From e835bf6ec80fa35fe9444cb4707aa8637630b79c Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Fri, 27 Sep 2024 10:40:32 -0400 Subject: [PATCH 001/121] Add a draft notebook --- gnomad_toolbox/modules/__init__.py | 1 + gnomad_toolbox/modules/variant_filtering.py | 40 ++ .../use_cases/toolbox_for_gnomad_users.ipynb | 511 ++++++++++++++++++ 3 files changed, 552 insertions(+) create mode 100644 gnomad_toolbox/modules/__init__.py create mode 100644 gnomad_toolbox/modules/variant_filtering.py create mode 100644 gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb diff --git a/gnomad_toolbox/modules/__init__.py b/gnomad_toolbox/modules/__init__.py new file mode 100644 index 0000000..6e03199 --- /dev/null +++ b/gnomad_toolbox/modules/__init__.py @@ -0,0 +1 @@ +# noqa: D104 diff --git a/gnomad_toolbox/modules/variant_filtering.py b/gnomad_toolbox/modules/variant_filtering.py new file mode 100644 index 0000000..ab7b230 --- /dev/null +++ b/gnomad_toolbox/modules/variant_filtering.py @@ -0,0 +1,40 @@ +"""Small functions to filter variants in gnomAD datasets, such as by allele frequency or by variant type.""" + +import hail as hl + + +def get_variant_count( + ht: hl.Table, + afs: list[float] = [0.01, 0.001], + singletons: bool = False, + doubletons: bool = False, +) -> dict: + """ + Count variants with frequency <1%, <0.1%, and singletons (AC == 1). + + .. note:: This function works for gnomAD exomes and genomes datasets, not yet for + gnomAD joint dataset, since the HT schema is slightly different. + + :param ht: Input Table. + :param afs: List of allele frequencies cutoffs. + :param singletons: Include singletons. + :param doubletons: Include doubletons. + :return: Dictionary with counts. + """ + counts = {} + + # Filter to PASS variants. + ht = ht.filter(hl.len(ht.filters) == 0) + if singletons: + n_singletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 1)) + counts["number of singletons"] = n_singletons + if doubletons: + n_doubletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 2)) + counts["number of doubletons"] = n_doubletons + + for af in afs: + n_variants = ht.aggregate(hl.agg.count_where(ht.freq[0].AF < af)) + counts[f"number of variants with AF < {af}"] = n_variants + + # Count variants with frequency <1%, <0.1%, and singletons (AC == 1). + return counts diff --git a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb new file mode 100644 index 0000000..d19f5ea --- /dev/null +++ b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb @@ -0,0 +1,511 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e77d32b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + "
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i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"bd49890a-4e5b-4f64-961d-42fb378aed46\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import hail as hl\n", + "from gnomad.resources.grch37.gnomad import public_release as v2_public_release\n", + "from gnomad.resources.grch38.gnomad import public_release as v4_public_release" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6fae5aa5", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the number of variant counts by allele frequencies" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4c6e7dd6", + "metadata": {}, + "outputs": [], + "source": [ + "def get_variant_count(ht: hl.Table, afs: list[float]=[0.01, 0.001], singletons: bool = False, doubletons: bool = False) -> dict:\n", + " \"\"\"\n", + " Count variants with frequency <1%, <0.1%, and singletons (AC == 1).\n", + "\n", + " .. note:: This function works for gnomAD exomes and genomes datasets, not yet for gnomAD joint dataset, since the HT scheme is slightly different.\n", + "\n", + " :param ht: Input Table.\n", + " :param afs: List of allele frequencies cutoffs.\n", + " :param singletons: Include singletons.\n", + " :param doubletons: Include doubletons.\n", + " :return: Dictionary with counts.\n", + " \"\"\"\n", + " counts = {}\n", + "\n", + " # Filter to PASS variants.\n", + " ht = ht.filter(hl.len(ht.filters) == 0)\n", + " if singletons:\n", + " n_singletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 1))\n", + " counts[\"number of singletons\"] = n_singletons\n", + " if doubletons:\n", + " n_doubletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 2))\n", + " counts[\"number of doubletons\"] = n_doubletons\n", + "\n", + " for af in afs:\n", + " n_variants = ht.aggregate(hl.agg.count_where(ht.freq[0].AF < af))\n", + " counts[f\"number of variants with AF < {af}\"] = n_variants\n", + "\n", + " # Count variants with frequency <1%, <0.1%, and singletons (AC == 1).\n", + " return counts" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "112c5065", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Initializing Hail with default parameters...\n", + "/opt/conda/miniconda3/lib/python3.10/site-packages/hailtop/aiocloud/aiogoogle/user_config.py:43: UserWarning:\n", + "\n", + "Reading spark-defaults.conf to determine GCS requester pays configuration. This is deprecated. Please use `hailctl config set gcs_requester_pays/project` and `hailctl config set gcs_requester_pays/buckets`.\n", + "\n", + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SPARKMONITOR_LISTENER: Started SparkListener for Jupyter Notebook\n", + "SPARKMONITOR_LISTENER: Port obtained from environment: 48351\n", + "SPARKMONITOR_LISTENER: Application Started: application_1727440474542_0001 ...Start Time: 1727441640185\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running on Apache Spark version 3.3.2\n", + "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:38205\n", + "Welcome to\n", + " __ __ <>__\n", + " / /_/ /__ __/ /\n", + " / __ / _ `/ / /\n", + " /_/ /_/\\_,_/_/_/ version 0.2.130-bea04d9c79b5\n", + "LOGGING: writing to /home/hail/hail-20240927-1253-0.2.130-bea04d9c79b5.log\n" + ] + } + ], + "source": [ + "v2_ht = v2_public_release(\"exomes\").ht()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c276fb7e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 1:===================================================>(9979 + 16) / 9997]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'number of variants with AF < 0.01': 14795986, 'number of variants with AF < 0.001': 14551940}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "[Stage 1:====================================================>(9995 + 3) / 9997]\r" + ] + } + ], + "source": [ + "print(get_variant_count(v2_ht))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c0243c4b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 5:====================================================>(9992 + 5) / 9997]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'number of singletons': 7763393, 'number of doubletons': 2194502, 'number of variants with AF < 0.01': 14795986, 'number of variants with AF < 0.001': 14551940}\n" + ] + } + ], + "source": [ + "print(get_variant_count(v2_ht, singletons=True, doubletons=True))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6f4a60c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From c9f0e22fa321df45eff6c48710a69caec3e69900 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Mon, 30 Sep 2024 10:55:23 -0400 Subject: [PATCH 002/121] Add more use cases in notebook --- .../use_cases/toolbox_for_gnomad_users.ipynb | 706 ++++++++++++++++-- 1 file changed, 650 insertions(+), 56 deletions(-) diff --git a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb index d19f5ea..93f80a1 100644 --- a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb +++ b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "id": "e77d32b1", "metadata": {}, "outputs": [ @@ -19,7 +19,7 @@ " \n", "
\n", " \n", - " Loading BokehJS ...\n", + " Loading BokehJS ...\n", "
\n" ] }, @@ -191,7 +191,7 @@ " \"\"}};\n", "\n", " function display_loaded() {\n", - " const el = document.getElementById(\"bd49890a-4e5b-4f64-961d-42fb378aed46\");\n", + " const el = document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", @@ -297,7 +297,7 @@ " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"bd49890a-4e5b-4f64-961d-42fb378aed46\")).parents('.cell').data().cell;\n", + " const cell = $(document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " }\n", @@ -313,40 +313,74 @@ " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"bd49890a-4e5b-4f64-961d-42fb378aed46\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"bd49890a-4e5b-4f64-961d-42fb378aed46\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/miniconda3/lib/python3.11/site-packages/hailtop/aiocloud/aiogoogle/user_config.py:43: UserWarning:\n", + "\n", + "Reading spark-defaults.conf to determine GCS requester pays configuration. This is deprecated. Please use `hailctl config set gcs_requester_pays/project` and `hailctl config set gcs_requester_pays/buckets`.\n", + "\n", + "Setting default log level to \"WARN\".\n", + "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SPARKMONITOR_LISTENER: Started SparkListener for Jupyter Notebook\n", + "SPARKMONITOR_LISTENER: Port obtained from environment: 52867\n", + "SPARKMONITOR_LISTENER: Application Started: application_1727699094620_0001 ...Start Time: 1727699722656\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running on Apache Spark version 3.5.0\n", + "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:43005\n", + "Welcome to\n", + " __ __ <>__\n", + " / /_/ /__ __/ /\n", + " / __ / _ `/ / /\n", + " /_/ /_/\\_,_/_/_/ version 0.2.132-678e1f52b999\n", + "LOGGING: writing to /test_toolbox.log\n" + ] } ], "source": [ "import hail as hl\n", - "from gnomad.resources.grch37.gnomad import public_release as v2_public_release\n", - "from gnomad.resources.grch38.gnomad import public_release as v4_public_release" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6fae5aa5", - "metadata": {}, - "outputs": [], - "source": [ - "# Get the number of variant counts by allele frequencies" + "\n", + "hl.init(\n", + " log=\"/test_toolbox.log\",\n", + " tmp_dir=\"gs://gnomad-tmp-30day\",\n", + " )" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "4c6e7dd6", + "execution_count": 11, + "id": "dd053d78", "metadata": {}, "outputs": [], "source": [ - "def get_variant_count(ht: hl.Table, afs: list[float]=[0.01, 0.001], singletons: bool = False, doubletons: bool = False) -> dict:\n", + "def get_variant_count(\n", + " ht: hl.Table,\n", + " afs: list[float] = [0.01, 0.001],\n", + " singletons: bool = False,\n", + " doubletons: bool = False,\n", + ") -> dict:\n", " \"\"\"\n", " Count variants with frequency <1%, <0.1%, and singletons (AC == 1).\n", "\n", - " .. note:: This function works for gnomAD exomes and genomes datasets, not yet for gnomAD joint dataset, since the HT scheme is slightly different.\n", + " .. note:: This function works for gnomAD exomes and genomes datasets, not yet for\n", + " gnomAD joint dataset, since the HT schema is slightly different.\n", "\n", " :param ht: Input Table.\n", " :param afs: List of allele frequencies cutoffs.\n", @@ -373,115 +407,659 @@ " return counts" ] }, + { + "cell_type": "markdown", + "id": "1ba4bfaf", + "metadata": {}, + "source": [ + "# Get variant count" + ] + }, + { + "cell_type": "markdown", + "id": "df28f17d", + "metadata": {}, + "source": [ + "## Get variant count by AF for a release" + ] + }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, + "id": "d9f96940", + "metadata": {}, + "outputs": [], + "source": [ + "from gnomad.resources.grch38.gnomad import public_release as v4_public_release" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "id": "112c5065", "metadata": {}, + "outputs": [], + "source": [ + "ht = v4_public_release(\"exomes\").ht()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c276fb7e", + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Initializing Hail with default parameters...\n", - "/opt/conda/miniconda3/lib/python3.10/site-packages/hailtop/aiocloud/aiogoogle/user_config.py:43: UserWarning:\n", - "\n", - "Reading spark-defaults.conf to determine GCS requester pays configuration. This is deprecated. Please use `hailctl config set gcs_requester_pays/project` and `hailctl config set gcs_requester_pays/buckets`.\n", - "\n", - "Setting default log level to \"WARN\".\n", - "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" + "[Stage 1:====================================================>(8782 + 7) / 8789]\r" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "SPARKMONITOR_LISTENER: Started SparkListener for Jupyter Notebook\n", - "SPARKMONITOR_LISTENER: Port obtained from environment: 48351\n", - "SPARKMONITOR_LISTENER: Application Started: application_1727440474542_0001 ...Start Time: 1727441640185\n" + "{'number of variants with AF < 0.01': 68398090, 'number of variants with AF < 0.001': 67709028}\n" + ] + } + ], + "source": [ + "print(get_variant_count(ht))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c0243c4b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 5:====================================================>(8781 + 8) / 8789]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'number of singletons': 34047562, 'number of doubletons': 10161819, 'number of variants with AF < 0.01': 68398090, 'number of variants with AF < 0.001': 67709028}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Running on Apache Spark version 3.3.2\n", - "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:38205\n", - "Welcome to\n", - " __ __ <>__\n", - " / /_/ /__ __/ /\n", - " / __ / _ `/ / /\n", - " /_/ /_/\\_,_/_/_/ version 0.2.130-bea04d9c79b5\n", - "LOGGING: writing to /home/hail/hail-20240927-1253-0.2.130-bea04d9c79b5.log\n" + "\r", + "[Stage 5:====================================================>(8786 + 3) / 8789]\r" ] } ], "source": [ - "v2_ht = v2_public_release(\"exomes\").ht()" + "print(get_variant_count(ht, singletons=True, doubletons=True))" + ] + }, + { + "cell_type": "markdown", + "id": "cc1178c8", + "metadata": {}, + "source": [ + "## Get variant count by AF for a gene" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "c276fb7e", + "execution_count": 15, + "id": "814e2af4", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Stage 1:===================================================>(9979 + 16) / 9997]\r" + "[Stage 7:===========================================================(3 + 1) / 3]\r" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "{'number of variants with AF < 0.01': 14795986, 'number of variants with AF < 0.001': 14551940}\n" + "{'number of singletons': 5282, 'number of doubletons': 1616, 'number of variants with AF < 0.01': 10733, 'number of variants with AF < 0.001': 10656}\n" ] - }, + } + ], + "source": [ + "# Filter to interval, e.g. for ASH1L.\n", + "gene_interval = \"chr1:155335268-155563162\"\n", + "\n", + "# Filter the exome release Hail Table to the ASH1L gene interval.\n", + "ht = hl.filter_intervals(ht, [hl.parse_locus_interval(gene_interval, reference_genome=\"GRCh38\")])\n", + "\n", + "print(get_variant_count(ht, singletons=True, doubletons=True))" + ] + }, + { + "cell_type": "markdown", + "id": "30663beb", + "metadata": {}, + "source": [ + "# Filter to variants by VEP annotations" + ] + }, + { + "cell_type": "markdown", + "id": "9c78ffb2", + "metadata": {}, + "source": [ + "## Filter to LOF variants" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a58ab4ac", + "metadata": {}, + "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\r", - "[Stage 1:====================================================>(9995 + 3) / 9997]\r" + "INFO (gnomad.utils.vep 928): Filtering to canonical transcripts\n", + "INFO (gnomad.utils.vep 931): Filtering to MANE Select transcripts...\n", + "INFO (gnomad.utils.vep 934): Filtering to Ensembl transcripts...\n", + "INFO (gnomad.utils.vep 940): Filtering to genes of interest...\n", + "INFO (gnomad.utils.vep 948): Filtering to variants with additional criteria...\n" ] } ], "source": [ - "print(get_variant_count(v2_ht))" + "from gnomad.utils.vep import filter_vep_transcript_csqs\n", + "# Filter to variants in ASH1L that are LOFTEE high-confidence (with no flags) in the MANE select transcript.\n", + "ht = filter_vep_transcript_csqs(\n", + " ht, \n", + " synonymous=False, \n", + " mane_select=True,\n", + " genes=[\"ASH1L\"],\n", + " match_by_gene_symbol=True,\n", + " additional_filtering_criteria=[lambda x: (x.lof == \"HC\") & hl.is_missing(x.lof_flags)],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6aaba993", + "metadata": {}, + "source": [ + "## Filter to pLOF variants that we used to compute constraint metrics\n", + "pLOF variants meets the following requirements:\n", + "* High-confidence LOFTEE variants (without any flags),\n", + "* Only variants in the MANE Select transcript,\n", + "* PASS variants that are SNVs with MAF ≤ 0.1%, and\n", + "* Exome median depth ≥ 30\n", + "\n", + "**Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "c0243c4b", + "execution_count": 17, + "id": "291d8b7c", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[Stage 5:====================================================>(9992 + 5) / 9997]\r" + "[Stage 7:===================(3 + 1) / 3][Stage 10:==================(3 + 1) / 3]\r" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "{'number of singletons': 7763393, 'number of doubletons': 2194502, 'number of variants with AF < 0.01': 14795986, 'number of variants with AF < 0.001': 14551940}\n" + "Number of variants: 18\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 7:=====(3 + 1) / 3][Stage 10:====(3 + 1) / 3][Stage 11:====(3 + 1) / 3]\r" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
freq
coverage
locus
alleles
AC
AF
AN
homozygote_count
csq
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>array<str>int32float64int32int64array<str>float64int32int64float64float64float64float64float64float64float64float64float64
chr1:155337668["G","A"]4593.14e-0414612700["stop_gained"]2.99e+0130218875281.00e+001.00e+001.00e+009.97e-019.86e-019.32e-016.07e-018.96e-046.02e-05
chr1:155337704["G","A"]64.10e-0614617140["stop_gained"]3.02e+0130220423721.00e+001.00e+001.00e+009.99e-019.94e-019.48e-016.21e-018.39e-044.93e-05
chr1:155337735["G","C"]16.84e-0714616940["stop_gained"]3.00e+0130219569141.00e+001.00e+001.00e+009.99e-019.92e-019.37e-016.12e-018.29e-044.51e-05
chr1:155338087["A","T"]16.85e-0714596480["splice_donor_variant"]3.16e+0131231113191.00e+001.00e+009.99e-019.95e-019.89e-019.59e-017.28e-011.35e-027.59e-04
chr1:155338161["G","A"]16.84e-0714618840["stop_gained"]3.19e+0131233084341.00e+001.00e+001.00e+001.00e+001.00e+009.76e-017.42e-011.35e-027.59e-04
chr1:155349380["T","A"]16.84e-0714617680["stop_gained"]3.21e+0132234449991.00e+001.00e+001.00e+001.00e+009.99e-019.76e-017.69e-015.01e-031.85e-04
chr1:155354631["C","T"]16.88e-0714534160["splice_acceptor_variant"]3.04e+0131222097141.00e+001.00e+009.95e-019.83e-019.70e-019.28e-016.53e-014.38e-044.93e-05
chr1:155357583["A","T"]16.84e-0714616800["splice_donor_variant"]3.13e+0131228588871.00e+001.00e+001.00e+009.99e-019.97e-019.79e-017.47e-018.14e-046.29e-05
chr1:155370984["C","T"]32.06e-0614557260["splice_acceptor_variant"]3.12e+0131228021811.00e+001.00e+001.00e+009.99e-019.97e-019.54e-016.97e-011.14e-033.69e-05
chr1:155415924["C","A"]21.48e-0613520320["splice_acceptor_variant"]2.73e+0130199323751.00e+009.84e-019.41e-018.72e-018.17e-017.64e-015.70e-011.35e-031.76e-04
chr1:155478020["G","C"]16.84e-0714618880["stop_gained"]3.26e+0133238300811.00e+001.00e+001.00e+001.00e+001.00e+009.72e-017.53e-013.11e-031.40e-03
chr1:155478203["C","T"]16.84e-0714618740["stop_gained"]3.27e+0133239248711.00e+001.00e+001.00e+001.00e+001.00e+009.72e-017.55e-014.07e-032.20e-03
chr1:155478439["C","T"]16.84e-0714618900["stop_gained"]3.28e+0133239948001.00e+001.00e+001.00e+001.00e+001.00e+009.73e-017.59e-014.64e-032.58e-03
chr1:155478528["G","A"]16.84e-0714618760["stop_gained"]3.28e+0133239977761.00e+001.00e+001.00e+001.00e+001.00e+009.73e-017.59e-014.67e-032.60e-03
chr1:155479767["G","A"]16.84e-0714618580["stop_gained"]3.30e+0133241434591.00e+001.00e+001.00e+001.00e+001.00e+009.74e-017.66e-016.55e-033.89e-03
chr1:155479862["G","T"]16.84e-0714611600["stop_gained"]3.24e+0132236512031.00e+001.00e+001.00e+009.98e-019.91e-019.42e-017.13e-016.42e-033.91e-03
chr1:155521291["C","A"]16.84e-0714618620["stop_gained"]3.30e+0132241144561.00e+001.00e+001.00e+001.00e+001.00e+009.81e-017.72e-011.94e-023.68e-03
chr1:155521474["C","A"]16.84e-0714617240["stop_gained"]3.29e+0132240775511.00e+001.00e+001.00e+009.99e-019.98e-019.79e-017.69e-011.94e-023.67e-03
" + ], + "text/plain": [ + "+----------------+------------+---------+----------+---------+\n", + "| locus | alleles | freq.AC | freq.AF | freq.AN |\n", + "+----------------+------------+---------+----------+---------+\n", + "| locus | array | int32 | float64 | int32 |\n", + "+----------------+------------+---------+----------+---------+\n", + "| chr1:155337668 | [\"G\",\"A\"] | 459 | 3.14e-04 | 1461270 |\n", + "| chr1:155337704 | [\"G\",\"A\"] | 6 | 4.10e-06 | 1461714 |\n", + "| chr1:155337735 | [\"G\",\"C\"] | 1 | 6.84e-07 | 1461694 |\n", + "| chr1:155338087 | [\"A\",\"T\"] | 1 | 6.85e-07 | 1459648 |\n", + "| chr1:155338161 | [\"G\",\"A\"] | 1 | 6.84e-07 | 1461884 |\n", + "| chr1:155349380 | [\"T\",\"A\"] | 1 | 6.84e-07 | 1461768 |\n", + "| chr1:155354631 | [\"C\",\"T\"] | 1 | 6.88e-07 | 1453416 |\n", + "| chr1:155357583 | [\"A\",\"T\"] | 1 | 6.84e-07 | 1461680 |\n", + "| chr1:155370984 | [\"C\",\"T\"] | 3 | 2.06e-06 | 1455726 |\n", + "| chr1:155415924 | [\"C\",\"A\"] | 2 | 1.48e-06 | 1352032 |\n", + "| chr1:155478020 | [\"G\",\"C\"] | 1 | 6.84e-07 | 1461888 |\n", + "| chr1:155478203 | [\"C\",\"T\"] | 1 | 6.84e-07 | 1461874 |\n", + "| chr1:155478439 | [\"C\",\"T\"] | 1 | 6.84e-07 | 1461890 |\n", + "| chr1:155478528 | [\"G\",\"A\"] | 1 | 6.84e-07 | 1461876 |\n", + "| chr1:155479767 | [\"G\",\"A\"] | 1 | 6.84e-07 | 1461858 |\n", + "| chr1:155479862 | [\"G\",\"T\"] | 1 | 6.84e-07 | 1461160 |\n", + "| chr1:155521291 | [\"C\",\"A\"] | 1 | 6.84e-07 | 1461862 |\n", + "| chr1:155521474 | [\"C\",\"A\"] | 1 | 6.84e-07 | 1461724 |\n", + "+----------------+------------+---------+----------+---------+\n", + "\n", + "+-----------------------+-----------------------------+---------------+\n", + "| freq.homozygote_count | csq | coverage.mean |\n", + "+-----------------------+-----------------------------+---------------+\n", + "| int64 | array | float64 |\n", + "+-----------------------+-----------------------------+---------------+\n", + "| 0 | [\"stop_gained\"] | 2.99e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.02e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.00e+01 |\n", + "| 0 | [\"splice_donor_variant\"] | 3.16e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.19e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.21e+01 |\n", + "| 0 | [\"splice_acceptor_variant\"] | 3.04e+01 |\n", + "| 0 | [\"splice_donor_variant\"] | 3.13e+01 |\n", + "| 0 | [\"splice_acceptor_variant\"] | 3.12e+01 |\n", + "| 0 | [\"splice_acceptor_variant\"] | 2.73e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.26e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.27e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.28e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.28e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.30e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.24e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.30e+01 |\n", + "| 0 | [\"stop_gained\"] | 3.29e+01 |\n", + "+-----------------------+-----------------------------+---------------+\n", + "\n", + "+------------------------+-------------------+-----------------+\n", + "| coverage.median_approx | coverage.total_DP | coverage.over_1 |\n", + "+------------------------+-------------------+-----------------+\n", + "| int32 | int64 | float64 |\n", + "+------------------------+-------------------+-----------------+\n", + "| 30 | 21887528 | 1.00e+00 |\n", + "| 30 | 22042372 | 1.00e+00 |\n", + "| 30 | 21956914 | 1.00e+00 |\n", + "| 31 | 23111319 | 1.00e+00 |\n", + "| 31 | 23308434 | 1.00e+00 |\n", + "| 32 | 23444999 | 1.00e+00 |\n", + "| 31 | 22209714 | 1.00e+00 |\n", + "| 31 | 22858887 | 1.00e+00 |\n", + "| 31 | 22802181 | 1.00e+00 |\n", + "| 30 | 19932375 | 1.00e+00 |\n", + "| 33 | 23830081 | 1.00e+00 |\n", + "| 33 | 23924871 | 1.00e+00 |\n", + "| 33 | 23994800 | 1.00e+00 |\n", + "| 33 | 23997776 | 1.00e+00 |\n", + "| 33 | 24143459 | 1.00e+00 |\n", + "| 32 | 23651203 | 1.00e+00 |\n", + "| 32 | 24114456 | 1.00e+00 |\n", + "| 32 | 24077551 | 1.00e+00 |\n", + "+------------------------+-------------------+-----------------+\n", + "\n", + "+-----------------+------------------+------------------+------------------+\n", + "| coverage.over_5 | coverage.over_10 | coverage.over_15 | coverage.over_20 |\n", + "+-----------------+------------------+------------------+------------------+\n", + "| float64 | float64 | float64 | float64 |\n", + "+-----------------+------------------+------------------+------------------+\n", + "| 1.00e+00 | 1.00e+00 | 9.97e-01 | 9.86e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.94e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.92e-01 |\n", + "| 1.00e+00 | 9.99e-01 | 9.95e-01 | 9.89e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 9.99e-01 |\n", + "| 1.00e+00 | 9.95e-01 | 9.83e-01 | 9.70e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.97e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.97e-01 |\n", + "| 9.84e-01 | 9.41e-01 | 8.72e-01 | 8.17e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 9.98e-01 | 9.91e-01 |\n", + "| 1.00e+00 | 1.00e+00 | 1.00e+00 | 1.00e+00 |\n", + "| 1.00e+00 | 1.00e+00 | 9.99e-01 | 9.98e-01 |\n", + "+-----------------+------------------+------------------+------------------+\n", + "\n", + "+------------------+------------------+------------------+-------------------+\n", + "| coverage.over_25 | coverage.over_30 | coverage.over_50 | coverage.over_100 |\n", + "+------------------+------------------+------------------+-------------------+\n", + "| float64 | float64 | float64 | float64 |\n", + "+------------------+------------------+------------------+-------------------+\n", + "| 9.32e-01 | 6.07e-01 | 8.96e-04 | 6.02e-05 |\n", + "| 9.48e-01 | 6.21e-01 | 8.39e-04 | 4.93e-05 |\n", + "| 9.37e-01 | 6.12e-01 | 8.29e-04 | 4.51e-05 |\n", + "| 9.59e-01 | 7.28e-01 | 1.35e-02 | 7.59e-04 |\n", + "| 9.76e-01 | 7.42e-01 | 1.35e-02 | 7.59e-04 |\n", + "| 9.76e-01 | 7.69e-01 | 5.01e-03 | 1.85e-04 |\n", + "| 9.28e-01 | 6.53e-01 | 4.38e-04 | 4.93e-05 |\n", + "| 9.79e-01 | 7.47e-01 | 8.14e-04 | 6.29e-05 |\n", + "| 9.54e-01 | 6.97e-01 | 1.14e-03 | 3.69e-05 |\n", + "| 7.64e-01 | 5.70e-01 | 1.35e-03 | 1.76e-04 |\n", + "| 9.72e-01 | 7.53e-01 | 3.11e-03 | 1.40e-03 |\n", + "| 9.72e-01 | 7.55e-01 | 4.07e-03 | 2.20e-03 |\n", + "| 9.73e-01 | 7.59e-01 | 4.64e-03 | 2.58e-03 |\n", + "| 9.73e-01 | 7.59e-01 | 4.67e-03 | 2.60e-03 |\n", + "| 9.74e-01 | 7.66e-01 | 6.55e-03 | 3.89e-03 |\n", + "| 9.42e-01 | 7.13e-01 | 6.42e-03 | 3.91e-03 |\n", + "| 9.81e-01 | 7.72e-01 | 1.94e-02 | 3.68e-03 |\n", + "| 9.79e-01 | 7.69e-01 | 1.94e-02 | 3.67e-03 |\n", + "+------------------+------------------+------------------+-------------------+" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 7:=====(3 + 1) / 3][Stage 10:====(3 + 1) / 3][Stage 11:====(3 + 1) / 3]\r" ] } ], "source": [ - "print(get_variant_count(v2_ht, singletons=True, doubletons=True))" + "from gnomad.resources.grch38.gnomad import coverage\n", + "\n", + "coverage_ht = coverage(\"exomes\").ht()\n", + "\n", + "#Filter to PASS SNVs with AF <= 0.1% and median exome depth ≥ 30.\n", + "ht = ht.filter(\n", + " (hl.len(ht.filters) == 0) \n", + " & (ht.allele_info.allele_type == \"snv\")\n", + " & (ht.freq[0].AF <= 0.001)\n", + " & (coverage_ht[ht.locus].median_approx >= 30)\n", + ")\n", + "\n", + "print(f\"Number of variants: {ht.count()}\")\n", + "ht.select(\n", + " freq=ht.freq[0],\n", + " csq=ht.vep.transcript_consequences[0].consequence_terms,\n", + " coverage=coverage_ht[ht.locus],\n", + ").show(-1)" + ] + }, + { + "cell_type": "markdown", + "id": "bf479bea", + "metadata": {}, + "source": [ + "# Get 'freq' for specific genetic ancestry groups" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "ee3cb2dd", + "metadata": {}, + "outputs": [], + "source": [ + "ht = v4_public_release(\"exomes\").ht()\n", + "\n", + "# Filter to interval, e.g. for ASH1L.\n", + "gene_interval = \"chr1:155335268-155563162\"\n", + "\n", + "# Filter the exome release Hail Table to the ASH1L gene interval.\n", + "ht = hl.filter_intervals(ht, [hl.parse_locus_interval(gene_interval, reference_genome=\"GRCh38\")])\n", + "\n", + "# Filter to variants with adj.AC > 0 \n", + "ht = ht.filter(ht.freq[0].AC>0)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "01c5051c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ami', 'asj', 'fin', 'oth', 'remaining'}" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# For this example, we filter to the ancestry that we included in the FAF calculation\n", + "from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX\n", + "\n", + "POPS_TO_REMOVE_FOR_POPMAX[\"v4\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "905996c8", + "metadata": {}, + "outputs": [], + "source": [ + "from gnomad.utils.filtering import filter_arrays_by_meta\n", + "\n", + "# Remove unwanted stratifications\n", + "items_to_filter1 = ['sex','downsampling','subset']\n", + "freq_meta1, array_exprs1 = filter_arrays_by_meta(\n", + " ht.freq_meta,\n", + " {\n", + " **{a: ht[a] for a in ['freq']},\n", + " \"freq_meta_sample_count\": ht.index_globals().freq_meta_sample_count,\n", + " },\n", + " items_to_filter=items_to_filter1,\n", + " keep=False,\n", + " combine_operator=\"or\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "c7cfb183", + "metadata": {}, + "outputs": [], + "source": [ + "# keep the necessary stratifications for your analysis \n", + "items_to_filter2 = {'gen_anc':['ami', 'asj', 'fin', 'oth', 'remaining'], 'group':['raw']}\n", + "\n", + "freq_meta2, array_exprs2 = filter_arrays_by_meta(\n", + " freq_meta1,\n", + " array_exprs1,\n", + " items_to_filter=items_to_filter2,\n", + " keep=False,\n", + " combine_operator=\"or\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "f39a0853", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[{'group': 'adj'},\n", + " {'gen_anc': 'afr', 'group': 'adj'},\n", + " {'gen_anc': 'amr', 'group': 'adj'},\n", + " {'gen_anc': 'eas', 'group': 'adj'},\n", + " {'gen_anc': 'mid', 'group': 'adj'},\n", + " {'gen_anc': 'nfe', 'group': 'adj'},\n", + " {'gen_anc': 'sas', 'group': 'adj'}]]" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "freq_meta2.collect()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "f57f9535", + "metadata": {}, + "outputs": [], + "source": [ + "ht = ht.annotate(**{a: array_exprs2[a] for a in ['freq']})\n", + "ht = ht.annotate_globals(\n", + " freq_meta=freq_meta2,\n", + " freq_meta_sample_count=array_exprs2[\"freq_meta_sample_count\"],\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "4792f7d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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locus<GRCh38>array<str>int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64
chr1:155335497["A","C"]11.79e-0356000NA000NA0000.00e+0013800NA0000.00e+00200NA00
chr1:155335570["T","C"]35.26e-0357000NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335571["TA","T"]35.26e-0357000NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335746["G","C"]11.77e-0356400NA000NA0000.00e+0013200NA0000.00e+00200NA00
chr1:155335855["G","A"]11.74e-0357600NA000NA0017.25e-0313800NA0000.00e+00600NA00

showing top 5 rows

\n" + ], + "text/plain": [ + "+----------------+------------+--------+----------+--------+\n", + "| locus | alleles | all.AC | all.AF | all.AN |\n", + "+----------------+------------+--------+----------+--------+\n", + "| locus | array | int32 | float64 | int32 |\n", + "+----------------+------------+--------+----------+--------+\n", + "| chr1:155335497 | [\"A\",\"C\"] | 1 | 1.79e-03 | 560 |\n", + "| chr1:155335570 | [\"T\",\"C\"] | 3 | 5.26e-03 | 570 |\n", + "| chr1:155335571 | [\"TA\",\"T\"] | 3 | 5.26e-03 | 570 |\n", + "| chr1:155335746 | [\"G\",\"C\"] | 1 | 1.77e-03 | 564 |\n", + "| chr1:155335855 | [\"G\",\"A\"] | 1 | 1.74e-03 | 576 |\n", + "+----------------+------------+--------+----------+--------+\n", + "\n", + "+----------------------+--------+---------+--------+----------------------+\n", + "| all.homozygote_count | afr.AC | afr.AF | afr.AN | afr.homozygote_count |\n", + "+----------------------+--------+---------+--------+----------------------+\n", + "| int64 | int32 | float64 | int32 | int64 |\n", + "+----------------------+--------+---------+--------+----------------------+\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "+----------------------+--------+---------+--------+----------------------+\n", + "\n", + "+--------+---------+--------+----------------------+--------+----------+\n", + "| amr.AC | amr.AF | amr.AN | amr.homozygote_count | eas.AC | eas.AF |\n", + "+--------+---------+--------+----------------------+--------+----------+\n", + "| int32 | float64 | int32 | int64 | int32 | float64 |\n", + "+--------+---------+--------+----------------------+--------+----------+\n", + "| 0 | NA | 0 | 0 | 0 | 0.00e+00 |\n", + "| 0 | NA | 0 | 0 | 1 | 7.25e-03 |\n", + "| 0 | NA | 0 | 0 | 1 | 7.25e-03 |\n", + "| 0 | NA | 0 | 0 | 0 | 0.00e+00 |\n", + "| 0 | NA | 0 | 0 | 1 | 7.25e-03 |\n", + "+--------+---------+--------+----------------------+--------+----------+\n", + "\n", + "+--------+----------------------+--------+---------+--------+\n", + "| eas.AN | eas.homozygote_count | mid.AC | mid.AF | mid.AN |\n", + "+--------+----------------------+--------+---------+--------+\n", + "| int32 | int64 | int32 | float64 | int32 |\n", + "+--------+----------------------+--------+---------+--------+\n", + "| 138 | 0 | 0 | NA | 0 |\n", + "| 138 | 0 | 0 | NA | 0 |\n", + "| 138 | 0 | 0 | NA | 0 |\n", + "| 132 | 0 | 0 | NA | 0 |\n", + "| 138 | 0 | 0 | NA | 0 |\n", + "+--------+----------------------+--------+---------+--------+\n", + "\n", + "+----------------------+--------+----------+--------+----------------------+\n", + "| mid.homozygote_count | nfe.AC | nfe.AF | nfe.AN | nfe.homozygote_count |\n", + "+----------------------+--------+----------+--------+----------------------+\n", + "| int64 | int32 | float64 | int32 | int64 |\n", + "+----------------------+--------+----------+--------+----------------------+\n", + "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", + "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", + "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", + "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", + "| 0 | 0 | 0.00e+00 | 6 | 0 |\n", + "+----------------------+--------+----------+--------+----------------------+\n", + "\n", + "+--------+---------+--------+----------------------+\n", + "| sas.AC | sas.AF | sas.AN | sas.homozygote_count |\n", + "+--------+---------+--------+----------------------+\n", + "| int32 | float64 | int32 | int64 |\n", + "+--------+---------+--------+----------------------+\n", + "| 0 | NA | 0 | 0 |\n", + "| 0 | NA | 0 | 0 |\n", + "| 0 | NA | 0 | 0 |\n", + "| 0 | NA | 0 | 0 |\n", + "| 0 | NA | 0 | 0 |\n", + "+--------+---------+--------+----------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 7:=====(3 + 1) / 3][Stage 10:====(3 + 1) / 3][Stage 11:====(3 + 1) / 3]\r" + ] + } + ], + "source": [ + "populations = ['all', 'afr', 'amr', 'eas', 'mid', 'nfe', 'sas']\n", + "ht.select(**{pop: ht.freq[i] for i, pop in enumerate(populations)}).show(5)" ] }, { "cell_type": "code", "execution_count": null, - "id": "d6f4a60c", + "id": "027a2120", "metadata": {}, "outputs": [], "source": [] @@ -503,7 +1081,23 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.7" + }, + "toc": { + "base_numbering": 1, + "nav_menu": { + "height": "213px", + "width": "374px" + }, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true } }, "nbformat": 4, From a44e72d3bbc41fc41fe6aabeb89d90e11d3e0419 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Mon, 30 Sep 2024 13:21:13 -0400 Subject: [PATCH 003/121] Add example to get AF for one ancestry for one variant --- .../use_cases/toolbox_for_gnomad_users.ipynb | 150 +++++++++++++++--- 1 file changed, 132 insertions(+), 18 deletions(-) diff --git a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb index 93f80a1..ca5208e 100644 --- a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb +++ b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb @@ -366,7 +366,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "dd053d78", + "id": "e69953f7", "metadata": {}, "outputs": [], "source": [ @@ -503,7 +503,7 @@ }, { "cell_type": "markdown", - "id": "cc1178c8", + "id": "725f9a57", "metadata": {}, "source": [ "## Get variant count by AF for a gene" @@ -512,7 +512,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "814e2af4", + "id": "9f8e1ba4", "metadata": {}, "outputs": [ { @@ -542,7 +542,7 @@ }, { "cell_type": "markdown", - "id": "30663beb", + "id": "7bff63bb", "metadata": {}, "source": [ "# Filter to variants by VEP annotations" @@ -550,7 +550,7 @@ }, { "cell_type": "markdown", - "id": "9c78ffb2", + "id": "b1031947", "metadata": {}, "source": [ "## Filter to LOF variants" @@ -559,7 +559,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "a58ab4ac", + "id": "5b08a706", "metadata": {}, "outputs": [ { @@ -589,7 +589,7 @@ }, { "cell_type": "markdown", - "id": "6aaba993", + "id": "f9b2d921", "metadata": {}, "source": [ "## Filter to pLOF variants that we used to compute constraint metrics\n", @@ -605,7 +605,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "291d8b7c", + "id": "6ce87a77", "metadata": {}, "outputs": [ { @@ -814,16 +814,24 @@ }, { "cell_type": "markdown", - "id": "bf479bea", + "id": "b104a39b", "metadata": {}, "source": [ "# Get 'freq' for specific genetic ancestry groups" ] }, + { + "cell_type": "markdown", + "id": "135565fe", + "metadata": {}, + "source": [ + "## Get 'freq' for multiple groups for an (gene) interval" + ] + }, { "cell_type": "code", "execution_count": 85, - "id": "ee3cb2dd", + "id": "4f78166f", "metadata": {}, "outputs": [], "source": [ @@ -842,7 +850,7 @@ { "cell_type": "code", "execution_count": 86, - "id": "01c5051c", + "id": "8f625a41", "metadata": { "scrolled": true }, @@ -868,7 +876,7 @@ { "cell_type": "code", "execution_count": 87, - "id": "905996c8", + "id": "15729719", "metadata": {}, "outputs": [], "source": [ @@ -891,11 +899,11 @@ { "cell_type": "code", "execution_count": 88, - "id": "c7cfb183", + "id": "d2886179", "metadata": {}, "outputs": [], "source": [ - "# keep the necessary stratifications for your analysis \n", + "# Remove the genetic ancetries/group that you don't need \n", "items_to_filter2 = {'gen_anc':['ami', 'asj', 'fin', 'oth', 'remaining'], 'group':['raw']}\n", "\n", "freq_meta2, array_exprs2 = filter_arrays_by_meta(\n", @@ -910,7 +918,7 @@ { "cell_type": "code", "execution_count": 89, - "id": "f39a0853", + "id": "5471689c", "metadata": {}, "outputs": [ { @@ -937,7 +945,7 @@ { "cell_type": "code", "execution_count": 90, - "id": "f57f9535", + "id": "16170ed7", "metadata": {}, "outputs": [], "source": [ @@ -951,7 +959,7 @@ { "cell_type": "code", "execution_count": 91, - "id": "4792f7d1", + "id": "e3bcf7a0", "metadata": {}, "outputs": [ { @@ -1056,10 +1064,116 @@ "ht.select(**{pop: ht.freq[i] for i, pop in enumerate(populations)}).show(5)" ] }, + { + "cell_type": "markdown", + "id": "fe2e98b8", + "metadata": {}, + "source": [ + "## Get 'freq' for a specific group and a specific variant" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "9a4f26a2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[{'gen_anc': 'afr', 'group': 'adj'}]]" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from gnomad.utils.filtering import filter_arrays_by_meta\n", + "\n", + "ht = v4_public_release(\"exomes\").ht()\n", + "\n", + "# Filter by the location of the variant\n", + "ht = ht.filter((ht.locus.contig == \"chr22\") & (ht.locus.position==15528692))\n", + "\n", + "# Assign th\n", + "items_to_filter1 = {'gen_anc':['afr'], 'group':['adj']}\n", + "freq_meta1, array_exprs1 = filter_arrays_by_meta(\n", + " ht.freq_meta,\n", + " {\n", + " **{a: ht[a] for a in ['freq']},\n", + " \"freq_meta_sample_count\": ht.index_globals().freq_meta_sample_count,\n", + " },\n", + " items_to_filter=items_to_filter1,\n", + " keep=True,\n", + " combine_operator=\"and\",\n", + " )\n", + "\n", + "# if you want to further remove 'downsampling', 'sex', and 'subset'\n", + "items_to_filter2 = ['sex','downsampling','subset']\n", + "freq_meta2, array_exprs2 = filter_arrays_by_meta(\n", + " freq_meta1,\n", + " {\n", + " **{a: ht[a] for a in ['freq']},\n", + " \"freq_meta_sample_count\": ht.index_globals().freq_meta_sample_count,\n", + " },\n", + " items_to_filter=items_to_filter2,\n", + " keep=False,\n", + " combine_operator=\"or\",\n", + " )\n", + "freq_meta2.collect()" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "7e044201", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
locus
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locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>
chr22:15528692["C","G"][(793,5.43e-04,1459438,7)]
" + ], + "text/plain": [ + "+----------------+------------+\n", + "| locus | alleles |\n", + "+----------------+------------+\n", + "| locus | array |\n", + "+----------------+------------+\n", + "| chr22:15528692 | [\"C\",\"G\"] |\n", + "+----------------+------------+\n", + "\n", + "+---------------------------------------------------------------------------+\n", + "| freq |\n", + "+---------------------------------------------------------------------------+\n", + "| array |\n", + "+---------------------------------------------------------------------------+\n", + "| [(793,5.43e-04,1459438,7)] |\n", + "+---------------------------------------------------------------------------+" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht = ht.annotate(**{a: array_exprs2[a] for a in ['freq']})\n", + "ht = ht.annotate_globals(\n", + " freq_meta=freq_meta2,\n", + " freq_meta_sample_count=array_exprs2[\"freq_meta_sample_count\"],\n", + " )\n", + "\n", + "ht.select('freq').show(-1)" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "027a2120", + "id": "6fc82c5c", "metadata": {}, "outputs": [], "source": [] From 4bc936c3c94c046b44b29ca593b5db63ccf9a570 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 1 Oct 2024 10:27:26 -0400 Subject: [PATCH 004/121] Add table of contents and screenshot --- .../use_cases/toolbox_for_gnomad_users.ipynb | 47 ++++++++++++++++--- 1 file changed, 40 insertions(+), 7 deletions(-) diff --git a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb index ca5208e..791362c 100644 --- a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb +++ b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb @@ -1,5 +1,16 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "8e609a46", + "metadata": { + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "
" + ] + }, { "cell_type": "code", "execution_count": 6, @@ -420,7 +431,9 @@ "id": "df28f17d", "metadata": {}, "source": [ - "## Get variant count by AF for a release" + "## Get variant count by AF for a release\n", + "\n", + "**Note: this will take long if your notebook is using multiple nodes.**" ] }, { @@ -588,6 +601,11 @@ ] }, { + "attachments": { + "Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png": { + "image/png": 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JRJoDIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIrAKEpBItAoOum5ZBERABERABERABERABERABERABERABERABERABERABERABCQSaQ6IgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIwCpIQCLRKjjoumUREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERkEikOSACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACqyABiUSr4KDrlkVABERABERABERABERABERABERABERABERABERABERABERAIpHmgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAisggQkEq2Cg65bFgEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAGJRJoDIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIrAKEpBItAoOum5ZBERABERABERABERABERABERABERABERABERABERABERABCouKwS//vpr+Ouvv8I///wTlixZEtZZZ52w1lprhTXWWCPvSy5evDi89dZbYeHChWGbbbYJ9erVC2uvvXbGdn7++We7thdac801Q8WKmW833zp//vln+O233/wSoUKFCnafqQMFO9w/7UatcuXK0a/aFwEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIHlTmCZeRLtscceoWrVqqFatWphiy22CNWrVw/HHntszjf4xRdfhJNOOskEoU022SQ0a9YstG/fPuy+++5h/fXXDw0bNgyXXHJJQIyKG+LNBhtsYNenD3xGjx4dL1boO2JOvM6oUaMKlYl+ef7558O2225b6BrU//7776PFAuW8D7595513CpXRFxEQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARFY3gQyu9YUszdz584N8+bNK1L74YcfDosWLQobbrhhkXPRAy+88EJo27Zt+Oqrr6KHC+3TPp8nn3wyjBs3zoSoQgViXxCB8rWkOngODRgwIAwdOjSxuXid+HcqJR1LbEwHRUAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERGAZEVgmnkT//e9/03Z38uTJac9xgvP77bdfRoEo2sDMmTPDLrvsEhYsWBA9vEz258yZE/baa6+0AtEyuagaFQEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIFlQKDURSK8ZMaMGZO2q3fffXfac3/88Ufo2bNnkfOHHHJIGD58eLjnnnvMi2ezzTYrVObHH38MV155ZaFjpf1l+vTpoVGjRuHNN98s7abVngiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAgsdwKlLhLh2fPhhx+mvZGnn346fPbZZ4nnydUTP3feeeeZd9Hpp58ejj766NCrVy8Tavbdd99CbTz44IOFvpf2l/fff79QkxtvvHHo1KlToWNl+QWBDbGMfEy5GGHzZs+eHR599NHwySefpA2BR7lffvkla5PZytEv2snWP87feuut4Y477khd87vvvgs33nhjeP3111PHHnrooXDLLbfk7HGWqpjnDp5tXOebb77JWvOvv/4KiKDLei5m7chyLJAPn+J265lnngm33XZbTvOwuNdYUerxfmQ+Tp06tVS7vKzaLdVO5tkYgj7vDXLXPfvss3nWVnEREAEREAEREAEREAEREAEREAEREAEREAERWDUIlLpINGHChELkTjvttIAnUNQmTpwY/Zra//jjj1P7vnPkkUf6bmpbpUqVcNFFF4V111039fnhhx/C77//niqzLHfatGljgkVS35bldTO1jWhGrqeTTz45U7GwcOHC0Lp1a+O26667hlatWoU6deqE6tWrm7dWPF/SNttsE9Zff/2AgJfJatasaeU++uijIsUef/zxsOWWW9r5Cy+8sMj56AHEpm7dugXmjdt1111nHmYtWrTwQ+Gaa64JZ5xxRkZBMlW4BDt4qHEdhLRsxkL0iSeeaGLmBx98kK14kfMIZNlEtCKVyvhAPnyK01WEz+bNm4euXbuGTGEsi9N2ea7Dc8hc+Pvvvwt1k3xvzMeRI0cWOl7SL+napQ+InyuaPfLII6Fx48b23mCOPvfccyvaLai/IiACIiACIiACIiACIiACIiACIiACIiACIrBcCJSqSMSCYjycHAv7RxxxRKGbSReOLh5Gjkpjx45NXKRs1qxZWLRoUaHPGmusUeg6pf0FUYqQd9wjokp5sXnz5gU8uLBx48aZR1FS3xB6WDhlAZV7Oeigg0LHjh3DjjvuaHXw2konMsXFo6T2ORYth+CDmHfooYeWyOOHnFMYY16eDbENrltttVXYdNNN8+oqC/HrrLOOffCcki0lULlyZcsDBtfttttulcEydOhQmwt9+vQps3t+4YUXrA/kYVvR7Nprr7Uut2vXLrz44osBT1SZCIiACIiACIiACIiACIiACIiACIiACIiACIhAUQIVix4q/hF+rf3VV18VaqBp06Zh8eLFhY699tpr4d133w1169YtdByxgsVgvAfcbrjhBlvk69ChQzjggANC/fr1Q8WK+XcbLyNEi3QW/8V+vByeOnPmzAk1atSInyrz7+PHjy/UB8KdwStuhHFjfFj0ReyK3sukSZPCsccea6LcueeeGxo0aBCvntd3vMLatm0bGGvGdM899wzTpk3Lqw0vfPjhh4effvopVKpUyQ+Vy+3mm2+emv+rr756sfsYFdqK3chKUnG11VYLTz75pAnFxXnuV1QMPgd8Wxb3UZbXLun9umjet2/fgDekTAREQAREQAREQAREQAREQAREQAREQAREQAREIJlAqXoS3XvvvYWuQg4hvCPwECK0WdSSQkexsJ4kbiA0nH/++aFRo0bWHh4liEfx/EXR9uP7PXv2NLECwSLpQ0i1TIYXQ1RUyVR2eZ7DewvxByMsG3bnnXfaNv7nvvvus0Pk6IjfC6HzPMcSglFJjdBrjBveSohrhx12WLGbJJfRp59+Gr7++uusbSAEkhOLD3maoobn2fTp062t6PF89gk7Rz6YpLnHovqCBQvC559/XqRJ+kVOJeoioEUX4LmvaJg+9un/zz//XKQdONDGF198UeQcBxDTqPvtt9/aeQRX8oA9//zzqWOJFQsOEiKP0IDF9WRinLh20v1zTe/bl19+WagL33//vfWPfiaNMeXhylyPtlOceyzOHGCsuS+EZDy+Zs2aFZ544gnzYvQbYTzx1CPHFwJ4dHy9jG9pg2cDT524cM09ci36ibHle7rxhjntvPzyy0Xa8nmUFC6OY7SbFOKT6zLPfI4vWbLEytIe9+V1fZwZP54rPBqjxo8D6BusCHMZN58P+Y5jpvnC/OG+/IcG/j7gHqIGN+Yb76akHwjE+waLhx9+uMh9wIBnBv7xa0Svl+n5j5ZjP9P8iJfVdxEQAREQAREQAREQAREQAREQAREQAREQAREoKYFSE4l+/fXXEPdoId+NWzx/T7qQc8OGDUuJFV43vsVj6ayzzrKwXghGxcn/Em9zRf3+zDPPpLyDCBWHAIbnRaYcOiyQJtlNN91kAgEh4kpqhAm76qqrwuTJk4sIUvm2zSJzvXr1LL9IprrcF8IkZU866aTUojnhpvCeQhjDK2nrrbcOO+ywQ0iXGyvpGggVDRs2tPxNLVu2tLlH6L7o4jeL4njH7bzzzqkmWFQfPny4jcvuu+8eqLvtttuap9abb75p5c4+++xCodT22GMPu4fHHnss1c6oUaPsGH2njdq1a9s9xHOAIfBx/5deemm44IILLE/VgQceGPbff38LgYe4GrfbbrvN6uClR2jAjTfeOOy3335FFv3j9eLfeQ65NvmnWGSPG3mkOI93B4YIdsopp4SNNtrI+kc/EZR5V7hIQjlCVlLP82IV5x5LMgd8PKZMmWJs/u///s/yrDGfCD3J3CDvGp6QvPPwwoMlQkvUEIAYa/jSJox5Xtu3b58SGN577z27Vw+Xdvvtt9v3eF432h0xYoTl+aKdJk2aWFvXX3+9XZJrMT/hhlATNwQSzlE3SdBirh933HFWzfvE3P7mm29MWKIuc4r5xPjxXDFHMZ4DxP5NNtnE2qfvtWrVsmcwKnblO465zBe8IOmbG88i3wnDifG8Mr/4UQDzjR8ebLHFFqF///5exbbRvnEvhJCk3owZM+w8ghh8mes8M/CvVq1a6Ny5c+q9Q8Fcnn9rsOBPLvPDy2orAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAqVFIP+4bWmuzK+p/dfbXgQvEjc8SfBgcWPhEc+K6II65wgpdfPNN9sCHIvXvpDu9eJbBCNC2uFdwcL/qmbkbMLILQQ7hCIWmFkURSSIGuH6Ro8ebQIKi88s3kbzOFG/tEJ6xUXBpIXoaN9Kuo9ISbg8QtqxeI2nWpUqVcL7779vIgNzE2+2ffbZx7wHEGDIV4Kgtvfee2e9PF5Wa6+9dujSpYstkuMxwtxEFECAICxakrFwTq4nrFevXmGDDTYwbxMW6VlkZtEd0YdF9KuvvtrKdevWLSCykeMIw0PPc6rQ/9122808F1j8Z9zXW2+9cPDBB1tZ/8MzhHnbd911lz2fiKvkeCL8H3b//feHrl272j7PKAIWDGmb7y+99JIt9luBLH94/mgbLxk8S1q3bl2ohgvD7i3IPTFPEYa4Z0Q+hGY8Nhgb2shkud5jac0B5jT317x5cxtz3j0nnnhi6p7ZZ87hyYc3C+EWowLNhRdeGBBxEIYYN+YTIipzlfmL4Lfhhhua1yTiL94pfj2EpahxHk68Yxkz6hJKEhEKARFhjbmKUIvggYgRNY5hiHQVKlSInrJ92pk7d27A+5D++vxjXvp7nntkPuFliXCG4IIh1MKGPjP/8NShHULAIdYyHtHnJddxzGW+cN88S1deeaX1pUePHmHNNdcMO+20k4mSbdq0Ma7MOXLlwYy+DRw40MYOkSlq3jf6zX1SD087RDQMYdB/qECYT96veMQiuGO5PP88v1gu86O03s92Qf0RAREQAREQAREQAREQAREQAREQAREQAREQgQICpSYSxb2IoEsoHg9JlESbxbm4SEQ5FtlYNObDL7ZZyGbBlBBmvkAZbY+FPhYHWbRMZyzmRX9hHi+HiJEUAi9erjx9/+GHHwKL/xgLnhiLoIhECGyE6IsuAF922WXGEE6UY/GXRWYWkF1MsEYS/vTp08d+KZ9wyg4ljUu6sqV9nAV2RAWEG+4HUYUwhxiCBH0788wzA54sbnisIdogLBHKLMrJy0S31atXN3a+WI8QgjcI2/nz5xfJr+V1PQQjAgmiAcYCPN4HCFT0mT4wHi4S4WlTtWpVK4vnhXt03HjjjbaobycK/rAQjvDK2OG9wQJ+1Mg7xYI9Rlk8JxAteFYRifCq8D5xzIW9QYMGWZ9Y9GbhGkExV0MogQnPdlQkQlBj3rHIjtCFV4h7dyAocBxDCEBsQERjXFjwz2TZ7pG6pTUHEM24LxbqET5gi1DD/XIPeIpheJPwXkMUITQaXiuPPPJIyqOMd5kLgP369bN5RDvMEfghWAwePNjeeQi7fI8bcxoBCBEEY3wRMnmH8eF9iIcSZZiDnPc8WXis4AGFUSbJevfubSEAuV/6mtQH6iGAXnHFFakm8PZygYj79HFlTm266aYWwu6tt94ycSVVqWAn2zjmOl+Yz3y4XwwPIX8ueFboE2IxIrG/I3g34FGFByXPUjxXHh6H8HS74447bJc8dYyr50pD1MSjCmZ4WME7l+ef/uYzP7wf2oqACIiACIiACIiACIiACIiACIiACIiACIhAaRBIdn/Is2UWLH3BN1oVT4noJ3qOfRbbkvJBRMvx620W7xCK8LogHwgLmHEjZ0S6/BqURXBiYTTTJ95mef/OIj7G4rWLFyxUszDLgryHRvL7qFmzpi2+42FEecaNBWW8AUjuzi/i8e5KsgceeMDEAgSDpE9SneVxjFwfLHSzyAoHPCp88Zdf/LOAjxg2dOjQQt1BqIETAmOm0HxeCUbOmGN4eODBgb3xxhu2TfrjeZE8VBpl8KLA8+33339PiTRJdTlG/zG8o/D6iBoL9Iif2OzZs6OnzOvBBSJOsJDtog2iFvbqq6/aloVxF4g4gAiCiIY99dRTts31j1+TxXEW9t14fjE83Vys8HNRNjAmTBtssglEvBv8erSVdI+lOQe6d++e8rRjDJ0ZXiYuENGP7bff3sKTse/CNTmhMIRaF4j4zv26yIJHWq7GnEbccGPMEBox915ibvBhjkfbdrGdfvPcl8QQEaOGEM/YMYYuEHGeUGw+VuQBilou4xgtX9z54uEbEXD8HUG7cPBnC2EvaghKUYGIcwh53COikAtEHOf9ybjwXvV8Trk+/6U9P+iPTAREQAREQAREQAREQAREQAREQAREQAREQARyIVAqnkTk6iiOsXhJOCsW6XIxvD3I98GHX+fjKRM1fiVOqKVVxfwX7SzyRxfkjznmmPCf//zHfp2Pt0vU+DX/5ZdfHvoVeDAgIvEhvBgLqHgA4LlA6MC4hxdhk8jLkc4QMcrCCP3lhjcMoaXcXDhh0TY+VyjjC7mUyzZvPLyUt80WzxzCJn777bfRw4X28QKC64ABA0xIRajBcwte0b4WqhT5giiKxceRYzwPeAThpYO45yHkOEdIuri5kOFzxQUoxEZEs6h5eEAYIc7iSZWLIXowH2mTRXT3cPNQc3hNYSzS4/2FuEzYQ7yL2OKdQT+zeXbRRi73WJpzIO6JSGg5LOm5IGwcApFzdBGA/FQu0lnlgj+8tzDehbka8yEaso16/h5lvNwQ5RhbRDoYYx5q7oQTTvBixdoiAnmotHgD5Kdi3iPc4/GIp5t7ajoTr5PLOJZ0vhDK0Oc7oeDWWmstv7xt8bzEKOPhEPkefw9yDPvrr79MmGXM8MhDjMRrjHcN5j9+yPX5L+35YZ3QHxEQAREQAREQAREQAREQAREQAREQAREQARHIgUCpiESECiqu4XHgi5uIG3isuJ100kmp3A5+zLeE64ov/BN2bFWxjz76yLyCuF9yynhemej984t5wlbFF0Qpg+cB3PngJfPuu+/aL+ER7m699dZw3XXXRZuyEE3169cvdCz6xX9BHz22vPcRugj35OZeHHwnF0w6I6xZNktiSE6ZbMYiPXUJJYeYQxgsPvAi/NU555yTsQnyt2Cbb755Yjn31mBRPmpJ/Y178ETFikx88ArJVSSiD4gPiESIAohEHmoOr4xoKC/mGd5YLNojKPDBEF2Yf4hwmSyXeyzNOZCLcJWuvzDA6E861oTWzNWSBMYkHnjvIBIhxg0ZMsREK89j5p5luV4zXi6az8zP4Tlz8cUXW8hLP5Ztm9Tv+FyljZLMFxeEaccFIfbjxns1anEhjnOIQscff7yFi4yWTdrP9fkv7fmR1BcdEwEREAEREAEREAEREAEREAEREAEREAEREIEkAiUON8ev1smnETU8AtxLJb71EF1enpw6HpInuoDMecKaxdvmODk1PKE439223HJL313pt9EcUHilxD8OgJw3GN4jiALpckTB3pO2E45qRTHmE55RGOG/ELvcfK4RGo5f+qf7nHbaaV5lmWzxTCC0GyIAcxpvGTwOCNUVHceki++www522MWieBk/ToizfM1FP8LWpWPD8SQvqkzXIi8UIhjCBPPOhV9E36ghMpDrioV53hOIZwhECCmIfXHhK1o31/3yMgfcEwyPnnSs8UQpbcNzEJbutYkQx9wjNJ3nvSrNa5KPh5xojD8hHpn3X375pc2D+Pjne92SzJctttgidTnyfKUbAxfQUoUTdvDCJJ8YYfIQrt5+++3w9ddfB36k4KJttFouz39ZzY9oP7UvAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiKwahIosSfR5MmTi5Aj70a6MD0sHHouDyqyYEneE7wGDj744FTCcW+UsohOe++9d6hRo4bljyH/TDx3BGGuPJyW1y2v29NPPz3jAi2Lj56nJOkeCNdE+DeMRWdy8cSNnDK9evUKd955p+WiIQ8I4aZYvMVzJpqTw+v6L/q33nprP1Tut3Bg3Am3xgI184X8K4TB8nw9hJCCmSew95vCQ+ann36y3Dd4VpW2IWYSbguvCARM8r/w6dixY+jRo0cYOXJkeOKJJ4KHYPPrE8rKDYEL4xnheNTDgnxMLuh5Oa+Xy7ZRo0ZWjIX8pPmA4Aa3eJi1bG0zj/C0wGOGkHPuaXjUUUelqi5ZssQ8MhgThAzeF3zIs7PTTjtZGD/GsaRzsazngN8wXlR4uhH+Lv68EgqNvFiMQTwPkwvo3k5xtowFYjvvCm+PMGi5Wj4emoSqxMgBh4di1JjDxbWSzhcEJsaAfzcQdfj3JGqEjEToIUxgNq85zwXHv2X8m+WGAB/1WMrn+S/u/PBraysCIiACIiACIiACIiACIiACIiACIiACIiACxSVQYk8iXwD2DiDWZPI82G+//bxoauveFE2bNk0lX0+dLNjBGwaPg86dO1vi97hARFlEFRc5onXL4z6J5Vm0TffJtphKHgy8LRB8yCGUZJ4knmsQHom8H3hVIMohUi1atKhQNfKiDBw40I7FF7ELFSyHXwgDhmcZ4ho5gkhCT04Q+HgoQxbFSTbv9sorr5gAgYiAZ8GyMBb/6RNeWoyDG31DnMJcBImKP9H5jZDDfSB04cHguU4QjPAAYh7wzHGdfM3bxisCL56oIWCR+wuBlsXufM3zuhBCkjFBvGMB3g1Rjz7jKRUN94cg4ddLyvXj9XPdlvUc8H76e+/SSy8NzzzzjB827xMEFeZh//79U8fpN+a5alInirHDGNIeHj6EoGQ/Wyg/LuOiKh5w5BTKxTbZZBMr5vl/vM6gQYNsrvr3fLelMV8IUYrxLog+83g64YnJGPADhGzm3kIeIo7yeMx17969UNV8nv9850ehC+mLCIiACIiACIiACIiACIiACIiACIiACIiACJSAQIlEIjwxnn766UKXb9++fcak8yw8ktg+aqNHj7ZFNo4Rfs7FimiZdPsseLIAz6/lVxW7++677VZZiE/KTcJJwit5onpywyCk8Mt3jDBgeGU1adIkMF6IeuwTkgpRxQUmK7yC/KlSpUqYOHGiLYAT3szvFRGTRV3mCGXIw9KsWbOUJwHeDnj3LAtjbnoIP0QSvA64HuPCPOc8Yb/cfLxatWplosqsWbPM4+yhhx6yIuSUYVxbtGhhwhC5fLApU6aknQdWIM0fRBuYYeRHIu8Rog7zAU8nDGGhOF5WLkpaIwV/OnXq5Lu2xZMD7ydESzyImId4ESFMIXwhIHkIrkIVi/GlLOeAd5c5N2DAAPvavHlzYwzrOnXqBEKcMRcuuugiL24c+IJgiJDWu3fv1Ll8d8idFeWPiFqpUqWszbiASUEEFN43ePRkMuYmNm3aNAsdSB080fr27WtzNlPdTOdKY74Q3hEBHG+f2rVr23uA9wHPFEIY861t27aZumHnGDeM3EvU4TseYHFxP5/nP9/5YR3QHxEQAREQAREQAREQAREQAREQAREQAREQAREoBQIlEommTp1apAu5eKGwYB43D5uFR8X5559vv6BHTPJfbcfLE56nS5cugTBqufwqPuqpEW8r3fdsdZKSmsePZWsj6dpJCeG9HF4WLhJlW9AkrBk2ZswY25KbhF+/uxiB9xACEgukLGgiDDCm1apVs/LRP/H7ip6L7qcr58cRqzJZ0nk/5m1Q37lGjyH2OBv32EAMwxMNFnjc4ClAXhb2hw8fHgYPHpypO4nX8Qrxfvl3P88WwfOqq66y6+GxgycHC/8IBXgzRQWqESNGpELkIWohwmJ77rmnCUGeW4YFeIznCJGWBXw35+FbP8426Rj5kQgZyXOLSIjAxnxAwEF8QrDKZEnj4OVPOOEE22VuxZ9RWBF+De9AjHmIN9gPP/xgx/Cg8bZdpPL+x7fWwP/++LnosZLOARdU4m37d+9n9Jp+LDonCP/IXEBYgDGsYc44ElovOhcYF+YO8xRPLA9x5tf0bfSamfajIQ0R5OLm7fmW84RsxMuTPtBPwjniHRMtE2+HuTpp0iR7byPGUIe8dXfccYfdZ7S8t+PbpHN+LJ/54nXYRvkzjrwLe/bsaV6VvAfccwgxF8bujep98m20TcryfmFe4zHFOG5ZEE6Sd6d7v/l183n+85kf0f5oXwREQAREQAREQAREQAREQAREQAREQAREQARKQqBCQQiuf0rSwPKoi7cB+VFI7M6vvlmQ84Xj5XH9lfEaiE0sPpOLA48BFtJXdiO/DqHNWAhm4Xt5GyH+mMt47GSav3hrEL4KsS5ejpwyLL5na6M490Y4PvjAhgXwkhrh8BDiWJSPh7OLts24EP6LhfVlPQ/Leg74fSOGMR9q1qyZ0QuMsILkysFjsGrVql497y1CBh4veGi98cYbedf/7rvvLJ8R+XpcAMnWCGHceM8wpi6aZauTy/nSmi8IX8z54vaPfiDkIup7mL1M/c/1+aeNXOdHpuvpnAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjkQmCFEIlyuRGVEQERKB8EELKmT59uof3o0fz5803cLR+9W7V6gZAxd+5cC1GIuEhoOw+XtmqR0N2KgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAgkEaiYdFDHREAERKA4BAjhRX4Vtz59+kggchjLeYtYRw44N0LYHXXUUf5VWxEQAREQAREQAREQAREQAREQAREQAREQAREQAREIEok0CURABEqNwPrrrx8OOeQQC+FF3iV5rZQa2rwbIiwcuaaqVKkSGjduHLp161aqYd/y7pAqiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIlDsCCjdX7oZEHRIBERABERABERABERABERABERABERABERABERABERABERCBZU9gtWV/CV1BBERABERABERABERABERABERABERABERABERABERABERABESgvBGQSFTeRkT9EQEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIHlQEAi0XKArEuIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQHkjIJGovI2I+iMCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACy4GARKLlAFmXEAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIHyRkAiUXkbEfVHBERABERABERABERABERABERABERABERABERABERABERABJYDAYlEywGyLiECIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiAC5Y2ARKLyNiLqjwiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAgsBwISiZYDZF1CBERABERABERABERABERABERABERABERABERABERABERABMobAYlE5W1E1B8REAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERWA4EJBItB8i6hAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiUNwIVS9Kh2bNnl6S66oqACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACJQRgQq///77P2V0bV1WBERABERABERABERABERABERABERABERABERABERABERABESgjAgo3FwZgddlRUAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERKAsCUgkKkv6urYIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIlBEBiURlBF6XFQEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIGyJCCRqCzp69oiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiUEYEJBKVEXhdVgREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQATKkoBEorKkr2uLgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQBkRkEhURuB1WREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREoSwISicqSvq4tAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAmVEQCJRGYHXZUVABERABERABERABERABERABERABERABERABERABERABESgLAlULMuL69oiIAIiIAIiIAIiIAIiIAIiIAIiIAIikI7AggULwg033hgefujhMH/+/PDnn3+kK6rjJSBQsWKlUKdOnXB4i8NDt65dQ61atfJujbG6sWCspkyZEt59993wxx8aq7whqsIyIVCpUqVQt27dcOihh4auxZzfy6RjalQEREAEygkBiUTlZCDUDREQAREQAREQAREQAREQAREQAREQgX8JjBo1Kpx5ZvfQ4fgTwiX9B4Y629YLLPbKSp8Ags78994J06c+EurVqx+uu25EOPnkk3O+EGPVvXv30KNHj3DbbbeFBg0ahDXWWCPn+iooAsuSwO+//x7mzJkTJkyYEOrXrx9GjMhvfi/LvqltERABESgPBCQSlYdRUB9EQAREQAREQAREQAREQAREQAREQARSBBAdrhg0KNw+dnzYbvsGqePaWTYEEN/gzOfAgw8Ll1xwtl0oF6GIsRo6dGh49tlnQ6NGjZZNB9WqCJSAAIIlc5PPMcccEzp06GCt5TK/S3BZVRUBERCBFYZAhQI1/Z8VprfqqAiIgAiIgAiIgAiIgAiIgAiIgAiIwEpNgLBleLNIICq7YZ43d044qeOx4Z133s4Yeo6xwjNDAlHZjZWunD+BWbNmhSZNmoS33848v/NvWTVEQAREYMUksHqfPn36rZhdV69FQAREQAREQAREQAREQAREQAREQARWNgKDhwwJ29StH1q0OnJlu7UV5n422mjj8PMvP4W5b80JzQ84IG2/hxSM1R577BE6deqUtoxOiEB5I1CjRo2wZMmSMHPmzHBAhvld3vqt/oiACIjAsiKw2rJqWO2KgAiIgAiIgAiIgAiIgAiIgAiIgAiIQL4EHn7oYQt5lm89lS9dAoSdYywy2ZQpUyx8V6YyOicC5ZEAYeeYvzIREAEREIEQFG5Os0AEREAEREAEREAEREAEREAEREAERKDcEFhnncrh2RmvB/LkyIpP4LNPF4QXnnsmPPn49NCo8W5h/Q02CHvt0zRstnmtnBr9448/QpPddw4///xT2vKVK1cOixcvDuR8kYnA8iTwwQcfhGnTpoX777/fQsdtuOGG4aCDDgpbb711Tt0oSL8RqlatGn76Kf38zqkhFRIBERCBlYCARKKVYBB1CyIgAiIgAiIgAiIgAiIgAiIgAiKwshBAcJj55rtpbwfx4pOPPzSxY6211k5brjye+Oeff8K4saPDHv+3V9i6zrbLrIvdTj0xzHjphcT2Tz+jZ+h8+hmJ5+IHd92xbmAxPZ0xVpnOp6uX7/E333wzPPfcc6Fr1675VlX5lZDAIYccEp544onEO+vbt2+45JJLEs/FDy6v+Ru/rr6LgAiIQHkjoHBz5W1E1B8REAEREAEREAEREAEREAEREAEREIEiBD7//LNwbo9uYc9GO4Rjj2wR9tmtYTjrjNPCV18tLFJ2WRz4dtE34cEHJoZffvml2M3/8P334eohA8P0qbmFuSrONRF2XCBqvNsegc9pXbvblo6PvP7aQJllYcOHDzevIvK9xO2CCy4Im2++eUAoy9fGjRsXevbsGX744Yd8qxYpv3DhwjB69Gh5kBQhs2IcQNhxgWjfffcNfAryrduWO+jfv78821aMoVQvRUAEyhEBiUTlaDDUFREQAREQAREQAREQAREQAREQAREQgaIEPv/s09D5+Pbhh++/C6PuGheeevHVcPd9k8Pff/8d2rQ6JPy8HEJGLfjkk9C/94Xh++8WF+1gjkcI+fb0i7PCqV3PzKlGvte8deT1qXYRhm4adZd9Tuu2dJ9jbngblba1adPGmnzkkUcKNf3nn3+G2267LZx66qmhQoUKhc7l8mXAgAHhm2++Ceutt14uxTOWef/990Pnzp3Dt99+m7GcTpY/AgMHDkx1CmFo+vTp9vF9tm54G8lEQAREQARyIyCRKDdOKiUCIiACIiACIiACIiACIiACIiACIlBGBB68f2L4syDM3NXDbww77dwoVKmybqhbr364fMjVYdu69cLL/wuthhhx/bVXh7atDwsH7buniToLv/wi1evBl/ez82w5f1jzpmHSffemzv/44w9Wh3N8KPfzzz+Hx6c9Gs4/a2mItpM7tQsD+i4NZ8X5EcOuCpdedH5o+n+7hBkvv1ggXP0Vxo6+PRzV4iA7duoJHcJbb85OXaPLyZ3C008+bt8z9SfdNVMNxXbIQYSXkBvCEPbqKy+Hm28YYfscw7MIw9uIOqVpNWvWDCzO33PPPYWaffrpp8OPP/4Y2rVrZ8enTp0a9tprr0AemcaNG5tnj1fo3r27hQs78cQT7TxeIxMmTAitW7f2IiFbfcQC2sFzaauttjKBisoTJ04Mbdu2tXaaNm0aunTpkmozusM86tevX2jYsKG10b59+/DFF//OI/ZPO+00O0cZwpvlGnYPL6ZmzZrZvR199NHh2WefjV46vPjii+GYY46x83vssUe49dZbU+cz9Yt69erVs/nqFe6+++5w4IEH2te5c+fa+TvuuMPuiz5g9P/666+3PuGlw3z36+ywww5W57zzzivULnXoV6tWrVL95PpuXj8TP9gzPswDrp/NyEGEl5CbC0LMLUREjGN4FmHMG+rIREAEREAEshOQSJSdkUqIgAiIgAiIgAiIgAiIgAiIgAiIgAiUIYF5c+eERrvuHtZbf/1CvVhvvfXDbaPHhf0PWLoQftXgy8Ptt94UWh91TOh5bq/w1pzZFpKOhW/sq4Vf2vnffv01nNvrkrBNQV6ggf17pxbAh105OMx69ZXQb+CQcN6FvcMjDz0QRt18Y9i+wY6hfacTrQ28cVoffYzt096do24uCEH3c+h1cd8CQWKb8Ni0qWHYlYPC4S1bh959Lw+rrb566HrqCbbwTqVPPvko5fmUqT/prmkXTvjzwnPPFDpKSDkEKT4337hUJCpUoOBLvE78fHG+H3fcceHhhx8OX3/9dar6+PHjw6677mqCw4IFC0LLli1tH6GB43j2zJo1y8p/+umn4corr7RwcNdee23YbrvtwnfffRfefXdpnqpc6g8ZMsTCAl599dUBoYNcRoTA41qIRxiCwsknn2z78T+IIldccUVAqEKAQGDZb7/9wl9//WVzBcHq+eeftzL0feTIkal2421FvyN2UR5hjDrYAQccYO2zP3/+/NCiRYvw22+/hRtvvDEgZHXr1i3ce+9SITNTvwiD+OGHH1ofaQv7viC8IW1ivxbMec6fe+654fjjjw+9evWy4/PmzQtnn3122G233QICEkLRRRddFEaMGBHOPPPMQJjA22+/PZxxxr95rKhDvxB4GCvYRvMAZeon/UAcmzlzZhg6dGg4/PDD7fpcO5NNmzat0Gn6iQDGx0WiQgUKvsTrxM/ruwiIgAiIwFICFQVCBERABERABERABERABERABERABERABMozgblz3gxtju2QsYuEnLtv/N2h7+WDQ8sjjrKyuzTeNbQ6pFmYNXNG2KfpfnZs623qhEsHDLKwZwhPhx6wj3nV7NeseZgz+/XQqPFuYa99mlrZBg13Dr8XLNjXqLmZHefg/+21d9i0Rk07zx88mYZcMyKsttrS3+HuuFPDMG7iQ6HOtkvz/tTecqvQsW3r8OEH71vZVMX/7WTqD33B4tf8X9VCm+8LhJS44UW0NCfRmXaK73zcnnx8ejimXUf/WipbRA7s/vvvt/ByCHQIAO4tUqVKFRNYdtlll1CxYkUTRe67774wY8aM0KhRI6u74447BoQlZ2oH//cnl/oIS7fccouNMUJL7dq1zbMEz5cmTZpYS4gLtWrVijZt+3g83XDDDSbiuIiEGDJ48ODw2WefhXfeeSe89tprJuzUqVPH6uARhaBEmapVqxZp0w8MGzYsdOrUyYQVjtEf7nnMmDEmOOFlREg9cjCts8465vVUvXr18OWXX5onVqZ++TWybRkLRLqoke8JYQ1D8EGco0/udbXpppuGI488MlxzzTWp++vRo0e48MILrc76BeIt3lZfffVVWHvttTPye++990wgIuyf8yfXFKIUDNPZokWLipzCiwjPod69e9s5vvNxYw6efvrp/lVbERABERCBNAQkEqUBo8MiIAIiIAIiIAIiIAIiIAIiIAIiIALlg8AGBQvv3xfkI8pk77//np1uXCD8uNXcbPOw2ea1wjvz5qZEogY77ZzKi7PRxhuHatU2NA8j6hza4ggLR0dbTfdrFpo1Pzgg4mSyHQvai4oZCEozZ7wURl53bVi06OvwztvzrDreS0mWqT9J5dMdwwMqychLhCEO4VUUNRehosdKuo+4gbfM2LFjTSQiNBx21FFLhTtElHXXXTcgmHxSkOdp8eLFJoC4txdlCbMWZcoxt1zre+6jGjVqhI0LxhkPpVxszpw5VszDsfGFMG5402B49Wy22WbBBSKO4WWEUZdQay4u2cGCPwgwiCx4z+Cd47bmmmtamLdXXlk6dnhTHXTQQSYQeRn3+PFwbun65R5DXi/ddueddy5yCg8rt7feest2ySH1+ONLwyIuXLjQjnF/LrIh8rn5/ueff24eXBxP10+8qbDLL7/ctvxxtnhQwSTJ4mH5vAx5iTDEIQ+t5+e8r/5dWxEQAREQgWQCEomSueioCIiACIiACIiACIiACIiACIiACIhAOSGwQ4Odwvz3loYbi3eJRfnVVqsQ/vj9dztVqdIahYqw6PxHQT4jt0qVKvmubStGvp/UuUvYuVHj8NjUKWHihPEm9Fxw8aWhbfvjCtWJfqnwPw8iPzbmzlHhP1cNDnvu3STQ750bVQ54QqWzTP1JVyfpOCH3yDMUN89HlBRybv0NNogXL5XvHTp0MJHgo48+CuTFIbwY3jYYYdr233//gLBAqDW8fDycml88nUCUa31CkUUtzjh6Lr7PfMLS1UHIiLePRxRGXiLEI/ewsYMFfxo0aJAKNxgXQfhOCDaMttNdN1u/rIGCP//884/vFuTH+ju17ztJbF1Qo4z3BW+sjTbayKrh8bPJJpvYx9uJ9nP11Vf3w6n7jJ5PnSzYISwetsUWW9iWP+R34lqE80tnhPgjz1DcPNScb6Pnfc5Fj2lfBERABESgKAGJREWZ6IgIiIAIiIAIiIAIiIAIiIAIiIAIiEA5IkB+ngcfmBhemzUz7NLoX6+HTxd8Elof1jxcNujKsPc+SxPWz31rdth34+bW+x8KcrJ88P78cPoZPXO6m28K8ugg7HCNcy64OPTudW4YN3Z0IZEoKjglNTp50n3hiIKcSH36D7TTs1+fFe4evdQLJal8LseyXZM2PERevL3Gu+1e4EU0I37Yvqerk1g4j4OEZ8Pbhrw6DzzwQJg0aVKqNiHAOPfSSy+ZRxeixMUXX5w6n22npPW9fQSdJEPQwegf4hZGfqV77rknHHvssQFPnP79+9sxF1E8nxL5jwjN1qVLF6sX/0MYvWi7CDovvPCCeU5RlrxA5HNCEHLhie+IPfvss481F60f7VflypXtPB5FHraPHET5GveAkSvIPXPoD15C3Fs2y8avYcOG1gQeVYSpw34qCBX57bffFvKgshORP3hYJRnhBJ955pmkU+aVlXhCB0VABERABAoRWBowt9AhfREBERABERABERABERABERABERABERCB8kOg9dFtTbjpdXb3gAjz8UcfhueeeSqccdpJFi6u+YGHBLxiCBE3sF+f8MJzzwTEmUt6nWPnyT2UzfBi6NCmVTivZ7fw0YcfhAWffFyQg2ZB2KbOtlaVsHXYpP/eGz77dIHtJ/0hX9FrBaHf8B5647VZ4fJ+S/OlJJXNdizXa9IOZXf/v70KNUk+IstJ1K27baMnEc68/ejx0tjHW+WUU06xkHKElnOxgbYRGsjtM2XKlPDmm2+aF0k+1yxp/a222souRzi1JBGFcHZHHHGE9QuB5uWXX7Z7GTp0aNigYI7tueeeJnIdd9xxgRBwhNPr2rWrCRLZRJTjjz/e8v1w7ddffz0QSo4QdG3atLE+EZJv3rx54ayzzrLz5CgiTB28svWrbt261sagQYPCq6++Gv773/+G4cOH54PWypID6bDDDrP7594IMUdeHzykyFeUzbL1c++997bwfx07djS2MGR+4H2WybbeeutCIewoSz4iPn369LFttH7fvn0DdWQiIAIiIALZCUgkys5IJURABERABERABERABERABERABERABMqQAKGrhlwzPDTcpVG47NKLw9EtDw5nnXFaqFngkTLm3klhjYKQXdiAQVeFnRvvGnp07RxO7tQuLPnxxzBi5G2BnEZYhQrplkEqBEJmDRk2wgSoNq0OCXzwzji31yVWt3pBOCzCzo0edUsYfHk/O5bUXvezz7ewWce3Pzqccny7cHir1v8rW8G20T9J9ZeeX1o26ZrR+vH9G265o9Ah8hARbo4tn6h1Pv2M6NdS32/fvr21SX6eaHg2vjdv3jwQPqxx48YmGGy77bapPFHZwqEVpz4d8ZBq5Cjq1q1buOqqq0L37t0T75scOXiuINCQ1+b9998PkydPtnw51apVCw8++GAghxICRcuWLc1zZ8yYMYltRQ/27NnTQtEhYOy+++7mNXTnnXcGhBOM3EDjx48PTz75pJ0ntxN1ENywTP1CnLnpppssJBtCFt5Z1Hfz+/fvmbb0ib74vZEPaNq0aQWCa7XEat62j12mftIGAuH3BV5+sIUhzxlhCbPZo48+WqgIeYgIM8eWT9QuuWTpcxs9pn0REAEREIFkAhUK3Gv/DVaaXEZHRUAEREAEREAEREAEREAEREAEREAERGC5EEBQmPnmu2mvReg1vHzw2FlnnXUSyxEei3Jrr7124vlsB5cs+dFyw6y55lpFitI2i+LRPCxFChUcINTdOgWL3x42LKlMrsdyvaa3d+vI68PI66/1r4W2eBvFxaRCBSJfdt2xruXZiRwqtMtYpQvbVqhgwpcfCwQ8GKYbw4QqhQ6VtD7zg3HMND6UIYfOeuutV+ja/oUwaQiYURHMz2XaEmbuu+++M++gdOUWL15s102aZ5n6hUccdcnH4+JNumtkO87Yco8IUMWxTP2kPdrGPFSefcnhz8CBAy3kX1LRZs2ahbiYlFSOYyWZv+na1HEREAERWBEJSCRaEUdNfRYBERABERABERABERABERABERCBlZTAOutUDs/OeN0W31fSW1wut0VIPMLufV8gRsx69ZWw/wEHWt6iXEPMscDfZPedCzxmli7kJ3WaxX0EiXxFkqS2dEwE8iHwwQcfmHfTokWLAp5OeKbh/ZVriDkEMMQvF6ryubbKioAIiMDKRqDiynZDuh8REAEREAEREAEREAEREAEREAEREIEVlwC5T+a/907YbvsGK+5NlIOeIwYd065jsXvCGDAWmYw8OOSsadSoUaZiOicCpU4AMYhcScU15q3ncSpuG6onAiIgAisLgXTBeFeW+9N9iIAIiIAIiIAIiIAIiIAIiIAIiIAIrEAEDm9xeJg+9ZEVqMcrZ1cZA8Yikx166KFhwoQJmYronAiUSwLMW+avTAREQAREoCBvn3ISlc00IP4sVtL4sGXTe11VBERABERABERABERABERABERABJYNgQULFoR69eqH28eOlzfRskGctdV5c+eEkzoeG9555+1Qq1attOUZq/r161u4L3kTpcWkE+WMwKxZs0KTJk3C229nnt/lrNvqjgiIgAgsMwKl6klEHM/XX389/PDDD8usw2Xd8NNPPx0uu+yy8MADDxS7K7/99lvYeeedQ4MGDSwBIoLRgAEDrF1i+SYZiQe5Lp/vC5JfZjOSWs6dO9cSIWYrm+08/Xv33XfDt99+W6Qox0ePHl3keNIB4sSSmDGTffHFF+HXX3/NVCR1jrK33nqrJSNNHSzYYf5xPKm/0XK57HPvn3/+ecY+/fzzz4G+yERABERABERABERABERABERABEpOAFHiuutGhEsuODsgVsiWLwGYw54xyCQQ0SvOjxgxInTo0CGw8C4TgfJOgHnKfGXeZpvf5f1e1D8REAERKC0CpSISkSCuWbNmlvBt9913D9WrVw977LFHmD17dmn1s9y08+KLL4bLL788PPJI8V3flyxZEubNmxfee+89E3z+/vtvE4loN53ARhnOZyoDpIULF4bjjz8+bLLJJiZEbbzxxqFx48bh5ZdfTjGk7ySVjH74BcXtt9+eKsPOL7/8Es466yz7RxNBa9NNNw0NGzYMMHCbMWNGOPvss/1r4hZhrV69eqFGjRqB/tDGa6+9liqLEMM/zhtuuGGoXbt2WG+99UKrVq1MnEkVSth58MEHw8CBA4skM/3mm29Ct27dstZPaDJ1iD7dcMMNNpe33HJL69ORRx4Zvvrqq1SZDz/8MBx88MFhgw02sH5vvvnmYdSoUanz2hEBERABERABERABERABERABESgegZNPPjlcfNFF5s0yfNhQE4v++OOP4jWmWlkJwBZxCNZ4EMGeMcjFKHfBBReYZ8ZFBfVYhC+IWpNLVZURgeVCgPnIvGR+sv7FfM11fi+XDuoiIiACIlDGBCqW9PrPPPNMaN68uTWz4447hl133TW89NJLJgKwj4iA14zsXwKIIQgMCBEIL3gJlYbh8XLggQeGKlWqhPvvv988lT7++OMwceJE+0fwueeeC4h4bozN+uuvb949U6ZMCV26dDHhqGPHjiYQHXPMMSYI3XXXXSb6IW6NHDky7LvvvuZKjhCYzT777DPr0xVXXBFOOeUU8/rp1atXaNmyZfjoo49CxYoVw6RJk8K5554b/vvf/4b999/fhBj6cuyxx9p10l3jqaeeCgcccEC60yU6DjMEssmTJ4d99tnHBKf27dvbsbvvvtvGrkWLFmHbbbcN8+fPN4EU8a1Tp04mqjEOMhEQAREQAREQAREQAREQAREQgeITYBGX/7e64cYbw8C+l9j/e/35p4Si4hNNX7NixUqhTp06loMoW4i5pFZ8rG4sGCv+35+oIxL1kkjpWFkQqFSpUqhbt67lIFKIubIYAV1TBESgvBMosUjUvXt3u0e8LMaNG2c5dvB6YQH9scceC9dee23KQwUPlOHDh4dp06aZZ8lhhx0WLrzwQhMKEJvwSNluu+1M0LjmmmtM7Dj11FND586drQwXYoGeD20hSh1++OEmMKy22moh2sbee+8dhg4dav9RgvfNTjvtFM4888yCeLrvBM7bLUbVAABAAElEQVTddNNN5tlCm4QKI9wbQgneNfxH6KWXXmpeL5xPMg//hvcNHjIIK2eccYYVpS1EGticeOKJdox7wHuGPiGu4CmDPf7442Hddde1/eifL7/80kLL0Sfap81sRl+oh5cSHjtY1apVTaRD4IF7VCTaaqutTCSi3C677BLGjx8fHn74YbuXF154wcrjJeZiEOLWoEGD7Br0y49TnzHBq4dQeMwFxo9/hAmth8jUtm3bVP4l5sbYsWMDfcILB7GI/iIcYYhc7Pfv3z8wlxjbuCGwTZ06NQwbNix+KvX91VdftTF/8803i4w5jAjdBxP+Q7hv3742/2CAqFmzZk2bDy5C8R8T/NoEYQ3jVyh4gvHrky222MKOtWnTxkSiTz75xL7rjwiIgAiIgAiIgAiIgAiIgAiIQMkIEA5qUMGPDvnIyjcBxoofiPKRiYAIiIAIiIAIrDgESiQS/fjjjyZIcLuIKhUqVLA7Z1H/vvvuC+SgQSjAWKjHAwXDw4iFdBbpCY9GqDHaogyfe++918rxp0ePHhaebM8997TcN4gtGAIHQgYfQoBdeeWVads47bTTTIQgfBrXefTRR8M555wT7rnnHhM1DjnkELsPvEKwm2++ObzyyivhiSeeCJUrV7Zj8T/RPDx4y8ycOdPEHkK9kbiR+4iGJsPbhGPk5EFgYh9L8iIinxAeKYRpw2j/0EMPtf1Mf3CdxavLBaJoWUSbTEYeoE8//TQcffTRVoz+IV5FhSCvHw9LB1PEQOYAOamuvvrqsPXWW5vowpYPYg/CCiId/8GIaIRAhLVu3doEpt69ewe8deCHmMa4JQlE1OFXSVx3r7324muiMcZDhgwxMQqRrWvXribeMQZcEzEK0ZJxgjftMUcw5hvGWCB0MRaIXbSHrbnmmiYKci+EmSO832233WbnED9lIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIlDeCRR10cijx3Pm/JtAEm8MDA8NvE0QDcaMGWNCDMevu+46NuY5hLCDlwciBB49ixcvtnP+h3Ms5BPmC8O7BcPbA8N7BLEBsQfjWniwRA33UXLTuJcOuWMQrRCAMDxkMNqmz4gNb7zxhn24Ll4/hDPLZLRBDiG8iDBCsZWG0XcXiAjF9vPPP4fzzjsva9PkHcK7yg0PKTx2/EPotKgR3g1BDndwvKcQSVzgQCSKthWtl7SPgEJ4OMYeloxx1MjVw1ggECKo+DhQBhGJMUUYwqMJL6vNNtssEJYundE+Ytg222yTroiJVYiKhI3r2bOnjTkeSLAl3B/eXowdHmzpRDQ8wfDGQlSinIuUXLRPnz4mNCHgNWrUKFx//fXhzjvvTHmope2YToiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIhAOSBQIk+iqJcHi+8YgouLOXxHGGBxnTxF2ODBgy0sHPuIEhjeJ25487g4gbBDHh28jvD2wKMGI6wX1qxZM2ufdhB43AhZh/CAsXiPKOBhwzw/knuM4H2DITp4biUXODhHOLskw/vIvWwIM4cQgzcRniclNQQwjPvw6xO67qqrrsrYNPmF/L4o+MEHH6SEFvjhfeVh7jh//vnns0mNA6HXEHGwatWqpcbMDmT5Qzg2N0LawSJqXJeQfzBFlCJ0G95azCGEKsQX8hLBlZB5hCFEmJk+fXrKGy3aHqEFGX/3Xoue830fa74zD5gnxESePXu2scAV3m2//fbz3UJbQt6dfvrpJhgiZCEKImYh3CGs4YGFRxqh+BCdTjjhBAvh52Jbocb0RQREQAREQAREQAREQAREQAREQAREQAREQAREQAREQATKEYESiUSIDm543hCiC6GFRXgW91n0JxQXxuI6Rh33OsLTBbGAsF8sumPsu6299tq+ayG//At5g9wow+J/VBxZa621/HRqu/rqq9t+VNjiwE8//ZQq4/36+uuvQ+PGja2vqZOxHcKNuUX3CavmFhWMCLWWqxHeDIveh4fty9QGQk1ULCP3EiHkMEK3EaItaohICEsTJkwwj5qKFf+dDg0bNjQPLUSQaD+o7/fl5d1by9tO6iteP3wQkPAWYtyff/552xKeDo8evJDcGAvmE0JSUkg55hcCTibz/lEmOu7rrbeeeZlF6/r8jB5jH08lPniXMdcuuugiC6uHeIm3Fd50LpAhNH388cfmNSeRKE5S30VABERABERABERABERABERABERABERABERABERABMobgRKFm2PR3z1PWDwnRBwL8LVr1w5Tp061e8WDA0MUwAj9deutt9qHHC5HHXVUxpBhVqngD14fLkZ4GDi8ljzvD6JGccz7f8QRR6T6RT6ldu3apfLSJLVLviJELoyQcBiePwhYnsdoxowZdhwu7h1kB7L8qV+/vpVAeOMeMbxUslnLli0D3kBRzyzqcP1HHnnEvGmS2mAM6Dtj6B5hzpNQflFDINphhx0sH0/0eLr9G264IeARFjUX4/AYwpYsWZJi5uVcIIyKf37u/ffft3H3vEF+PNctIhXCYpSph0P0Nsgt1a9fP/9q23r16tkWQQnxDPOxti8Ff1y09O/aioAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEB5JfCv60gxe4gXyL777mvh2vC4IAQbnkQu3iA8YHiKjBs3znLDvPXWW5YfiJw4CD/z58/P6eqEdSNcHblvjj766FReIbw2Nt1005zaiBdCwKAPDzzwgLWJGIWwgT3++ONpBSxEhgYNGpinlAs57du3t3qEVcMQZvBI+vbbb1Mh3exElj/bb799IOzee++9FxBrCL+H10o269Spk+XhIbQeId0Q6GB72WWX2Xgg0CUZXlZXXHFFOPLIIy1HU4sWLeyaF1xwgeXdQTgi7B3eXnj7kM8H76NcDA8crktOpZNPPjngUeUeQPvvv781gSA3YMCAgAhDCLeFCxeGc845x8YlyYuI0IWMmYtpufQjWgbBskOHDqFt27bmIcT1ol5HlEWAIo9RzZo1LVQhIQ/xjENU5BhzHqPvQ4YMsbxFDz74YEBgJBydTAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQATKO4ESeRJxcyymk/MHUQPh5LHHHjNBAs+UJ598MhWKCzHmjjvusHI333xzQCBC/MDjqGrVqjlxwrMDsQEPJurjZcIi/d133521vueu8a1XIBweYhDiFov8CES0T1/d+8nLRrfkReK8C0SEc/McPwgQnksIcQdGnr8ofv1om75PeDfuz+vQRtSjJ10bhHnzXD3HHXdcQGwiFxAh9Z599tmwxRZb+CVsG20HoQ0BpFevXilvIngjlOBlg9jl94snlwthhRqMfPHwfpTD04r7IUcQ4eYIc4fQU716dauBkEgOIq5HeYQiRKAXX3zRPHMizdou93jQQQcVCiEXL8P36P1Fw81xfOTIkdYvxubMM88MEydOLNREly5drD/0jbnM/GWLmIgxZ1999VUTvQgzh5iHWIQA1qNHj0Jt6YsIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIlEcCFQo8O/4prY4tWrQoLFiwINSoUSNssskmaZv95ptvzHNjgw02SFsm2wlCvSHmuBiRrXwu5wl7hrcM7eZq1CEnUVIenu+//97y92y44Ya5NlekHMIbIczini5FCsYOcB94/HAvG220Uexsfl/xJCLEG6EE82ETvwoeVYwXeZCSjOsQgo554eHmksqR1wovp65duyadznrss88+M+ELjzSEKwzPKwSxzz//PCVecdz7VK1aNRtnjsUN1oSgK643W7w9fRcBERABERABERABERABERABERABERABERCBVYHAX3/9Varru+WF2cp6X+WFr/pRugRKVSQq3a6pNREoSgARkrCGM2fOzOrNVLT20iN///13aNasmYXwa9q0qQlD5H9CJLryyivTVdNxERABERABERABERABERABERCBMiDAj0TJtUsEk3x/QFkG3dUlVxACRJAhtDyh94l0k874wevHH38cttxyy5wj4aRrqyyOf/TRR/YDWH5wGzciA/EhOgq5uld0I/IN0WfIRU3e7nTGj33fffdd+yE0KQVWNOOHykTpqVu3blhnnXVWtO4X6i/RoU488cRA1Cm2K4uRaoU1R9KlcG8yESjvBEocbq6836D6t/IRIPTeDjvsUOwbI/QcYRF5SZO36ZhjjgnTpk2TQFRsoqooAiIgAiIgAiIgAiIgAiIgAqVPYPjw4fb/frVr17ZFbBZDu3fvbqHnc73ajBkzLHIEYeZl6Qnce++9YY011kj8kCagvBi/zCcSCB8WyktiY8eODZdffnmYO3duYjMcJ/Q+UUNIB0DEHLaIC27kVoYbIfajdtVVV9nxbt26pSLMUI58zFG75ZZbUswXL14cPVWi/T/++MPSCSAMISTQT9ItkCaBXNFuhPmHATm1VwZ7/vnn7X6mTJmSeDswJp84EWxIh4DwB5fo/ZOSgLGK5/V++eWX7biLbSz+U44UBFFj3nCcz6OPPho9VeJ93mNcj/QN9J/7IH3Ep59+WuK282mgNN+r/vwhquRqpXn9XK9JOSIT8e4ZPXp01mr8yJ3oUMwbmQisCAQqrgidVB9FwAnUqlWr2GHmvA22/PqMf9D5yERABERABERABERABERABERABMoXgSuuuMLyxNIrctLya38W4m+66SaLCkEO5Gge2nS95weBl112WTjppJPCAQcckK7YKn+cUOtuLDpHzfMJR4+V1f6ff/5pUUC4/qGHHmph8ZdFX/A08fD0zD/Eoeeee85yb5PbmFD1pB5wwYU81QhKiA6Y82TL+gN5rW+//fbw0EMPhT59+lgZ/rg4ccghh5SalxJ9OuGEE1LClXsIkWOZH93iWYRAhoixqlmnTp3sR8LknD7qqKNSLFq3bh0QHhhzos9g5Czff//9Ux5WftyZsZ5EbvMXXngh4G1GmgJs+vTpXqSIgJQ6UYwdBCLmPIanFPMNLzB+BI0n2Jw5cyz9RzGazrtKab5X+xXkJ+f5yOfH4KV5/Xxu/r333rP3z2abbRaOP/74jFV5pl9//fVAWZkIrAgE5Em0IoyS+igCIiACIiACIiACIiACIiACIiACqwgBwsuxcIjhTcTCOpEgCBOOsSjLgi5GuDAW6/h1P/mADz744IB3BHbJJZeEYcOG2f59991ni6rz58+375TBo4A6e+21l3kfIEC48etv8tjiZUA4MjxtWJSN/tCQfuJ1wOIm5RCi3nzzTW8idOjQweogDiC8cK3JkyfbMUSH3377zcriEUPbfN55551U/bLYYfH8/vvvL/ShX+5Vw76PA7+o5zscycnMPh+8J2AKky5dutiv7/1e8HDp37+/iS7wgBHh5KN266232jhyHk6MIWILuZLxnnA7/PDDw0UXXWRfs7XL2OLFw2I6XjWIjZns2WeftdPrrruuCQEIO9wXYhCiEX2JGh4DCBD0I8kQJDDYMW8whKiHH37Y9lkkxxAd8BTh3plXeLOQCzsfw/PLPZsQUydMmGAfBAUMsciv6+3ioYWYynPE2F1//fV+yjyhhg4damOLsMSYjBgxIiWEURDPCp/jbMePH5+q/5///MfqDhkyxJ4R5gXnfb6QTxujD02aNLHj7n2RqV3q3HPPPZZOgDZh9euvv3I40ZijiAvYXXfdFfr27WvCHf3juY6PKeVOPvlky73OftxcsOE4nN14X2G8l9Zaay17dniH0Ec+RLMhVFy+1qNHD6tCX+GDiMWzs+uuu5rHCmHbMJ/rjCPzCAGMMXcjHB/sjzvuOHsOeB74PnLkSKtLOTyu8ILjHG2QssG9MdO9V6PtksqBucQzx7M7atQom1fMH+Z1r169Uu8/3iOnnHKKzSmu7fMFbzzuGWatWrVKeWWluz51o+bvX+ZaixYt7D7Y8vzx3uC+4u+CTO8Rnine8RgeRTDj2Yq++/zfiQMPPDDg1cY74fTTT091i7CVvEO4rs+F6Lwrjec/dTHtiECeBORJlCcwFRcBERABERABERABERABERABERABEVh2BGbPnm2Ns0CPyOC2xRZbmMjy5ZdfWh4OftnPYhyLzCxI4vHCYi0fFvHcy4P6LOKzgEwdhJx9993XmmWBFaGJBfKFCxfaQiVhgljopg7GgrovqvsiNDmSWHCkLfq59tprm3cGIhACFrl0EXw4H72HBg0apISkV155Jeyzzz7moUK5jTfe2PIu2UXL6A8LzD/99FOhq1euXDlsv/32AQGJRVAEExbZe/fubYyuvvpqW+DnHjAWdN0QyBCYWGBmgbhr164mKMCMNhHvEDVYUK1fv74tfHuYL8YUUYUPi7csjjt/2kdkcaEtW7uDBg0qFOqNsIWZjNByGHOAhWoW1Hfaaadw3XXXpa3Gwj0CGOHb4obwwz3THixoz4UoyiJ4cZ8uQrZt2zYwPxACWISGY6726quvWlE8iPbee+9UNTygaJ/F7lmzZpn46Sej4bN4dhAf6C9CB4IaY838bNeunT0L5557rp0nhwx1O3fubE0hTODZwgdPP+6DRXnmhs8PCnoeHY7xvDLWb7zxRio0F6yztQtHPKbcYJXJqlSpkhoDwu6dffbZJnghhvBJMsaLhX7uJ25Vq1a1cePdQHg72PJecDEOYZB5i/BFO4iLCH4INojUeHTlmmMNIRkvFoyxcOO9g7jDeLrHCqKEjydc8Vbjw7PGs0lffDx4nt2Y54iozBPGE8GCXF28s9hHFMNbKd17Nald+s01XCjhfcvcQvjl3gcOHBh4n9MftljSfEGg5cOzkO76fh++9fcv4+fPHiLhbrvtFr766is7xr8dvAuYt9xrpvcIImZUBOZdxPuS4z63ea4x2nIe3p9vvvnG5gDXpD8Yc+GJJ54wgZLjpfH8+/W0FYF8CciTKF9iKi8CIiACIiACIiACIiACIiACIiACIrDMCBCiB2PBMh5SjhA+LEyzCMeCIZ48LNyTz+Ltt9+2xWbqItTwa3YWgjF+AU4Z8rP4Qv+FF15oXkksqrNox2I4v6CfNGmSLfCx6IpgxEJn8+bNrR0WBTEWhVkYpAy/BGfBF+GJhcH4gj6eFywAImpsvfXWqYVA9z7wbceOHQM5dMvSWIhm8Tv64Z4weGIstNJXjiMiIIBEjQV3FsPdawPxhDHlV/QsXsOaxWZEEhea7rzzTmuCMIMY3gUs8npuGb4jvLmASBm4XXPNNTm1i3cCxpgzDngAZDLmly/YMi9Y6F9//fXN88nnZ7S+eybhceMeF9HzhKZzbyEXHD0sGXMar4aXXnrJqhDi7I477jDhjPtGCM3H3MuO5ydu3BfmZaLnGQ/mOmOLIT5gTz31lG1dfOA7/SJXGIZHDsZ9cc7HDM+hqDHuiIG//PKLCQ54emDOwa/DnEL8yNYuHmcYzx3h3hAjffHdTsT+rL766iYGc5i5g2CCGIiXDN5zcWNuIoziuejzMl4GryCMdwZigQtE9IN2582bZ88JZcaMGWOCAF41iIn5eIjxvLjxHEQNQY13IvMEwdsFIoQoniEPbzh48OBoNdvn3YewhViN+dxEFMIQgBEVOc6Y4xWU7r1qFf73h/nL/cGNvEm0A0c+cMAQfTIZAtfXX39tz7eXg28u1/fybPHcWbRoUfD7RyCCJ3mcuAaGyJbt/YTg6X3nvc+/J3hRRo13H2KQP8vRcwh1/DvANbkW/YA771GEcq9T0uc/ek3ti0A+BMr2vz7y6anKioAIiIAIiIAIiIAIiIAIiIAIiIAIrPQE3Msg6jWSdNMIGSy8s+jMAjb7LMJhcW+YaH1fjGPREIGDsHIuhCAAuBDBIjaLyISMOvXUU6NNBPfWIIwUi6D8Kt4XDD1Ulldg4ZBFRfcaYLER88V0z0vjIcm8Xllt8aKKfrzfNWrUsJw29AvPChbC8dCJG/eL1xBsEcgwFvBdXIE1rDjvXjcsaONBwMIpBleMPFLMg2hIJjsR+ZNLuz6+iIrcD142vkAcaSq1i6DAYjveF4hFLj7wnZB38f7gBcUiOoZ3CzmL4ubjy6I7HlBsMZ8PeDhgCBjMuzPOOCNUqlTJwoXZiRz/ILBgSc+PH/NnzJtEqGKsmOtcF8PjA1EUgQzD244wWYiszGfGj/HC88jPc6xfv372HREVQc6N++QeYcsYOA/ERK7j3jo8x7m0y8I+xpjiJVSnTp2UwOXXjG+5N8Qw3hc+/uSaom8+Hl4Hji4KME+TxD8POcf8oj8ufDB/YYmg43MHTyy8D/FY5PnivZGr+ZhSPurNEq+PNxq27bbbWjg09n1+MZ68K90og2jIXCBMJ4bIhOHZhuH9hJcNAh5jx/hnM+YG707eAcxf2uZaeM1w3L1tsolkMEKY5b2DcIL5XMvWh+h5vze8mDD6x48FYIrAhsEl23vECmb5c84551ieNO47bj5fCf/HfOU5YE7xTPqzQZ2SPv/x6+q7CORKoGKuBVVOBERABERABERABERABERABERABERABJY1AZLHYyxqsoDGYqvb2LFjbSET8YDFQ8qyQMsiJOHLcjG8JTAWiVlYxhAoWAxn8c6vF13g9sV1F0z8nJelDRZFsbhAxUJn1HzRkvtjURfvHRaQXSSIll3e+56TKN11fWGd8yyy4iETt+gx54Mo4swo79wpSwhAhBf30uJ8dJE1m3dVtnY9JB3t+hix731jP8noA94mfPASYaHcRQEEjbhHB/lrEPzwwojm9PG2WehGMGC+4k3iC94shmMsYuNVgwcPwgneKXwI7efeNt5Wpi0s6UNcrKSOH4vPteiYRfcZG8JxMYcRTPDkQIjFWw5PH0J5ubHw7p5/iDw8T1H2iLpRYy55+DGEGxce4YSQ4pauXQ87Fp0r0b57/fgWMczFS7xB8FxC0HJBMFoeEQUvGMLrIZLFjXtCUMHTBoHIPXBcACNUI15b5HBCJOEe+eBhg3DqYQ3j7ca/81y64QVDSDg32CF0wcmfId5jblE+UYEpWiYqQlEPzz7EQPL58J7igwcPHi8u2Hj78S3vsqiRQ8i9EPHI9GcgWiZpP9qnuKiZVD7dMcSYXCzbeySXNlwQTCqbNF95XvyZKa3nP+naOiYCuRBYLZdCKiMCIiACIiACIiACIiACIiACIiACIiACy4MAOTDcCPlEiCOMhUrCxrE4zWI/i60suPNLdxZ7SWLPr8STjBBAbu4ZQe4bQlbxue2228yzgYX/XXbZxYqy0I8wwAKye7z4IixJyzEWTf2Yh1fzBWgrkPCHBU8P6UX4M4zF9mxiSEJTy/UQi6g9e/a0ayLKIQQ4l2hH3CMDMcHDb+Fp41xZRB4+fLhxZzGakIAIMSRy90VWr8dCOmG78PaKG6EBsWztksvKjYV6jIV2QoGlM7yCCAGHRwgCEQvNTZs2TeWM8n5G67PYi8ATXyT3MsxZ9+rwvDIIRNWqVbMihEukDbyzCLPFfMZg4fdqB7L8cQGIeh56jCqIPHgpYPE5Sl4UhFIMYQpDmKDPeN4h6JBbhhCPHsKR/FuwdRbMDZ4l2F177bXm2ZfNW8afg0svvdSuCR+EnlzadTHZQwfyLqBP6QxBljHlXghPh+2www4mULK/3nrrsSlieB/hUZPOfEwJacgzAQ/mLAZTxMATC8LB8Tzgpch5+ureiOnajR5H0HRPGAQr9wjienhF8U4kp4+HGOReCaeG+ZjzzKa7x+i1eM7xquE96e14Dre4WBl9r0bbiO572ELelYiozI3Sslyun+u1sr1Hou3wQwN/70ePZ9r39mHodS+++GKbKwiMpfX8Z+qDzolAJgISiTLR0TkREAEREAEREAEREAEREAEREAEREIHlSoBf53teH8SEWrVq2UIaYhDGgiULxHgSYeS5IOQUv1J37ww7UfCnUaNGtssv/PHgIZySL0yzqE09Qp+xqItQwyJ9u3btLAwTFVkcRhBicTxqtMViLwIS4gGL/ePGjbMivmgcLR/f91wmvoBLCKLyYAgnLKJHP88884x1DW8AFrpbtmyZyunDwriHafL+I3zBzENTIdwxDixSM4YsbOON0KtXL/OI4N4RKWDfuXNna4aQbYSmgj9eEi70ICD4YiveGnh5ZGsX8Q1xESNsIH3zeWEHE/5wXRby8RCh7CmnnGJzDq8vLJ03xUYbbZSauwnNpkKs0TbmeYrYZy4wh8lTglDkghb3F/fCoXw6Yy4T8g+DJ0IIHzydMM7hiRc1+oM4i8jgwp+HT2SM6Rch6RBO8VzB9t57b9viaYTxDJx//vn2LCH6DRs2LOUlYQUS/hD2D3MPpyiPbO36M4R3Ife35ZZbpsJNJlzK7g2RhHdENI8PAjFGKLgkY/7ccsstKTEsXgYumI+ph5rjGCIG7PDuuuCCCwJ99XLZ5iD1o3bzzTdbHxDL8eziHYSQ556IhEX0Z4F6iDww6tq1qzUTD5kZbTu6jyDKPTBHEGcRcl1Q5T4w73v0vRptI7rv7wHeCz169EjNw2iZfPfzuX6ubTu7dO8n2vEfMDCGzH8PL5jLNXin8G8Gwhv/ZvCMXHXVVfZ+47krrec/l76ojAgkEZBIlERFx0RABERABERABERABERABERABERABMqMAEIOAhELdyzaIRRgLPa7twCLryQlx1hAxWsh7iFBMndfiGYRjsU9FlfvKEisTtvUw/OFsFL80p3FeEJEsfh3ww03mDjAYreHD/Nwc3i90B7XY9EWryYWbCdOnFgoFJR1LuEPC7AsGGLlIdQcAo0bi+jRD14L7777bhg4cKAVIVQWof26dOli330x3+sTWuujjz6yrwg68OVeWXzGSwWRiYVSuHIdhAU+GG0j3GGE/2LsCQfWv39/O8YfvE4QnqiLN0Yu7V5zzTWpPEcIe8wjxBgseu92oOAPHmX84p/rsEBOyDcW45kziJIuUEbD13ld5px7XMXbRuD0cae8h5pjnz7hpcM9c7+IjswvOORrHiKN/tNvPuzTPgvTcUOcQVRwMYDnyscEkYh+MmZ4PjDfEVL79OljzTAe5513nu3jJYLgwxi7F5N7yMVZUIHnyMVfuLB47patXUREPLKox/1F56Rf09tiyzG8qxh3GN999932nfp4QNFnL2c7kT+MN++MJOOd4XU5HxW6eK/QNteA45AhQ2wceE9suummSc2lPYZAwfPjvPx9xv3g7UV70WeB5wOhkfcLnkZ4TuZiCLF4/PA+oz7h4ngHMCddSEt6r6ZrmznB3GP+IJL5s5GuvI9d0nzxY/lc36/jdaPPbPRaUXbp3k/Vq1e3nFuMJ2UWLFhQ6P3h1/BrRre1a9e2f1d4J/IM8W8MXPBAZX6V5vMfva72RSBXAhUKYiIu9dvOtYbKiYAIiIAIiIAIiIAIiIAIiIAIiIAIiMByIkDIJjx5yGPjIk300uQLInxPNMdG9Dz7hKzj44uCfp5f+tNmNCwWnjMebszzkCBcsFhP2CiEpaiRg4h8E7l6exC+DO8bzysyePDgQMLzFdkI3+WL3uSTwWMDQS7KNXp/5EaBPZ43SWPKWBE+K9152vIwcPm0y1whz04+OU4QFD7//PPAIm+uYxztU7779A+GeIvk08901/n4449t7uNpk82WLFli4d6ieWy8DvmFCIPHOGcaM8Y8mk/G6xd363MhXbvMA/odz/2V6XqU/+CDDwKL/jVr1sxUtNTOMY/gWhpziGcLIRYxM90c4Z3IM+bPZXFuhPcu4edoI0kASfdeTboWIRN5LyDGlJblc/18rpnt/URbSe+fXK/B+HENDzUZrVfaz3+0be2LQCYCEoky0dE5ERABERABERABERABERABERABERCBVYoAC6OEVSK0WtT4Rf5DDz1kYceix/PdJwSYe1nwa/3nn38+o8CVb/tlUT4uEpXGQnhZ3IeuKQIiIAIiIAKrIoGKq+JNozQTY5UtbtFJ//GSS5lVkV1J7hmm/NIB4xcsSb9C4By/dsCVnV9zpPvVEeX4JQ+/TkkaP85jtMOvfXCXTTKSzfE/AMS4TmelcZ10bWc6Tr/45RS/DEn6lU6mun6OX3zQjrvC+3HfMib8eoZfnqQr42W1FQEREAEREAEREAEREAEREIFVgQAeCYQSmjBhQpgzZ479+p9QVoSpw7OlpEYuC/5ft27duuG4445L6wlQ0uvkUz8aggmvqHy/R70V+P/LfOur/Bqp4UrHn+O5mFhmZ+kc07Fenuf9WtqKgAiIQFkSKHFOIhKQ8Q9Q/OMxMsvy5tJdG9e9AQMGmFCESJBkuZRJqlfWx3w8+HUThkt8fGyIIUqc33T37vdA7Nt4Xb7fdNNNVoS4pHxHiIkbcWWJ3+zGr6Rw46V/fNi/5557/LRtcacm1vAmm2xiv8ziV1oklPQEhl74nXfeCfvtt58JTZQlEd93333np21L8jyuQ8xWxCb6iiDiRjgA/mOcfiDCMF/jyTZL6zrOiW3UiGNMckUYRvv/6aefWgI7/ueD+LXcIzGc3XAVTxqXfv36eRFLxsr/wPA/HohkjAfxdqM2fvx4E8f45RplSG5J2+ksXX+9PO7afj/xsfUy2oqACIiACIiACIiACIiACIjAikAAoeOEE04IV155pf3/M///WBoCEfdO7gnywpD3JV2oqOXNiMVy/3Bt32eb73d+pFmS+vleb1UpbwORwx+xL725vDzmVg5DqiIiIAIisMwJlFgk8v9gYDGbRHb+4ZcxsuVPwMfDBRHELoxkaIwNSRZJioagcMghh4RFixZl7CT/8Uriwujn2GOPLVQHMYJYmumMxIC40/MfwQhBfEh4yX9wP/vss1YNbx1ECoSZ+++/38rMmDEjHH744ZY8kX0Mr5ijjjrK/uMcQYOEk4hUxIX2e0XsOfroo619rkXcaP7DnsSNbiTYxKV/1qxZlmgOQQnxjBixWGlch34gUpHAFMHLx4T2SRaJ0LXVVlvxNXWOuKQkVSVG6+zZs008GjRoUGjfvn145ZVXrCz3hM2dO7fQuPTo0cOOw+HQQw8NG264oXkJEYMWgQpGb731lpV54oknQqdOnew4c4AwCpRnjuB9FLd0/Y2WwysvnxjA0braFwEREAEREAEREAEREAEREAEREAEREAEREAEREAERWP4ESiwSeZcRHVjc9w+L8th//vMf8wZBIGARG4+GVq1ahUcffdSr2mL3kUceaec4j2cIIbDcECD69+8f8GxhIbtDhw5h5syZftq+43GCZwQL/ZRhS3LLiy66yL4jArgHTKpiwc4bb7wRDjzwQLs2gkiSV0y0fHQfzwyve9ZZZ4Vhw4bZvQ4ZMiRVDA8aFt59AZ4+uuXChrKZ2vC2sm3btGljY4NI4IILDBmXTOaeNnjb+CcaAg7xCY8kPJPSmYsbiBKEhuNz+umnm3hCfez222+30Gp4ADVp0sTK7LzzzoEkoXhDIWxhzz33XHjvvffCiBEjLDwa4wrHRx55JOUFgxcLouWFF15o7ey///6hT58+Vo42iJV877332ndEHDx1GDsEounTp1OkVK7D3EKMos9+n9Z4wR/EsFdffdXuzY+xffLJJ034IhFq/fr17Vdl/LoMYYh7wkgUSXskbfUxYesJ73755RcTf6699lqb1yQGbN26tdVdsGCBbeG8yy67hK5du4Z1113X2hs6dKixnTp1qpWJ/knXXy8zZsyYMHbs2CLeYX5eWxEQAREQAREQAREQAREQAREQAREQAREQAREQAREQgfJHoNRyErEwTQgvN8JhVapUyYQavD34uCEQ8cFbg1wriAJ4UBx00EGBkFUPPPBAePHFF8NHBflrOM9CNkIJi9nEAcYzhIVsPEFYSGcBm/YRIShDW4gKu+22my38cwxPCTwdCCtGG26IJ24sciOg8FlrrbX8cOIWUQLPDLdoaDXuB6PPeNBgXBePEj7k4mnbtm1WNuTbydaGNZ7nH8QXRD0+8VBuSU1FPWA4H80lRB6bG2+80YQ/BLOkMIN+DG+YCy64IDRq1Mg8ZZo2bZq6HB49zZs3N4+b1MH/7VxzzTWpQwhEeOVE4x03bNjQziMs4pnDfEA0XG21fzVQrokIxBx1AXKnnXZKtYvwhQgzf/58O1Ya18FTKp0xf7DPPvusUBFEyl133dXmNp5P9PmAAw4wsdNzLyEYwb13795h0qRJds+w7dixo7VVuXJlE075gsCKWHrppZdaOffw47niWYuae4PFk7NSJl1/Obd48eLQs2fPMHjwYAtbxzGZCIiACIiACIiACIiACIiACIiACIiACIiACIiACIhA+Sfw7yp6CftKfF73EmGLB0jUWLzHAyKa84QwZIQxQ9TB8EYgDNaoUaPMc4hFbMq7QETCSMKTsThOnTvvvDN6CYvnS+gsFqsxFtipQ44XD+uFGBG1du3amScM+VYwxAEPgRYtF9+/5ZZb7FDLli1tsR3hAfEC87Bnffv2te8PP/xweOqpp8KUKVPse9TTiAPp2HAu1zYom48hFGF4uXh/k+rjEUY84ejHQ5Z5ecLWnXLKKSZSIPzFDZHouuuus7FFpIAT4hmJQN0Qq9xThmMIG4h2/pk8ebIVff/9982DxuuxdQ8azmHMqVq1atm+/yEvEYbw6CIIQkvUtvx/9s4DTIri6cMlOeecBckIgggGFEE/MYCoGFAURQUDqIiCiooJUYyYUYLCXxEREUHMIEkFJOecJecc9Zu3j1pm53bv9u5WglQ9z96knu6ed3bndvvXVVWmjBOY2BePdvx1x7qOSISHFzwRTfEi4n0DY8QYbN26da4M4egQPrl+vOD43PgNDghGeBzxWUPoQTDF6tev797reHAhAhJiTnMmwT4lRuhARDs8vsyMgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRuDkIRA3TyJCV/kH3cl94zdCrpGvhBfhvwirhRcFXjfq/YN4gDcJnhOElMO7g8FtDFEIbwlMBR28jfxGbhwMTwyMkFwVKlRw63iNIA7g8eQ3BrbxGuIc+shAOrle8IpJyjSEGoP5eE1pmLz33nvPnYZApV4iCGYMpB8+fNgdw+vJn/clGptY6khtkkvlAHO/x03wmsmPwzX6rVSpUv5Nt47whXdYhw4dpE+fPomOI3bg6YUISEg3PK8Q/xDkCCPI+0L7xMmIbo899pirBw4IaYQppJyKitqIesBwDCO0X7CMerkRek2FEvb5PcYQJQk9h8WjHVdRCv+ol9bAgQND70FCxVWuXNlxwwONEHuEXySUIkboPrzO8Dy68847Qy0ievFZ4X2P2FanTh2Xg6l06dIu1xPnEQ7v0Ucfdbz4DNCOMghVlMQKguHbb78tCKF8DsyMgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRuDkIRA3kQjvHrxqohmD2Gp+YQNPB8K7kWOGMHMako0cN4g1fjEFjwgMrxa8Xxj09lv69On9mzGt+0UC6sX279+f7Ln0O1h23759bh+ii66zA6FKB/8RPBDG/G1EYxNLHX6WrvEY/6jARki+pIy+48WSnCG+fPLJJ07YQPSKZFznVVdd5V4IQIgV5A9CJKIdcvio4XGEBxiGwKS5ongP4A3kN/V8KVu2rNuNiOL3WGOn5uLBo0jFqDVr1jhBSeuiDb3WeLSj9aZkibCDIaSqkW8I0XX69OkuTGGzZs30UGgJV7yCEMdUBIM3wg8vjuNdhVjUuXNn934kZ1Hr1q1dyEG4IM5yblDgDTUSYaVr167OM2zlypVh4iCf4xw5ciT5TIhQne0yAkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGIFjSCBuIlFq+8wAPx435Pfp0aOHzJ8/XxAIGOyeMmWK4KGE4fGCxwKiCOG2CBuHx0habciQIYKXEf1gYBurVq1astUibOChQWg7RAlECPWgQcDC20Y9pMjXQht4vIwcOdLVjZdUchaPOoJtILqR0wmWWL169YJFUr1NCDOuVT2+tCI8VfDaIXeRGl5neK2oITDi2YIQoqHwOLZt2zbn3YUHDYaYxHuD8H0XX3yx24e4iCGmYOSpeuWVV1x4Ns3jwzXzXkJIRJzCOE9D3BH6jvch9xKLRzuuohT+0f6QG4nrwGBHGEQVSdu3b+/283lR03CHvOcI3deyZUvHSL37+NwgQKmghoce4uwjjzwSYqAc/fdF64+2RPwk9xhikd+GDh3qBN6khGN/eVs3AkbACBgBI2AEjIARMAJGwAgoAYtSoCRsaQSMgBEwAkbACJzIBA4cOHAidy/mvsVNJELkwZtErVatWvLVV1/pZtTlpk2bXIg5CjD4jTePhgqjjoIFCwo5bQibhXcFwsCgQYPcgD4htxBr0mJ4LH399dfOa0W9MM4777xkqyR/CyG2EBdU1EDIwlNIjXwx1I9nDcLJhAkT3AA+16EeK1o22jIedVA3XiMIJRoCj314lzz44IOsRjXEB8LD+Q0RgfB6kYx7Qv4gzftDGTy+yFmFYIEXDJ5W5J8iZw58MELRES4NbxbEJO49Qgmh+mCqwgj3hlw9iBuErCNPz7PPPisdO3YM5SbCOwaRCMEKrxlCGw4fPtz1i7Z4jyFq4P1Wo0YNF5aQsryXNGRhPNqhrZTaFVdc4cSzFi1aCHmv8HTTXEENGzZ01cGTz1vVqlXdPUQw7dSpk9x///3uOGIYnyvKvPDCCy5sIx5bhDlU3uQxol74Ic6RBwmmhJ9DIEPQJOQi713qjmYDBgxIdIgfdOSgCoqFiQraDiNgBIyAETACRsAIGAEjYASMQAQC/5UBlwiXZruMgBEwAkbACBgBI3DCEUizSKQzfBBYVNzhKtWDQfPdaLg1PwH24TnRu3dvefjhh50nEcfJJUT4uSJFirjiiDh33323G+hHlMEQBXhFMm1L+0aZaP1ATGAgnb7TFzxsChQoEMofFKl+9uFxgnCF9wWhthB+8IIhN4+GvevSpYvw5ZbcO4g0GJ4ViBtYtD5xTK8huToo6ze9Zj1f26CMCkSILHgQIRDhBZKUIfQFxT7uDWICpteqdeCxwvUSUk/thhtukPXr1zsRQj1O8HihHoQqjH6MHTvWCRYISmoIUuQt0jxIXBcCBAKItkHeJBVSOA8BBPGC68ODi7YQhPzCHO+32bNnO9GKcwjJhieYXk+82qFujHr1niTsCf+rxygHb0Sic8891xVCfCSXU5kjoegQX5YsWSJt27Z1LwqRw4v3MsZ7gHCCMFJPMero27dvSATjvc42OYxee+01d961114rL730kltHJEKk05B/bqfvj/bXt8tWjYARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbgJCNwmidi/HOi9BmPEcQCDREW7BcD13hI4F2UIUOa9a1Q9YcPH3YiUSwh4PSknj17CiG78HjB24cQeHih4EFDbhgG+dX++ecfd5z6/fmH9Hgsy3jUEUs7/2YZwvCRKwiBAdHHL2D52yUkHhwRNrjX0YzQaXjaRHu/cB5iCrl4VDwL1rVjxw537xEmo1k82olWd1L7CWHIfVeBLFiWvFZ4ehE+L1puKlgigBYuXDh4uts+dOiQY4RnmObZ0oJ8LlQ00322NAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBiB/w6BE0okOpmw/vnnny53UrDPhCwj3FpSwkXwHNs2AkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMwLEmYCJRGogTvm3o0KGyaNEiyZ8/v1SrVk0aN26cbAi3NDRppxoBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkYgLgRMJIoLRqvECBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAicXgXQnV3ett0bACBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAvEgYCJRPChaHUbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBE4yQiYSHSS3TDrrhEwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYATiQcBEonhQtDqMgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACJzmBffv2yZw5c2TPnj1pupJ41ZOmTtjJRsAIxETARKKYMFkhI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMwLEnUKJECcmUKVPE16RJk+LSoUOHDsmjjz4quXLlkpo1a0qePHnkscceE/ZHsr///lv69+8v+fPnl7p164aKpLSe0Im2YgSMwHEjkOG4tWwNGwEjYASMgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIxAkgTGjx8vhw8fDpVBiLnuuuskS5YsUr169dD+tKy8/fbbMnDgQEF0qlatmowbN04aNWokVapUkdtvvz2s6q1bt0qzZs1kxowZUrFixbC+paSesEptwwgYgeNGwDyJjht6a9gIGAEjYASMgBEwAkbACBgBI2AEjIARMAJGwAgYASOQNIEyZcpIuXLlQq/hw4fLokWL5NNPP3VCUdJnx3Y0a9as8uqrr8pZZ50lGTJkkAYNGsiZZ54p06ZNS1TB+vXrBe+mBQsWuHL+Aimpx3+erRsBI3D8CJgn0fFjby0bASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AEYiYwdepU6dy5s7z77rtSuXLliOd99dVX8sorr0Q8ds4557hzgwfvu+++sF0rVqyQWbNmycMPPxy2n41KlSq5UHOJDng7UlJPpPNtnxEwAseegIlEx565tXiEAG6y6dOnPyY8li5dKj/99JMMHTpULrzwQhcv9bLLLpOyZcsek/atESNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI5AWArt27ZKbb75ZmjRpIq1bt45a1emnn+7C0UUqUKpUqUi7w/bt37/fhZhDhLrpppvCjqVkI171pKRNK2sEjEDKCZzSIhHxO5csWSKnnXaac9dMiWCBmt6vXz+XzK19+/YpJ3+Kn9GuXTv5/PPPZeTIkc6N9d/Ecfnll8uoUaNCTfjXn3nmGXnyySdDx47Hyp4Dh2XV5r1SMn9WyZYpdaLZocP/yKote6VMgWze+znyVezad0h2eq+iebJELuDtXbZxjxTPm0UyZYgciTIe7fzzj8iGHfslT/aMkjlKO7v3H5Z12/ZJCY9JtDIc9y7b9TfqBSVzIDkmsfTV38Thv/+Rjd61FcyVWdKni3IjfCcw+2fVqlXStGlT394TY3XhwoVSunRpyZw5c4o7tHbtWsmbN2+aXN737Nkj27dvl6JFi0Ztn3Yoh9t9LM/vzZs3u3Ik3zQzAkbACBgBI2AEjIARMAJGwAgYASNwshHo2LGj7N69W3r27OnGM6P1v1atWsIrNfaPNxhy7733Oi+iP//8UzJmzJiaaiRe9aSqcTvJCBiBFBGIPBKcgipITpYpUyY5++yzE52FkMIxXpEG4jlHj69bty7s/EGDBoXq3bdvX9gxPYfBVQzBQfcFlzt37gw7lw3EoRdeeEGyZcvmYmuSjI14mS+99FJYorVEJ/p2rF69Wrp27SpvvfWWb2/aVv/66y/54IMPorprUnssZdLWi2NzNoPj3Bu9h7G0SuI8+CAsxWq8H1QUql+/vvB6+umn3ZI6nnvuOffeibW+MfM3S5XHRiV6ndNljKti595D7tgzX81PVOXXk9e6Y3oAoaRNn+lS++kxcm2PSW7Z9pOZsnnXAVfk0QFzErXjbxshBXv/l2Vy3nNj5arXJkjDl36TEdPXu/36Z68nQlFXnWfGyiUv/S43vjNZ5q0J/1yMmrtJGnX/w9VxwfPj5I3vl3j/zLWGhGVa26G+z35f7fVjjDTo9pvUfHK0cL1bjlwvrWzfc1Du+3iGwLPJGxNdmc//+CusI3P+2un62tC7lv97+Xe3vmDtrrAyuvH4F3Mj3o/kmMTSV22DJfU9O2S+nPnEr949+N0tuw1bKPsO/u0vlmj9tddekxEjRiTa/91337n3JUJq0O666y654YYb3O5t27a5ctdee22wmNtGQNFnYsQCEXYS15jnOs/FYsWKuWc3X+zU+DLK85x6/c91yrzzzjvOSw9xKVeuXHL11VfLmjVr9NSw5SeffOLqYHaT35YtW+YSYyLkUA8xjvv27esvIjNnzpQaNWq448xqOuOMM2Ts2LFhZfwbY8aMcdeE4FSoUCF3bqSYyv5zbN0IGAEjYASMgBEwAkbACBgBI2AEjMCJRIDoOH369HGT1gsWLPivdY2xss8++0wYm0hLBJ541fOvXahVbASMQIhAmkWiAwcSBrSJUTl9+vRQxazwQFHzDzKyb+7cuU6R1uPffPONrrqllqfep556KuyYbmiZv/9OGIgtXry4XHnllWGvSLPLe/To4USinDlzyiOPPCIPPfSQsI5XyUcffaTVH/MlCefoC/2IZrGUiXbuibT/hx9+cInvcI+N1QgXBx8ExFjsxRdfDBVDGPr555/dS9dZquFtlBL78sFz5IdO54VeQx6qE3b6l5PWyMg5G8P2BTce8YQbvIiGd6grU7vWl6/b15GNOw/II5/NcUUfa1I+VP/tF5aUfDkyhbZpO2umdDJo4hp59+dl8tot1WTicxfJnReVko6fz5Gpy7eFmnt68HyZ4m0PfbiOjHu6npQtlE1a95kREmcQjNr1mynX1i4qE569SN69vbr0Hr1C+o5dEaojHu38NGuDvPjNQnmjRTWZ/EJ9GfHoubLG8wbq6u1Ta+v1Y/OugzLsCBP68sLQBYLAhiG63PHhVLnszEKuDvpbuXgOuav3dMGLx29D/lzr7gHeVUFLjkksffXXiaiGgMj7gnv5edva8t2MDfLRr8v9xcLWeW79+OOPctFFF4Xt92/cfvvtgtjuN33e+fchNC1fvty/y60PHjzYLcuXL5/oWKQdPMObNWvmXMpJQsn5JK18/fXXXXGOIx798ssvTmzRZzAHv/76a/c8RfzZsmWLzJ8/X/bu3RvRLZ3neps2beT8888P6wb1NW7c2In2ixcvFjx/aJsZTHx+MequXbu21K1bV1auXCkbNmyQe+65Ry699FKZN29eWH1sIKz/3//9n3PD55oQ+JlNxbOHCQNmRsAIGAEjYASMgBEwAkbACBgBI2AETnQC/LZl0ij5gS655JJku8tkS500Glxec801Uc/v3bu3dOvWTZhAyu/u1Fq86klt+3aeETACKSOQZpHI3xzh19QYyHz22Wd1M9GSBGrYVVdd5ZaRZsy7A96ft99+O+Jsez2uy+uvv97lnEFZ1xfeQkEbMmSI24UnEN5DDIIyGMrg6KZNm0LF//jjDxfnM3/+/G4wk/JJDSoS+uj+++8XZsYzEx8PJwYw1fCIYuY9D1nqZOASsQSDR6tWrdw6D368rL788ku3rX+ilWEGPeVvvfVWdy20j6cUNn78eCea0R4z8olbSj8x/3kffvih6zP14LKq18mSRHfq9UXf8RbwDw7z4G/UqJG7Jo6/+eaboeO33HKLO/fjjz8O9WPr1q2OTcuWLYUZ/oSUon5e8GDgmL4y8AsLDG7Ui3GvKMsgcjQjBxEzFtRUEKI9ZcM+PIswvI04J1Yr5YVB878IFee3KsVzCl4seAtFMgSNKcu2ySVVC0q5wtklS8b0UrFoDnmn5ZnSukFp58VTMGemUBt5s2eSjOlPC23Tdjovrtznf6yWlvVKSv1K+SVnlgxym7d+9ul5ZMgRUQXPnO9mrJcnr64gFYrkkPye0MQ63jvjF25xXft22nrXdpuGnvdH1gxSt1xeaft/p8vHYxM89SgUj3YK5c4sfe4+Sy6smN+F1Tu9YDap7fV19ZYET8FNnkA2dfl27oC8FAAAQABJREFU1/YZR5g0rFJAePX6NUEo2bhzv1zjiVl3X1zK1UF/m9Yq6q5ny+6DIdRLvbB5Tw2eJy/eUNmJYqED3kosTJLrq78+1ld64QLhVtW779zLGqVySY2SuWS1tz+aEc4Nb7rzzjsvWhEnhvBc0M9j1ILeAcSZoL333ntuV+7cuYOHIm7zHD7zzDPl8ccfd+HiGjRo4DzvENaxGTNmuM8lzxVEeb8hUuGlg/iSI0cON9uIdSYE+IUtnoM8q3lW8sz1G5MOEMLxjCJGMuI9ZTEEIYzPMNa9e3cpUqSI4HFEf7FIIj/xj//3v/85AYsQePQRIYpnM7GczYyAETACRsAIGAEjYASMgBEwAkbACJzIBMjpfeedd7oukmdbJ0HrkgmRQWNCKpP3I72ipc3Ac4jf6owFpEuXLtTOxIkTXfWMSxLVQ8cVg23qdnL1aDlbGgEjcOIQiGtOIgYkEV2yZMmSZOgfBIZPP/3UUXj++eeF+Ja///67mwlPbolIhqBAiKHgwKS/7MGDB11cTt3HA40wckHTHBeIHwye8uDkAchLjZnuKiAwa50BSvrKgxeRJGgIHXijMJNdZ+0zYMm1IUDQD7wCmG2PcR0MdvL69ddfXZg7+q/GQGpwYJh/CpHKMNBMf3mpl82OHTucB0LDhg1dlTVr1nSiCg90xBAe8JHOo/CDDz7oHvoMXiMe4cnFwGrz5s2dWIf3FYO3d9xxh7z//vui/1wQpwjhxIt+durUSRYsWOD6heCjxnXofvrANn3HCE+lhrDEADPh5dRjjWOcAx//wLOeo0u8jvzGrAnupw4wq2jkL8M5eCzEZJ7Tii8CV6I8QA9cVlZ6jlwmeKx80Kq6E3T89ZKv5vzy+VyouNxZM0rDqgUkT7aMUtgTUnjFYn97HSDMWivPe8hv1T1xQj2JyFOEVSyWI1QkpyesIEit2LTH7Vu6YbdULZEzrI+IXAhJhFHLnDFdXNqpWTpBqEAgO3Dob5m01PNumrJWOl6V4OWiOXw45jdC4WFcb8l8WZ3I5T8+bsFmx6yAJ4CpdfO8kxCj8DgaNjU8lGUsTJLrq7ajy2bnFJX2n852QhEC0WRPAPx13iZ5/47qWiTRkmcen6ty5colOqY7+AwgmiKQEnc4mrVo0cI9lxBLeP5ikydPdp9FjvF5i8UohwDLs1MNrxsEFcLM8QyLZsxEwnuP5wViNKEkecbiMeSvjxCd1MUkAsQbv5H/qG3btm7WEkJx4cKFnSs9ZfASxTJkSPi35X8mqGhNjrmg4RrPi+cF53CNzIq68cYbncAULG/bRsAIGAEjYASMgBEwAkbACBgBI2AETiQCRNRg7BC77rrrEnXtiy++cJMt/QcIy84rJdarVy9XnLa0PXYwLsoYBmOijHsS2t5v5Hr3W3L1+MvauhEwAicGgaMjgWnsz6OPPupq+P77791SB/8QFII2ZcoUIe8EQgmz1tXNEQEjaJdddplzp0QYQJQICif+8ggWzBTXVzT3S+0rXirUyQAinj/kB9H63333XVc1g648COkzwgiiCZ4wQSPcEw9KBnSZbc+rXr16bpB29OjR7npVICIfD9ev+TxQ9RmwVOEMLnPmzHEDrf52YimDNwGz4xkEZUmoJjySEIUQ2TBEHP7B+I3rI78JfcY0Twp9xxBP+vfvL2yTF4hcIRjtYOxj8FXvP9t+w8OIa+YfSYECBfyHwtaZsUC/VeSh34S4wtsLl1oMzwr4VKhQIexc/wZhqoKGQIRQpGGrVKTTcnifxWp1nx0rVR8fFXp1+Gx22KkZPBGo241VBAFj4IQEb6iwAt7GyzdV8TxPcjmPl/OfG+fyEpGzZ39AJAmep9sbdxxwq4VyhYtKRTyRafH63e7YqiOeLMEyxfNmkWWetw22dMMeKZo7QVhwO7w/hY/UiZdPvNrRuh8bOFfO9vIwkXsIgevGusXcobzZMzrxitxH6hU0ccnRz9o2n6eQ1jXaE2Lg+3jj8iGh7pfZG+X3RVvkyaaR3x+xMNH6o/VVj+sSj7Bm53j5e76cJ41fn+jlJ1ogN51b3AlVWia4xJMPb8KkjGcBzyKeFTw3ohlehDwj/WE7+VLG87NKlSrRTku0n2dYyZIlw/aTlwjDUygp4zmKmIUwhCiN4Ev/H3vssdBphHpDoOXzjAdQJOM4nkhXXHGFCwvH5AO8VFXcr1MnIbQj5RB9EJk1HF4wNJ+/flzteYbz5RbxKZLXkb+8rRsBI2AEjIARMAJGwAicGASYsMT3VP+EyROjZ6deLxgLIay0jptEIoCHAZM6zf7bBOw+H9v7S/4hfv9Ge0XLU5zSXjJuGakNxkUxxuwY1wtOdiXyknobUS65eihjZgSMwIlFIG4iEQIGxsx3xAZEIgbkIw3kqxjEMUIuqeCACBHJGFDES4VBfUIMRTMGJP05iS644IKIRfGQQdDo0qWLG8ykEAJG586dRQWkCRMmuHNffvllufjii11eDQZhsWDuJfbpAC4PTnJj8CIkkx5DNMIQxcjpgZF7iC9PKki5nWn4w/UT3g2vmYwZM0rVqlUFN1TEIcQwZuirkStEDc8n+kVoPsLGYRraifMxvKgQ0ugr7cCELwUaTk//ISHMcU3BGf38I+E89QJwlUb4Qzn6T/0ISxjeRCm1cePGRTwFgUg9ioID9HqtEU8M7HzTy6vz4Z01Qi9CxAWNcGpPXVNBug5dGBJt/GUKeOHkPrmnpsvN8/Q1FQVPGnL2PBIQnPzn+NezZU7vNvH28Rt5jnJ4oecwXQbL7N5/2IWWo0xuz4OJbb9p+RxZ0ku82tH6H2xUVv53by1p50LarZRXvj0aNvC1m6t64ef2Sr3nx0m9F8ZLq4+mydW1irhT6affyKV0/yczndDUqHohd4icRS8OWyj3XlLGhebzl9f1WJho2aT6qmVYklfpqz/XuPvNtT3miVZfeOLVWz9GD2FIXp9Y3nN45fD5uu222xLN1tE+5MqVy3kA6rMEkZRnccyecUcqIiylPue0brx+MNpIyvBivPvuu11YNz5n/A/g+cAkAP1BzzMWIR2vxEjGl00+lzxDCH2HWMwzAQ8m3NUxBB4EdQRxxCS8sfBgJXRpUODyt4FoxTOZ8KVMEIB9Ut6I/nNt3QgYASNgBIyAETACRuDYE5g0aZL7TYuHOeGNsmfP7iZPMYBp9u8QIBoAvz34Ta7fv2lp48aNbqyF7+LVq1d338nxXFDDs5+IK/yeYHyH3w58/16zZo0rwqQv6oz08v92Z0JopDLsS0qY0n7Y8iiBaPeS30CMvXGvdMzl6FkJa7Nnzw7dBx0b4khy9zlYj23/NwkkN67337xqu6qThQBjWpqChsheZrETSBhJjr181JJ8WeDLBGqx5q/gHzz5IPzGP3adwY3rogomlGF2EIJAcOY7A4F42zC46M8z46+XdXJXICjFYohOhEXihdjBkkFNXsyGJ1wbRjl1z6QcA4v0J/jFVAdSOUfL80WK0E3UoYOkhFPymz8Mk39/atYZLPUb3kAq3vDP3z+AquGZKM/1qAXD8z3wwAMuJB+Dsoh0iGkMPiNwEQJQDVFKLdI1xZoTxc9Hw2YF30PaTlJLBqYjPQw0H5Eu/XXwJSlWO79CPpcDKLnyzT1vkp9nbZSOn8/xPGbCc7jouYhJvG4+r7i88f0S6T16hctlFPT+0fK6JAdRPi/E2trt4bO01mzdJ5W8cHJYCS88G7Z++34Xzs5teH+We6HmLvLyGGFlC2WTNduOiobsW7stoU76gHgVj3aoF9NcTuROIn/Pa98tlnaXne5yDJGfadQTF7hwbVs9z6GzT88t4xdskTl/7XT9SKhBhGts3WeGIA51uKKc7nbh67hW2Hw5KeEHyQrPm2qzFzpv0MQ1nrdP0ZiYaIVJ9VXL7Nx7yAlCXa+vLNd59WNcG9b920VODMuUIZ3b1j+IqAgoPNNiMX5wVa5cOZR7J9I5JLBEAEHExkuJ54GGwIxUPtI+hOCgNw4/LjD1KIp0Hvvw5iG0HYKNGs9CRHnCbvJDceDAgU5IwqsHQ1TnWYvXEyIPgjahJ/lRohMMEIzpEwKYhpxjUgLseCbx7EGcJqxdpUqVtOlES3jwwhMJTyee5b/99ltMQl2iymyHETACRsAIGAEjYASMwL9KgO+IRLngOx6Tq/AIZ5Y63wMRi5h8ZBZfAgMGDHCTS/U7t792fmvgwc/3ciK34O3PJDbGO/jOzzgQUWSYEEwYf37rEHL+pptuEkQgxiU0aonWO3z4cBeinogEakzSZWzGb0QQYNJt+vQJkyT9x2w9MoFo9xJPMPLCwpPfftzToDHAyn3j95Z6j2iZ5O6zlrPlyUkAMVaNMc9/e1vbsqURMAInBoHwkcs09knzVWgIMn9+Ga2aQTmdqd6uXTuXZ8Ofa0O9jLS8LgkRFGlgX4/HukSsYSYSggADqRhhjPD8wRjIJJamzvAn307v3r3dq0+fPi72Z9CtkvPoH9a0adNQ+cGDB7sZ8wyS4qmDkScEsQlj5j05g4LeUXwhTm6WTCxlCDOH8aWKL2a4f6oF44Xq/uASjypELvpK3iUN+TZs2DDHkS/rGIO1GCIf18SXitSY3n/EKK0zOPC7aVNCjpqk6ifMViQj/1Q0i3ZOtPKx7E/nvZe63lDZ5fR5YejRvDCEemvyxkTBG8Zv5LNJiSEGfT9jQ+iUfQcPy0gvh88ZhRNEIsLKYSPnbAyVmesJLggpZQtld/tY/rZwi+zwxA61H2ZuEPISaZ6geLTTyRPK3v5pqTbhlohjGEILuYoINbfUY9OgSgEnuCByDfY8dKoUS3ifUZZ+3t17uhPCCNmnfeTY4cP/uPxE/cevcnVR3xIv9N6MlTvkg5HLXBuxMEmur7SlpuEBs2QKf5xmPbJ90OtT0HgO8tnhi3kshriBONuzZ08nmEc6BxEJLzlCYvKjjdxiKZ3hw2eNH+H+kJo8xxBVkvtRRphIfrD7TUVnPBcR0vEmJCQlzyJeeArx/4D/Gfzg1LAUkerR/xuEveNc+oMoxUAB9fMjE+/JoBGGVD0k9ZgK+evWrdNdtjQCRsAIGAEjYASMgBE4gQgwgRRjjIHf60xsZGIQvxH53cZEQryLGAz3G2KE/u7lOL/lGZdQr4k//vgjVJyxAX4rU44xArziCY+sltT5lNXcvFqesMuNGzd2m0y2ROxA5KJtBtwJ00aIe9riFRzfwLuD39KUZyDf72HD93siDPiN9snzieEdwmQtvg9zPpMmyUWcEmNiF2MIeP34DSGB3y1w5V4wmfOOO+5wRTSkPd/R+c3SpEkTd68QflhnfIC+Mc7COIq+GGNggi6RD/iNoMbkYy3Dkt8T/B4hskmsYxha16m8jHYvySfDe4/IOv6c3H5WjPlwj4PjVJRJ7j7767H1k4+AP8wcvf+3t08+QtZjI/DfJhA+qpnGayVMkIoGeBH5PVS0ahUu8Pp54403XKJzkp1rDhs8VvxeLnoeS2amBGef+I+TEJ0vG/6X38OHssw6Z2YMA44IQ4gZvFTgol8Yg4/YQw89JIhZfCHjCxuzZSJ9OWEQkmsnJwhf6PjCxpdKvpzhYs2seBWS+IJIfeQRIfwRg7uYelXRN0Ll/fDDD26//08sZbQ8//wxBo35R+/P0RSNsZ6rS7zC4IRHAl9M9Ysq/YMDX44x+BHqji/sXFOpUqW0ihQtyQHFbCQdPOcLY61atVwdumQwGN5+t+dgI3wp5X75jQF0XvwQYOk3PKP8M5j8xyKtT1i81QkriCu8/LlzguURJV70hCK/lSmQTQ54odE6efl5OJ8Qa+O9/EWEXqvoCT/JeRFpXfdferpMWbZNeo1e4XIMvfjNItnvCUW31Uu493iwPOx52rz78zIZ5YlHeOQgViFG1fO8obCbjuQE6urtX7phtwz4Y7Ur+5AXFk4tHu2cVSa39By53HndrPS8e2D28vBFUq1ELifsIPYs8gSd9p/OctdEXiXC7y1Yu0vaeqHpMASZtv1mOk8ochlNXrotdB/wPGpxQQn5tfMFYa8GlQvIDXWKuX3wiIVJcn1FyLry1Qnyh5f7iLCBtbxr6zZskRPjEAC/n7FeXh2xWOp73lrZj4QFVJYs+YzwHo70LPGX86/z41afS/79/nVCsyFmI7L6Pf38ZZJab926tTvMc48fsTyX+bwl5cGp9RFCDi9RvJ44lx/wPC95LvLM07xk9E1ffO74jLONmK6fS+pi1hqDA/zY5ochz1WsSJEiTixjNiM/bmDJ/xuEeJ5B/NjH+4jzMP5n4LVKKFF+pOJppc+taD+M3In2xwgYASNgBIyAETACRuC4EdDJP3xvI+yZhkvn9zC/q4lAQRm/SITAQzQNvldifJfk+zHfRYk4wqQmzQvM8Q4dOriB8DvvvFPwYGFgnUmflMOSOh/vdCYjab8oz/dwzZ9JX/g+Skh3viMz4ZLf0IyHMG5xww03OJGIyWMY+7lWfgszMQzj9zvfXzFyCgc9/gmhrBNQCdXMd2vygRKFBU8exhtSYkRU4fqDxuQs+J1zzjmhQxq1gzEPjHEPWCCMkT+YsQyuH5EsUqQRxk2IIKDh/kMVB1Zol/D41G8WO4Fo95JJgYiR0fJE8xuM47y3/V4k2nJK77OeZ0sjYASMwLEigFMFKRAYR9JUMseq7ZO9nQxpvQD/Pw4EGGaC8I88OEDJYCjeMXx5wXSGjbavM70ZLJw2bVrEwVNmxTObHrEAIUUHWP1fOvii5LdIOScQPqiLWUW4y6oxMIpghdGfTz75xG1reDwGIRFccK8OGoLMyJEjhRlDDKpizKThi5F6JeF9o19y8Zhh8JS8SOpxxT9qZgIRtgkGGubJ31akMtHCQNEX/skziIrAwxc2nVWl7Px1R1qHFbO0+GJOnzAGcBFaMDwAGJRlthEfQIx7H8ugsisc+MMAs4YOwHuBQWoVHhEh+dLKF0oGfdWzIFBFaJMvpv73J4PWzNbCm0i9lLSw/8eC7ktq+dD/ZoUdRgj48/lw4clf4Jqzi8pPszbImPmb3W7v4yB929SUxwbO8cKmTQ8VJUzZ67ck9oagfKb0iTVdxIlHriznxJc3vVB1hXNnlm43VgkTmVpdWEoWeUJLO09cwRBlXrqxcsgDh1w/5Fh6wcud1Pj1iU7UQBS6sGL+UL/i0Q6h93bsOeTCy2kOJESUF73+qj3VtII8OmCO3NZzqttV0vMkom+EfcMWrdvtBCTW2386m0XIyBHl73PoQISV5Jgk19fd+w45oWrrnoOu9vdury7PDJkvD/Q/+r5ofFZh6XJtxQiti/uHhSCanAU/pwjr/LOLZjxXee4gkCKmYME6op3L/nz58rkf2nghEWuczx7PDUTioPFj0V/3E0884Z4FPMMQ9DF+kDNbUz2KgnUEt3m2TpkyxT0nEXow+sDzhD5hzGrjGYCgrx6atMMziMEC8hotXrw4NAuU6+A5j9hPOD4MYZ4vC9F+HLlC9scIGAEjYASMgBEwAkbguBFgwuno0aPdZCAVCJiQiOigv5+ZIHTdddc5AYXvsQhEfBfWiUd0nu+Q+r2bEOhM1kRA4fspv+0ZE9CxCyYXMbmTiCMaci3a+fSJ75f8LqUs4ZIZy9CJTbSNmKWh+PHGQbRhjIHvovSb3+ich3jEoD4TrBhLwbhGxj2YRKuRWtyBKH+YCMVkWX6r8xsYsSqYJzjKqSnejTcJ4z688PbBmHDJNTDeoZO1+D2PaBU0WPCbgXB0SU0uZTwERoheyUU1CLZh2yknwHgdY0hMxmOCn47/+GtKyX32n2frRsAIGIFjQYCJF0zCYBye/y+Mw/MsU4eVY9GHk7mN0zz3wcSxkE7mK0pB33Gd5IscA538s4sWmonwZhzLkychz0hyTTDziIFKvqBGMkQV6mQQ1z/I6i/LF6fkvgjFUoY6EVMQ8DTHj7+dWNcRisixRJ8jccIzidBNBQsWjHg8qXaYFaUD2rg/44pOn6Pxpi1efnEwqfoR/qKJVgymR/LYSqq+eB/b5QkOa7ftl2Kex1Ekr5NY2iNU219enh5EFQSlSEY7iDMISdEMD5+ieTJLxgiCFOfEox3v1snGnQk5kvDqiWRbPK8ghJiSR8ShSGXisS85Jkn1FRb+UHf0h32bdh5w3kXBY9pfPOAId4YYoiKHHvs3l4SbUAE7Ujs8i1SI4Tg/Kslj5hdaI50X3KfPAj6/sYpDwTrY5hnKDBB9NgTL0A4sea4RlsJv0Z6NPGt4rsaaI81fp60bASNgBIyAETACRsAIHHsCTPokFxGeK0wgROBhUiWD2YQqxiud3JWIP0z0xLNFRQq+xzLJVL3x+T6MJ8WkSZPcREgGkRYuXChlypQJXRgRLYiSwcTFpM4/66yznHcL4dfoF55IDELxHR8j7B3H8MjAmOhFDs5t27Y57yL20Vbbtm3lvvvuc2GbEZAQvtS4Rjz0OZfJjoRtZhKoGgNhiGOIXUT8wPMIHuxHuGJyZKy/mbVOlng98buexN8qlulxwlIzmYvJXfRHv+8Tnh4RjMliTDBjbABxrnTp0q7/jEeoMWGW+7Vo0SJ3XPcHl/w2YSIuIe2SGxsJnmvbCQSSupdMlCXUOJ8vDE+4Tp06OQ86xrIYWEUsYgKeinkpuc8JPbC/RsAI/FcJbN682Y2tRBojPl7XzOQMJivo5BL6wf8SvHQjTVo4Xv08UdtNsyfRiXphsfSLL30a1iyp8imdbc6sp0ih9rQNviDxhTEpi+VLUCxlaEM9cZJqL7ljzNDX8HWRyjLAnNw1RTov0j6uK5pARHnaiiauRaqPLz98CSX/CQ8xvkDzwGAmWkpCzEWqOx77cmTJIOWLpO2jiCCh3jbR+kQ7vJKy5OqIRzve7QvzdIrUn3zZMwqvf9uSY5JUXyOJQOxLSoTjevCs4TNZpcpRD6p/+zqpnx9qfs/JSG36RSKdFRipXFL74vUsIDQHr2hGO/zojGTRno38iDYzAkbACBgBI2AEjIAROHkIIHIQPo4XockQggjHhoDCZCF+5yFm8NsOrx6NnKFX6Bcn/N8RmQSJ8TvXb9TJpE61aOdzHEEHgYdoGAhEwfBusQo0mo842Be2NWen9se/9EdNQfAiNN3AgQPdxDA8mIjCEc/Z0zDDU4k+Ua8KRPQJ4Q4xjrxIakyM474Rxg/Pf4xrJZoKHmHRvstTjtB63Gc8qfz3jWNm8SfAfUEgQmTViYV4jGHcAz5fDL7Gep/difbHCBiB/yQBxlWZjEDUKsa2eKYTmet4G+kIcNrwC0T0if9N5AOMRSQiihb/n6iDCSinmiU9Ynyq0bDrPS4EGAjWL5NJDQqnpXOIQbjDmxmB402AWVi9evU65j92+GE22gvZYWYEjIARMAJGwAgYASNgBE50Al988YXL3YMniYoRiCbkxWEQR+3GG290OXSJEEG4NQayYzHNeTR58mRp0qSJOwWvc/IQEa44FrviiitcBAwG0cmvo/mNYznXX4brI8IA4ZA1XB1e83gN4cmBUYa+Mpivs7bx8NAJrQza4V1PqGheeDcxaEfOonhM5kSQIjwc+YboF55GfmNgLnv27P5dofvmz9uEFxEeRP57GHbSkY3u3bu7XESE5TP79wnwfiOiC5F2VGhVsRRvOO4tn61Y7/O/32NrwQgYgeNFgJz3+fPnd56x/M/UiRyak+949Sut7RKBihQFiOX+SRhprfdkOt9EopPpbv1H+8psLc1h8h+9RLssIxAiwD9QMyNgBIyAETACRsAIGAEjYASiE+A7Mzl6yBeEhwMDUuSVfeutt8J+O5LPh9BYeBYxmznWiBPUR/g3PIHwFiIcMWHKqUtzCkfvXcIRZlDjTcSsY/IZEao5tcZ1duzY0YXDIyzOgAEDnCikOYqqVavmqiY/EUIS3lMMzp177rluP15WhKVDhKEf5Ciif0ExJ7X9gy19QrxhtjYvDGEOjyE8jAiJR6QWcgkTRr5Dhw6uD8l5EdHn559/3oWBR9AiLCC5ohDfVBBLbb/tvNgI8BlAIPLb1KlT3fsLUVDDzcVyn/112LoRMAL/LQKIx3isElqVSf78v2rVqpXLG328RSL+/xDVi/+Pfm8i8tqRvzs5QywnTyCTLEgRcyqaiUSn4l23azYCRsAIGAEjYASMgBEwAkbACBgBI3CCEmBQmnDhhHBDCFIj18Czzz6rm847nxxCiCmEv0nKVEDSMHD9+vWT1q1by9VXX+1Ow2vn22+/DXnnBOsKns9xPJkQT2INNad1aN26jdcP3kDPPPOMy7tUvnx5oX967YT74trx8uB16aWXuhBgei0INGvWrAkJXIg3DJSlRmTRPmkfWWqep2C4HgQ8+vPEE0+4MH3cG50AijhEqG31BEMMQohA0PIbghKCFzlJMcICcf3mReSnlLr1SPdSa0rqmJYJLmO5z8FzbNsIGIH/DgHyQmM8o9VIpTB27FjdPK5LJpLwXYDws3yPIN0IEyeC/7siddL/3SLS8VNh32kHDhzw0sibGQEjYASMgBEwAkbACBgBI2AEjIARMAJG4MQiQG7Nbdu2uXyy5BUOGrOYCb3GYFBqjJnR3rhIonBpsdSlQhN9jEf+S2Yyc6158+aN2Dz5gHhFy+FLaDeuBc+o42H0Hxb0T8WhWPrhD6MXS3krc3wJpPY+H99eW+tGwAiklQDCf/369d3/Ga0LT5333nvPhSLVfcdz+ddffwlet9u3b3e5wDt37pyi7uA5iycRoTZPNUt3ql2wXa8RMAJGwAgYASNgBIyAETACRsAIGAEjcHIQKFKkiAvDFhSICKmGBw8DVAzqpNYItRXMp5NcXQw+tWvXzoVUYwAqHgIRbeLdEU0g4jih2qMJRBxHmDleAhHt039CxqVEIOK81Hg8cZ7Z8SGQ2vt8fHp7arVKLpU+ffrI6tWrj+mFL1y4UPbv3x9Tm3gNki8tKeP4kiVL5PDhw0kVCx2j7Jdffhna1hU8LLdu3aqbSS4Rq+EWa5tJVpaKgynp686dO50HJpMGUmojRoxwefRSeh7lixcv7k7bvXt36HQmBpQuXTq0fbxXyK9G2FPyBGpI1uPdp5OlfROJTpY7Zf00AkbACBgBI2AEjIARMAJGwAgYASNgBBwBQq2Rg+CXX35xA0LHGgvC0LvvvpsmgepY9zmp9hDh9EU5XVdx7kTYTqr/dswIGAFxwgG51vCQPBY2fPhw9xwmb1qxYsXkySefFDzNIhnhJhs1auSEbkSFEiVKSN++fcOKzpw5U2rUqOFEh8qVK7ucZ8mFMkPcIYcdYoUaedMuvvhiKVOmjBQuXNiFIMNLM5IheNx///0ux07ZsmWdyE1ONTwz1e65556wZ6I+D/1iiZZN6TIlfd2yZYvLt0NePTghiPTs2TNqk3ja0lfeE2qIdAgoGuJT98ey5B5j5O5RIz8R/4vNTn4ClpPo5L+HdgVGwAgYASNgBIyAETACRsAIGAEjYAROKQLVq1cXXsfD8NZ5/vnnj0fT/1qbhKnz24m+7e+rrRsBI5BA4Pfff5dChQoJecn+bcObs1mzZi5PHCIE24hAeEMGc48hHDVu3NjlsiE8KGW+++47J+6QMwbPDwSQ2rVryx133CE//vij85xEACEH24wZMwTRKJLhQYTQQt42DE9PcpqRK2fFihWyY8cOlz+OeocMGSKay03rQtj6/vvvZcKECc5rddasWe66EGA0Tw2eTR07dpQ777xTT3PLbNmyhW2ndCOlfeW6eDbDGgFs5MiRrq/0o2XLlmHNf/LJJzJs2LCw/EEU0Hv20UcfSfv27cPOSW4Dr882bdrIc88957x4uS+DBw+WadOmRT0Vz7ZjaenTpw953OJFpu3fddddx7IbJ2Vb5kl0Ut4267QRMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAqcCAbyDnn76aedBghcOokgwrBy52RBqMDxsEDmqVq3qPD0QblLjPRKN7eeffy5nnnmmPP744070adCggetfjx49Ep2CsLFo0SK59tprpVSpUpIzZ07nzULBlStXuvJjxoxxy+7duwthRgmtSd0YgkY0GzRokNxyyy2hMJfjx493bb3zzjsu/CVeLvQJUQrRKGhLly51Xke1atVy3kR169aVc845R9ivRp4bOJYrVy7sRejFtFhK+oqnFCLgU0895QQwhKEmTZq4F8z8hmiGmNOrVy8nfPmPIZLBi/uXGnvhhRfc+wgPLQSnN99807GJVhcC4bF88b4npxAvRCJtO1r/bP9RAuZJdJSFrRkBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYAROKAKEQPvwww/l1VdfFcKNsWzatKkgruTIkcMNhpNv5o033nD9fuKJJ1w4t65du7qQY506dZLNmzfLxx9/nOi6vvrqK3nllVcS7WcHggmhNYOGEHH22WeHeeYgtGzYsEEIw+bP9ZY5c2Zp27atdOvWzYWZQ2BQD48rr7zSVa25yfxejAzwY+QcimQc55rhooYYhTcVQpMaodkwhJ+glxUeRs2bNxdErjp16ghCG3V+/fXXerosX75cNFwewhyeT3gWaY6eUMEUrqSkr5H40Bwh/zDyUamX1MMPPyyXX36586gib1/QzjrrLOcNhIiSVB684HlsU3706NEurxRhV7m3SZl6eCVV5kQ69l/zEk4JWxOJUkLLyhoBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASOEYGdO3c6IaR3796hsGL16tWTChUqCDl7EFoQHCh33nnnya5du+Stt96STz/91IVao5uIJnjyICIFhQGEE0KZRTI8fyLZvHnznEjkP6Y5axBV8LzxG15Q9PWKK64I7e7Xr5/z9mEHAg1GOUQpwobhpYJF8gBiv+YZ8vcRQemMM87gcMgQMzCOXXLJJaH9rFx99dVC7h6/mIEXDiILhgcXXAmx9swzz0jGjBnlgw8+kJ9//ll+++23UGgzVziFf1LS1wIFCjjPLTx5LrjgAilYsKD8+uuvoRbxnEEc++abb1yuPu5PNCMnFLZx48ZE74Vo5wT3Fy1aNLjLtk9yAiYSneQ30Lr/3yDAbIaffvpJhg4dKhdeeKGbFXLZZZcJSfPMjIARMAJGwAgYASNgBIyAETACRsAIGAEjYAROTQJz5851F37RRReFAJCTBnGH/DSIRH/88YcTCcqXLy+TJk1y5fDWIW8Ntn79erecPXu2G3dyG0f+4AHEKyWGNxPiid/wIMLI5+M3wtzhfbNv3z4X5oxzGf+6/fbbhRxv9B/vIkStW2+9VcinQ0g67KqrrnJCjb8+XccTBiM0nRr1BfuF0INxLGjk5cG7ClGN8HlTpkxxXkKUffHFF2X//v0udNs999zjjnP+9ddf78K4jRo1KqK4hhjHfVFDvJk4caJuhpYp7St8EAfxYKJOvLZatGjh8gMhhO3du9flGercubMLixdqKLCivJRf4LBtnqIETCQ6BW784cOHnQJ/Clxq3C5RXVrTGl80lg4xO4F/LGr+dWYpkETPzAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbg1COAUIEFQ3tlyZIlJKDgpdOwYUNhHAsxBqtcubLzOGG9ZMmSTohBjImHkesn6OGzatUqV7V6FGk79G3WrFmCQIX3E3bxxRe78/Ea0pBzN954o5x//vkuhB4eO3j94NVTqVIlrSpsmSlTJrdNHho1vIjwZPLb2rVr3WZwIjaeSOQ74kXYOQwRBiOcXJcuXVwov2C4PeqB7bRp0yKKRAhd6olEXVmzZmWRyFLSV06mTbyPyGWE5xB9/fHHH2Xq1KlCODpEQfInIdJpOL/Fixc7MQkvNDym8NBS0QzGZkZACZySIhECADE5WT7wwAMRXetiKaMQT+TlgAED3IPO/8BLS3+JdcqLeJ7EPv0vGv98cXPln8zkyZOjPszjce36D4266tev76pkZgj/QOGMOysvf0zWeLQbax2Ef129da8rXiJvVu/LRqxnhpfbte+Q7PReRfNkCT/g21q2cY8Uz5tFMmVI59t7dPXQ4X9k1Za9UqZAtqj9iEc7h//+Rzbu2C8Fc2WW9OkiX/DGnQdk74HDrr+RyvztgVu+aa/ky55R8mRL/T/d5JjE0tejBEX2HTwsO/YekkLetZkZASNgBIyAETACRsAIGAEjYASMgBEwAic+AQ3dxhhVkyZNXIe3bNkihBSrXr2620YsYKIxpuXxwsGDB2OMa82aNWG5etwB70/fvn3l3nvv1c2wJQIOXj9BQ7ghj5E/r83gwYOlZs2aiSaqq2jlz1NEfYgniB0Ywg6eMnfddZfzjmEf+X/IuePPOcR+NQ0jR64lNUQoPInIm4MQhRGCDStXrpxb6h/tV7Zs2XSXW+o2Y3H0i5B0w4YNE4QxjP2wJ/dPJGvWrFmk3Yn2paSv3L+XX37Z3c/GjRu7utjHvYM5xjZeRu+8847b5g+iEQbL2267zd0b9SDCo8vMCCiBNItEfEBI3hW02rVry++//x7cfUJsk8yLGI4Yro3BWJzsj6UM5U50U5fUOXPmxKWruK8isKE+RxKJcGnFNZMHXTDOZ1w6cAwqIXarxu7cvn17zCIRSe3WrVvnZm7oP46kuovbqhoxV3n5jfeovk+ZgfDDDz/4D0dcf+P7JdJ79IpExxpVLyRvtqgmenzEo+fK6QXD/wnWe2G83HdJGWlxfgl3/teT10q3YQtl9/7Dbjt75vTyzLUVpXHNIrJk/W5p8kZiV1ltuE2D0tL+8nJOSHl68Hz5bkaCW3O1ErnkuWYVpXKxBLdhyo+au0m6D1/kBCDauPm8EvKwd65fkHr/l2Xy8diVri+Fc2eWR688Q6466+jsFwSbtLaDgNL928XyxYSEf6D07a76peWhRmUlQ/oEsWjB2l3y6Odz3PVznL680ryqnFP2qGvzN1PWSddvFoS41auQT165uWoisWiP1+fr3/rTE5P2eHVUcVypE0uOSSx9Tagp4S/iWhfvPkxckuCKnS9HJmnvXdf1dYr5i9m6ETACRsAIGAEjYASMgBEwAkbACBgBI3CCEWAwH8HnvvvuczlxNBQaIcdIWcC4LKHH8MLByF+DuIMXTs+ePZ1w0KNHD+nfv78bt1JxRS+TycqfffaZboYtyX0TyVq3bu1EooceekgIb0Z+HAQdxBTsq6++kueff96NZemk6ObNm0v37t3dOCxlEZUQmjByJtFXxpLJRUS+HCZNEwLulltucWWCfxBzCEu3YMGCkDcSOZlI3/DII4/I+++/70SsZ5991nkGcd2UJf/S22+/7cYtYdahQwfBKwvha8aMGfL444+7+qib8U1EIcLNIb7gzcW4KKYiVLBfsW6ntK+M7eIh9L///U+4FnIj4aE1aNAg12Tbtm2Fl98IfQdbyqrNnz/frfL+MTMCSiDNIpF6OPChxXVRjRiYZsefAA9CYmXqLIJ/u0fk1eGfACLSySoS8c+Xf7B4k/EgjdX4Z4HLJ7FTkxOJyEHEPzs1FYjwHsKLSEUj9SgiBB3nBF1j9Xz/Ek+bnnfW8O+SrJnSh23f2Wua/PzY+SHxI+ygtzFp6VZ58st58sL1leT/qiX80xgxfb10GjhXinjeQDVK5ZIfOp3nTsOb5arXJkjHq86QS6omfHnIlTXh0YJwM2X5Nhn6cB3J7wkTr45YLK37zJBh3jZCxbw1O6Vdv5ny4GVl5RZPnGK71UfTJHe2DE6goYFBE9fIuz8vkw9a1ZBaZXLLUE+86ugJNUXzZPa2E8SZeLTz+ndLZPKybfJ1+zpSKn9WmfPXTmnZc6qUyJdFbjq3uGzfc1Cu7TFJrjunqHzcuqZk9jyePv9jtdz+4VQZ3qGulCucXWau3CFPDJorPW49UxpUyS9rtu6T+z6eKS97ItjLN1VxvPTP818vkBxZEj+CY2GSXF+1DZZ4g93jMS9dIKv88vj5HtuMMnrepiMMs8gFnohlZgSMgBEwAkbACBgBI2AEjIARMAJGwAicuAT69esnCDN4tWB169aVb7/91glCjMUhaBCOTI3yd999d8jziPxFlAsKRJQn7BmvlBj1EL3owQcfdN5MtP/UU0+FwqyRA4kJ2OQjKlq0qJtQTn9UWKE842KcjyHS4PHDGCZjzBgCDtcRDLPnDh75g9eO5mBiF+H2CA9H+DgENIxJ7prKYceOHbJo0SJR76MhQ4Y48Y121RCzNMQc9f38889CO2effbYrgrjCvjJlyugpqVqmtK8IfS1btnST02mQe/r5558n8pBKrjN4pCGkRQuDl9z5dvy/SSByXKdUXCtiBO6H+nr11VddLbyB+RC99tpr7oNfokQJ90Dze0Xg7YKyyTFeN9xwgxsQ124QK5EHBw9ABvBRkHlDq7FNG1988YXgckcZlsScfOKJJ9w2g/aR3BNRiHG9pF2EjYULF2q1yS5xXbzppptC9fNgwwsFQ2DA5Y8HGiHFEGkee+wxl/CM4wz+02fiVNIv+sc2qjnugWo8cPWaeJDCFS8nLJY6UIpx1fS7GhKHslGjRq7fMEWhp79Ycv12haL84YFLXRizAbgeYl/qe4DZAjCGNQ9TEtpxDqHrYAQrmKnpeWl57+A6Shv63uFe63sPLyH6yAvVnSXHuYf80+WfCPFJ/ZxR7LmXvIhNqnE8ifuKQIR16tQp9E9PryW45B+z37h+2ual3kP+46wHzwke1+1snjcOIof/VTBnQpxWyuD9Qli2D0Ym9gDUOmat2uFWrzm7qCD48Lr5vOLS755aUtgLVZYxfbpQ/YgPGCHMtE1CrCGq4EH05NUVpEKRHE4kYn3LrgMyfuEWd86309ZLxaI5pE3D0q6NuuXyStv/O93zGlrljvMHIaZlvZJSv1J+yemJKrd562efnkeGeGIRFq92aPuNW6q5/iCq1fbaQHBD6MEmLtnmlp2uKi8FPJ45PSZtGpZx+wZOTPA+IqRe20tPl8vOLOgYlfbOv7hyASceuYJH/uBtNGzqOnnz1mr+3W49FibJ9dVf6cHDfztvpcvOLCTFvHB+eGtd7nmWYWu2JVybv7ytGwEjYASMgBEwAkbACBgBI2AEjIARMAInFgHyzDDuyVga4cLGjRsnZ511lusk65deeqmkS3d0mBdvoy+//NKNcSHY4EHDuGI8jbFF6kUMYgyWcTK1+++/3wlEGuIN4WfixIlunG3lypVOpGHM1t9nxuWYII2IQ36j0d64a7VqicdNtA2WjN3htaTjsexDvOFcciZx7XDQEHLnnHOO6xf5jzDELrgiZhFabu/evc7jCt5qCGiMH1MffSN0m3pHaZnULlPSV/JJEVaQEHJ4A8E+udB2RD3yexGRt54xUMbhzYyAn0Diaez+oylY50PEg0qNQW8SYPGQwPWNlxqD9LwYpCexFsou8SJRMflQoxwT1owPJ8dxp8QlEpW5SpUqToDgQ/Hbb785V0A+FNRPbEXKUBcD6nzwcbdkH54h5B9CiKAONb9SjGsl6jMvFOykjAcb/cWI90j9CDyIIt999537wGk8T0LvIWohoHA9hBmjj8pF3QKpC6EJ0QSXQ64ZlR1DHcblkhfXhFgUSx2EP6Mdlhiulu3bt3fr1EmSNV6IHYgb9CWpfrsTo/xRrzIO0zcEGgStSO8BjpEEDsGK+0P8TBjxwnsHt9hI56XkvcM/GsIJ8kDEuE+aUwk3WGZY6PsSsQ7jnxYPTN3PejTOxAKF20svvRRKCkgdfBZ4JWU6Y8Ffhr7xT4aZF5j2VcsgwOq90X3Rlkc0v9Dh0xKipbntgjkzy/PNKsk9fWd4XiT5nXdOqOCRlZqlEzx0Hv5strS+uLRUKZ7T5efxh1ULnhPcJsQZVrFYjtAhhBVEoRVeiDVs6YbdUrVETknn6yBtISQRQi5zxnRCiLdWF5UK1cFK9ZK5ZKrnoYTFox1EoUurJXhB4RmF2DN0ylonrlxeI0FQ0ZBziC5qynmll38IwyvH75lDXeMWbpaangeUGjmBCEeH5xU5mIKWHJNY+uqvM5Pn8dTighLSc+RyKeIJhPk9gevLSWtcEQQsMyNgBIyAETACRsAIGAEjYASMgBEwAkbg5CDAWCsvvzFWxpheJGN8lte/aSoEBdtgDDRoiDUq2ASPsY13TenSpSMdiriP8UPGehlDRZjyGx5MkSxSv9hXrFixSMVD+6LVFyqQhpVodUfqKyEAo4UBTK4LjC0iIDKGbmYE/ASOSsz+valYZ5Cd3D76IuSY3xAkiCeJ6qrGIDhqM4PwGAnKCKuFNwmeQwhGlFeBaPbs2U4pZxCdc3A59BuxNhl8Z/AeQ0zhHBRe2semTp3qlvoHF0JcDTV/EoowCnxy1qtXL1eE8xGIaAOx45dffhEU8Tx58sjrr78eEna4NowHd9DI4YPHSr169dyhESNGuKUmnENcQgjTc9966y0nsPnriVaHvwzr3bp1c7tQkanz+++/D22zkpJ+uxN9fxCuNGkbHkPEyiR2pxp8EPYQUK644gpBPIMR9wjRjfuHBfmn9r3DfVGBiPvOtrqXBmOt8o+EZHkTJkzQ7kZc8p5jdoHGH6X/eH7x/tH7h9cW15OUBa9Ry+JhhVDEZwOvIr+pm6x/X6T1uV6YtKqPjwp7+fPscM6FFfPLDV4+mkcGzHaCSLAewrqRf+iPRVvkpncny7nPjnXh56g7Vlu1OUE4wcPIbwgjyzaqSOS5HecOF0rwVMJWb9knG3cccOvBOhA7Fnt5kbB4tOMqOvKHaz3/uXHyipefiBB3mj+JEHvYmz8sEYQiBKC+YxOeZ9E8cnr8sNTlL7r3kjLuXP6QXwmh7lZPuIlkSzckzcR/TrS++suw3s7zbsLD7K7e0+WaNyfJZ7+tdnmQ/B5mwXNs2wgYASNgBIyAETACRsAIGAEjYASMgBE4sQkwJoVA1KRJk2PWURWeVHw6FtvRLg5RiQnojGcei35oG/RHrz9a35Lar/VoHcdqG9GJMVFtN6k+2rFTi0BiSTeV148niF/1DOYkQtlFqeTVoEEDl9AM9zgGvhEPEH2I6Yh7JLlsCCHHB5zBcozjN998s1tXQQdvI7/h5oghPmB4jqhIUb16dScSBD082rVr57yGOIc+4gVE+LvgAL2r0PdH2ybcHEbfEcEwPFhKlSrlQrfhFfXGG2847yeO+d0f2YaTxtqk/4QsQ2RC4IIPpt5OhDRTVrg5qkWrQ4/rEs8c6sXUrRDWePWo0QdCziXXby2fkiWunHh3qXE/cfnEswpxD/dQTEVDLZfa9w6CIwZfdU9FeNP8PwhzaiSp87uS6n7/knus70ESwanHD66w+j7zl09q/ZprrnGCaLCMhprTpf84YRRjsZL5sspT1xwV5zinXKHsiU7Fk2Xsgs3y0rBF0u3Go3FrtSB5eJp64eYmLN4iv3nh4T77fbV87YV4+7xtbZeTSMtFW2q+HecR5HmzqO3ef9iFa2Ob/Dhs+43yWI4s6Z2wwbruYx3b45XR+nWZlnYSak342//eWrJ55wEZM3+zl09ohrx125leXqaCLlzea7dUlUcHzJEhf651Yds4o4HnjeP3LtK6vvpzjfQZs0Leu726cE+wRet2S//xq6TXXWe5cHRa1r9Mjom/bLS++svsO3hY7vDyPO0/+Le82aKa5MmeUX6ZvdHll8qZNaML4+cvb+tGwAgYASNgBIyAETACRsAIGAEjYASMwMlBgEF/IjAdS/NHEqLdY70dvFbNqcRYst+Od7/8fQmuH+u+BdsL9se2jUDcRCIGzJNSrf3JsPxuhdmzZ3fh3fC+QJjAE4cXHi+INXhtqGkSNRKWEcasTp06esgt06dPH7Ydy4Y/rJwmQtu/f3+yp2q//G3642jisaJ5mRC+VNwJVpwjx9FQXH5G/j741V3KIKL4xa5odQTb8uc68rumpqbfwbpj2cbLTA0hiliohLqDzUUXXaSHEi39XFLy3tF8QXpftWL/9eo++pCc+Zn53T1T86DVUIXBNuFADqRIFu2cYFlCuuEplJwhrnRvXkXu+HBaVKEgixfujZBkvMi/U7/rePl22rqYRKISR4SR9dv3CzmK1JZ7oeYu8vILYWULeTl/tiV4HOnxtUfy5OA9lD7daZIvRyZZu/2okEk58gRV8sLWYfFox1V05I96DtXzGK70vKHwukEkwq6sUVhqlc4tk5ZuE8LPnV8+nzz15TzvOsJFuHGe+Pb04PkuH1ODKkdDur3v5YHievA80pBv1Pv7oq2eIJZBGnplk2NCebWk+qpl/vT6Ssi+EY+eK6cXzOZ2k9MIhv/zBCtyPZkZASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI3BqEjg6vf84XT/eLXjM3HHHHc7TZ+bMmSFBhRBqeChheHG8/fbbzoWQkF+ENbv66qvT3OshQ4a4OugH4hSmXiduI8ofcgZh6q2CaIN3CN4+1EV+IozcSXgnESIuJVayZEnHgXNGjx7tTiU0n3oCkbcopVaiRIlQneqhhRBHn9UjKq391j4Rui0pI6wfAhGG5xRh4Vq2bJnUKYmOJffeUQ8t8hxRFiPnEtfbvXv3RPUlt4P3KfmqsG+//TZUXMVL3REp35Ae02XZsmVdP3SbJWHmeOHpxNJveEBxTrytTtm8cvuFJYXcQ+QBUnt5+CJ55qv5uumWhCYrVzhcDAkrENjQfDsj5yR42HGYcHWIRiqqsMRLiTw9aj/M3BDKgcQ+xKDvZyR4wLGNZ8zIuZvkjMIJIlE82tnnedlc+eoEGT0v/H2LB5aKVqu9HEuEikufPp1cXcvLm+UJRvs8j6ZRXl/KFznKhWsk39P9Xog3cgH57TRvI6MnLlGPvjj+8+wNMtwT37DkmMTSV1fRkT+Ux7J5eZf8Rr6n3fuPcvcfs3UjYASMgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBE4NAnHzJELk8YfrqlWrVih8WFIoERPwtMHat28veH1ouDHqIBEXCcgIMUeYOrxPGOhnwJ68RXXr1k2q+mSP4bGEQEFOIdrFo0QFoKROxoVx8ODB0qNHD1myZIksX75cZs2aJZUrV3Zh9ypWrCiIE48//rjro4ovSdUZPEZIM/IrIeA0a9YslKuH8GtFihQJFk92mzidd999t5Dj6PbbbxdCy+G1gvD00EMPufPT2m/uGTZ8+HBXv+Zucjt9fxD91Hjv4KUDz5RYcu+dAgUKuNCD3AfeQ+eee27oPcl7LTVG3iHEJ+41Rtg+9UajfsIFPvvss0I4wgEDBiTZBDmm/F5iCHeEmcObSEU8rUBzKel2UsudnuCC8OK3Ap7AU/GI541/P+sPNSoro+ZsklWeCKJW3cu/Q1i1gp43T6MzC4nn0CPfTF3n8us8ckU5LZbkMpMXYu5hr+yb3y+RSsVySmEvj1DXoQucF1K9CvncuTfVLSa9fl3u9pO3Z8KSrU50+fDOoyIoYsutH0yRXqNXyKVVC0rfMSu90GmH5bZ6CQJMPNrBY6pC0ezy3NcLXJLEsp7HzfSV2+WTcSulTYOEhImwGPDHXzJ1+Xbp3LSCbPVEtXd+Wua4NqmZ8HlESLr9w6nu2mp6Hkf++3DuGXnlDS/cW9CqPDbK5X9qfKSO5JjE0tcfPaHtXU+I6tu6puA1hLX/dJZ0vKq85PY8zUZ5YhhiXKfGZwS7Y9tGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACJxCBNIsEukgNwKLijvw0/xEGtoLgSJo7GPAnQRjeAYhuGDkEiL8nAohiDiIGwgP6n3SsWNH4RXJtC3tG2Wi9eOVV15xA/P0nb7grYS4cPhweJ6UYDuNGzeWjz76SF566SXXL44jRPTv398VxXOIHDz0d/HixU6ESak3EWIDIeKok3w9CFjNmzcPeSkF+xTLNqIYYdjwxlJRBg8eBDcs1n4rz2Cb5Fpj8msAAEAASURBVHJq2rSpCx3466+/uveEltX7wjnkm/rggw9ccj3CDJJXqUWLFi55mtYZ6Tw9Fut7Z9iwYe69gzeXMuzSpYvzQtu6datW54SB0EaUFbza8EJ6/fXXXQnEOr/4d9dddzmvL0QpPMhiMULVkZNJ+QdzEdEeYlJKDLGndZ/pYac0ql7I5aNhp+cIE2ZZMqaXV26uKje/Nzm0/4rqhV1enpc8jyI8XrDsmdNLl2srutBzoYK+lQgfcWl1YSlZ5IU6a9dvpitZrUQuecnLf0QYOYz8O+TJeWHoQmn8+kTXBqKQP1xerTK55ZEry0nPkcud4ITY1O3GKkI4OrV4tNP1+sry7JAFLg8R9XK9eFk9cFlZ10xmT/Tq2aqGPNB/pjR+bYLbR98I2YdQhY30xDbNsRS8B5OeuyiUR8kVjvInFibJ9XWTJ2AtWb/b5XLCA+zr9nXkSS8sHmIbxrUhDrasVzJKL2y3ETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAKnAoHTvEHqf06UC8WjBY8Sf+4af98QN/AewbvInxPGXyY16whCiEQIF6kx+kSuHH++HK0HIQIPK3/uIj2WkiXh0vC+SWs92iY5gdatWxeVZVr7Tf28VOjRdoNL2O/YsSPqPQ+Wj7Yd63sH4dEvVkWrz79/xIgRzmMIkWjixImyb98+lxMr0v3mPK4bS0k7S5culZ9++kkIVTdu3DgXupAcRP9GiDnXuRj//O1dy19e7hoknWJ5s3geRYnF3liq2rXvkBNPEHiiGfl/iubJ7IVjC6hYR044/HdCX0p6uY6idSNe7WzaecDzosoU8Xq5veQUwqMnv5df6N+0WJhE6yu8VIzTPhKqb9e+w4JnmZkRMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbghBKJ7HYYgRORQFAkOhH7aH0yAkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjkFICaQ43l9IGrbwRONkIVKpUyYUVLFy48MnWdeuvETACRsAIGAEjYASMgBEwAkbgpCMwc2ZCuOqTruPWYSNgBIyAETACRuCUIlC9evX/xPWaJ9F/4jbaRRgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkYgZQQiJ/9IWR1W2ggYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJG4CQjYCLRSXbDrLtGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARiAcBE4niQdHqMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AEjMBJRsBEopPshll3jYARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBIxAPAiYSxYOi1WEEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBiBk4yAiUQn2Q2z7hoBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkYgHgRMJIoHRavDCBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjcJISWLlypdzV8mYpVCCPZMqU6Zi8aIs2advMCBiB40fgtAMHDvxz/Jq3lo2AETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJG4HgRQKRpeOG5ctc5p0mbC/NIoZwZjklXNuw8JB+N2yZ9/vxHRo2bIKVKlTom7VojRsAIhBMwkSicx0mxdfjwYUmfPv1J0VfrpBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACJy4BvHnO2DNWnrqywHHpZNfvNsnibBdJn/6fH5f2rVEjcKoTiGu4uT179sj06dOFZaw2ZswYef755+Wbb76J9ZRTutycOXOkUKFC0qZNm/8Ehy27DsiS9bvl0OHUO7Tt2ndI1m7blySP9dv3y469h6KWof1lG/fIP0l0Ix7tRO2A78Du/Ycdk/2H/vbtDV9d513vX1ujX3Msdew7eFg27NgfXnEqt5LrTyqrTfFpew4clgVrdwnL1Nqxei/Eq53UXqf/vHi8F/js8Bnavuegv+qw9YOH/5ZF63bLvoPR39thJxzHDT5/k5ZsT9N76Th235o2AkbACBgBI2AEjIARMAJGwAgYASMQM4Hh333vPIhiPiHOBfFeog9mRsAIHB8CcfEd/OOPP6RLly6C4KNWv359eeaZZ6RevXq6S77++mtZt26dNGzYUCpWrOj2c27Xrl2lVatW0rRp01DZf2MlUvv/Rjv/Zp2rVq2SnTt3ysSJE2NuBs+jjz76yJVv0aKF5MqVK+Zz/QXf+H6J9B69wr/LrTeqXkjebFFNxszfLPd9PENeaV5FGtcsElau86B5smv/IXn7tjPd/pkrd8jjX8yV5ZuOCop3X1xaHrjsdMmYPp2c02WMIHREsmolcsmgB2rLXk8IeHrwfPluxnpXjP3PNasolYvlDJ221Bu07jJ4nkxdvt3tu7RaQXnx+sqSM+vRt/77vyyTj8eudO0Vzp1ZHr3yDLnqrMKhOuLVjlY45M+18pTXJ9r6tfMFutsNrsMEjmpPX1NRbj6vuG7KnL92SodPZ8uqLXvdvpL5ssrbLc+UikVzuG0G6JOrg3O7eNwmLtnqzsmXI5O0b1RWrq9TzG3Xe2G8IN4FrU7ZvPLJPTXDdifXn7DCgQ19vwR2S/bM6eXP5+vLTk/Uq/vsWLnB69dzzSqFFft68lp58st5Mrd7Q7cfsespb3v8wi2hcg0qF5Dnr68k+b3re3TAnND7JFTAt0J7tHus3gvxaKdBt98E8dNvNUrlks/b1vbvcuvR3nPJvRc4OZZ2Pvt9tXs2aH9g3/WGypI3e0bXPuJQt2GL5Ntp60Kf6zYNy8gD/3e6pE93mivj/8Nn7oORy12dPBc6XFHOf/iYrK/dtt/r8xLvPVReqpc8+kwJNv7+Lytl9qqd8n6rqsFDbvuAJzbd1Wu25MmeQd5pWSViGdtpBIyAETACRsAIGAEjYASMgBEwAkbgeBLYtmPPMQsxF+k6CW9HH8yMgBE4PgSOjpSnsv3x48c70YfTy5cv70ShCRMmOMEIMejnn38WBCPsnXfeEcp/8sknIZHIHThGf453+/G4zMsvv9x5axUvflQ4SK7eQ4cOyUMPPeSKXXHFFakWiaigTIFs0vPOGmFNZs0UHvqu08C5UrNMHimeN0uo3N8+Fx08cpq/N1luOre4Ex1yZMkgM1Zslwf/N0uyeXXde0kZGdK+Tsir57GBc6RInizy8OUJA8WZ0icMKiMQTVm+TYY+XMcJAa+OWCyt+8yQYd42wsdOr512n8yUM4pkl9FPXuCJVIflIa+NTl59791RXdKddpoMmrhG3v15mXzQqobUKpNbhnriQ8fP50jRPJm97Tyu//FoR0Hg5YJARFtBT6C2/WbKgUP/yLAOdaVEvizy+6Kt0s7blyVjOrm2dlHnfXHHh1Ol+bkl5L5Lyzjvqy5fzZO7ek+XMd71MdieXB3chns8RqULZJVfHj9fcmfLKKPnbTpyzVnkggr55It2teXw30ddqv7xTrrjo2lSvmh2vQy3xBskuf6EnRBl48sHz5Gc3ntALSgZfDlpjVxUKb9cUrWgFkm0fMQTgejncI9dcY/dik17nYD4yGdz3HvssSbl5UFPCMM+/2O1DJ+2Xgbcf3aonqyZ0h2z90I83nPcRwQZxNnKxY8KGJkzJHYOjfaei+W9EEs7w6aukxe/Wej6wvtnsycwdvx8rnQeNNd9roD83NcL5DdPwPvorrOciDt79Q4nKOfJlkHuuDA83vC8NTvlfu9zi7jH55g+nIj23YyNMvevXTJ+wVbJ5ROdg319efhS9yzKmSX8ORksZ9tGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbgeBBIPKKYwl60bdvWnYF4MXPmTPnwww9l2rRpctVVV7n9999/v1siGCEQYZ06dZKLL77YresfBng7d+4sJUqUkKpVq0rfvn31kBw8eFCee+45qVu3ruTPn19uueUWmTx5cuh4jx495Oyzz5bu3bs7jyTqQJzyW7T2o527e/duefLJJ6VGjRqSKVMmOf/888P6RB9oc9CgQXLNNde4ft1zzz1CODi1uXPnyrXXXuuuiT7dcMMNsnTpUj0sK1ascGHj8KrS40uWLHHHx44d6+q/9dZb5dVXX5XTTz9dXnjhBfntt9/ktttuk3vvvdeV0/6/8sor0qFDB1cPbPv37++OU1+dOnVCbXJfnnjiCbedXP9CJ/lWsnneFqXyZw17FcyZyVdCBI8evFn8QoO/wJINCTMDrvOEj0K5Mjth6Lzy+eQzb9C+XsV8rigeMtpO9swZJLc3CKvbCEZ4zOBB9OTVFaRCkRxuQJl1PGDUm2Tysm3OU6mL541DO2ULZpOnmlZwnjoq0CAYtKxXUup7IgRCxW3e+tmn55EhnliExasd6iJ81YP9Z0mL80tIozMLsStkm3YecN5ObT3PijMKZ/eEofTSsEoB9+r1a4L31sad++Uaj9ndF5dyzBiYblqrqLvmLbsPSix14NGB99ZlXvvFPBEP75nLPU8wbM2RkH2Ie8qaJeHBECTuvCh8MD+5/oQuLpkVf1usl/RefqviiSC8n6KFxuN9NsW714hI5Y6ww7PqHc/DqnWD0k5k4D2q7eTNnsnzVjsttM1+BMNj9V6IRzs79h50iKqWyBl2HXin+S2p91ws74VY2unleRe28TjjUYjgW9oTkj+9r5Z0b57gWcN7E0+mFzxvsJqlczvRs7b3GRvzVD25sW5isXv+ml1ysycgD/Q8oooErsd/bZHWe/26Su7sNUuavTXNE4TnyZh5Rz3LXh2xTF7/bpm85r1ueme6NH93ulBeDe+lLoMXuWN3954tv8w+6tGnZfzLLyaslZkrd4r31olqP3hC0gyvTFIiEiff03eO9B2z2hPXFsj1b0+Thz+dJzwnHxuYsN2y50zvPZ7gDUn51Vv2eQKy9/+lx1RX/sVvljjPSo7Rb+r73mv7lvdnhK6D/a0+SmDT9pO5rl+UNzMCRsAIGAEjYASMgBEwAkbACBgBI2AEjIAROLUJHJ2+nwoOu3btknnz5rkzCRmXPn3CTOl06dI5QWPEiBGyaNEi2bFjh+zbdzR/yt69e4WX3/AuUtuwYYMTQWrXri3Vq1eX++67z4keOXPmlCpVqsjgwYPlxx9/dIJJpUqVZO3atTJr1iz30jr87bHPv+1vP9q5HTt2lN69ewtt1qxZ04lSCFNFihSRK6+8UhYsWODaQ8RR+/jjj4VwcN99950Tti688EIXGu6yyy4TWJF3ifB6y5cvl23btgn7ly1b5tqgDo6PGjVKEHYIKafXhBCFwVH3ux3en0j9h9/vv//uhCv4+K+dfFH79+9Ptn8ZMkR/awRn9gcHSV/2ws3d9M6fLoQboaKCVt7z7ME6e+HBCO1Wt1xeN3CM2BOrabi1isWOnkMIOcSBFUdC2LHEE6GAT8SqdCQs2+rNe52nE14WrQLiR/WSuTzBZpvrSjzaQfDC+o1bJbu9gegHLysrQ6ckiFDugPdHQ24Rmspvo+Zucpt4YlEPQpjfxi3Y7MLWFfCuc9uRXDBJ1ZHJ8zRpcUEJ6emF8mIAPr/HBk8d7GIvRFjQEGDe+mmp571U3POuOuoZRrnk+hOsK+q25ynif08F308PeLx6jlzmPIM+aJXgAeavC3bneyIjIdxyZ80oDasWkDyehxSCSVA08Z/nX4fvsXgvIMDFo53NnvCC4W02aOJsJ3JdXDm/E8UI16iW1HsulvdCcu3w/iCnWGvvc/7MV/NlrPd+LJ0/mzQ9u4jzfqMfq7zPGobATDjK+d5n7ixPLLrVE0sRZIOG11xqbPCkdTJi+kapWjyHJ8IWkJ89UeSdn1fIueXzCB5WCCt4mOGZd2m1/E48ofz55fNK1RI5PA/DBV5f98npBT3R0HtPfTttQ5Ld6HdPdXf8gf5zPTE5cb4z2vvQE6HuaVDS81zb6DzdolWIADps6gb3fiW03dTlO+SRz+Y7ZvUq5PXu8zZB5BrY7iznUdjeE5EyeH1sfm5R2egJzAhAXb5aLK/eXNGJxYi6H45a5YnnGZ1X5Ki5mz2PyRXe8zCjF0qzoCekb/Xu1yJXXp/H0fpm+42AETACRsAIGAEjYASMgBEwAkbACKSWwJ4Df8u2vX9LsdzRxxlTW7edZwSMQPwIHB1NTEWdiBhqFSqED14Tek6NcogWmp+IsG/BnDqFChWSv/76S9asWRMSTRBl8LbBKwaxZvbs2TJu3Dh56qmnnFjSr18/bcItKYOnDSIQYdX8llz7wXMRqF5//XXXJn1t06aNq472/da8eXMnwpDvCPvll19k69atTjxD0ME+/fRTJ/7gHYVHFIIRQhICER5CXCPCDnw4BwHMbwhonNOtWzf/7rB1+o8HF+KTenHh1VWuXDnn4aWFf/31V3njjTeS7Z+WDy7nejlxqj4+Kuz1xYS/woohPjxzbUUhhxE5a4JGSLlP7ztbDnjhyhg0rvXUaGnTZ7qMnLMxWDTqtg484yHkNwbhl3l5iLCV3oBwGS+smt8Ir4at9AauN+5IyLsTrIP+L/YGvrF4tEM96zwvnR4/LJHHG58Rlg+JYxi5WxC4EDrwvMA0ZxDr247sY12NMHEDPfaPNy7vvBliraPdpae7wWfC1F3z5iT57LfVLo9U0COMdn6ZvdGJAJHEPu2HLoP90f3JLck75H9PdfhsdtgpDIZ3u7GKIIhxvZHs5ZuqeOJALhfK7//ZOw/4KKouit8EQq+hQ+gQQu+9Swfp0pEiHREQBT5FAaWooIAUqdJFqoWOdAHpvXcINZQkEFoSkv3euctbJsu2kKXfy293ZmfevPfmP5MAc/bcW+abLcphoc5L1cmBk8aVeFn3grvG0TWjZqpaWg2LpqNSOZJz2sQBqvaXDmf3HNo5uxecjaPdZ0P+OsnpEztXzqJSQ8blelH6WmnRtsv0gyyCwN11U4kiH07aR0cuP/v7Qc8/ukuklyyWLSkNa+qrUlmmo+5VM3FKxgMX71q6ggA5+aN8SkzLSOPbmesDHVfOpTMBD1ggqpzHm0a3zk0/tfRT4nVSy3HRXYF49rVyJfkqQbxmQftpEo39pkoSR80tLw1smIOFHez7Uc2jd80sLHohvSPcTn/sDuDz+rJ+dj7PHtUyU7V8Kdnxd+PJ7zQc27BYGvq1U34WwGb+e4WQFnS6+vxRRR+a0DYP/85AXxJCQAgIASEgBISAEBACQkAICAEh8PoTQAYiZDqy9dq1a9dLPYG2M65SrK7HqcvcqF+ANk7i3K1wqjranxL3PEkZ+5+mdH1P069bzV/INraTdSEgBF4PAjGScePHf/oAPiwsjOLFe+o0wGcdxnZ6m/US6eBSpTI/TKtRowa7hW7fvs31d9AW4kmLFi34MAg+CLhyjNG0aVMqXry4cZPL69bHYqxFixbRsGHDCPNYsmQJ96WFH90x0snBOQVXkI6AgAAWZyDcoD0EsKpVq1KVKlU4VV6yZMlo37593Byp6hIlMrth1q9fz98291BPMiEiIVB7CKntnEW9evU4TR/a9ejRg+Di0mPYOhbikaP52ToG2+Ae+aqBb5Td2VObnUHGje8XTkvrlOjTV9WK+aP3s9cENXlW9SulUh7dpR1ngviB/icqFdsXqnYMUr45C6S1QuChqbEOy31Vd0g7h5A+Dp+N8TjCXOAEx8PZgEAfxnigPuv+9TIm46BvCGY45zqF0hqHirL+Y4u8XKup3Ldb2AGFB/T1iqRVDoPrXDvI2FjXbYELCmm+dDjr41F4BNcXClUPnFHPJpkSpyAEoY5UYuXCQdo9HewiWnOOa0chNZ2jsDcfR8fofZiHvhbYpq+f3o9lVqQKVPfd0L9OUYlsyY27eB3HzOxSmAVC3E9Ldl/lOjnbTwfS+LZmx8czBxk26PFf9L3grnGQJhBpC3upOkv6HoUjpL+6jn2VaAjBz9k958q94GwcnWkNP2eTVa0yuLpalM5AsVU6vxmb/dmBpp1hqEEG5yCiUbH01EgJebhO+XxyGa7E86+2r+CjhMQg+mH5OboWFEqXlJMHofQaS3ir+z2pqoOE0KJosHIBabGq/JN0l9gPh9HOs09TvGGbqzFq1QWuQzS2TW5XDyE/Q80vpKfDvNIrwQ2RIrFZ3MY3sE6p9I9gmk+5n3QUyZpEOaducYo6va1B0TS8ip/juw8fs0g05C9zOlPsQIpFzUgfI0shIASEgBAQAkJACAgBISAEhIAQeD0JoIRHRMTT51eoP96oUSN+FossTC8rZvwXTH8fDCHfNHHsDolsMXXG+VOO1HHo3PAc5J0gFq04fI9a/XqFMnl7UbU8zz5HtNuZ7BACQuClEIiRSJQ799MHYAcPHiSkV9OB+kQ6kCLOWRiFpAQJEliaIz2ajhw5zA8Y48aNS5GRkVFq7aBN8uTPPjzWxzpbGo9FfSTU9YEzB0JKhQoV7B6OuSC8vMwP8XTDhAkTEpR8uKaQRg4OI7zgBkItIC2iGY+DOISXMSAwuRL4JoEOvW6d0k/vx9LZ/NKkMT9gNB6DdaR0K5/rqZBgvd/4GbWAaozYrtIknTFutqzjISVqlODVoWImVUtkv6oLc8UlkcjnSQo3pFVCajEdqLdT4YnQgdool1XNIWPoujaoQwMRCenort15mgoRba8GPVIPbM0PYN0xzqnr92j5gQBVfyU9LVYPxRH7Lt6he8r5sHDnFSXMpGSHBerpbPiiLKGWUpByDhXNmpS2ngxkN5ZOR4djMb9Ovx5kcahPrezYZAlnfSBtFdKdrfi8FAsvOBDp/tDnnK2XoohEaw7f4PpFUzsWsvRva8XRfGy1t95Wxtebr4X1duvPSHm39rASHn8/arOWDdpDTMILQgVEkmmqXg6uubVbzLrvl3UvuGuc3OkT04D6iaOcRllf88/lyashFKTccM7uOdyXzu4FZ+OUymGuIYa0ccZ7tLyaC+oQQZxI/yRNYYGMT505SPlWVl33/ernwF0xcb0/rTl0i0UgCGa+SnT557A5XaMewygo621Yhjw0/0PbWANJi0nGdq6ub1Pp3MCjt6qLhNDuwA6qXtLQJr4Wp5CxP9wbxogTO+rfBXoffm/gPPD7U0fEE/FbDWkJPX8tfKK5XkejXIoPUutJCAEhIASEgBAQAkJACAgBISAEhMDrTyBLlixRJvnjjz9yiQ88jzV+aT9KIzd/OHE9jDrOvkYLO2eguTufZu2wHiZM/R/1VEAY9a+RgjIrUQjRpGgSFokuBpqz51gfI5+FgBB4tQRilG4OAkmZMmX4DOBeQc0cBJbdu3fn9ZIlSz7zywrOHFcD9YAQEEvGjh3LdYKQZu7TTz8luGeeJ5yNjzpKEIgQ/v7+hFRybdq0idZQqBWE9Hnt2rXjtHIQzbRzZ+/evVznCB2uXbuWoP4jvvzyS4KjavXq1fw5Om9wDgUGBvIh2vVUsGDBZ7pAKjyEs/k9c+BzbIAAgzRgEH7ghtGxAoLJuD1cW0NvQ32U/KoWkKuBtHIIY4o6pMKDaJTtibMpixIL4HAwpm3T7SESISAGrTp4g9fxBnfFelUHKEcas0jkjnEeqm//ozbO5hO3OZ0cUsptVanTMLeJ6y8QUnbh2/7Yfk6lyqucJyU1Kp6OIFBBVMqjBAEdeOjeUaWJw7zB1vhg3pU+kLIKgZR/xoirHtrfD31aVwV9jf/nPLuINANje73uaD66jbuWeCg+tEluFjaQ3kwH0gvWHbWT4GYyRsFMrt9POO5l3AvuGmfZ/uv8M4TrpONq0ENezaDuG1fuOVfuBWfjwDGENInp1P1tDNzHcCFB+MDPIeLOw6j/EDytHDHZnuwzHvs866Cw8Vgg1xlDraCvVCq2gpme/tw46zODt3n+e88/Fa12n3u67ux46/0V/LxVCsBk5KdqpuEVR9WJQq0orEMgi0kgnR+u3e17T3keumS+9/XvNWP/cJpBIEJ9ouEqFZ9+QSDK6O3YIWjsR9aFgBAQAkJACAgBISAEhIAQEAJC4PUggMxBeIY4fvx4Mn6B3zg7PBvEM1lbLzzDfZ7oteA61cyXiBoXcfy8Ja760mOPyt40bOUtWn/8Ph2+Ekp9FgXwkHXym5+3Pc/4cowQEAIvjkDUry4/xzhTpkyh0qVLc42blClTsmik08FBFJk6daql11KlShHskYMHD+ZUcfPmzbPss7eC2kYQotBn5cqV2eGzcOFCFmBQ3we/7FwNV8c3uncg8sDts3jxYleH4Xa3bt3iFHP40Lt3b4KgplPVFSlShFPDgQ/EKDiVwE6LQ7/99pvDVHG2JoKaRmCVNm1a/iYB2jRp0oSbYmyIbRgLVlT8ZYD0eEiBh7A1P95h4y1EiRTbTpnFKL0bqb7woNhWVMmbypIyTe+HcwhukH7zj3LBeziBdp0LollbLql6GZl0M4dLiEqfKhfNaOUW8VMiCkSYoUo4gDBQTjkUEBgH698vO02DG/kpV0M4jf3nHKG+jq5N1F3V52k9cS9NVY6Tqmqu01WKrFAlFH1Yzof7cMc4BTN50cYvy3J/+m22cu3M+Nc/yvbTqg7Sol1XCSnjML95qqYOnB4/f5ifD0N9nY9nHWJ3T++a2WjPuae5XPNkSMx1jZz1AdcQovfcw9S3Tk5KqpxhG1Rto9WHblA/VS9Jx8qDATzOlA5RhcYRy8+olG73aWL7glzvx9l8BgwYwA7DmjVr6q6fWSI9nFG0gvCg52ndGILVMCUUDVhkdmhgfxblGEN9K6TMQ32mzKoO1QUlUmCuuC+duYj0GC/jXsBY7hinkLq3kVpu2N+n+GcGqcm+UPWIwAIviALO7jkIfAhH94KzcXB8typZuY88Pomper7UdMD/Do1TP2etyvo8ESfi8PqI5ac5xVsO5Zj7c881/pkf09p8bxvvK/RpL+zdT+p0OcUdRN6bIWF0RaWam7DOn7vB7yxnUVoJOhPW+qufv+tUMHMSrkm29shtZ4fZ3d+nVpYo+3opR1G4+vntWydrlO3P86GWqnG0VaXVG/fPRfqsdhblAgylTccD+Xdguifp6az7LazOad+FuzR321XlBE2uHIzX1e/xIOryXkbrpvJZCAgBISAEhIAQEAJCQAgIASEgBF5jAqhZjhIZdevWpU6dOtmdKWqg4xmgrciUybVnb8Zj/zoQQuuU4HNySNSMNsY2xvVB76ekf0/dp+o/m/9vjn1zPkqvvmQa40fRxmFkXQgIATcRiPFPpq+vL61atYrw8G7z5s0s5mBuFStWpKFDh5Kfn59lqh06dKBNmzbRnj17aM2aNZbtWEFdH+tA6rVYsWKxk6djx460bNkyi8Onb9++hBdCH2udqs26P1vj2zoWNYMmTpxI/fr141RxEF9atWpFEG9cCcwjf/787HqC42nMmDF8GOoLIf0chBwERCEINuCBwP4ZM2ZQunTp+HN03tq3b0+XLl3ilHYQnz766CPq2rWrpYuBAwfyWHA3wcn02WefOZ2f5WDDyqXAhyrV2QHDFuK0Z6growMPbI3xZT1f2moQllDfZkbnwvSZqlfUbLz53NEe6dh6Vs9mPNSybuvati+fiU4rEaWHEk4Q+XyS0HdNc1vcNXhQPrBhLvrfgmPUYoJ5nKr5UqmH9Fm4Pd5QJ+iz2tlpknL0QHCC2DS8aZ4owoI7xrEM6GDlq/q+9Lli8uGkfdwK9Z/AVbsD4LzYq1LRIXrPPcJL/YZ6MEgD6KwP1Dr5s3cJFlkgjiHg+EBtmzblzA+MI1W6RbiIcD10uj09zmXlVrl466Gq82IiZ/Px846kkSNHEsRZR9FrzuEouzGf3d9WjLLN+KFB0XQqjdgNdmZhO67zdHU/9YfoaLg3kQLtp5Z5jYfyOtrD2WEdL+tecMc4uDd+aVeAxdb5O67wqeD+H9chP/OwPjdbn125F1wZp2q+lFxrCEIPXghco/5KhNTRS/1c31VCVveZ5p9VXOMB6vdC9fypuInxvjKmUcM6rhfi5s2bDu+nFqXTKZH3MnWaZv7ZeC9vCvpPCSG/KLEIrh57ge7jK2cdhJ0fV56nnrOPcdN8SvRCrSJjCjdbfeB4PUdb+/U2W7/D9D5bSyMHRYGbYC55MySi+kVT0997b9CHE808E8WLRV83ePIPdXPTKF32rpmF/jf/JC1WIhheiBLZkxIEJwkhIASEgBAQAkJACAgBISAEhIAQeHMI4Fno/fv3adKkSer/ojb+A/jkVPAFdbzcEQ/DTdRrQQANqJ2ScqR6Wu7CXt+op1tl1EV69NhECzploJSJYtMf++/Sh9OvUrL4sai2uInsoZPtQuCVEfBQtXGe5iuK4TRQP+js2bOUPXt2MtYVsu4WNX8Qjn6ZWR+Dz+Hh4QSHTqpUqSh27OfXt1wdHwXhkDrPWK/I1rycbYPLB24ke/3AYYRz8/b2dtbVM/v79+9Po0ePps8//5zrHd25c4frDdnjg3OC8GYMZ/MztnX3+i31rX84fDKq9G9Ix/Q8gRodSN0GgcdeoC5NPK9YhAfjtgJpu66oujx4KG7v71h3jGNrbOttqF9yX50TmDxvuNIHXBf3HkUQnGCuBn50TeqP8QG2vWN37tzJLqKAgAC79769Y593O67RteBQghAJIeJ54mXdC+4YB2LdZeWaQUox74Rez3O6fIyze8GVcdAH5uKjUpjhZ81WBD8I5/o8mdW9bUyV6Mp95cr9FKbcOqeUmOqrahLBBQjGuB8wJ1cCadxOXL3HP3tIz/Y6R4i6109du0+pksRV8433REZyPGNcn+sqJSfqdr3u5+f4TGSvEBACQkAICAEhIASEgBAQAkLg3SPw119/UdOmTfnL+lWqVHErANQ3j5iU22afkzYH0ce/X6cRjVOrjDTm/+//vD6QksTzpHZlktFHZVGn+Omhq4/cozrjL9Gxb7JTrjRPnznV/+WSSp9uojW9bDuZYnU9bqnh/rQ3WRMCQuBlELD9xPw5R4YwBAeNs4iuOKT7g9DyPC4bfbxeujo+xBR7wo7uy5WlMX2drfZw/rgrkiZN6rAra4EIjZ3Nz2GHMdwJgSI6IoWt4fCAHC9H4SzlGB5Ya8eOvX7cMY69vo3b8bA/Jg/80ZcrfeBBvr2H+cb5GNchoGlXg3G7rXUIxkgV6Y6fIVv929qGa5QzreN7wdZxxm0v615wxzgQ65zdt8Zzs7fu7F5wZRz0gVRyjgKpJfGyDlfuK1fuJwhDcADpAGNXBSIcg3pBhVRqtjchUO+paFbHv++tzwMsosPD+nj5LASEgBAQAkJACAgBISAEhIAQEAKvhgAyAyFDEjIWuSIQTZ8+PUqGIeOsa9euTRCcXI3H6guYPsm9aOwGc51zHHc5yFwn93LwY2pTOmmUL4LCeYRIGCeq0ym++j/3rZCn9XW5kbwJASHwWhCI2dPU1+IU3u1JoNYLRJ6iRYu+2yDk7F87AllV/tuvv/76tZuXTOjNJCD305t53WTWQkAICAEhIASEgBAQAkJACAgBIRAzAsgKhLISiPLly9PatWujdFigQAFKkyZNlG2of26vbAYyNEUnelT2JryMAVdQ2iSxaXJrc8mMxXvv0jfLb9E/vTNRpVwJuGmzKVdoxAdpKHkCT1p28B4tUm1+VJ8lhIAQeP0IuDXd3Ot3ejIjISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEwJtJADV6UcfcXixYsIAaNmxob7dL2x2lm7PVgbVINGFTEPWcf51ODsnOdYsOXQmlDrOu0j7/R3x4YpWarl+NFPS/mint1v6VdHO2SMs2IfByCIhI9HI4yyhCQAgIASEgBISAEBACQkAICAEhIASEwAsgcPv2bZoxYwanYnqZ6a5fwKm8Fl2CJ1LVJ0uWzO58kPoK9Z59fHxstgkKCiLUp82RI4fdmtLXrl3j9OTx4jmvIfro0SOugY0MA45qYNuczDu6MTIykk6fPk0pU6akFClS2KTw+PFjun79Opd2sFWeAAeh/jjqXzsq/+DsfrA5uGx8rQhEVySyNXmkpYut0r4b40FYJN19FMmuI+N2W+siEtmiItuEwMshYCgr9nIGlFGEgBAQAkJACAgBISAEhIAQEAJCQAgIASFgj0BwcDDhgaW9b8bjoTX244XYuXMnffnll7Rv3z57Xcp2Fwhs3ryZcuXKxWIA0toXLFiQ9u/fH+XIjRs3UsmSJQliTbZs2Shv3rx0+PBhSxs4HlDvBKmvdAosuBx0QFgaN24cixaZM2emJEmSUL169ejq1au6SZQlRIzPP/+c2xUuXJiFq/79+xO2S9gnMHfuXEJKMdQNh7jz/vvvE8Q/Hffv36fu3buz4IbrGD9+fOrTpw89fPhQNyF/f3+qUaMGM8e1giA4b948y36sOLsfojSWD289AWuBCCecII6nSwLRWw9HTvCFEcDfO6ixdeHChRc2xrvQsYhE78JVlnMUAkJACAgBISAEhIAQEAJCQAgIASHwhhFYsWKFzYc+ixcv5jPJmTMnLyFKwLXiSjH3NwzBS5sunCDVqlWjTp06McvLly9TkSJFqG7duhZB5siRIywa1KpViy5evEhwApUrV47w+e7duzzXDh06EOqnYD+2Qbz78MMP6cyZM7z/zz//pM8++4xmzpxJgYGBdOLECRYmmjVrZvNcx44dS/Pnz6ddu3axo2XNmjU0evRou7VWbHbyjm0EK9SvmTZtGt27d4+OHTtG58+fZ7FNoxgwYACtWrWKduzYQRBlt2zZQgsXLqQffviBm8CFhOsKB9K5c+fo1q1bhGPatWtHR48e5Tau3A96PFkKASEgBITA601ARKLX+/rI7ISAEBACQkAICAEhIASEgBAQAkJACLyzBCAmWMeECRN4U9KkSXkJAaJUqVIEYQMB4QHCER5ww+kCoQEOFmf7sB8OjPfee4+PbdOmDac44wPV2yeffEJff/01L+GqgJvm119/1bv5YXvHjh3ZcYH9aI+H9Doc9a3bvKplaGgozZkzhwUcpOyDkwjukxs3bljOYdasWewiGjhwILtTwPeXX35hJ1HChAlZHIITaejQobwfaeQgKiAOHTrES3zTG31DfEqUKBG7kbAOIQPChHXA4TJy5EgqVKgQp62rXLkyu2OsHU7Wx73Ln5EaDvcpnHhw2yHlX506dVho01wg/FSqVImFQKTvgzusePHiLAihDRxFEIV+/vlnvp/h+GrQoAEffunSJV46ux+4kby9MQSSJUlAN0JenUMPY2MOEkJACLwaAiISvRruMqoQEAJCQAgIASEgBISAEBACQkAICAEh4IBAq1atODUZ6tHo2LNnD6dAwz4d2A+nRFhYGItBcEDg4fiSJUtYpEC6MjhQIBTZ24e+li5dyg6M0qVLs/gB8Qmp0EJCQngoiFBwWuAB+k8//cQCVLdu3SwiSr9+/diRAQfHqFGjODXX999/z8c665sbvcI3pByDmweMIBhB1Bk+fDg1bdrUUpvo+PHj7BwaMWIEp6IrWrQoC0IQGVDPBi8IOhAbdGzYsIFXkboOAaEB/L766it2pKxevZrQX+fOncnT89lHVODbokULPhZvcCghvV2xYsUs22QlKgE4wiAS6UBqPtz/uK91QLz77bffaPbs2SyqTp06leDc044uiH4tW7bklHXh4eGceu6LL75gYbRs2bLcjbP7QY8lyzeDQN3atWjKluBXNlmMjTlICIHoEsDfr4sWLeK/82192SC6/b2r7T3UP6LMX6d5VwnIeQsBISAEhIAQEAJCQAgIASEgBISAEBACrw0BpL+C2wTpsCDqwOGiH1536dKFkBqtYsWKhNRl//33HyHtFVKjwUEEx0P69Olp+vTp1Lp1az4niAopU6YkLy8vu/tQt6V8+fIsfowfP56PQ52DDBky8DhwYsCZAQfGgQMHyMPDg9OtoVYL0t9BTIIQAoEEIhECggYELLhrnPXNB7wGb5g7atUgqlatyucGEQgBdxScRUjzB1EHKcggmjVu3Jh+//13bmN8g2sIQhIEPbi5dECYgONKB2oNQcRIliyZ3mRzCfEKNXKCgoIIYiGup4RzAkj59+OPP9LJkydZ5MEREI4+/vhjmjFjhqUDXFM4hyD26YAQpAU+/CysW7eOsmfPzrujez/oPmX5ehJADar3ypeiDsU9qHP5ZJQ6ceyXMlE4iCAQ/brbRBu27KBMmTK9lHFlkDefAL5wAMcrvmCAfwOcPn2af8/h3wa+vr5v/gm+5DN4OT/xL/mkZDghIASEgBAQAkJACAgBISAEhIAQEAJC4M0mAMGnZ8+eBNEGItHt27f5ofYff/xBeHhtKyAGQdxATRY4iSAqQNxJkyYNN7e3Dw/Nd+7cyS84J4xx8OBBTteFbUjLBYEIAWEJYpZOcwfnBRwcSJ0GUalRo0aUO3dufiDvSt/c6St+g9hVoEAB2rdvH02cOJHFrd27d/NDOC3KwB2keUIAA2sIdxARdEDIqV+/PqeG03VusA91byAQoS5RzZo16fr16/S///2PHUZr1661K/zA4dS1a1d2EWE+ei56PFnaJgARCAIRfmaQHlFH7969+WcJolD+/Plp79691LdvX0IKx2HDhulmlCVLFhZi4dSD86hEiRJ8b0Ac1dfAlfvB0qGsvLYEIM5ApPnmq/6Ue8gqCr774KXMFSnm4CDasOUHEYheCnH3DIK/j/H7InbsVyct4O8OiEGDBg2ynBRSwPbp04eWL19u2SYrrhF41svr2nHSSggIASEgBISAEBACQkAICAEhIASEgBAQAi+UQIcOHVi4gXsHD6khykBccBT4FjGcLXCmQGTKmDEj94Fj7O3TwhBcMnhYql9Iy2VMn4Y0dsbQD8qxrX///oQH5hCS4MiBAwM1e1zt29jvq1oHXwgBEGSmTJnCosy2bdt4Ovqb2VogwsYqVarwPghpOuD4ad68ObuokAIIdYV0IE0fnEXfffcdu8Eg/sGJBUcYxB978c033/D1X7lyJdcxstdOtj8lgFR+cN6NHj2av22v98Cph2uLF9L5lStXjnr16sWpApEuENdPB64dUvs1adKE0ydiO34OEa7eD9xY3t4IAvi99+vs3+nGrWBO34kUni/6hbEwJsaWeP0JQBxC3T58SQJ/F0BoflWxbNkywt/RxsC/Gf79919LGljjPmfr+PsFDskePXo4a/pW7n91ct9biVNOSggIASEgBISAEBACQkAICAEhIASEgBBwFwE4cZBabvLkySzAQPRx9M1lPNCEiwUuHqRBGzNmDBUqVIgfbCOtmb19EHYwFlxHAwYMsEz/6tWrlChRIstnRyvXrl1jQQkP3fGwvU2bNjRhwgRO3xbTvh2N6459ELP+/vtvTvum+8uRIwevwu2DgOh19OhRdkbpa4D0Pgidggz1ICBMoB2En1SpUvF+/Xbv3j1CvRtjaBEJqYNsBQQ31EfCA0FcJwnnBPbv388pEFH7CWnljKFrfOk0gnqf/oyfIYiyuH83bdrED4PRBvvhLMJ9jnDlfuCG8iYEhMBbQ+DTTz+lFClSEMRmOHpR6wwvfLngZYd29bpjXHwRAWlR8XvtXa1r9EY5iSIiItxx3aUPISAEhIAQEAJCQAgIASEgBISAEBACQuANIYAaOUghg5RXeHDtKFAHB99IHzhwIKeBg8Pl7t27LAA52oc+8Y1kiBEzZ87kh194YISH4o4cLnouSFcHxxEcF6j9cvbsWcJ4efLk4SYx6VuP8SKXELY2btxIn3/+OafLg0ig6wZVrlyZh4ZAh5pEnTp1olOnTtH27dt5HQKYdpWA+7x58zidHDgghRxeZ86c4T7gMIKDZdy4cVxDavPmzfThhx9S4sSJqUyZMswub968tH79em6Pb3bj+mMOqDuh+0P6PgnbBHDfIa0iAg9vNTMscZ+mTZuWWSMlEwq+41rC8YXUTbVr1+ZrAUEVNadw3+7atYvb4CEq6nuhBgjClfuBG8qbEBACbwUBuGLnz5/Pf79CNEbNufbt2/MXDF7FCeJ3Ef6+Ngb+rVChQgWXv9yhj0VKU9Q3xN+F72q8MU4i/CMDfznhHxNYvisR/CCcYnl4UOL4ti+Vuofpxt1QSpbQi+LGfj7NL/RxJPnfekgZU8SjeF6xbKINefSYboaEUeYU8SmWp4fNNnfUXKHiJrEzV5sHvSYbnZ1fTDkH3Akl/MJJmyyezTO++/Ax3QoJpcwpE9jla/NA2SgEhIAQEAJCQAgIASEgBISAEHjLCeBBENKgIcUNHnAjjN8gNq5DrIAA8eWXX5KuhdO5c2dq27YtOyHs7UOfqNOCB+OolxMSEsIPy5FKR6dUg0hhKzA+nDV4eIZUN6jxgoCwMWrUKF531jc3eoVvqEOEVHxIswNxDAHxZ8eOHYQ6Twik4YGogHRxOuUYnD2oM6SvwYgRI7gtUu8Zo1+/fjR06FD64osvOP3e4MGDmTPaQByC4ARHEQQ9uJOQ0ggxdepUXkLAwksHUqDBqSTxLAE4wnD/IlAbyxi4v1HrCzWKkGrugw8+sOyGgIf6XwikVcQ1wfM3/dAUP4PTp0/nOl9o48r9gHYSQkAIvB0E/P39+USQllUHvgiB9G6vIr7//nsWrfFFgyJFivDfHfhyAn6/RTfwd9K7Hh7KRqpkhucPFCnEt3mMgb9A8O0ed4o5sMjiHxs6T6pxPHeuPwyLoInrL9C0TRepY6XM1KdWdrvd77twh75adJwu3HpAOwdXsAg5lYdvI4gCxiiYKQn9/nEx3rRs/3XqP/+YcTevT+1QiMr6evP6rnNB9PWiE3Qp0Gy3zp4mIf3QLA/lyZCY90O0mLf9Mo1ZfZbuh5odVpVzp6QhH/iRdyJzjuSvF5+gJbuvPjPO3iEVKX6cWBQRaaKRK87Q7K2XLG3aV8jE56yFoOvBj2jQHydpy0nzP9ASxo1FQz/ITTUKpLYcs+NMEA1aEnWu3zb2o8KZk1ra2Fuxx9sVRu7g7Oz8XOFs7xxwzjvPBjHjY1fM/0DLokSg0a3zUa505nQFgffDqd/vyoZ/OpARge/gRn5Up1Aae8hkuxAQAkJACAgBISAEhIAQEAJCQAi4QMBRYW1H+/AFP+z39vZm94oLQ0VpcufOHX7IrtOoGXfGtG9jXy9qPTAwkGLFisVFyW2NATfKuXPnmI8WkGy1c7QNHJDGDnWjrDmhf53OzlEfsi/mBMAa7jDUFsE1txUPHjxg0clYi8rYzh33g7E/WRcCQuD1JADhGOlfkZJSB74wgLSqr1K0h3sI9QAhFCEdnr0vdOg5O1rCDQsxHSlY37WwbU+JBgV9Y0AYypo1K1uztm7dSnghnyy+meCOgKKHbzjAdvyi4vjVEOo+8xClUCILhBYIBDq2nQqkpfuUuFM3J3kr186wpafot22XqZwSdSAS6aY4BgLR6Fb5KPcTQQd9GF0+t5Qjp2jWZDSsSW7dPS9TJzGLOzi+3eT99Fnt7NSkRAZ6HBFJI5SY03n6Qdo8oCw7Tf45fIOG/X2KJn9UkPvCMX1+O0JD1bZRamwEHEYQuj4okZ4/6zftFoI4tPxAAC3pVZx80yaiPeeDqf2U/ZRDCVINi6Vj8amb4pFeuV+Wf1aS0iePR3/vvU6fqnEWJC9G+TMm4TE+mrqfulfNSh+W9aEIdf5TN16gVr/spR1KOHPkKnLE2xkjd3CGuObs/JxxdnQOp67fY55dq2ShX9oVoDjK6fXTyrPUYdoBWt23FCWKF5u+WHCMxbpN6romTeDF91RfJRrl80nMriJ9zWQpBISAEBACQkAICAEhIASEgBAQAtEjgLoJ9sLRPrhinlf8wHhJk9r/wmRM+7Z3Pu7cDnHMUUDA0enlHLVztA8cUPjcVohAZIvKi9kG1unTR31mZD0S0krpekXW+/DZHfeDrX5lmxAQAq8XgQwZMvCE7t+/b6ktB7E/c+bMr3Si9erV4zpJqD8YE4HolZ7EazC4ba/0c0wMSh2UO+SFhZUbsWDBAktPs2fP5tym+IcYcpwa96ER0slVq1aN7aqwYY8ePZpzG2p7+MSJE9m2DWu4DhwD2zn6rFSpEheG1MWlYHVDbsTWrVtzgUs4nvB50qRJnINV92Fcnrh6j1qUykDzleMnbdK4xl0s/GxTbo/QcLNr5/yNB/Rn7xL0YbmMUdrdfRjOn/Oqh/yZVGo2/Upj6A8CCBwlep9eavEmTKV/G9kiL31UITOLLBCs4BIKvBdGD564hlKr/n7tWIjK50pBCZQrKGuqBFRMCU+XAx9Z5gORKGfahM+Mo/4txhHXy5P+934Oyp0+MQtPJbMnZ4eLdr3sOBNIJ6/doyFKzMqWOiGnomum+ED88X3ihLkebHZM1S2chkUOCGjvFzbb/5E+zVE44u2MkTs4u3J+zjg7Ooc/91wjOMh6VMtKqZPEpWRKBBrcKBet/LyUxcmVLXUCdm5hP4TERsXN/0gGdwkhIASEgBAQAkJACAgBISAEhIAQEAIvgwDSi+kXxtPrWL7On3ly8iYEhIAQEAIvnIAWlFG7R8fevXsJz9wl3nwCMXYS2UKgLahwEiEgEOmCh8g1u27dOn7hmyNNmzYlFCJsp/Kc6jBausqXL8+boUyiQB6WCGOfcDDB1oYXbLIjR45kKyza44X8uDpQWK9gwYJcvE9v00u4Z+wFHt7rB/hoM00JNAgIMca4rdKHIf47HUQLdx4hT3WOlXKnoE6VM5NXLLMmd1MdE0cJNEgzdtD/LgsJLcr4WNKzZVTiEl6Ryi4T/thE528+oEkbLlDtgmksKe10KjekjIOotOtcMP219xr1rfM0L+RllaoOohHcQXAaIZUdnEVasGpZ2sc4dboS9IhFoXblM/F2OKTy+SShvcphNH3zRSVShVPpnMmpy3tZlHhlrq2TL2NiQiq84UtP0yfVs3HdHbiekBYPwpKjcMTbGSN3cHbl/JxxdnQOZwPus8tr2saLtEw5tmKrWk7v5UnJ/HQ6v/7vP71eYLX9Sdo5PyXcSQgBISAEhIAQEAJCQAgIASEgBISAEHgZBHSWGD3Wm/ZZz1uWQkAICAEh8GIIwDUIY8g333zDdekOHjxIixcvpv3799sdEIaSFx1eXl5ca23btm20fv16m8OhXqCEYwJucxJt2bKFlixZQt9++y2NGTOGR61VqxYvBw0axMsVK1bQpk2baNWqVfxZu4R0IcK6devSvXv3OLctCuIhtDOIPxjedJ9wHKEo1erVq3kvikoi/68xoGoGBwdbiu1hHi8q4PZBzPzXnxoWTUelciSn8WvP04CFxy1D3rgbRn/svsaCTxvlRLqq6v4gPRtSlxlj8a5rVGjAJmo4ZhenwBvaxM+4m9dR26jo15up24yDhHpCTUuabcKPVd43pFMb9885KqEcQg2VyLXu6E0WjEIePn6mHwhN/ecfZcFH18OBOHXk8l36cuExKqccS3ARbT5xmzpMPUB3n/QBEexH5XpCzaKm43ZTs/F76IQ6j+FNo6bSe2ZAJxucMXIHZ1fOT0/THme939bymHKmTd/sr8S76/RB8fRU0S8F17vqr1LM2QqIegNVbacWpTOw+8tWG9kmBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQeNkEhgwZQqhTBoNI48aNOROYo9IwqD/3ol/4UgPqCMGsYm+sl83pTRzPbU4iLQxpCHXq1CGkjYOz58qVK7wZAtKwYcMoIiKCP8Plgxtr9+7d/BmqHqzMPj4+1KRJEy58pfszLo19ok4R4r333qPEiROzgwhKpo6cOXNS/vz5+WONGjW4VpK/v7/e7fZlwrixqJVyBfWqkY1rzmAApHyDyNBXuUZSJY5DJZVw1LJMBqqemBquAABAAElEQVSe3yyENVY1gxopIQjC0YD6Tx0kVfOmJL90xejolRCat/0ytZiwl+sHQZjR0VONA1Fh59kgmqGEqXuPIqifSiEXpuoYQdRprl65nqSGq6nGqzFiO21XaeT02OgH9X0gTiDF2R+9SlDsWOb+Pci8HNM6P7uQ0LZqvlRUU/Wx9dRtdjZBaIGIhXR4zdU8lLGJBTKIXkv7lKR0qp7Rx6qu0TGDAIaaT4t7Fkd3dsMZI3dwduX89ATtcdb7bS29nnCc060Ii3xokzVVQvpCiW4B74daHF3YDtEN9ZFwrYxuMOyTEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAo4I4Iv2M2bMIDz/xLPVlxWnTp3iujRx40Yt3fE84wcGBhIyTyVPntylw2FaQB2asmXLRmnvzjnBjIDMVtmzZ+caXFEGiuGH6Mzz8ePHPA/Uc4sVK5bTkfH8fdasWQRTRqpUqZy2d6UBrgsMINeuXSPUr3N2zXVmMVf6fh3aQLt4V8NtTqJ+/frRokWLLByrVq1KyZIlo0ePntbIQWHDHDlycK5CpIhr06YNhYaGWopdYV2HPs5WwSljO50fF8fFjx+fD9dp7vAhUaJEvA1ver9lQzRWkFZuhUoZhvRujgL1fQbU97UIRGhb1tdcLPPkE6GkW5UsUUSaeCr1XEWVkg6p54yBWkQFVE0biEBDP8jNIs6+C1FdUqhnVFTVIupeNSt1q5KVZm7xpwdhEVynaFDDXBaBCP0ihR1Sw+maQ3qscWvP0dJ911UKvcLcRm/38TanlIMbSgfGQz2l41fMNXNQdwfzHNsmP9dHgltm8kcFufmy/QG8rFckLadYQ5o6vCCiOQtnjNzB2ZXz0/O0x1nvt7UEJwREMR1I14c4Yag5BBdXrzmHKSw8UnEsoGo/ue3HUg8rSyEgBISAEBACQkAICAEhIASEgBAQAkJACAiBt5jA8ePHqVu3bhQebi6F8aJPddmyZfyMN1++fIR6NQMGDGAnh/W47du3j1LnDM9y9QvCB2LDhg3cV9q0admlglIh27dvt+4qyuerV69SgwYNojzvdXVOEE/0HKyXEJ4Qly9fpnr16rHAAgMC3DPIouWOcHWeGOv+/fvUvXt3SpAgAWXLlo3Pt0+fPuyc0XNBG/DHuWCJgJCEVHB9+/bVzdy2hFDlTCBy22DS0Ush4Lan0SVLlqT69etbbsSBAweyqydTpkzs8MHZ9OrVi6ZNm0YTJ04kpIWDCwhCEo5FfP/993Ts2DFas2YN6ZyFttLNZcyY0dIn1EsEfhHCYYTALxJ3x9xtl6mvqiF0XKUQcxTL9l9Xadf2RBGTrgaZazNl8I7Prp0Pxu4mtDPGBeXIyZbaLCr89t9lTgtn3J85pVkAQ60eBOoZjVWp5IyRNZX5eKSTO6f6q/HDdl7qNuHKXYQ6OZlSmNth+8KdV2nS+gs0RQk7BZUgZQzftGaB7aKqTaTjoRKgUMtHz+dReAQljBOLdI0dtNProWofokaB1OxogqsJL0d1fNAeziZnjNzB2ZXzc8YZ87UXELIgoBmFxYu3zPcCBDsE6k59vfgEnVbXZUbnwuSd0Mted7JdCAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAI2CSAWu0o34Ev5r/oOHDgAKcba9u2LQUEBHBtGtSI/+mnn54ZGs978dzW+MIzYWR/gpABV0rNmjWpVatW7JRBRqrq1atTxYoVKSgo6Jn+9AakPqtUqRIVKVKEN0VnTg0bNowyH8xtxIgR3A+EmJCQEH7OjfkdOnSIy5h899131KJFC0tGLD2P6C6jM0/0DdEHpVt27NjB84CItXDhQtJlXNAfhLp169bx9UfKNR0wdcybN4/27NmjN8lSCNgk4DaRSPfeo0cPXsUP0y+//MLrn3zyCS9r167N6iVSw0GJRT0hWAhxsyNVHNTNQoUKsQ1O1yTS/VovP/74Y97UrFkz/gGtVq2aZQyozu6OJiolHBxCudIldNh1ocxJuY7PsL9PEWrMHLkcotKLHWf3DZwlyBQH8eCH5Wdo26lAuq7qEaFuDWr9vJcnJfddTDmDkD7u+2Wn6YwSD1CrSNc0KpXDm9sUypKUxZ0FO66Q/+2Hlvb5fJJwGrOsaiyknBu4+Dg7kJAWTvdRInsy7gNjDv7jBJVUNYtwHTAfvLSjqYJyBcF59Nm8o5zyDnP5XK0jtLvovTyp6JI6T/SDecKl9KU6X9RDqqRS0D1PuMLIHZxdOT9nnB2dX5tyPoTaSQMWHSfw33/xDn2l1sEU1wfx8+pzLBh2qJiJ2+hrYBTmHI0h+4SAEBACQkAICAEhIASEgBAQAkJACAgBISAE3m4CcAd9/fXX/MV4pJJDGi84XYwB8QCp5hBw6AwePJhQLyZXrlz0+eefc8kPY/uYrP/+++9c3uN///sfp4arXLkyz8+6HAnGgAMHqdr0C5meFi9ezHXt8Tzy0qVLPJWWLVtyCjOkRoMYg4AAZStQhwZCSfPmzS27ozOnJEmSWOaDeUFYmzNnDnXt2pUyZMhAGzduJJRJmTJlCvn5+bGLp3PnzjwfXdbEMnA0V6IzT3R97tw5ixgGNxHMFsWLF+ft2I+yK126dOESK5i7MWC0gNj2559/GjfLuhB4hkCMaxLBxobADzUiRYoULPqg9hAUWPxwwVWEH97Zs2ezgwjtkA9Ri0j4ZQW1+++//ybUC8IvFqigUETt5VjELzr8wkOfsPpBZMIvhkmTJqH7GAfq/jw5Je4Lzg9HadLMZ69Suim30C/tCrDraL4ScBAQbsZ1yG/p7391c9Kdh+HU6dcDvB9vqCOk6wShLg36+ObPkzR7q/kXJYSFRaqOT/InThM4cu4+eEw/rjzDggz6QKq3YU3zYJXHmqmcKT1mH+aaQdgGV8vMLoXJR80RsfDJ/CBI4aUD8134STF2BI1XaeQgEjVR7icE+pjTtYilD4hFo1vlo++Xn2ZXEtqAwcT2BSl/xqjOJOyzF9a8nTFyB2c4npydnzPOxvOxPoe0qh4TUu/1+e0Ip/NDW7i1fv7w6b0wddNF7mLkijPGrqhTpcz0aa3sUbbJByEgBISAEBACQkAICAEhIASEgBAQAkJACAiBd48A0otNnjyZ4NbBs1cskdFp8+bNXGoD7pEVK1bQqFGjGM4XX3xB06dPp6FDh3IKMjhKbt++zTWLrOnhuap20Vjvgxgxfvx468108uRJKlq0KNcD0jvh6EGWJ6Q+S5jQ/pfsMXe4iJAqDlGsWDHKnTs34RwHDRpEyCrVv39/Kly4MAs0un/jEsIIDArGbFIxmROeSUMU0mIKagVhXtu2bWN3FM6rSpUqBK6u1ksyzte4Ht15tmvXjp9543l5iRIlCGIgrrWeK9xcjgLnsXr1asKzegkhYI+AhxJvnnrQ7LVy03b8wkKhL6SYM9YHgrCDX2r4ZQLXEdpA+T5//jz/8oLd0FHAlgjnkT1BydGxL2of0ohdDnzEtYnspRALefSYboaEUWYlQOkUbdbzufMgXP3C9aDE8WzreXAQ3gwJpWQJvChObNvGMNRTCo8wUYbk5hpD1mO48vmacjxhrPQO+rir0twhksS3PVdXxrFu44yRuzg7Oz9XOFvPXX9Gujm4vXCNtMin98lSCAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAI2CMAMQTCEEp4oL474sKFC4Ta73/99RchcxNEDaQcO3LkCNcH8vb2prlz51LTpk25PUQFpFiDM8da5Ni3bx+tXbuW21m/oYyIdvUY9+EL/61bt2b3kN6ODFFwuWAJB5OtgGsIzp3ffvuNmjRpYmmClG4QM3TADIBnxTgnW7Fy5UoWmYKDg9nlgzbPO6eIiAh+Jl2hQgUaN24cDwfX0MyZM9mU0Lt3bx4DYhmcPFu3bn2Goa052tsW3XnCJIGMWjNmzLB0ifmhlIv1s3Dwh5g1fPhwS1uIi0jNZ+08szSQFSGgCLjvab4LOOE2QmEr64Dy3LNnT3YE6eJaaIMbG7/onIWtPp0d86L3w1WS6UndGXtjQfixJ/7oY5IqYcFRwO2UOklcR02c7nd48JOd6ZQrxlm4UxzSYzlj5C7Ozs7PFc56ztZLCIC6XpT1PvksBISAEBACQkAICAEhIASEgBAQAkJACAgBISAE7BFA/XYERAwdWbJk4RRpyMSEZ6fbt2/nL9DDobNr1y5uhnrv69ev53Wdtg0iUvny5XU3vMSX9nVdnyg7HHyAaAXxyhhwECGQys1eaBdRo0aNLE20a6dOnTqcNg1OIgggSJOG80PKNOsIDAzkTRBtdDzvnCC0oSbR0qVLdVcqQ5M5Z9T8+fNJlziB8wmOJwhqWnzTB9y9ezeKqwnbGzduTD/++KNuYllGd54QqSAQgQlS3e3du5fLuSRNmtQldxBEQTihwNXT07bBwDI5WXlnCbxUkcgeZVgX4RrCD+Xp06dZHYdS/P7775OXl2ORxF6fsl0ICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASHwJhMIDQ3l6ceNG/VL4vHixSPUKkL8+++/hBrwEDcePXrE2yBooL4PAkILagPh5Y6AG+biRXMJBd2fri2UPn16vSnKEu2RTQoOJ6MDZtasWSxwoU6R3g43DPqZN28ep56L0pH6oMufIGuVFnSeZ05w6aBMCpw5mTNntgwDEQ6BFG864ICCCAfhylokwrVAfSZj5MiRw/jRsh6decIphbpIeCHtHKJcuXK87Nu3L8/d+r7gnYY3fY+IQGSAIqvPEHgtRCLMCoW1YJ2TEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBsqRu27NnD9d4BxM4aeB+KVCgACNas2YN1/PBB53qDc4c7YKBGHL16lVKmzYttze+oXYRasrbCriU8KV+6/Dz8+M6RkFBQZbUaxB5UEdICz3Wx6D2PEQWo4sIbR4+fMg1jIzHxY5tfmStBS/rvuDGQdy5c4fLmmD9eeaEekwwLCB9nTHg2EGcOXPGUhcJTim0tSX+QLTq0qWLsQu769GZpz5/o2MKHevPqowMOROJUIsKz90lhIAjAuIxc0RH9gkBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIAReEQEIIhB8unXrRqtXr+bUcqhNhPrsSB2H7ExIJ1amTBmeYcqUKTkFHdwxEI+QYg4iEMSNe/fuPXMWSGOHGkG2Xkh1Zis6derEm3v16kUnTpygiRMn0rJly+ibb77h7RBfChYsSKgjjzh37hzXVBo0aBBpAYh3qDdkksI5dO/enV06qGnUoUMHTmdnrwyJj48PHw7RRoezOZ08eZIFNJ2Cz56LCP3VqlWLU8u1atWKULMJKf9QgwkBx1ZMIjrzhKiH69qnTx9Oh4fUfIsWLWLXEtigdpOzOHr0qE1hy9lxsv/dIvDaOIneLexytkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAHnBJCSDeJCvXr1uDHquC9fvpwgCP3zzz8sFiC9nA6079ixo8V5lDVrVm7n7e2tm1iWEI9suWMsDWysoB+kgkONebiZIFZ89dVXVLNmTW6NGkhwOj148IA/ozaPLRcRdkJ0+f333+nzzz9nIQnbMF84mFCixFb4+vqyOwYCjm7jbE6oGwRRCc4aBIQsiFMY1zrgasJ+iESlSpXi3RDlNmzYQDoVnfUxrn6O7jz/+OMPFgg/+OADyxDNmzen8ePHWz7rFcxbp9/T27Zu3Urt27fXH2UpBGwS8FC2NJPNPbJRCAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIAReCwKoL4MUYwkTJrTMBw4jpJ9bsGCBZZteQVukSUuePLne5Pbl2bNnueaRrhOkB4BTx9o1pPfZWyJ9HcKV+X7xxReEFHxr1659pjt3zgm1llD7KFOmTM+ME9MN0ZkneMIxhrpSxtR8juYA9xRS5x06dMiSNs9Re9n37hIQkejdvfZy5kJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAIvMEE4Lrp168fu03e4NOI9tQhmMA9hfRxhQoVivbx78IBcHqFhobS5MmT34XTlXOMAQFJNxcDeHKoEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJC4FUQgLsEAlHdunVf2vBGxxCcSi/6s70TQ/o3pIS7c+fOC5+D9Tnam5Oz7db9vMjPDx8+pDx58lCjRo2cTUv2CwESJ5HcBEJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBB4Bwl4voPnLKcsBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIgXeegIhE7/wtIACEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICIF3kYCIRO/iVZdzFgJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAE3nkCIhJZ3QImk4mGDBlC3377LQUFBVntlY9vI4Hl+6/TmYD7dk/t/M0H9Oeea3b3u2NHwJ1Quhr0yGFXgffC6Kya5+MIk9126vYlzPfOg3C7bWRHzAmEPo6kXWfv0IOwiJh3Jj0IASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEXhEBj7CwMPtPnF2YVK5cuej8+fPPtCxWrBj9999/z2y33hAREUFTpkzhza1ataIkSZJYN3mpnzGf+PHj85inT5+mzJkzv9Tx34bBQh4+ppKD/7V5Kn99WoJ80yayue9VbSw3ZCv1qJaVmpfKYHMKf++9TkP/Pkm7v61oc79xI8SZ0t9soakdClFZX2/jLpq5xZ+mbfKnrV+Xs2z/7b/L9Nu2y3Th1gPe5p0oDn1aMxs1Lp7e0uaQ/13634JjljbY0bFSZvqkelbyimXWeR+FR9JPK8/QqkM3CGISInuahPRtYz8qnDkpf7Z+G7XqrJrPxSibi2ZNRr1rZCMsJewTuHDrIfWec5y+/SAnFciY2H5D2SMEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBB4jQnEjunclMjEXeTPn58yZsxo6S5nzpyWdUcrjx8/pl69enGTWrVqvXKRyNFcZV/0CHyjBIqS2ZNHOShdsrhRPr9tH6KjuE5Ye54mrDtPAxvmoop+KShubE/adPw2fbX4OIU8ekztymeie2rZfMIeaqYErJldClOieLHp4MU71HPOYUoQJxZ1rZKFIBD1nH2I9qvtP7bMRwUzJaH7oRH0+/bL1OqXvfT7x8V4mz3W//QvzbuuBYfSLCVkfThpH20bWJ6SJ/Syd4hsFwJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBt4BAjEUizWDw4MFUt25d/ZGXjx49otq1a9OdO3eoe/fu1KFDB9q5cyevx40bl8aOHcvb9EF16tShevXq0XfffUfh4eE0fPhwWrlyJZ05c4Zq1KhBffr0ITiUEC1btqSTJ09Sjx49aNGiRdwv2n388ce8vX///jRv3jzasmULffDBB9SzZ0/KmzcvH+vv709fffUV77t79y73ifmXLm1+WM6N7LyNGTOG5syZQ02bNqUTJ07Q2rVracaMGVStWjWaPXs2zZ8/n+dSsmRJatu2LTVr1ox7ghg2atQoWrBgAR0+fJgKFy5MrVu35vl7eHhwG8wXr+3btxNEN/D47LPPyNPTk1asWEEDBw7kOY4fP57bT5o0iaZOncpzwfk6mtu0adOY0549eyhHjhzUvHlz6t27N+mxYzp3W7jSJo1LmVKYXVnW++uO2kkfls1I64/epH0XgilzygQ0oL6vxfVy7sZ9Gr70NB30v0OpEsel5qUzcPsnqAjunsW7r9KJqyFUOXdK6lE9m2Us9N2qjA+tUa6aI5fvsqPny3q+9POac/TP4RuUNlk8dstUyZvKMq3g++HU7/ejtPH4LcqVLhG1VMfXLpjGst/dKw9VmjIIRJ/Vzh7FwdSoeDrCvn9P3qY25TLS2Rtmh1GjYukodRKzwFY6pzf91r0ohamUZ4j9it/WU4FRxKBkCbzo89o56FZIGG0+ccuuSJQnQ2Ly8TZfIywjVb66Dcdu0bXgRywSffvnSRamkA5vw7GbNK5NASqVIzmn3/tDpeADfwiB7SpkomLKfQRn1H9qLhPaFeC57TgTRIOWnKCpHQvx9YmINFGDMbt4buVzedPUjRf5Wt4MCeXjcZ0yPrlnbtwNpXH/nOdrkl5ds/pF0lKrsj7cL9L/rTgQwHOZueUStVT3R/eqWXmfvbd/Dt+ipftuqHMLJe9EXlQmZ3JqX8HsHhu54jx5qh9DiHy7VRo53GdV8qagTpXNwjeuyXdLz9HJa/cpcfzYalz7TquLymU0cMlpqls4Na09cpuC1L2VX7mNmpdKSxjn9r1wSpMkDg1p4ksp1DwQ208H06QN/nRXufDiecWi9wunUvdgesJvhp/XXKBQJQRCFNx0IpCmdcxHsdVkf1h+nk6p+Xiq9WJZk1DvmlkoFk5CQggIASEgBISAEBACQkAIvOEEDh069IafgUxfCAgBISAEhIAQeBcIFChgfgb6pp+r20Sihw8f0v37T+u6xIkTh+LFi8diDgSifv36UZMmTVicgUgCwSN58uQEIUnHgwcPKDQ0lD9269aNRZfEiRNTnjx5aPHixbRmzRratm0b+fn5sRCEfrp06aIPJ6SKg3CE7RBgdEDEuXTpEgtOkZGRLOggRV7WrFkpZcqUtHHjRn5duXKFvL2jpgjTfejltWvXuH+MoQPnAJGlY8eOvKlMmTK0bt06fkGEgaA0efJkPvfUqVOzQAPRBwIQzq9du3ZRjse8kKoPrxs3btDIkSMpODiYx02T5qlwgX2YR/ny5Xlce3P75ZdfWBBCI/S9f/9+fkGIw3WJ6dx5cBtvSg8g1MjRoQUefEZtncF/nGCxplq+VDR9sz8h/dmcrkX4mA7TDpCfEmvGty1Ahy/dpe+XnaasqRJQ+VwpWFj6YuExQso1pIibpUSCrtMP0qKexSlh3FjcN8SNAUpwqKWEHoyzTQkXEJOGN81DC3ZcoU9mH6b9wyqxewfzGfvPOXq/UBpOz7ZRiSSfzzvKogyEjxcRZ5UIhsD5WAeEEC2G5EybkHd/ueg4CysQZOJ5eUZJ2Xfy+j0+bziIrOP7ZnmsN0X5jNo6qHH0WF2sU9fu8XWASKZTAkIcmq94VVXX6Kv6uShb6gS06mAADVDzaVs+I4szEGvaKPfR0j4lKZu6RsP+PsUOKLietiix61LgQ4JYBMEQtZ9w7XOqVHjL9wewcDesSW5KmTgOC0KfKe4LPylGmFePWYcpPCKS+r+fgy4HPqJhS09RfCWUQEiD0+q/04FKcAmjnirtXv6Mz5678UTPKbHtl3X+6prGoSYl09Jh/xAlTimRKXtSyp0hEfcPcQdsq+ZLQeuUuLPiwE0WkvL6JKJ+80/SpduP1D0Yn0WZ5ftvGLuPsv4wLFLVhHpMc7ddpdzpE1Gc2B609/wdfqVXTrrSOZPR1pNB9PPqC5yu7uiVe0rwOUfY16BoGtpz7g4t2nmdBZ/mpdKpuYXS6evm+yVzyvjkpRxn3WccZUGpZPZkqn9P+leJR7dCwum7Zr5R5iIfhIAQEAJCQAgIASEgBITAm0jgbXng8iaylzkLASEgBISAEBAC7x4Bt4lERlEGGOFsGTJkCLVv356mT59OcLDAvbJ582YWKiCQoPYPviEEoQQBsSZLlix08eJFi0B05MgRSpcuHX377bc0dOhQmjVrFjuN+AD1BscO3DsQT2LHfno6GGvmzJm0atUqatiwIQs2QUFBfBjcR3AyffTRRxQrViwWnSAa7dq1i2rWrKm7drjEnFevXk1FikDUMJFOrwfxB66i9evXE9Ln/fDDDywSbdq0ifvr2rWrRSjDeLrm0aBBg3j/6NGj2Q21YcMGnsvPP//M7R1Oxmqn9dzAFDFx4kR2bum54TNEIj32886dO7fx1m3GwShbIeAY6/rAKdP5vSzcJrESFD797QjX04GwBnGid43s7FKBMAIxRac/Qx0dpF/rUys7HwtnTblvt9Duc0FUSQlBiE5KQNJCy3Hldlm+/7p6gJ6HH7z7eMej/34OpIuqDpAWQ4pkSWrZXz1/ahY2Fu28yu4W7tDNb/qhf6YUCRz2DPfI3G5F6QtVk0jzLKdqHeH8tRMKbhIIO88TEGwKfLnRciiu0dgP81PsWE8dKeh7TOt8ymlj3jbj30tKzEinxBtzSknMo8HoXewI+rhaFu7ryOUQdvlAcIN4tf1MIDUtmZ4OqJR4GZVjKX3yeHR8Wwivv184DddWKqCEHv/bD/n4feeD2QW2/osylE65iBBIvTdn2yUWiXiDevu1U2HydiEtXsCdMBZhRrXOzULQB8XTUpNx+9npA5EIgdOb/FE+SpogNjUoloY6TTtCx6/eo7hKOIJAVDmPN/WqkYXbfrf0LO1UjiNH8Z5yIvWsnpndSQ1H72NB8pf2ZjfjhZsP6eKTc52++TKPPbZNHubeUI3dZfpR+mtPgBJB01mGgAAE0WnD0dssQnWs5KMcR6l5P8QtOKUgTmH+EkJACAgBISAEhIAQEAJCQAgIASHw7hGIpEgKV38eqz8RT/7wmilC7YlU/z999o/63/AzfzzJk2J5qJf6E5vfsYxNXuoPtkkIASEgBITA20XAbU8TkT4NYo4OLZpAhIGTpUSJEizUYD/SpEEgshcHDhzgXSEhIdSiRQteh6sGgVRsxoBLKUMGc8oo43YIQ0jTVr16dcvmgIAAFoSQfm7hwoUEwQbCEQQihNEJZTnIzgrcQcWLF+e9cPTAhYSAmDVs2DB2NeEznD5wSMHt8/fff/N+pKurVKkSi1dYGo/H3BDvvfcei2dgcPBgVLGFGzh4M84N7iL0jwATRJUqVSwOLuPYzzN37tDO28fVshIe/OtAiixjINWZDr0eoFKM5U6fWDk4vAluIaSHK6cEour5U6nUXHEI6coO+t/lFxwwxjihHuhrkcjXIJokVanXIHToVFzeCePwYeGG4zGe3o9lWd8UtFI5ZF5UYE4ICB/xvMzzsTcWBKxV/UrxOcORg5RucEJ9UTcnfaiENogCgRfD7R3udDtS1yGQcm+TSrcHF9d0Jb4grRwCIo8WiMAcKfw+LGdO+4b9cLKgLRxfSJUGlqiPBNfRBSXEwR3WVQmGuHZ7lfhTOY9ZyIO4BBdY9R+2U60Caahi7hRUPJvZuQVxBvHLugu8xNsSlV4QodPspVHpDF0RiHAM3DspE3vRlA2XlAD4iJ1DcLnhn8c60JcWWFIpZxMiWIkuELwQSI+nA6nqnIlERTKb733c9RDd4EjSgXSASDuHuBoUShmUaGYU5vJkSEgbjwUyM7RJoMQ7CESIY8p5hMD4+y+a5wZRFQHXVtIET3+ueKO8CQEhIASEgBAQAkJACAgBISAEhMBbRQDCTxiF0iP1CjOFqXVIQ+F0l0LoGl2nANNNCqRgCqI7anmHbpuC1ad79JCPeqyOwytcvSKU7ONJ8ZUAFFe94qlPcdUrCSWgFB5JyJuSqlcy9ScppfZIQekpHX+Oy628KI5HHLVm/uOljofcJCEEhIAQEAJvHgG3iUSo8WNdk0jjSJs2rV7lZZIkT4WDKDuefICoogP1cxBw/iBVHMQmYyRNmtT40bKO9ggvL/PDeL0DaecKFSpEEF8gZCGV3fMEUuXpMKbM8/X1Va4A81+KEGAgDiGF3ieffEKY69y5c9lNBWEKafDg4mnTpo3uipCmTweENMwTqfx0IEWcDtQ5shXGuRnbGFlAQEPEdO4DBgywNQXeBoHIVjo1fYDxoTjqqhjjF1XTBi4U1Aga8tdJfv3+cTGLYyaLqmEEN4oOpCAzphx7cgn0bqdLpPAyBgQtiBrRDQgmiDsPn14n3QfqzSRUziAE0q0hzt98wKnW+MOTN9QFwthesZ7OCSJN4cxJ+dWhYib6aOp++n37FRaJ/NIlZrEFKdriWp2HPgctgBnHwTrEOfSrAwLOKZW+7g8lyGiRSN/PaIO0dIg4hrnxZzVu6OMI3ldWOZ2Q3g/p5eACK6xELsSxKyH0nxK5hnzgx5+Rym9V31JcWwh1kGZu8ee0dnAyPQo392W8xnBPQcgBH4T1HHijnbftZ4Lph2XnmI9vuoSq3lQq+mPP9SitrdnpnSEPzXNBjS0dWkzSn20tE8WL+u0qpMqzFRC9rMUuawEUjjIdd5WwiECbiEjz3JKoOkl5lCMqYVy3/UrXw8lSCAgBISAEhIAQEAJCQAgIASEgBF4hAXy5UUlBSuBRf0wPWRi6QTfotOk8naAzdEItTyth6ISSg0iJPm4L60cils8elJYSU25KTb4emdQyB+Wi7OTjkUFJS/Epvod68Z94Sn56+lzDbfOSjoSAEBACQsDtBF7KE0WkNENAlDl9+jTB/YPaQsb0cNgPV0+WLFkIriQE6veMHTuWEiRIQNevX6d9+/ZRihTP1nDhxi6+oVYQhBfUDUIKOIgomJd2ArnYTZRmmTJlsrh+evXqRcifDDEHad0QyZIlox07dnCaPTiYUKtp8ODBhNRyS5cuJQgtSBGHeWFOcPwcP37c4gAqWLCgxUEFBmFhYSx+7dy5M8o8bH3w8fGx9I1Uf++//z4dO3aMkHIvVapUtGDBAsv+5527rXFjsg11aCCoIO1bjQKpaUB9X6o3aict3XeNvm6g/umhBBbUMepWJYtlGDgpkCrteQPpzUilqNMBJ0wen+g7MvAwH/NDvZzaqh6SDog1W08GWgSTDCrlHeY7deMFKpq1oMWpg/aDlpykXWeD2D206uANFoBmKzcOUoohIERBENt8/DZ/9nviMIErp3Plp+eAMWuP3EEllFCjhRk+wMEbjrmuWGZIbtvphznAlXXA/w5fG3QFzWbfhTtU8IlzBuONXHGGhY8Kfin43HAtF6r0fYGqhlCxLGa3UPCDcCWQxaXuVbPya972yzT0r1N0Q7nJ/JSbDNFa1WdCKkLEw7AI5ewJZ7cSb4jG2/zt19gpNrNLfq5rhL6W7I4qEtnrLoO3WRxCXaH0yc3p3XarukHuCgg8pwOeCuPo99T1Byxo2RL3kC5xl3IRtS6XnvI+SZV39PI9Jbbd4JpL7pqX9CMEhIAQEAJCQAgIASEgBISAEBACr4YAEsbdV38emB4ob9A9OqXEoN2mA7TDdIQ2kb+aVNirmRiPqp4bKM8SXhtNZ9SWDea5mNSzCkpFpT3yUAkqRAXU0puSU0KPhKS+0khx1B9xGr3CyyZDCwEhIAQcEHCbpN+uXTsWQbJmzcrLxo0b87AQSlAzCIIPBBCkhtu/fz9NnTqV98Pxo0WhRo0a0U8//cSiDUQcOHEqV67M9Y1Kly5NDRo0INTqiUnolHj/qfR1EEqqVq0aI4FIzwVOIUTt2rWpb9++nC6uXr16LATBiTFmzBgeCzWPJkyYQFu2bOH2ZcuW5eXHH3/My2bNmnGKPdQ1QqA/OLHy5jXXMoGQlC1bNipVqhTXcOJGDt4wdseOHblF27ZtqWXLlpyCb+vWrQRxCxHTuXMnNt6QfgyOEuPrgXo47ywuq3RgFYZupZ/XnKXrwY8IaeSQlg3iC6JRsXQ0af0F5Xa5RqipM3vrJao8fBunO3PWt739m0/cVqnNzqsaNCG83HLytkqBZhYErI+BqId6VPYC88Pcxqw5Ryev3eM0cQMWHec0bbWeCEdwBo1qlY+2Kj5fLz6hRJZgbjtq1VlOq9ZeuYW0ewjp3frNP8rnd0nVsUHaNQhC7+U1p22DaIMaTGNWn1Xp1C5Yxuwx6xCnH4PQYi/g7tmlajnhtUzVbfps3hGuB4XUb/YC9Ygw/qJdV5kXBCHMUfPyU/OBALZcpevTbiS4izBvpM5LrEQRxPfLTlONEduZD0S+4yqNGo5DWsGiqp23Wn6m6lQhvSBEu7aT91Mf9dleOLouKRKZHUjXgkNVCryH9NXi09zNg1CVl/mJM8lev6VzJGOBadGu66pm0iPacOw21zKy1z662yv4JWcB7I/dAYSfD9QWAo+KuZ+mtzP2WTWf+bqPXXORU+GhRtHQv88q4S4kRkKpcQxZFwJCQAgIASEgBISAEBACQkAICIGXSwAp44JUUrhLpsu007SbJkZOp48i+1L+yLbUOHIwfW/6SwlEEGVepUDkiEkkHaYAmmLaSB1No6lEZCeqH9mdhkWMpn8iN9AZ0zm6SbeUH+pRlNTvjnqUfUJACAgBIfByCJif1sZgLJ0eDeIFXjogxsDxAtcQAnV64FwZMWIEtWrViuBagSiUJk0aGjhwIAs2cPPs3buXUMfozz//ZHFj2bJlLCqhD4gveD1vQDCBKNS5c2eaMmUKvyDClCxZklxx5WBcnaYNfRkD54DznT17Nv3888+8C+n3UI8JAZEIaedWrlxpOZ/mzZvT119/zfvhLIKrCccvWbKE3T3Yj/pNCKSxQ2q6b775hsUzOKogzM2cOZP3483e3IYPH87OplmzZtHixYu5PVLcoS9ETOfOndh4G7/2/DNb//q0BPmmfVqbRTfQNCGMZE2VgAY2zEU/rTxDUzZe5CZINQbxBdGufCYKUvVzvlt2iu6rh/wQFr5q4Kvqzth+qM4H2XjTY2JXRyWyIOUZ5oz+elbPxk4m68Nu3rxJI0eOZJHOep/+3L5CJq6bM23TRRZtsB2Cx48t81JF5azRgVR8cPj8usmf/txzjTdj7EHq3HG+CKRbm9G5sBJvjlKz8Xt4G96alkzPc9QbetbIRmHKgTV722UWp7A9o3d8mtutqCVFn25rvWynxBcdqD/0bWM/qlvYnCLS6jbnZm3LZ6Qg5ej5WYlgcAYh9d+I5nmUAGR2CMH9UtEvJW0+ccsydols5vSMqFeko7eaM8SQFhPM54X5TmxfkAUZ1Gz6tWMhGvzHCct+9PFds9x8uPHaYYOz69KyTHqC26bPbyf4+Kyp4ivRMQHX9Vl3xOzI4h1WbxgHaeL61MpCP648Tz1nH+MW+ZTLDLWKrLIkWh0d9aORpXG9lZrb8SsQO6/wC0dhbh9VfCruGdunTxaXOlXOSNM2XaKvFpnFLqTKG9w4R9QB5ZMQEAJCQAgIASEgBISAEBACQkAIvNYEIikSfiG6Y7rLbqHNpv/ob9M2OqbSyb0NcVSdx1H6h8ab/qEEpgTUjIpSNY/yVNAjr6p5lELVPkqsKhnF+NHk24BKzkEICAEh8EoJeChhQyWLej0iIiKCBSLjbJC27datWywwWaenM7aL7jpq8UCUSZToWcEiun0Z25uUKwGp8ZBiDjWFrANCER5owx1k73yuXbvGziuIZdaB4wMDA/l4a6HKuq31Zz03iHW2xtb7YzJ36zFj+hnpxZBuzFbaLRhAsB/1YSAuuSO4bpASamyNh/4hJqLOVEBAABlrP9kaG/O7HPSQa+ekMdSzsdUWKdbCVY2Z9MniqZpWtloQ3QoJU2n4wimjqvVjrFdkbI0x/W8/YGYQpl5kYKwQ5fBCurSYxKPwSHXukZa0ctZ9ITUcwl5NH+xz9bocV640MNY1ha4GhXKKNmN9LPRnKzBPuNrAH84kd0fAnTB2fkEkdaV/cDl57T6npcuVPqHbfgbcfV7SnxAQAkJACAgBISAEhIAQEAJCQAhEJRCuXEN3VLq2G6abtMm0leaZVtMuuhS1UbQ/4WECXkgaFEtVBcLLk+Kql5d6JVDviVTKt7hqGdfDi+KYYqu12KqKUQSFeTxWdY/CKdT0WElWYUq4UuvYrkQsLEPU0lzvCEu81AOBGEQqJQ6186hEdTyqUWbKRN4eySmempmko4sBVDlUCAgBIRADAq+VSBSD85BDhcBLITBv3jx2oCF1osTrQ0Cuy+tzLWQmQkAICAEhIASEgBAQAkJACAgBIWCbAFLKBas/p1QtnwWRS2kybVENw203drjVLATFUyKPtxJ+MlJSykZpKaNHOvJRy/QeaSm1qg+UTG2HWyepRxIlEiVwWYQJVdLQXVOIkrHusph1iwLpmuk6XVGViC6p5XmPq3TBdJtuq3Z3eP74cqfz8gK2Tqkm5aa2Ho2oqEchSu2RSglb6suzLHbZai3bhIAQEAJC4EUQEJHoRVCVPt9aAtu3b6cHDx5QlSpV3tpzfBNPTK7Lm3jVZM5CQAgIASEgBISAEBACQkAICIF3g8BjekyBpiCVeu0EzYlcQnNppzpxOHJcDYhCXkr+iUt+Sv4p6uGnljkop5KGcnhkU6nbvJVvKJarnbmlXYgSkc7TRa41dIrO0T7TMTpCl5Uf6oGSjiB8PY7WOGUpC3X2aEKlPIpROiVyxRVnUbT4SWMhIASEQEwIiEgUE3pyrBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAELBBwKTSsiGt3EnTaZocOeeJOORqqrbYlFwJJQWUNFTeo7Cq5lOACnnkp7QeaV66IGTj1GxuCjYFsxC233SYtpj20H4lIl1QyevMbinXzrukSj/3mUdbKuFRlJ1FUrPIJupnNg4YMIDWrVtnt+Y66rGjTjtqxrsjIiMjae7cufTpp59Sjhw57I7rjrGkDyEgBF48gZgVE3nx85MRhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEwBtF4JHy01w2XaH5pj/pW9Nfau5hLsw/lkofF58qKpdQZY9SShwqRTk9cqiEcu6vi+vCZKLdJJlHMipLpaismncP6kRXTFdphxKLNpr+o010mE4pwcwZh53kT01NQ6ilqSR18WxNeZRrKrGqpuSp/ki8HgSCgoKocePGdPDgQcqVKxehxryEEBACbzYB+Q37Zl8/mb0QEAJCQAgIASEgBISAEBACQkAICAEhIASEwGtCAO6hIFV3aH3kJvogspcSiBaqmTkSiDyU/BGPylFW+sGjNW32nERzY02grp7tKa9H7jdGILKFP4NHemrsWY/Gx/qe1nvOoNkevamxckWlU5WSyInoM0+l5KsY2YdmR85XdZCuqOR10UtfZ2s+b/K2ggUL0syZM6lJkyaUIkUKqlatGq1YseKVnFJAQAD5+PjQyZMnqXLlyq9kDjKoEBAC7iUgTiL38pTehIAQEAJCQAgIASEgBISAEBACQkAICAEhIATeQQIRFEFXTddphmkeDVUOIlKf7YencsjEp/oeBaglNaCyniXVp/j2m7/he9J4pKbmHo2pGTWio6bjtMi0lP4wbaWTSlKzX78onD4z/arkooPUmzoq0cxPyWnxyEP9edfi+PHj1LlzZ+rQoQNNmjSJ5s+fTw0bNqRdu3ZRoUKFYoRDp6qz1UnXrl2pffv2UXb5+fnR7Nmzo2yTD0JACLzZBEQkerOvn8xeCAgBISAEhIAQEAJCQAgIASEgBISAEBACQuAVEwhTbqHjppP0ZeRIWqukD/vhQckoAbVR3qFWnh+88W4h++dpew8EnnweefjVSTmE/jAtp9mmlXSIbqoDbItqC1XKun9Np2icx6dUybOs8iEleSeFotq1a9PEiRMZbP369alw4cIs1sRUJCpevDglSQJ317OBdHISQkAIvP0ERCR6+6+xnKEQEAJCQAgIASEgBISAEBACQkAICAEhIASEwAsiEKrqD+0w7aZPIr+nEyx22B7IQ7lg2ngUpW4ebZRIkveNTiVn+wyjt9XHIwP19OhCjU11aZZpPs01raMzFKg6MT3T0XVVz6iJaShNjOxG9T1rq9pN3iph3bvlKKpevbqFS5w4cTjl3L59+yzbnnelQYMGz3uoHCcEhMBbQkBEorfkQsppCAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAi8XAKhykG02bSNOkV+R9eUkGE7YinfUCb6n0dnquBZhlOm2W73bm5F7aIvPfpQQ1MdGmWaTHNMeymSHtmAEUHdTBPoYeQjau7ZiFIooehdSj0HYcgYXl5eFBbmqN6VsbWsCwEhIATsE/C0v0v2CAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASFgi8Bjekz/mXZSRwcCUSwlCX3l0ZDmxRpL1Twri0BkC+STbbk9ctEEz+/pV88eVIDS2mlpoj6mabQkcikF0x3lOXrWdWTnwDd+87Zt2yznYDKZCJ9jmmoOHaLmEAQoW6+ffvrJMqasCAEh8PYSECfR23tt5cyEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBF4AAYgTR0zHOMUcUqHZilyUkkZ49KbKnuVFHLIFyMY2JVVQK48mVNgzPw2KHEV/02HVKvKZlp+YppB3ZDKq7VmDEqoaT+9C/Pbbb1SwYEGqVKkSLViwgPbs2UNDhgyxnHpwcDCtXbvW8hkrmTNnJl9f3yjbrD906tSJUO/IVuTNm9fWZtkmBITAW0ZARKK37ILK6QgBISAEhIAQEAJCQAgIASEgBISAEBACQkAIvFgCAXTj/+2dB3gUVReGz24IkNBbAOm9o9Kb0hUFRLCigoiK7VdAaQoizYYFRBTBgiCIqAhIExCVJggKUqT3EjqBUAKk7H+/G+4yW7IbAgm7yXf22d2ZuXdueWdm4blfzjnyasJ7sk2Oe+2ouZSTd+z9pIqtomRSL9rVEaisuI2xvy3FHCNltON3dXKsWwMJ8qTjI5nvKCK1bLdKqHqld3vttddk/Pjx0qdPH4mIiJARI0ZI8+bNndPevXu3tG7d2rmPjV69eslbb73lcsx9p0GDBu6Hkr1vs2WsvFDJBsOKJBBkBGwqdmXG8csMsovD4ZIACZAACZAACZAACZAACZAACZAACZAACQQWgYtyUYbGfyDDZZYamKeXSyupJCPsb0gpWwmxqxct5QRiJEaGJLwvHzjmq0bchSKR1lJFsR4kJWzF0nV+IoSCmzRpkjz44INy/PhxyZs3r9jtvLdSfmfxTBIgASsB/ppYaXCbBEiABEiABEiABEiABEiABEiABEiABEiABHwQWOX4R0bJAlXDUyBqKRXkQ/tACkQ++F1NUZiEyev2XtJTWqrTQjxOnSP/yQLlaXROvTKK5c+fnwJRRrnYnCcJpBEBikRpBJrdkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJBDeBGLkgoxMmqM8Yj4lUlALyrgoxRw8iDzTXdCBcCUX97S9LR1tN1Y5neLO3HZPlgCNSSXaeot01dRxAJ3/88cdyyy23BMSI4NVk3hiQdTsgBshBkAAJXDUBhpu7amQ8gQRIgARIgARIgARIgARIgARIgARIgARIICMS+C1hibR2vC7xSiZytRCZocKeNbc1lizqRbv+BLY7dkjHhFdkvRz2aHyE7WnpbH9Yckh2jzIeIAESIAES8E2AnkS++bCUBEiABEiABEiABEiABEiABEiABEiABEiABLSnyo8yWwlEFz1ovCQtpK6tFgUiDzLX70A5W1l53vaAajDUo9FvHHMlyhHlcZwHSIAESIAE/BOgSOSfEWuQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQLonkJCQIFu3bpUTJ06k2Vzj4uLkwIEDEh8fn2Z9prSjkxIlixzr1ekOtyYyyWP2+yW35HI7Hpi7wcTcneADtnbSQG5yPyxr5KAckEiJUy8aCZAACZDA1RGgSHR1vFibBEiABEiABEiABEiABEiABEiABEjgBhOoW7eu9O/fP0WjOHXqlDOHhsmlYb6jojKuJ8KkSZOkQIECUq1aNSlcuLC0adPGRSz69ttvvXJbuHCh8zqUKlXKo85tt93mLLdunDt3Tp5//nkJDw+X0qVLS1hYmLz88ssSE+OZ68d63o3cXutYJ3u85CJ6QupLESVchKhXINj+/fulffv2+lrMnTvXOaRgZO4c/OWNHLYccp+thdrL5F4kfzv+lfNero9HRR4gARJIMwL4PZoxY4b8999/adYnO7p6AhSJrp4ZzyABEiABEiABEiABEiABEiABEiABEghyAmPGjJHNmze7vHPlCg5PkOuNftWqVdK1a1f54osv5OzZs7Jp0ybZvXu39OrVy9nV4cOHpVGjRi68wK9hw4a6jsPhkIMHD8qUKVNc6nz33XfONqwbEPnmzZsnK1euFAh3S5cule+//17effdda7WA2v7PsU35EHl6qrSwNZLstmwBMVaIeWXKlBFcD3cLRubuc8B+E1tD5bXlmfdpvWxRmaLcc0V5a4HHSIAESIAErAQoEllpcJsESIAESIAESIAESIAESIAESIAESOCGEoBAUaFCBZkzZ440aNBA8uXLJx07dtTCQ3IH9ueff8p9992nz23WrJl89dVXHqcWKVJEL6ZjQd287faMuUxy+vRpef31153eJ2XLlpXWrVsLxCNjR44ckXLlyjlZGWbwBIIZL6yaNWu61AFnb7Zr1y5p0qSJ1KhRQ3sTwTusdu3aguOBavtUSDNRGYncrZKtvGRVr0Cw1atXyw8//CATJkzwGE4wMveYhDpQ3lZOCkqYR9FOR6TEOmI9jvMACZAACZCAbwIZ838/vpmwlARIgARIgARIgARIgARIgARIgARI4AYRuHDhgvZi6dy5s9x7773y0UcfybZt2/R2dHS031Ghbtu2bXWOm88++0wgPjz77LMCDwuadwItW7bUIpEpRc6a+fPnS/369c0hOXTokPZOwXWBiIfvFStWOMuPHTumt3/99VfNHALfsGHD5NKlS8461o0uXbrI5MmTZeLEibJlyxb5/PPPtTD40EMPWasF1PYJxykv48ks4eplU69AsBEjRki7du28DiUYmXubSBbJLEVseTyKjsgZJeF5ingeFXmABEggTQjAMxUeoj9+P1V+Vf+m0AKXgGcAz8AdK0dGAiRAAiRAAiRAAiRAAiRAAiRAAiSQQQggNBZy1MAQ5qx8+fLy22+/abHIFwLk1smfP78gzFnWrFnlgQcekJMnT2qx6ZFHHnGe2rdvX8GCurHbb789xXmOTBvp5XvgwIHacwt5JIxBJFq8eLEW3F566SXtrdK4cWPtbXTLLbfI0aNHdVUwRa6hyMhIGTJkiBb4IAS52z333CNPPPGEPPXUU86ibt26SatWrZz7gbYRq0PNuYZxC1e5cewBIhD54xWMzJOaU1ZHZo+iC+r6uF4djyo8QAIBQWDfvn0yuF8fmTXvFzl17nyqjSl3tnBpe1creeOd4VK8ePFU68dbw+vWrZMOre6U+jkyS9mQBJm3arnMnTpFfvrtD8mWLTDCc3obd0Y9RpEoo155zpsESIAESIAESIAESIAESIAESIAEApgAwsQZK1mypA51tmHDBr8i0Zo1a3QYMwhExlq0aCHjx48XeCkZq1y5slSsWNHsahHKuZOBN8Dp/fffl59++klKlSrlJIHQcM8995x06NBBH0MOo1q1asnXX38tI0eOlBw5cmhxCMJQzpw5dZ0qVaoIvFeQZ6hw4cLOtrDRo0cPfU3gKVatWjX5559/pHfv3oK8UG+++aZL3UDZCdch5eAxdEWKOC+XlDQRHN4rwcjc27V3KP6n5ZxHUU6VpyhYBDuPwfNAhiEAgahZ/XryZHisbK6aRyJC86fa3I/Gxsu41Yt0f7+tWHndhKITJ07o3+pMmZKWFno987T0zWuX5wpeEXT/t2evDOn5krw77stUmzMbThmBpK9kytrjWSRAAiRAAiRAAiRAAiRAAiRAAiRAAiRwzQSyZHFNTI/FqIsXL/ptF3Xcz82cOXGRKj7+ymL+448/HtBeK34nmgoVfvnlF3nmmWe0h1WbNm1ceoBnl9XCwsLk7rvvluXLl+vD8CaCWGQ1hLGDrV+/3kUkOnXqlIwbN06/ISLB4C0Gg1AETyb3a6gLb/BHQVs+pQ+5ikSiJKKTEqXDnIVIyA0eYdLdBytzbzOKlmjZL56h/4pKi0OCMAAAP0hJREFUXuXXxaVOb8x4LHAIwIMIAtGAYrlSfVARoSGJ/ew/rT2Xvvz2u2vqE+IQvHOXLVum/zAAv9Xdu3f3aPPw4cOydccOea56XpeynoXCpeXcefKuy9HA2Jk7d67AmxVhbkePHh0Yg0rDUTAnURrCZlckQAIkQAIkQAIkQAIkQAIkQAIkQALJI7Bq1SpnxaioKB3+DB4n/qxGjRpauEhISHBWRVvlypVjiBsnEc+NtWvXCsKRDRgwQF544QWXCg6HQ+cZcs/rtH37dp2fCJVRhjxEyGdkbO/evXrT6pGEA8ajKzw83FTV32Y/qTxGLpVvwE5pKaF69RSC/nGsk/MScwNGlPwug5W5txmuTdggkeIpGJe3FZfMtiteC97O5TESuNEEEGKuW6HsaToM9Id+r9V69uwp+fLlE4jOCxYs0KK+9d/qa23/Rp0/ePBg6dSpk/Yotv7f4UaN50b0S3n9RlBnnyRAAiRAAiRAAiRAAiRAAiRAAiRAAj4JIB9Rnjx5pGDBgjr8GMKZIW+QsV27dsnChQvNrv6GNwvCoSEvTp8+feTRRx8V5EV47733AjaEmcsEbtDOnj17BCH5YPXr13fh2rRpU4EXF9iCaYECBaRSpUoydepUwV9eI/eTOQ9eQVhEfOWVVwR/cf7kk09qcQ4C3datW/W1GTVqlDRv3lwLSrjGCAuIsH+4Tv369dPeSbjWgWjVbZUlhyNUzkisy/BmO/6Qto47Jbstm8pOBE+jwLNChQoFJXN3kvHKZ2uB43flv+UpEt0qVVRAwCthJt3P5T4JBAIB5CBKzRBz3uYIj6JrzX0UGxurf+8RGhSCfs2aNXVeuZkzZ0qdOnVcusXvTYWyZWXMkQMq3NyVPwYYcfi8dLi7rUvdQNjBH0Js3LhRxowZI8ePHw+EIaX5GCgSpTlydkgCJEACJEACJEACJEACJEACJEACJOCPwKBBg/QC1JkzZ7QoMW3aNJeQZdjH22ozZszQIsOUKVN0yDIIEhAcevXqpd/Wuty+QgCLfOAMa9269ZUCtYUFM+QYQp6ikydPupQPHz7cmaMI3kLTp0+Xzp07y9ixY3UbyFn0448/is1mk+joaIHnEcQjGHIeIcfR/fffr/fx8fDDDwd0mJ8qSiSqIHnkb+U3ZLWFslU2ObZKhK2AZFavQDAwd7dgZO4+h/2OAzJNEOLwiqdgYh273GKrJmEUidyRcZ8ErgsB5FKCQfQ3htx+S5YsMbsu3++P/Vw6tLpTlsRekrIhCbIy1i6OAsXlpxGjXOoFwg7+v5HRzaZceK9k28voNDh/EiABEiABEiABEiABEiABEiABEiCBG0pgzZo1Uq9ePdm9e7fgr5ER1gbhbVJiCFOXO3duLVKk5Hye40ng9OnTgnwTZcqU0R5G7jUQqgfXDsISvI6shlB07onOcezo0aPaYywkxDOUm/X8QNh+M+EDGeyAOHklvxXGdbfyYhlpf0OKq5Bn9gD1JjL8go25GfdF5T00NP4DGS6z1CFXkaiD3CojQ4ZIIYkw1flNAgFJADny4hsVT/OxhSzbJ9cSynPFihXSuHFjlzYmT54sn3zyifz5559e53P27Fn9RwP4d7127drSo0cPr/UC5SByLOEPIz799NNAGVKajYM5idIMNTsiARIgARIgARIgARIgARIgARIgARK4GgIQDVIqEKEfhKvz5lFxNWNgXVcCuXLl0nmI3MUeU8tut2sByV0gQrm3c3DspptukmAQiDCHe213q8xEObHpYnPlP5njWCjR6uVQr0C2YGMOlggzt8yxQkbKArXnKhCh/CFba8nl5bqgLKMYBNovv/xSDhw4kKZT3rZtm1y86Bn+z9sg4FG4f/9+b0UpOvb999/Lzp07Xc5FWDSMCSHEkmORkZGCPygINDt4KV6i4jzv9WsZ56FDh2TixImSkrw7RYoU0V2fO3fOOQT8wUCJEsjV5t2yZ88uDz74oDzwwAPSsmVL75V4NCAIUCQKiMvAQZAACZAACZAACZAACZAACZAACZAACYBA0aJF5aOPPhKIEbS0IYC/bDdv9Gi28X2j9nXHAfhRxVZJOkh9NTLPJbWejvGyPGGlxMiFABx58A4pQYlC2x075bWEj+SSoutuTaWs1LXVyvCh5jZv3qxDOEIkSQubNWuWFoyrVq2qhd7+/fsnKcxAWHjsscckf/78WkRu0KCB/Pvvv3qYO3bscPnNsf7+vPXWW0lOZc6cOYK8ZhERV7zHhg0bpj1QMabSpUvr3GlJNYA8aU2aNJGSJUtqT0YIGfBcNbZ3716v40rt0GRbYmLl9vVHpPiqg5J/5QG5b/NxOeVDLFoefVEq/RMp8BTyVQ/zAv8PPvhAJkyYYKaZ7G+I+TDk7jGG/EQVKlQwu/wOYgLMSRTEF49DJwESIAESIAESIAESIAESIAESIIH0RgALfshVQ0s7Au4hiAJtP+1IJK+np+2d5NeENbJBDrudECfdHO/JREcWaWCrl+FFCzc4KdqFQLTbsUd6J7wpayXSSxt26Wl/QvLZ8nopy1iHEPILv5/ID5baBoHnvvvuEwgm+L3G/p133qm9N5EDzt2eeeYZWb58uSDsGLwM+/XrJ23atNH78ESBwGW1gwcPSosWLXQ+Outxsw0vIQhEffv21XnncPyLL76QIUOGCHLTNWzYUHvMdOrUSf/hAfathrCZHTp0EOTUgRgEDyd4vHTp0kXnS4NH5JEjR/QpmzZtcvFIhYdqatlpJQa133RMKodnlv11ikh0fII8oESiTttOyMzKBTyk6e67omR05Bm5I09W2RYT59eHMTQ0VAYMGCDPPvuszgEXFhaW7KnAA7Fbt24yePBgQZi5devW6Zxza9euTbINeLZlyZJF4FG0fv16WblyZZJ1kypIrkdYUufj+FNPPeWrmGWKgOefPRALCZAACZAACZAACZAACZAACZAACZAACZAACZCAVwJlbaXlRdtDalEtq0f5MTkjTyhB40/HSjmnXoEees5jAgF0IE7iZJcSiHomDJH5ssXryHpKK6ltqyFZ1Cs9G7yDXn/9dbn55pu16IFFb/ewckuXLtVCDTgg7xQEnCpVqmhPDwg358+fv26IpkyZItWqVdNiD0STpk2b6vGNHDnSo4+TJ08KwsKhDB4+BQsWlBEjRuhcZAsXLhQIF8hxZn3//PPPUq5cObnnnns82sMBiE3IfQahytiYMWPkpZdekrvvvlt7or744ovSqFEjr14zy5Ytk+3bt8vHH38shQsX1owwvrlz52rRCG0eO3ZMEGKtbNmyLmPLmzf1BMmlyisIYs8nZfLITZlDpGJYqIwqnUfmnoyRPRfizFSd31vOx8raWwtL95tyOI/524A4d+bMGfn999/9VfUoHzp0qL6PcA3BHtcR91hSBoHnwoULOs8PvMmwfzVvtIuQsdf6Tmp8PH6FAD2JrrDgFgmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAn4JfCo/QH5N+E/+dTxm6rrmjfksMpL9JASNj6zdZdm9tslt8qWY+ffaftlaq1wQYXs2+jYLK8mvCeLxTXnjKnXSErJs/YukldSz7PD9HWjv+E1M3bsWHnvvfd0njZ8t2vXThYvXqy9NLDwjvBrH374oR7qq6++Kl999ZUg/BrCt/Xp00dOnDgh48eP95jKtGnTZPjw4R7HcaB27doyevRojzKEaqtZs6bA48ZYjRo1tPADMSBbtmzmsOzatUtvV69e3Xksd+7cWmRCqDl3gxfRqFGjtLdKUrnKlixZoj2ITAg05NjZsGGD9i6ytofxw8PK3SAQweuqUKFCziIIcDCMF95Y8CSCgATPm+nTp+tjHTt2lEcffdR5zvXe2K4EoojQECmkBCJjN2dPDPu5W4lEpbO6LuXPr5oYai/ykqeAZM53/4b3EAQ+iIoQ1K7GIAj+8ccfgtxGEMvgJeTLgs2DB55oGdVc76yMSoHzJgESIAESIAESIAESIAESIAESIAESIAESIIFkEgiVUOlv6ymHHMdluqxTZzlczoxWXkSPON6RdxMOSntbayliK6LO4DKcCyQvO/C8ipJTsixhhfR0fCT71bY3K6mEoeH2PlJMcVV+Bt6qpJtj8PqAQIRwap07d9bzgodM+fLlBWIJFvoheqBe/fr15ezZszqv26RJk3QINZwAMaR9+/ZaRHIPlwZBBKHXvFnx4sW9Hdbh4SASWc0INnv27HHxLoHHDwyCi9WQCwhik7tBAIMXUVJjQn14+UDoMHb4cGLoR/c+kOMO4eLcbefOndpDyHrceAihrHnz5oI2//77b53HB15JEJueeOIJuXjxonTt2tV66nXb3nEhVsqFuf5O5M2UKMTtUCJR8+vUE+4dwywlTbpzTkkbPCewCLjedYE1No6GBEiABEiABEiABEiABEiABEiABEiABEiABAKSQAFbfnnb3k/OJwxS4dCw2O0qFMHDqK9jgqyQf6WHPClVbJUkh2SnV1ESVxPeQwcdh2S841sZ7pijasV7rRmhKH5mf1Wq2aoq2S39L20akeP222938oDAAnEHuYAgEq1YsUJ7xkBcWbVqla6HfDCLFi3S2ya/zsaNG+W2225ztoMNeADhfTWWL18+LUpZz4EHESxnzpzWw86cQSjPmvVKiEaIWQhbZrV9+/bJp59+KhC4kvIiQv2jR4/q/EfmXOO55B5SD324jwfn5MqVy2P8COlnyvBdoUIFnX8HXlkw5PGBF84HH3zgVSSCNxZC3lkN/EuXLm095HM7V4hdzqg8RFaLVV5iMJRdL8P1g5hHIwFDIP3/kpqZ8psESIAESIAESIAESIAESIAESIAESIAESIAEriOB0rZS8qH9deml8hDNk82qZXehSGSGY51695L3bY/JXbYWyquosISpV3r3gEku5ksSq7yHomRlwt8y1DFW1kuiV4i384uq0H2fKWGuoa2eykKUGIbLW730dAyeKzD30F4QXIywAY+iZs2a6dwtyAEDq1SpkhQoUEBvFytWTAsy7qKMLkzBBwSUvXv3upy5f/9+vW88ikyhEUkiIyN1qDxzfNu2bdKqVSuzq78htPjzIkJF69yxD9EH4ePMGHAMBtHJhJFLPJL4iTxD7iIJQqjBzHit+Y4SzxJp3bq1DtkHr60cOVzzAEF8wzis5u61ZS3ztg0vok8PuYqjkZcS991DzXk7P7nHLl26pHNBJbc+66V/AhSJ0v815gxJgARIgARIgARIgARIgARIgARIgARIgARSiUA5W1kZZR8sAxzDZapjjerFdZE3sdtL0svxlXzjmCc9bY9KA1sdibBFKKkoa4YViyAOnVav/1TuobGOyTLN8a9C5SmymctWXQrJCHtfqWurpeShjCEQYe5VqlTRCBD6rG3btnr75MmTOuSbyfMzf/58eeONN3SZqQ9Bo2XLlvpYXFycQKSx5uDRBeoDuYvgJePN4KU0Y8YMj6KKFSvqPEZRUVFOj54ff/xRbr31Vg8PoBIlSujzZ86c6QwRt3btWkHuIYhNxhCWbty4cdqLKFMm30vWEIRWr15tTtXfYPHDDz/Ic889p/fhVTRr1ixniD5rZYRbg9CD/DpNmjTRRRgfrEyZMvq7R48e+nvkyJH6Gx/IV4S+3QUilCHUH97XYuXDQrUn0R+nL0iTXIleVzNPxOgmy7qFobuWfhCuD/OgkYAh4PuJM7X4TQIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIk4JVACVtxGSXDpKjjYxkrv8lZOe+13jo5JF0c70tjRxnpausgdWw1lViUX7Kpl129MoLFqLByp9Rri2ObTEmYKV/Ln2rariG2XDmESBupJMPsvaWCrZyEqFdGMoQGg+AD8SM0NFR7q7z55pt6kR/eKxBXEH6tQYMGGkv+/Pl1CLpu3brJZ599JkWKFBEIHRMnTtR5aEzuHcMQYewmT55sdl2+jSeSy0G18/TTT2uRqHv37vLaa6/J77//rgWZn3/+WVedNm2aDBkyRH755Redi2jYsGEyYMAA7dWD8eC8unXryp133uls+p133kmWFxFOQK4hCE0Qv4yghPYh+MAb6d5779Vh4WJiYgT5hGB9+vQReC9B9IKYc8cdd8grr7yiw9tB7Bo0aJD07t1bDJ86depIly5dtEgH/mvWrNFtPP/887q91PhokDOL3JEnq/TcFSVjyuaVU3EJMnDvaelTNKcgN9GWmFhpv+mYjC6TV5rnThSRUjIOhCl86qmnUnIqz0mnBCgSpdMLy2mRAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmkHYE8ttwyLOQ1qZFQVd5TuYj+1WHTvHvGLJadstjxnlR2RMiTtrbS1NZIiUUFJKd6IYxaegtFFytxSjg7K6cd0fKPY63yqJopc2WTujje+ZirFqp8rQbY2snT9s6ST/KmOy5mnv6+J0yYoIWZe+65R1eFwDJ79myBILRgwQLt2YLwcsZQHyKA8TxC/iLUMwKIqYdvhF7D+2oM7Xz77bfy0ksvCTx44FkDkcaEj0MOpM2bN4vJEdSzZ09BPiQTwq1WrVqCnEkm7xBCv40fP16++eYbp+jjazxNmzbVxVu2bJGqVavqbYhkb731lrz99tt6LBCj0EfhwoV1OcS07du3S0JCgtjtdhk9erQWgUyOpnbt2kn//v2d3Xbs2FF27twpL7zwgn6j4JFHHtEilLPSdd6wqfY+VQLQ49tOSMN1R3Tr9+YLl9eLJ4axi45zyLaYODke611Uxfn+DNcGXlwtWrTwV5XlGYiATcUg9P1rnIFgcKokQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkcK0E4CUz3PGJfKfyEcVJYrgo322GymMqjFo7uUOq2CpKblsu7V2UVUlGwSoYIZzcefU66zgre2SfLHD8IRMdv0ukCjHn30KknhSV123PyW32+iooX8q9Jvz3FTw1kIMI+WSyZcvmHDQ8jBB+burUqc5jZgN1z5075wwJZ45fz28IKch5lDmzawhAq5eP6S86OlqHeYOAc61Ws2ZN6dChg4uwgzbRL/IlIbeQzXZFNnE4HII3BCKrIRcRchwllT8IOaEQZg5h88LDw62npmgbnOIbFfd7LnIRhdltkkd5EFlN6USS6cq0rEU+t0OW7dP3DsILDhw4UOdkMl5YPk9kYYYgQJEoQ1xmTpIESIAESIAESIAESIAESIAESIAESIAESCAtCVySSzIr4Rf5xPGtLJP9quu4ZHVfWHkT3SO1pImtfqJgJLkkzBamfGrCJFQyBaxoFK9yMSGUXIwjRotDO5U09KdjlcxzrJDVciBZcxc1u6Jq/s/b7pHO9oekgOQP2Pkmc0KpXg1eQgilZnLxpHqHAdLBokWL5PHHHxd4E2XPnj1ARuV/GMkVify3dHU1IBLBswviWt++fbVX1NW1wNrpmQBFovR8dTk3EiABEiABEiABEiABEiABEiABEiABEiCBG0rguOOE8qD5ToVYmy//yQk1luSJRYmDDlWCUSWpZ7tZakg1KWYrqjyMwiWrDT5GmdU7i87Rk9beRhCEIIJdxNtxUUlDF+SIHJMNjk3yt2O9zNHB9qKvgrtNSWHh8rCtrjxne1zK28oqOYxZMvwBhNfM559/rsPKIU9PWpjVYwieSqm972tOCHmH0HvWUHtpMSbMGf2kxG6kSAQPqzlz5kjXrl09PKpSMheek34IUCRKP9eSMyEBEiABEiABEiABEiABEiABEiABEiABEghQAgccB+UHlYtnshKL1stxNcqrEYvMpDJLCymp5KKyUsFWRspJaSloi1DB2LIoLyP1suET8komLR6FXP60i2vIKtOat28VlEtJQFdecWo7Vr3iHHH6G2HkotRrl2OvbFO5lTY5VH4ltXVUznhrzs8xu8o1FC4P2erJ47YHledUZSV9hfo5h8UkELwEIvLkls1V80hEaEiaTeJobLxU2hglR6NOpVmf7Ci4CFAkCq7rxdGSAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkEMYHDjiMy0zFXCUYL5C85pLxxLqjZXEvKcLuUkdxKMCooxW2FpJgUlggVpi2POpZbhW7LZcupZJhwp8cRvI4SX4kQIQqZV4LagleQyhyjMgdFyynHaeUfdEIOymHZ5zgsu5W/0FotcKXMi+LKZcssFdTY2kkD6WjvoD2HIG/RSCC9E3jykYel7OpFMqBYrjSb6rD9p2VH7eby5bffpVmf7Ci4CFAkCq7rxdGSAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmkAwIQY5CzZ3rCPFkk62SH9sSJVTO7FsHIFxhku4f3gl3JMSEqbJ1dBYtzqPxB8eoY3gmX3+orVSyTEq2yyO1K0mpvbyl32JopL6K8ahTJ93JKlWGxURJIQwL79u2TZvXryZPhsdKtUPZU9SiCB9G4w2fly/Oh8tuKlVK8ePE0nCm7CiYCFImC6WpxrCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAumOAELRLXf8JQsdS+VP2aoEo7NqjhCMINwEq0GUyqSEoCxSR4pLC1t9aWprpLyGyjGkXLBeUo77uhCAUDS4Xx+ZNe8XOXXu/HVp01sjubOFS9u7Wskb7wynQOQNEI85CVAkcqLgBgmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAncWAJHHEdlpWO1LJe/ZYVjo+xS+X+OqaB0iTmMAlk0SvRUClcSUHHlp1RbiUENpIbcpsShUraSynsp040Fy95JgARIgAS8EqBI5BULD5IACZAACZAACZAACZAACZAACZAACZAACZDAjSUQp8LA7XTskrWO9bJGNsg6x3aVF+iEFo3OaU8jhIlDeLrUClGX1PwRIs6ugtZlkgJKFCqq8gtVtZWUW6WK1LTdLFVsFXUeJOQ+opEACZAACQQ2AYpEgX19ODoSIAESIAESIAESIAESIAESIAESIAESIAEScBI46YiSnUoq2ubYocLS7ZE9KlTdbjmsXtHqFasC1cWrbEdGPILnkRGRrEKSddsq5JhtiEDYtisZyC45dA6jECUIZZOSEiElbIWltJSQCrYyUk7lGCpkK6TqId8RjQRIgARIINgIUCQKtivG8ZIACZAACZAACZAACZAACZAACZAACZAACZCAG4Gzck4Qqu6o8jOKklNyynFafZ9WW6fV66wKWHdJLjnUW+LUO1a94pSsY9f5gTKrYHCZbeqtvnMoISi38gzKI7kkt0291Xd+lVmosBKCsG1XLxoJkAAJkED6IUCRKP1cS86EBEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABJJNgNJ/slGxIgmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmkHwIUidLPteRMSIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESCDZBDIluyYrkgAJkAAJkAAJkAAJkAAJkAAJkAAJkEAqE1i/fn0q98DmSYAESIAESIAESODaCVSvXv3aGwmAFpiTKAAuAodAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAmlNgOHm0po4+yMBEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiCBACBAkSgALgKHQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJpTYAiUVoTZ38kQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkEAAEKBIFwEXgEEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEggrQlQJEpr4uyPBEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABAKAAEWiALgIHAIJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJpDUBikRpTZz9kQAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEAAEKBIFAAXgUMgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIggbQmQJEorYmzPxIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIIAAIUiQLgInAIJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJJDWBCgSpTVx9kcCJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACAUCAIlEAXAQOgQRIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgATSmgBForQmzv5IgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIIAAIUCQKgIvAIZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZBAWhOgSJTWxNkfCZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACQQAAYpEAXAROAQSIAESIAESIAESIAESIAESIAESIAHvBA4ePCj79u3zXhjgRzds2CBjxowJ8FH6H15qzWPbtm0yceJE/wNIoxp79uyRL7/8Mo16C85uYmNjZfPmzXL06NGrnsCUKVNk06ZNV31eRjohtZ41MFy6dKksX748VXA6HCL7T8boN7ZTakdOX5TomDifp+8+dl4uxSUkWScu3iGo42sc16Of+ASHHD51QfDtzdD/vhMxcjT6ordifeyimsf2w+fkQmx8knWSU+CPib+xuveB8Xgb95w5cwT3KC39EbBdunTJ+52c/ubKGZEACZAACZAACZAACZAACZAACZAACQQJgU8//VQ++eQT2b59ux5xRESEDB06VJ544ok0mcH8+fMlR44c0qBBgxT3179/f3nvvffk+PHjkjNnTr/tXI8+/XbiVgEiTaNGjaR06dJuJVd2r3YeV870vTVp0iTp3r27nDhxwnfFNCr9+eef5f7775eLFy+KzWZL1V5PnToluKdbt24t06dP9+jr/Pnzkjt3bn1crd25lOOaPfXUUzJt2jRp27atS1ndunXlwoULsnLlSgkLC3OW7dy5UypVqiSrVq2SW265Rcw1dVa4vHHfffcJxBx3A5MBAwbIRx995CxCe+PHj5caNWo4j/naKFq0qLz++uvyzDPP+KqWrDIsVOO3oUOHDknWvxHPU5KDSWaBuS7J/c04cuSIYJ64btmyZUuyl8jISKlWrZosXLhQX6/7R62WTQfPeNQf2L6CPFyviHw4b6d88cdemdOrnpQqEO5Sr9HQZfJc85LyaIOi+vj0vw/JWz9vk3MXE4WObFlC5A3VTptbC8nOI+ek7Yd/uZxv3enWtIT0aFVGdilRZ+CPm2XNntO6uEXVAvLm/ZUkR1gmZ/XfNh2Xd2dt10IU+uhYv6j0VOdaH9VPf90t45fs02MpmCuL9Lq7rLS+paCzjevRDwSUd2fvkKkrDzrbfbJxCel+Z2nJFJL4uzHn3yPythrrybOJz27NUrnlnYcqS5E8WfU5EG3em7NDJi7b72zjiduLy8t3lZEQu+tvz/lL8XL/R6tlz/HzMvzhypqrOckfk+SM1bSFbwh9A3/cIn/tjNKH82bPLD3UvO6vc5Pe/+GHH/TvwNq1ayU83PW+0BX4EbQE7EE7cg6cBEiABEiABEiABEiABEiABEiABEggXRKAGNSjRw956aWXBIvbBw4ckGHDhunF5ZEjR6bJnLEYDhHjWgzzSO5iL/q5Hn1ezXgTEhK02LB69Wqfp13tPHw2xkIXAvjLfHgwuduPP/6oD5UrV869SL7++mt9LCkvLHj6vPrqqy7nOby4VaBt1LW+P/zwQ5fzzA7Ezq+++kqLDFFRUbJ7926pV6+etGzZUiB4pbXNnTtXXnnlFZ/dpvXz5HMwySy82mcNv48QDE+ePOmzB7TbpEkTF0Hvvto3yS996ru8W998RVBBg10/XyvwzEnKVu2Kkv4/bJZ+bcvJykG36/fLd5WVPt9tkr93n5Li+cOc7UNwgvVuXdZ5rIsSRs5ciJP/fb1eIEj80b+hzFb14BnT57v/JOHyfbs58oz8b8J6aV+rsO5j9OPVtYj11ZK9zqF9/1ekjF64W95/pKr8Nfh26ara7j3lPyU8Jd6f16ufD+bu1HOb3qOO/DO0sUx8toZ8uXivTFsdqceyckeU7vf5FiVl9ZDG8vtrDSVPtlB5+ot/nR5DEIdmKyFpWvfasuHtpjK+261a3Pp5zWHnfMzGkOlbJXvWK2KZOZ4cJv7GatrCN1A/8+U6yRpql1/7NdBjf1Vd14HTtsjybYn3F8TITJkyybhx46yncjsdEKBIlA4uIqdAAiRAAiRAAiRAAiRAAiRAAiRAAumFwLlz57TH0FtvvaVFoWLFimmPiy5dusiIESPkt99+E4gbsD///FP/BX2+fPmkWbNmehHbcEBYqwoVKgj+8hneQKhz7733yqFDh0wVmTVrlsDzAmWos2jRIl2GxdRff/1VvvjiC93Gv//+q8NkoT0s0N988826P1TesWOHbhdtoLxv374SF5cYLgl9o0+Yv/F461OfaPmAKHD33XfLxx9/LKVKldJveL/Ak6BKlSp6Ho888oiAobEVK1bIAw88oMvuuecevciPMggT8ASBPf/88/p8bL/44ovaywS8MSfwts4DdcAQ3iDwDMGc+/Tpo71vUIZr8/bbbzvHg/nv2rULRUka5oDxo71u3bppYc1U9sXXX1/WceL6wjPNar/88ovceeedep7t27fXYqS13H0b7WF8GCfuAXh9WL18cAz3DDiDHe4t8PdnRvSx1jNjzZUrl/WwFmeWLVum78OZM2fKsWPHXMqxc+uttwo88eA14suyZ88uZcqUcXkXLlzY6ynwTLrtttukcePG2mOlSJEi+nn87rvv1OJyooiA+37QoEH6WuK+6NWrl8AjKimDCIvnFqw6d+6sBWFrXZRDhEI5vvG8wyCADR8+XBCKEv1AvHI3b8+TeQbdn2E8P+Y3ombNmi4hEM0zN3bsWN0XxgIB2zzj/u5B6/OH+wH3hzF/z5oZL4REM76OHTtqUQ9t/PTTT/Lggw/q5m6//fYkPbRwj37//ffy8MMPm671N4SL4vnCXN5Wzx144pxVAs6YRbtdzrPubNgfrXfvrVlYciqvH7w71i8iE56pIQVzZpHQELuz/RJKMIJFqOOm39zhoVpwgZfMwHsr6LLSynNpQLvysnjLCTkYdUGfM3vtEalQOLt0a1ZC91G3TB55oWUpJaxc8cSZsuKAdG5UTBpXzCc5lKjSSW3Dg+cn5ekEg2h1PfpB3x8qIQrjCcscIrVUHyXzh0vk5bEu2nRMKhfJIY8oTyd4PIEjhDH0vWRLotiSRQkx/dqUlUo35dCeQ2gT7bl7d83857BAOBrxWFU9B+tHcpj4G6u1vdj4BD3GO6pFyE3K4wljb1U9QleJVGH1YHa7XfBvjDdvQ12BH0FLgCJR0F46DpwESIAESIAESIAESIAESIAESIAE0h8BeDXAWrVq5TG5F154QSAoYKEK+WwQais+Pl4+++wzvSD/7LPPyrfffqvPQ8gteDv07NlTL55CyEBODpNzBsIF/iq6RYsWAq8NCCZ33XWXDn2GxX/sowyeFSVKlNAhvNAevBewoA0xCAvE7dq10/lZMAb8RT+ELCzQw+BhgXHC/I3HW5/6RMsH2oN4NW/ePPnggw902DCER3vyySf1eOBthblARIJhjm3atNHbGB8WuBHeDKG6ChQoIO+//74u69q1q150xw68tuA1AqEJnhjgYJ0HFv0h/ECoePPNN7VoAqbGqwOLh2+88YYez+TJkzWbxx57TPfj7ePMmTNapILXGEKRwUMEAhXY+uPrqy/wxvX9+++/9dwwb9wLRpD577//tJiDkGzIGwXhB4v/SZmZN3K6QMDEtQZTLPQbw70LwQ0L+mB49uxZLSSZcm/fjz76qL5eGK8xjBnhnFDmbhBlIAJBLIBQg5Bz7gbhq3fv3jo0IzzZfBkEHus7qboQiHBtIALBewWGcFMQb/LkyaP3Id7g3vvf//6nhUOEosMz683wHOO+q1+/vn5eIAZCXMP9AMO8UI4Qabg+CKUG4QfPIIQRXE+Eg8TzCeHK3bw9T+YZtD7D+/fv178jEJsg4NSqVUtf2zVr1ugmzTOHew3eOJ06ddLjxf0P83UPYk54/hCqD3OAkIP7A4INzN+zZsaL3xs8c3ge8XuC7ejoaD1Wc//h2QEvb7Zu3TrNFSKmu0Hfs76t5QVyZJEPH62qRKI9zjBw1nJs31oitz7Uc/JGgWBk8vPULp1biikBKjm2V4kn8CLKnyOzs3pFJZjADqicPrBdR89JlaI5xG6JLQchBuHcYlQ4NngcbT10Vosz+oTLH9WL5dQh77B7PfpBOwiFV65QNj3XU+dj5eul+7S40urmREElkwoXd/5y6D3UhxlvrINRifOBgIRwfMYghmH8VYvmNId0bqZhM7dqgcmEqXMWqg1/TFDX31it7WXOZJdHGxaVz9T1XrH9pGw7fFbemZ0Y7rVJpfzOqghXid8neBTS0g8BT1+19DM3zoQESIAESIAESIAESIAESIAESIAESCDICOCv52HwcPBl8DLInz+/YNE8a9as2lsGIZewkIq/dDYGjwwIOTB4gpicChAJYFjsh1dOw4YNBSJT5syZ9cI3FuBLliypF6NRD4vTMIgMJg8MvAmwX7ZsWedCOTwu4D0A0cObJTUeLLa79+ntfByDEIZ8NXXq1BF4GWDhHgvJMHj+YFEY9s0330ihQoV0/dDQUJ2/BXxxPrx9sNAOw8K42cY+FuanTp2qxTjsWw0iCRYI169fLxUrVtRFCFsG4QoGryvwhNcCWGKMRlTQFbx8QGSCsAGDIAexAAvsyJPki6+vvjBOiC3oG95oMCysQ8SACAUGGCcW+SEUQXg4fPiwwDvHm5l5gx+uNwyiG9p65513nNcf171fv366HF5AEHOOHj2qveH0QbcPCGgQ09DvQw89pEs///xzueOOO6Ry5cqydetW5xkQc8ADQgNyJiE/F4QY3LfuBtFg9uzZWqTBtfRmuI5ZsmRxKRo9erQW/lwOqh0IKxBUIJDhjXsV/T/99NMC7yMIYia8m/Fuwb0HDy0IOUZIMu1CRINXFoRGGDyK0OaSJUv0vQgRFPeQCX+HZ+7ll1/W9wWeFXieQfS13rembXx7e568PcNY6Ma1hfCGMFoQdSC0IneTNdcSGBYsWFDfJ/DQwnMH0crXPQgvJOQiw28UBDVwwW8W7jNjvp41UweCF+YOQ/6w8uXL6+ccYhHEOxjma+5zfcDyYbzNIIRaDTmH8LbazJ51tQBijt1WIZ88oPLRvPLtRvn55braQ8eU4btGyVw6/9D7c3fIrxuPae8TeKI82qCoh2BjPc+6ve94jPLEcRWUcikPI9g+JRLVLwdB5LyLgIIyeCrBDpy8oL2LsA0vJasVUl48O1ReJNj16AfikLF6g5Y48zCNeeJm7RWEsjql88iEpft1+LkOtW6Si3HxMuKXRGH1aPRFc7rz+1JcgvRVofXKFMzmkj8J+ZUg1D2mhBtv5o9Jcsbq3u7/WpSSv3edkidVaDxjyINUwCLg4TcahvvK/bk25/A7+AhQJAq+a8YRkwAJkAAJkAAJkAAJkAAJkAAJkEC6JWAWnU6fPq0X75OaKP7SH4u0EIiMwfMHi+ZWr4zq1aubYi2GGC8fhH6KiIjQx7CQDZECC87+knHjr6iNYVEZQhKEpy1btui/rEYIOxPGzdSzfic1HmsdX9tYSIdABDPfEDuMYSEbYbhgEBSwDc8OYxAGwAciUVIGNvDW8mZYFMcYjECEOhB18DbbEAvgmYEwdwiPBw8KX2b1BDF1IcZgMdwXX/SZVF8YJwzeVcZwb8Dg2QEOuH8gEBmD91pSIhGEN8zbCEQ4B+fDNm7c6Fysh9hgzGxHRkYmKRJBRICwBHEGItGJEyf0PYxQYsarzrQHARJCBwRU9IlvzAOsIChZDc8Frj+uJcQKeDe5G+4b8LOaezumDPc6xggvMYgk06dP12zBFGIcRD0YBD8TtvHIkSP6mJUPDkBc/euvv/Q7NjZW1zEf4AyBDG1a71vj8WXqXcu39RnG7w08kuABuG/fPv0Mw5vJGiYPvxN4rozB+wnhGmG+7kH8RmEu1t8UeCBazdezZupBQDOG5wGiLLwBIRIlx0y+Ius4cB68TCAAWQ1hxtwNodKWbD0hb/+8Xd56sJJ7sTxUr4i0U+HmVu44qXPXTP7zgExXId6mvFBLbi5+xTPG48TLBxAa7lwSnjcmFw9EI/c68CCCZc8aIuEqNBrMHNM76uO8qmPauB79mHbxjVxEJ85c0mHxnhu/Tj7qVE1aKqbwukHIvdd/3CLvzNquxw0PIYhAVm8ptAEvLuT8gRfRT93rSKYQGw7L9sPnBHmLPn/yFh2yTx90+/DHxFo9qbFa61yIjZcu49bKxdgEGaE8yHKrcIQQ/pBfKkdYqA7jh/rm3x16ElnpBf82RaLgv4acAQmQAAmQAAmQAAmQAAmQAAmQAAmkGwLwEIAhrBI8EayG8GNYYIaHChb63b0gcByGEHTGzDHsW4UPLPxiQRqhn+AFg9BeWDRH3hN4iCRl1jawoG+EgA4dOmhhBOdh4T8pS2o8SdV3P249370sqf3ixYs7i+D5Urt2bee+tw3rHN3LsajvawzwbIBoAe8JCGYjR47UnlwQ0pIyCBDGQkISF3uRR8UfX199xcTE6Catc4fnCsLs4f6AUOY+D3hbJWW439zrm3Fb8xJZ2zBzSapNcxzhAkeNGqW9UuBJg3sTgpW7SGS8anCvWQ1h73C+u+HehKgDb7kZM2a4F+vFXm9hHT0qWg6AHzz18Eb4OQgVf/zxh/aQQTUIpKgDg2cLxBWrwILjRhiC2GG9PubeNM+v4YtzrqdZ7294ETVt2lQ/x82bN9eebCYcnOkToe6sZr2uvu5B3DPW+8Hahtm2jsUcc/92/50DF7SdXDP3LTzR4IFmDHl04CnkzyCyvKu8SbqMXesUCtzPyapy7EAcwbtbs5LSeNgymb32cLJEohJqHAdUPiGrGY8b5C6ClY5QOX9OJT7Tpt6hy3ly4D0UokK8IWTdodNXwjaiHvIEmdB116Mf0ze+kU8I1kgxhMfT5OUHtEgExAPaVZAH6xaRdfuidU6i+mXzyC39/5AS+cL1Oebj44W7dM4hCGrW8HyfLtqt54NcQD+sijTV5c/tUUoQyyTNKuf3y8R5ktpIaqzWOquVBxHEqjm96kkplRcKhpxGYPiNEqyQ6wlmnl9/97auzI+gIXDlX+GgGTIHSgIkQAIkQAIkQAIkQAIkQAIkQAIkkF4J4C/l8Zf9SEyP0ErWRdTnnntOL0hj8RyhoJCfB8KRqYMQUVh4dl/U9cYK4bFgaBNvhE9D2DUseCOXDczfQiwWmBFKDF4UZrEbi/zXsrjtr089sGR+IF8RvE8QrsoYhBezCG+OXU2f8MIYNGiQDt2HMGMweO0gxB44on2IAgMGDNBv5GLp3r27S33Tr/nG+WAPMx5AEBv88fXVl8m/Am8UhH2DIc8SvCrgUYF5LF682OX+AaukDPUHDx6sQywZEcTkrTHCZlLn+juOucKbauzYsTqMGDyL3O8heLbAywnsrXl+EK4NofMgILmfg36Riwv5f5LrdeJtrHjGMD6El7PmvUEoQWOGAbzxEPoMBkEXXlTuYi+8gjBnePBZ703UzZ49u/YOhMAFwdaErsMY4KVUs2ZNZxg4s1htxuDt29+9DfEMHmK49hBQIB6+9tpr3pryeszXPQgxFmHpwMFcG+xjLiZkpddG3Q7idw28YPDewO+f+xitQqXb6U7RG96ZxgvEvY6/fYRQe/y2YoLcQ1aDpwy8dwbfV9F5GKHJ4DWTXCupBAl4Cf21M0qLEjhv0X/H9OlXRKJs8vnve3SenpxhicvZv6w/qkPaQSCCQQyat+6oIN8PDJ4xizYdl/bKywl2Pfq5oLxsOoxcJX3alNWCmG5YfZSJyOYc86pdUbJeiUNdGxeXCpdzK8EjB2bl8v1fkTr/z7iuN3uIaZhRqPIqQsg5qy3ceFRi1LwSRSLfTJIzVmvbqA8Lz5wo1JuyLEoAPHnuktl15iLy9ccUzsrcCBoC3n2Hg2b4HCgJkAAJkAAJkAAJkAAJkAAJkAAJkEB6IgDBBzlaFixYoHOWQChAaCUsJiO0FXJzoA68KXAci+AIuYXQWshzAm+E5BjyjkDkgIcLchVhIRYGkQmGcGrz58/XQoI19JQuvPxhPCTgNYPcMRC2IFyl1JLT59W0DUYI64UQV2AFzx6EcINwAwNHzBeeE//880+ymkbYMiyqw/MKwhBCj2GxH+IYrFevXnpBG/1i0R+iD0Q/I6x46wSeNL///rt+w9sHeVrAwh9fX30hxxQ8cjBOjAVjhXhh8lVhzGCCNnD/gMkXX3zhbXj6GEKMYd7IIYS2cG9AFEM4MXcRJMlGfBQgzxBEEISTM/mlrNVNzicINRC9zBvsIFRC8PJm+Gt/E2bPvfzUqVP6+uEamjfEUnfDfQLRrXfv3jp0HfI8IRycEYzgTYNcOwgtiOsHNggxh1xJCM9nBFlru3hOkdsIzy1ED3hCQSBevXq1roZyhIbEdcGY8PxDHDPeCwhniHnj3jU5d6ztYzs5zxOuHUIygi/uB4z/aszXPYjnD3Pr0aOHfg4Q9g/3nQkHmdx+8JsHoQ/38eOPP66fJxOW0YSaNPeOtzZNLqLt27e7FO8/GaPDwy3flhgmDt+HL3vnuFS8vNP9ztJSLG+iZ48pr67CycHTZfTC3TpE2k6V/+fDeTsF3w3L5zXVfH7fWiKXNFJ1ITjB82apCm03asEueapJCTG5iR6qmxgWb9iMrSo/0Tn5Vnke/aYEIIzJ2PMqn84/u0/J5yrP0u5j5+XNmdtV6LR46dQoUTS6Hv3AY6p84WwyePpWHWZuv/IgmqU8pr5euk/uujlCDyWXCs0GBiPm7dJeOAs2HJU3f94mD6uwfMZDZ/GWEzLopy1aFIM4aa4B5g/7UIV7+/21hi5vHH+jfQUdCg7b/pgkZ6zDZ+8QhMqDwWsI1mPSBlmz57S+hmAJMe7O6olzQzlCq8Lw+wrDv30QiU2YSX2QH0FHgJ5EQXfJOGASIAESIAESIAESIAESIAESIAESSN8EEAILXhXvv/++XpTGbCE0ICeKWcRFLo8pU6bIwIED9QIzyrFgizfMGlZJH3A7hvwvEEYgIsBwPvKzmJxBnTp10l4AEBaWLl3qEWoM58D7BV4yxmsG3hbwfDCL4tYxWLdxLsz9mHufmKPV3OubMvfjWNSHQdiYNGmS9oBBzhUYQo9Z86K8+uqreg6oC28Ec66ufPnD2j48ERBmDAKJySWE62Vy2wwdOlSLQxAOYFjEhreG8aS43KTzC9whdCAnFAxzhnAAMcAfX1995c2bVy/8Q3wxY8F4v/rqK90Pjn3yySeaDe4riGW4l4YMGaLL3T/QHkQ267zhDYLF+aTMcPPG1P2cNm3a6EVX5J8xopM5H3VxHREWzXhvmfMhlkBUw7OAcGkw9/6QZ+jdd991ue6oB0EKnj9Wgxcd2nI3CKC4P3D/GINoBnHKLBZPmDBBlxsvGVx7iL1gZ8zMCcLJ8ePH5ZVXXhHkADLPn5kDOEP8gacUxCCU49phrjDkH4NgCdEOYpN57k0/+HZ/nkzYNWsdCF1Y3DaeVvDiwr1gxmm+redY+fq6B3H/Tp06Vf8+jBs3TjeB3wvc7zBrO/qA+vDWH7zHIA6CEzyKpk2b5rwPcD/gHsdvJcS02bNnm6ac3xCGca3g+WYNNTlfLf7jbbWBSoSAmAELSfwZcRZnDQ2R4R2rSMdP/nYeu6t6QZ2X520l8Bivl2wqPxDaQeg5b6Y0ERfDPur3m7rJ2TbyJT3foqSzHsQi5MkZOmObtPngL0EfEIWs4fJqlMwlr9xdRnvnjFAiTcFcWVQOpcqCcHSw69XPsPsrKYFnq1NcwVjgZfXiHYmCFbyH3lb9vvr9Jvly8V7d9x3VCkgvldvJ2PcrD+pNeE/hbQy5i75/sZbZ9fmdHCb+xnogKkb2Ho+RBBWKEB5a03vUkf4/bJbHxiT+0QDmBiGuc6NizrFAIIY4bvK5RUdHCwRIeNXRgpeATf3AqxRZNBIgARIgARIgARIgARIgARIgARIgARIIPALw8sHiNMK5eVtAxYgRggniRVLlvmaFUFAIw+QtdA5yeCBcVdasnsncrW2iDryN8uRJ/Etsa9nVbie3z6ttF4wQyst4YljPR/g5vL0tolvruW8jfBvys3jjg5xAuG4m1Jv7ue77qItQX97yOfnj668vjBPmLQwheIONVchwH5v7PtoDx6vl5d5OMO7jWkBcwr1uPL3c54FrCUbJeR7AH4vL4O9NNPF3fUxuKW/nYlzJfZ4gwOBeRijClJi/exD3GO5taz4jf/1A1KlXr57mDeEQnl/efqfQDkLv4fcvKTEWYjAW9+ExlhoGkeGgyl0D/eemPFnF7q4EJbNT5CKCGGVCynk7Dfl/CufOosKxualYlyvHJySOBV5PSQ3jevVz/MwlKZAzs9f5Yhx7MVYlVoW5hXDzNq9rOZYcJt7Gqi6bONTL/XohVN/ZC/GSX4UOtBr+nShRooQW143YiXJrSEVrfW4HDwGKRMFzrThSEiABEiABEiABEiABEiABEiABEiABEiABEiCBDEDAKhLBE+haDN5Y8EKC1xTya9FIICUE4MUGj0uE6MyIInlKmAXLOd4l12AZPcdJAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAumMAHIJIYxjcr3xfE0fIQGxwA+vSRoJpJQAPNWQM5ACUUoJBu559CQK3GvDkZEACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZBAqhGgJ1GqoWXDJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJBC4BCgSBe614chIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIINUIUCRKNbRsmARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgAQClwBFosC9NhwZCZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACaQaAYpEqYaWDZMACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZBA4BKgSBS414YjIwESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIIFUI0CRKNXQsmESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESCFwCFIkC99pwZCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSQagQoEqUaWjZMAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAoFLgCJR4F4bjowESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAEUo0ARaJUQ8uGSYAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESCBwCVAkCtxrw5GRAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQKoR+D8RE209v9CoYAAAAABJRU5ErkJggg==" + } + }, "cell_type": "markdown", "id": "f9b2d921", "metadata": {}, @@ -599,7 +617,9 @@ "* PASS variants that are SNVs with MAF ≤ 0.1%, and\n", "* Exome median depth ≥ 30\n", "\n", - "**Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**" + "**Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**\n", + "\n", + "![Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png](attachment:Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png)" ] }, { @@ -804,6 +824,7 @@ " & (coverage_ht[ht.locus].median_approx >= 30)\n", ")\n", "\n", + "\n", "print(f\"Number of variants: {ht.count()}\")\n", "ht.select(\n", " freq=ht.freq[0],\n", @@ -1200,18 +1221,30 @@ "toc": { "base_numbering": 1, "nav_menu": { - "height": "213px", - "width": "374px" + "height": "613.99px", + "width": "526.312px" }, - "number_sections": true, + "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "219.438px" + }, "toc_section_display": true, "toc_window_display": true + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, From 2f1156e60d8d27f012dd08fe275bca330a893d7d Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 1 Oct 2024 11:32:05 -0400 Subject: [PATCH 005/121] Format gnomad_methods in requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 2900720..338124c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,3 @@ # We're using the main branch of gnomad_method on github rather than the pip version -git+https://github.com/broadinstitute/gnomad_methods@main +gnomad_methods@git+https://github.com/broadinstitute/gnomad_methods@main hail From ff64d867263bf21ceb56811826b9a4941512f652 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 1 Oct 2024 11:40:12 -0400 Subject: [PATCH 006/121] Remove setup.py --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 338124c..2900720 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,3 @@ # We're using the main branch of gnomad_method on github rather than the pip version -gnomad_methods@git+https://github.com/broadinstitute/gnomad_methods@main +git+https://github.com/broadinstitute/gnomad_methods@main hail From bc85e6fcc89a269a576208f35fdbb5a1cf33d1ef Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 1 Oct 2024 11:47:29 -0400 Subject: [PATCH 007/121] Remove setup.py --- setup.py | 41 ----------------------------------------- 1 file changed, 41 deletions(-) delete mode 100644 setup.py diff --git a/setup.py b/setup.py deleted file mode 100644 index f0e9818..0000000 --- a/setup.py +++ /dev/null @@ -1,41 +0,0 @@ -"""Setup script.""" - -import setuptools - - -with open("README.md", "r") as readme_file: - long_description = readme_file.read() - -install_requires = [] -with open("requirements.txt", "r") as requirements_file: - for req in (line.strip() for line in requirements_file): - if req != "hail": - install_requires.append(req) - - -setuptools.setup( - name="gnomad_toolbox", - version="0.0.1", - author="The Genome Aggregation Database", - author_email="gnomad@broadinstitute.org", - description="Toolbox to help users process gnomAD release files", - long_description=long_description, - long_description_content_type="text/markdown", - url="https://github.com/broadinstitute/gnomad-toolbox", - packages=setuptools.find_namespace_packages(include=["gnomad_toolbox.*"]), - project_urls={ - "Documentation": "https://broadinstitute.github.io/gnomad-toolbox/", - "Source Code": "https://github.com/broadinstitute/gnomad-toolbox", - "Issues": "https://github.com/broadinstitute/gnomad-toolbox/issues", - "Change Log": "https://github.com/broadinstitute/gnomad-toolbox/releases", - }, - classifiers=[ - "Topic :: Scientific/Engineering :: Bio-Informatics", - "Intended Audience :: Science/Research", - "License :: OSI Approved :: BSD 3-Clause License", - "Programming Language :: Python :: 3", - "Development Status :: 4 - Beta", - ], - python_requires=">=3.9", - install_requires=install_requires, -) From e083fbf4de436e83e54acb5ee60f04ba76773eb3 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 1 Oct 2024 12:10:29 -0400 Subject: [PATCH 008/121] Reorganize the notebook --- .../use_cases/toolbox_for_gnomad_users.ipynb | 155 +++++++----------- 1 file changed, 56 insertions(+), 99 deletions(-) diff --git a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb index 791362c..6b42cfd 100644 --- a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb +++ b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb @@ -8,12 +8,12 @@ }, "source": [ "

Table of Contents

\n", - "
" + "
" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "id": "e77d32b1", "metadata": {}, "outputs": [ @@ -30,7 +30,7 @@ " \n", "
\n", " \n", - " Loading BokehJS ...\n", + " Loading BokehJS ...\n", "
\n" ] }, @@ -202,7 +202,7 @@ " \"\"}};\n", "\n", " function display_loaded() {\n", - " const el = document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\");\n", + " const el = document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", @@ -308,7 +308,7 @@ " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\")).parents('.cell').data().cell;\n", + " const cell = $(document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " }\n", @@ -324,7 +324,7 @@ " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"f884217f-12c5-4fe1-a1d8-6a1d52499205\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -346,8 +346,8 @@ "output_type": "stream", "text": [ "SPARKMONITOR_LISTENER: Started SparkListener for Jupyter Notebook\n", - "SPARKMONITOR_LISTENER: Port obtained from environment: 52867\n", - "SPARKMONITOR_LISTENER: Application Started: application_1727699094620_0001 ...Start Time: 1727699722656\n" + "SPARKMONITOR_LISTENER: Port obtained from environment: 49385\n", + "SPARKMONITOR_LISTENER: Application Started: application_1727797895584_0002 ...Start Time: 1727798616140\n" ] }, { @@ -355,7 +355,7 @@ "output_type": "stream", "text": [ "Running on Apache Spark version 3.5.0\n", - "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:43005\n", + "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:45603\n", "Welcome to\n", " __ __ <>__\n", " / /_/ /__ __/ /\n", @@ -374,48 +374,27 @@ " )" ] }, + { + "cell_type": "markdown", + "id": "8e713032", + "metadata": {}, + "source": [ + "# Import modules" + ] + }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "id": "e69953f7", "metadata": {}, "outputs": [], "source": [ - "def get_variant_count(\n", - " ht: hl.Table,\n", - " afs: list[float] = [0.01, 0.001],\n", - " singletons: bool = False,\n", - " doubletons: bool = False,\n", - ") -> dict:\n", - " \"\"\"\n", - " Count variants with frequency <1%, <0.1%, and singletons (AC == 1).\n", - "\n", - " .. note:: This function works for gnomAD exomes and genomes datasets, not yet for\n", - " gnomAD joint dataset, since the HT schema is slightly different.\n", - "\n", - " :param ht: Input Table.\n", - " :param afs: List of allele frequencies cutoffs.\n", - " :param singletons: Include singletons.\n", - " :param doubletons: Include doubletons.\n", - " :return: Dictionary with counts.\n", - " \"\"\"\n", - " counts = {}\n", - "\n", - " # Filter to PASS variants.\n", - " ht = ht.filter(hl.len(ht.filters) == 0)\n", - " if singletons:\n", - " n_singletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 1))\n", - " counts[\"number of singletons\"] = n_singletons\n", - " if doubletons:\n", - " n_doubletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 2))\n", - " counts[\"number of doubletons\"] = n_doubletons\n", - "\n", - " for af in afs:\n", - " n_variants = ht.aggregate(hl.agg.count_where(ht.freq[0].AF < af))\n", - " counts[f\"number of variants with AF < {af}\"] = n_variants\n", - "\n", - " # Count variants with frequency <1%, <0.1%, and singletons (AC == 1).\n", - " return counts" + "from gnomad_toolbox.modules.variant_filtering import get_variant_count\n", + "from gnomad.resources.grch38.gnomad import public_release as v4_public_release\n", + "from gnomad.utils.vep import filter_vep_transcript_csqs\n", + "from gnomad.resources.grch38.gnomad import coverage\n", + "from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX\n", + "from gnomad.utils.filtering import filter_arrays_by_meta" ] }, { @@ -433,22 +412,12 @@ "source": [ "## Get variant count by AF for a release\n", "\n", - "**Note: this will take long if your notebook is using multiple nodes.**" + "**Note: this will take long if your notebook is NOT using multiple nodes.**" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "d9f96940", - "metadata": {}, - "outputs": [], - "source": [ - "from gnomad.resources.grch38.gnomad import public_release as v4_public_release" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "112c5065", "metadata": {}, "outputs": [], @@ -519,12 +488,14 @@ "id": "725f9a57", "metadata": {}, "source": [ - "## Get variant count by AF for a gene" + "## Get variant count by AF for a gene interval\n", + "\n", + "**Note: This isn't necessarily equal to the number of variants annotated to this gene by VEP.**" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "id": "9f8e1ba4", "metadata": {}, "outputs": [ @@ -532,7 +503,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Stage 7:===========================================================(3 + 1) / 3]\r" + "[Stage 3:=======================================> (2 + 1) / 3]\r" ] }, { @@ -571,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "id": "5b08a706", "metadata": {}, "outputs": [ @@ -588,7 +559,6 @@ } ], "source": [ - "from gnomad.utils.vep import filter_vep_transcript_csqs\n", "# Filter to variants in ASH1L that are LOFTEE high-confidence (with no flags) in the MANE select transcript.\n", "ht = filter_vep_transcript_csqs(\n", " ht, \n", @@ -624,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "id": "6ce87a77", "metadata": {}, "outputs": [ @@ -632,7 +602,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Stage 7:===================(3 + 1) / 3][Stage 10:==================(3 + 1) / 3]\r" + "[Stage 4:=======================================> (2 + 1) / 3]\r" ] }, { @@ -646,7 +616,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[Stage 7:=====(3 + 1) / 3][Stage 10:====(3 + 1) / 3][Stage 11:====(3 + 1) / 3]\r" + "[Stage 5:=======================================> (2 + 1) / 3]\r" ] }, { @@ -802,18 +772,9 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 7:=====(3 + 1) / 3][Stage 10:====(3 + 1) / 3][Stage 11:====(3 + 1) / 3]\r" - ] } ], "source": [ - "from gnomad.resources.grch38.gnomad import coverage\n", - "\n", "coverage_ht = coverage(\"exomes\").ht()\n", "\n", "#Filter to PASS SNVs with AF <= 0.1% and median exome depth ≥ 30.\n", @@ -851,7 +812,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 7, "id": "4f78166f", "metadata": {}, "outputs": [], @@ -870,7 +831,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 8, "id": "8f625a41", "metadata": { "scrolled": true @@ -882,27 +843,23 @@ "{'ami', 'asj', 'fin', 'oth', 'remaining'}" ] }, - "execution_count": 86, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# For this example, we filter to the ancestry that we included in the FAF calculation\n", - "from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX\n", - "\n", "POPS_TO_REMOVE_FOR_POPMAX[\"v4\"]" ] }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 9, "id": "15729719", "metadata": {}, "outputs": [], "source": [ - "from gnomad.utils.filtering import filter_arrays_by_meta\n", - "\n", "# Remove unwanted stratifications\n", "items_to_filter1 = ['sex','downsampling','subset']\n", "freq_meta1, array_exprs1 = filter_arrays_by_meta(\n", @@ -919,7 +876,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 10, "id": "d2886179", "metadata": {}, "outputs": [], @@ -938,7 +895,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 11, "id": "5471689c", "metadata": {}, "outputs": [ @@ -954,18 +911,19 @@ " {'gen_anc': 'sas', 'group': 'adj'}]]" ] }, - "execution_count": 89, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# The order in the meta is order how AC, AF, AN and homozygote_count is stored in freq. \n", "freq_meta2.collect()" ] }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 12, "id": "16170ed7", "metadata": {}, "outputs": [], @@ -979,10 +937,18 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 13, "id": "e3bcf7a0", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-01 16:07:07.655 Hail: WARN: Name collision: field 'all' already in object dict. \n", + " This field must be referenced with __getitem__ syntax: obj['all']\n" + ] + }, { "data": { "text/html": [ @@ -1071,13 +1037,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 7:=====(3 + 1) / 3][Stage 10:====(3 + 1) / 3][Stage 11:====(3 + 1) / 3]\r" - ] } ], "source": [ @@ -1095,7 +1054,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 14, "id": "9a4f26a2", "metadata": {}, "outputs": [ @@ -1105,14 +1064,12 @@ "[[{'gen_anc': 'afr', 'group': 'adj'}]]" ] }, - "execution_count": 96, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from gnomad.utils.filtering import filter_arrays_by_meta\n", - "\n", "ht = v4_public_release(\"exomes\").ht()\n", "\n", "# Filter by the location of the variant\n", @@ -1148,7 +1105,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 15, "id": "7e044201", "metadata": {}, "outputs": [ From b5101babf02725304f619a0194deb1fd8b1628f7 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 11:49:33 -0400 Subject: [PATCH 009/121] Function to import data by version --- ...variant_filtering.py => filter_variant.py} | 0 gnomad_toolbox/modules/import_data.py | 32 +++++++++++++++++++ 2 files changed, 32 insertions(+) rename gnomad_toolbox/modules/{variant_filtering.py => filter_variant.py} (100%) create mode 100644 gnomad_toolbox/modules/import_data.py diff --git a/gnomad_toolbox/modules/variant_filtering.py b/gnomad_toolbox/modules/filter_variant.py similarity index 100% rename from gnomad_toolbox/modules/variant_filtering.py rename to gnomad_toolbox/modules/filter_variant.py diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py new file mode 100644 index 0000000..7e1594d --- /dev/null +++ b/gnomad_toolbox/modules/import_data.py @@ -0,0 +1,32 @@ +"""Functions to import gnomAD data.""" + +import hail as hl +from gnomad.resources.grch37.gnomad import public_release as grch37_public_release +from gnomad.resources.grch38.gnomad import public_release as grch38_public_release + + +def get_ht_by_datatype_and_version( + data_type: str = "exomes", version: str = "4.1" +) -> hl.Table: + """ + Get gnomAD HT by data type and version. + + .. note: Available versions for each data type are: + + | Data Type | GRCh38 Versions | GRCh37 Versions | + |-----------------|----------------------------------|----------------------| + | exomes | 4.0, 4.1 | 2.1, 2.1.1 | + | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | + | joint | 4.1 | N/A | + + + :param data_type: Data type (exomes or genomes or joint). + :param version: gnomAD version. + :return: Hail Table. + """ + if version in ["2.1", "2.1.1"]: + return grch37_public_release(data_type).ht() + elif version in ["3.0", "3.1", "3.1.1", "3.1.2", "4.0", "4.1"]: + return grch38_public_release(data_type).ht() + else: + raise ValueError(f"Version {version} not found for data type {data_type}.") From 2bd494e65c3ed12cc3081a7b6a67c8f28b813fa5 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 12:10:20 -0400 Subject: [PATCH 010/121] Modify import_data function to match gnomad_methods repo version by datatype --- gnomad_toolbox/modules/import_data.py | 37 ++++++++++++++++++++++----- 1 file changed, 31 insertions(+), 6 deletions(-) diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index 7e1594d..ac17b35 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -1,7 +1,12 @@ """Functions to import gnomAD data.""" import hail as hl +from gnomad.resources.grch37.gnomad import EXOME_RELEASES as GRCh37_EXOME_RELEASES +from gnomad.resources.grch37.gnomad import GENOME_RELEASES as GRCh37_GENOME_RELEASES from gnomad.resources.grch37.gnomad import public_release as grch37_public_release +from gnomad.resources.grch38.gnomad import EXOME_RELEASES as GRCh38_EXOME_RELEASES +from gnomad.resources.grch38.gnomad import GENOME_RELEASES as GRCh38_GENOME_RELEASES +from gnomad.resources.grch38.gnomad import JOINT_RELEASES as GRCh38_JOINT_RELEASES from gnomad.resources.grch38.gnomad import public_release as grch38_public_release @@ -19,14 +24,34 @@ def get_ht_by_datatype_and_version( | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | | joint | 4.1 | N/A | - - :param data_type: Data type (exomes or genomes or joint). + :param data_type: Data type (exomes, genomes, or joint). :param version: gnomAD version. :return: Hail Table. + :raises ValueError: If the data type or version is invalid. """ - if version in ["2.1", "2.1.1"]: - return grch37_public_release(data_type).ht() - elif version in ["3.0", "3.1", "3.1.1", "3.1.2", "4.0", "4.1"]: + # Mapping data types to version sets for GRCh38 and GRCh37 + versions_by_type = { + "exomes": (GRCh38_EXOME_RELEASES, GRCh37_EXOME_RELEASES), + "genomes": (GRCh38_GENOME_RELEASES, GRCh37_GENOME_RELEASES), + "joint": (GRCh38_JOINT_RELEASES, []), + } + + # Validate data type + if data_type not in versions_by_type: + raise ValueError( + f"Data type {data_type} is invalid. Choose from 'exomes', 'genomes', or 'joint'." + ) + + # Get GRCh38 and GRCh37 versions for the given data type + grch38_versions, grch37_versions = versions_by_type[data_type] + + # Check version availability for GRCh38 and GRCh37 + if version in grch38_versions: return grch38_public_release(data_type).ht() + elif version in grch37_versions: + return grch37_public_release(data_type).ht() else: - raise ValueError(f"Version {version} not found for data type {data_type}.") + raise ValueError( + f"Version {version} is not available for {data_type}. " + f"Available versions: GRCh38 - {grch38_versions}, GRCh37 - {grch37_versions}." + ) From 3cfb1bdac0743061c2e0bb1336300a26702a1bc2 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:07:32 -0400 Subject: [PATCH 011/121] Formatting --- gnomad_toolbox/modules/import_data.py | 1 - 1 file changed, 1 deletion(-) diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index ac17b35..fb56e57 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -27,7 +27,6 @@ def get_ht_by_datatype_and_version( :param data_type: Data type (exomes, genomes, or joint). :param version: gnomAD version. :return: Hail Table. - :raises ValueError: If the data type or version is invalid. """ # Mapping data types to version sets for GRCh38 and GRCh37 versions_by_type = { From 8d904cf8299fc843ac091d4f0ab668bc3f95f157 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:12:22 -0400 Subject: [PATCH 012/121] Formatting --- gnomad_toolbox/modules/import_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index fb56e57..f0fb127 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -16,7 +16,7 @@ def get_ht_by_datatype_and_version( """ Get gnomAD HT by data type and version. - .. note: Available versions for each data type are: + .. note:: Available versions for each data type are: | Data Type | GRCh38 Versions | GRCh37 Versions | |-----------------|----------------------------------|----------------------| From 49b2cb0633fb09235b8337bd80683ddf4ec31df8 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:16:32 -0400 Subject: [PATCH 013/121] Formatting --- gnomad_toolbox/modules/import_data.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index f0fb127..8dcf16d 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -16,8 +16,9 @@ def get_ht_by_datatype_and_version( """ Get gnomAD HT by data type and version. - .. note:: Available versions for each data type are: + .. note:: + Available versions for each data type are: | Data Type | GRCh38 Versions | GRCh37 Versions | |-----------------|----------------------------------|----------------------| | exomes | 4.0, 4.1 | 2.1, 2.1.1 | From a7c8e53314ef1c431290ee29e09cc8392354c902 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:21:06 -0400 Subject: [PATCH 014/121] Formatting --- gnomad_toolbox/modules/import_data.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index 8dcf16d..0698693 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -16,14 +16,13 @@ def get_ht_by_datatype_and_version( """ Get gnomAD HT by data type and version. - .. note:: - - Available versions for each data type are: - | Data Type | GRCh38 Versions | GRCh37 Versions | - |-----------------|----------------------------------|----------------------| - | exomes | 4.0, 4.1 | 2.1, 2.1.1 | - | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | - | joint | 4.1 | N/A | + .. note:: Available versions for each data type are (as of 2024-10-29): + + | Data Type | GRCh38 Versions | GRCh37 Versions | + |-----------------|----------------------------------|----------------------| + | exomes | 4.0, 4.1 | 2.1, 2.1.1 | + | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | + | joint | 4.1 | N/A | :param data_type: Data type (exomes, genomes, or joint). :param version: gnomAD version. From 6d3471fd24313854f413dbf99aaca9b29e217347 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:29:01 -0400 Subject: [PATCH 015/121] Formatting --- gnomad_toolbox/modules/import_data.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index 0698693..70f183b 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -16,8 +16,9 @@ def get_ht_by_datatype_and_version( """ Get gnomAD HT by data type and version. - .. note:: Available versions for each data type are (as of 2024-10-29): + .. note:: + Available versions for each data type are (as of 2024-10-29): | Data Type | GRCh38 Versions | GRCh37 Versions | |-----------------|----------------------------------|----------------------| | exomes | 4.0, 4.1 | 2.1, 2.1.1 | From 1b847b438bb48226b32b80bd4321b9e4404ea369 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:44:30 -0400 Subject: [PATCH 016/121] Formatting --- gnomad_toolbox/modules/filter_variant.py | 7 +++++++ gnomad_toolbox/modules/import_data.py | 1 + 2 files changed, 8 insertions(+) diff --git a/gnomad_toolbox/modules/filter_variant.py b/gnomad_toolbox/modules/filter_variant.py index ab7b230..9c7073c 100644 --- a/gnomad_toolbox/modules/filter_variant.py +++ b/gnomad_toolbox/modules/filter_variant.py @@ -1,6 +1,13 @@ """Small functions to filter variants in gnomAD datasets, such as by allele frequency or by variant type.""" import hail as hl +from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX, coverage +from gnomad.utils.filtering import filter_arrays_by_meta +from gnomad.utils.vep import ( + CSQ_CODING, + filter_vep_transcript_csqs, + get_most_severe_consequence_for_summary, +) def get_variant_count( diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index 70f183b..acd93ee 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -19,6 +19,7 @@ def get_ht_by_datatype_and_version( .. note:: Available versions for each data type are (as of 2024-10-29): + | Data Type | GRCh38 Versions | GRCh37 Versions | |-----------------|----------------------------------|----------------------| | exomes | 4.0, 4.1 | 2.1, 2.1.1 | From 22c40f746616c93eeb3072db8131c0bbaee0b62f Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 14:49:52 -0400 Subject: [PATCH 017/121] Formatting docstring block --- gnomad_toolbox/modules/filter_variant.py | 83 ++++++++++++++++++++++++ gnomad_toolbox/modules/import_data.py | 12 ++-- 2 files changed, 90 insertions(+), 5 deletions(-) diff --git a/gnomad_toolbox/modules/filter_variant.py b/gnomad_toolbox/modules/filter_variant.py index 9c7073c..7b1d4a6 100644 --- a/gnomad_toolbox/modules/filter_variant.py +++ b/gnomad_toolbox/modules/filter_variant.py @@ -45,3 +45,86 @@ def get_variant_count( # Count variants with frequency <1%, <0.1%, and singletons (AC == 1). return counts + + +def filter_by_csqs(ht: hl.Table, csqs: list[str]) -> hl.Table: + """ + Filter variants by consequence. + + :param ht: Input Table. + :param csqs: List of consequences. + :return: Table with variants with the given consequences. + """ + ht = ht.filter( + hl.any( + hl.map( + lambda x: (x.consequence_terms.contains(csqs)), + ht.vep.transcript_consequences, + ) + ) + ) + + return ht + + +def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table: + """ + Filter variants in a gene. + + :param ht: Input Table. + :param gene: Gene symbol or. + :return: Table with variants in the gene. + """ + ht = filter_vep_transcript_csqs( + ht, + synonymous=False, + mane_select=True, + genes=[gene], + match_by_gene_symbol=True, + ) + + return ht + + +def filter_to_coding_variants(ht: hl.Table) -> hl.Table: + """ + Filter to coding variants. + + :param ht: Input Table. + :return: Table with coding variants. + """ + ht = filter_vep_transcript_csqs( + ht, + synonymous=False, + canonical=True, + ) + ht = get_most_severe_consequence_for_summary(ht) + + filter_expr = {} + filter_expr["coding"] = hl.any(lambda csq: ht.most_severe_csq == csq, CSQ_CODING) + + ht = ht.filter(filter_expr["coding"]) + + return ht + + +def filter_to_lof_variants(ht: hl.Table) -> hl.Table: + """ + Filter to loss-of-function (LoF) variants. + + :param ht: Input Table. + :return: Table with LoF variants. + """ + ht = filter_vep_transcript_csqs( + ht, + lof=True, + canonical=True, + ) + ht = get_most_severe_consequence_for_summary(ht) + + filter_expr = {} + filter_expr["lof"] = hl.any(lambda csq: ht.most_severe_csq == csq, CSQ_CODING) + + ht = ht.filter(filter_expr["lof"]) + + return ht diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py index acd93ee..8616917 100644 --- a/gnomad_toolbox/modules/import_data.py +++ b/gnomad_toolbox/modules/import_data.py @@ -20,11 +20,13 @@ def get_ht_by_datatype_and_version( Available versions for each data type are (as of 2024-10-29): - | Data Type | GRCh38 Versions | GRCh37 Versions | - |-----------------|----------------------------------|----------------------| - | exomes | 4.0, 4.1 | 2.1, 2.1.1 | - | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | - | joint | 4.1 | N/A | + :: + + | Data Type | GRCh38 Versions | GRCh37 Versions | + |-----------------|----------------------------------|----------------------| + | exomes | 4.0, 4.1 | 2.1, 2.1.1 | + | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | + | joint | 4.1 | N/A | :param data_type: Data type (exomes, genomes, or joint). :param version: gnomAD version. From 66770eae25b630ca9cdee03ea3973f60bdee4072 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 16:37:55 -0400 Subject: [PATCH 018/121] Function to extract callstats for one variant in one genetic ancestry group --- gnomad_toolbox/modules/extract_freq.py | 50 ++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) create mode 100644 gnomad_toolbox/modules/extract_freq.py diff --git a/gnomad_toolbox/modules/extract_freq.py b/gnomad_toolbox/modules/extract_freq.py new file mode 100644 index 0000000..cb590b6 --- /dev/null +++ b/gnomad_toolbox/modules/extract_freq.py @@ -0,0 +1,50 @@ +"""Extract callstats from 'freq' of gnomAD HTs.""" + +from typing import List, Optional + +import hail as hl +from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX +from gnomad.utils.filtering import filter_arrays_by_meta + + +def extract_callstats_for_1anc_1variant( + ht: hl.Table, gen_anc: str, contig: str, position: int, alleles: Optional[List[str]] +) -> hl.Table: + """ + Extract callstats for a single genetic ancestry group and a single variant. + + :param ht: Input Table. + :param group: Ancestry Group (e.g., 'afr', 'amr', 'asj', 'eas', 'fin', 'nfe', + 'oth', 'sas'). + :param contig: Chromosome. + :param position: Position. + :param alleles: List of alleles. + :return: Table with callstats for the given group. + """ + # Filter to the variant of interest + ht = ht.filter( + (ht.locus.contig == contig) + & (ht.locus.position == position) + & (ht.alleles == alleles) + ) + + # Format the gen_anc to lowercase if it's fed in as uppercase + gen_anc = gen_anc.lower() + items_to_filter = {"gen_anc": [gen_anc], "group": ["adj"]} + freq_meta, array_exprs = filter_arrays_by_meta( + ht.freq_meta, + { + **{a: ht[a] for a in ["freq"]}, + "freq_meta_sample_count": ht.index_globals().freq_meta_sample_count, + }, + items_to_filter=items_to_filter, + keep=True, + combine_operator="and", + exact_match=True, + ) + ht = ht.select(**{gen_anc: array_exprs["freq"]}) + ht = ht.annotate_globals( + freq_meta=freq_meta, + freq_meta_sample_count=array_exprs["freq_meta_sample_count"], + ) + return ht From 71a33bdcce46d92eab923f31e50a106d46970207 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 18:18:19 -0400 Subject: [PATCH 019/121] Function to extract callstats for genetic ancestry groups --- gnomad_toolbox/modules/extract_freq.py | 72 +++++++++++++++++++++----- 1 file changed, 59 insertions(+), 13 deletions(-) diff --git a/gnomad_toolbox/modules/extract_freq.py b/gnomad_toolbox/modules/extract_freq.py index cb590b6..5411dcb 100644 --- a/gnomad_toolbox/modules/extract_freq.py +++ b/gnomad_toolbox/modules/extract_freq.py @@ -8,18 +8,17 @@ def extract_callstats_for_1anc_1variant( - ht: hl.Table, gen_anc: str, contig: str, position: int, alleles: Optional[List[str]] + ht: hl.Table, gen_anc: str, contig: str, position: int, alleles: List[str] ) -> hl.Table: """ - Extract callstats for a single genetic ancestry group and a single variant. + Extract callstats for a specific ancestry group and single variant. - :param ht: Input Table. - :param group: Ancestry Group (e.g., 'afr', 'amr', 'asj', 'eas', 'fin', 'nfe', - 'oth', 'sas'). - :param contig: Chromosome. - :param position: Position. - :param alleles: List of alleles. - :return: Table with callstats for the given group. + :param ht: Input Hail Table with variant data. + :param gen_anc: Genetic ancestry group (e.g., 'afr', 'nfe'). + :param contig: Chromosome of the variant. + :param position: Variant position. + :param alleles: List of alleles for the variant (e.g., ['A', 'T']). + :return: Filtered Table with callstats for the specified group. """ # Filter to the variant of interest ht = ht.filter( @@ -28,9 +27,14 @@ def extract_callstats_for_1anc_1variant( & (ht.alleles == alleles) ) - # Format the gen_anc to lowercase if it's fed in as uppercase - gen_anc = gen_anc.lower() - items_to_filter = {"gen_anc": [gen_anc], "group": ["adj"]} + # Check if the variant exists + if ht.count() == 0: + hl.utils.warning( + f"No variant found at {contig}:{position} with alleles {alleles}" + ) + + # Format gen_anc to lowercase and filter arrays by metadata + items_to_filter = {"gen_anc": [gen_anc.lower()], "group": ["adj"]} freq_meta, array_exprs = filter_arrays_by_meta( ht.freq_meta, { @@ -42,7 +46,49 @@ def extract_callstats_for_1anc_1variant( combine_operator="and", exact_match=True, ) - ht = ht.select(**{gen_anc: array_exprs["freq"]}) + # Select frequency for ancestry group + ht = ht.select( + **{ + gen_anc: array_exprs["freq"][i] + for i, gen_anc in enumerate([gen_anc.lower()]) + } + ) + ht = ht.annotate_globals( + freq_meta=freq_meta, + freq_meta_sample_count=array_exprs["freq_meta_sample_count"], + ) + return ht + + +def extract_callstats_for_multiple_ancs( + ht: hl.Table, + gen_ancs: List[str], +) -> hl.Table: + """ + Extract callstats for multiple genetic ancestry groups. + + :param ht: Input Table. + :param gen_ancs: List of Ancestry Groups (e.g., 'afr', 'amr', 'asj', 'eas', 'fin', 'nfe', + 'oth', 'sas'). + :return: Table with callstats for the given groups. + """ + # Format the gen_ancs to lowercase if they're fed in as uppercase + gen_ancs = [gen_anc.lower() for gen_anc in gen_ancs] + items_to_filter = {"gen_anc": gen_ancs, "group": ["adj"]} + freq_meta, array_exprs = filter_arrays_by_meta( + ht.freq_meta, + { + **{a: ht[a] for a in ["freq"]}, + "freq_meta_sample_count": ht.index_globals().freq_meta_sample_count, + }, + items_to_filter=items_to_filter, + keep=True, + combine_operator="and", + exact_match=True, + ) + ht = ht.select( + **{gen_anc: array_exprs["freq"][i] for i, gen_anc in enumerate(gen_ancs)} + ) ht = ht.annotate_globals( freq_meta=freq_meta, freq_meta_sample_count=array_exprs["freq_meta_sample_count"], From 80b1f91799a2cee5213152269574192cf30256f8 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Tue, 29 Oct 2024 18:24:22 -0400 Subject: [PATCH 020/121] indent correctly --- gnomad_toolbox/modules/extract_freq.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gnomad_toolbox/modules/extract_freq.py b/gnomad_toolbox/modules/extract_freq.py index 5411dcb..ee051e7 100644 --- a/gnomad_toolbox/modules/extract_freq.py +++ b/gnomad_toolbox/modules/extract_freq.py @@ -69,7 +69,7 @@ def extract_callstats_for_multiple_ancs( :param ht: Input Table. :param gen_ancs: List of Ancestry Groups (e.g., 'afr', 'amr', 'asj', 'eas', 'fin', 'nfe', - 'oth', 'sas'). + 'oth', 'sas'). :return: Table with callstats for the given groups. """ # Format the gen_ancs to lowercase if they're fed in as uppercase From 8dcce6c58fcbc1d1abf8829b4890022e19c6d240 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Fri, 1 Nov 2024 19:21:25 -0400 Subject: [PATCH 021/121] Add functions to filter variants by gene symbol, interval and csqs --- gnomad_toolbox/modules/filter_variant.py | 150 +++++++++++++++-------- 1 file changed, 97 insertions(+), 53 deletions(-) diff --git a/gnomad_toolbox/modules/filter_variant.py b/gnomad_toolbox/modules/filter_variant.py index 7b1d4a6..6e3bd87 100644 --- a/gnomad_toolbox/modules/filter_variant.py +++ b/gnomad_toolbox/modules/filter_variant.py @@ -1,13 +1,9 @@ """Small functions to filter variants in gnomAD datasets, such as by allele frequency or by variant type.""" +from functools import reduce + import hail as hl -from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX, coverage -from gnomad.utils.filtering import filter_arrays_by_meta -from gnomad.utils.vep import ( - CSQ_CODING, - filter_vep_transcript_csqs, - get_most_severe_consequence_for_summary, -) +from gnomad.utils.vep import LOF_CSQ_SET def get_variant_count( @@ -47,23 +43,29 @@ def get_variant_count( return counts -def filter_by_csqs(ht: hl.Table, csqs: list[str]) -> hl.Table: +def filter_by_interval(ht: hl.Table, interval: str) -> hl.Table: """ - Filter variants by consequence. + Filter variants by interval. :param ht: Input Table. - :param csqs: List of consequences. - :return: Table with variants with the given consequences. + :param interval: Interval string. Format has to be "chr:start-end", e.g., "1:1000-2000". + :return: Table with variants in the interval. """ - ht = ht.filter( - hl.any( - hl.map( - lambda x: (x.consequence_terms.contains(csqs)), - ht.vep.transcript_consequences, + if ht.locus.dtype.reference_genome.name == "GRCh38": + interval = "chr" + interval + ht = hl.filter_intervals( + ht, + [ + hl.parse_locus_interval( + interval, + reference_genome=( + "GRCh38" + if ht.locus.dtype.reference_genome.name == "GRCh38" + else "GRCh37" + ), ) - ) + ], ) - return ht @@ -71,60 +73,102 @@ def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table: """ Filter variants in a gene. + .. note:: + This function is to match the number of variants that you will get in the + gnomAD browser, which only focus on variants in "CDS" regions plus 75bp + up- and downstream. This is not the same as filtering by gene symbol with + our `filter_vep_transcript_csqs` function, which will include all variants. + :param ht: Input Table. - :param gene: Gene symbol or. + :param gene: Gene symbol. :return: Table with variants in the gene. """ - ht = filter_vep_transcript_csqs( - ht, - synonymous=False, - mane_select=True, - genes=[gene], - match_by_gene_symbol=True, + if ht.locus.dtype.reference_genome.name == "GRCh37": + gene_ht = hl.read_table( + "gs://gcp-public-data--gnomad/resources/grch37/browser/gnomad" + ".genes.GRCh37.GENCODEv19.ht" + ) + else: + gene_ht = hl.read_table( + "gs://gcp-public-data--gnomad/resources/grch38/browser/gnomad" + ".genes.GRCh38.GENCODEv39.ht" + ) + + gene_ht = gene_ht.annotate( + cds_intervals=hl.array( + gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS") + ).map( + lambda exon: hl.locus_interval( + hl.if_else( + gene_ht.interval.start.dtype.reference_genome.name == "GRCh38", + "chr" + gene_ht.chrom, + gene_ht.chrom, + ), + exon.start - 75, + exon.stop + 75, + reference_genome=gene_ht.interval.start.dtype.reference_genome, + includes_end=True, + ) + ) ) + intervals = gene_ht.filter(gene_ht.gencode_symbol == gene).cds_intervals.collect()[ + 0 + ] + + ht = hl.filter_intervals(ht, intervals) + return ht -def filter_to_coding_variants(ht: hl.Table) -> hl.Table: +def filter_by_csqs( + ht: hl.Table, csqs: list[str], pass_filters: bool = True +) -> hl.Table: """ - Filter to coding variants. + Filter variants by consequences. :param ht: Input Table. - :return: Table with coding variants. + :param csqs: List of consequences to filter by. It can be specified as the + categories on the browser: pLoF, Missense / Inframe indel, Synonymous, Other. + :param pass_filters: Boolean if the variants pass the filters. + :return: Table with variants with the specified consequences. """ - ht = filter_vep_transcript_csqs( - ht, - synonymous=False, - canonical=True, - ) - ht = get_most_severe_consequence_for_summary(ht) + missense_inframe = ["missense_variant", "inframe_insertion", "inframe_deletion"] - filter_expr = {} - filter_expr["coding"] = hl.any(lambda csq: ht.most_severe_csq == csq, CSQ_CODING) + filter_expr = [] + if "lof" in csqs: + filter_expr.append( + hl.literal(LOF_CSQ_SET).contains(ht.vep.most_severe_consequence) + ) - ht = ht.filter(filter_expr["coding"]) + if "synonymous" in csqs: + filter_expr.append(ht.vep.most_severe_consequence == "synonymous_variant") - return ht + if "missense" in csqs: + filter_expr.append( + hl.literal(missense_inframe).contains(ht.vep.most_severe_consequence) + ) + if "other" in csqs: + excluded_csqs = hl.literal( + LOF_CSQ_SET + missense_inframe + ["synonymous_variant"] + ) + filter_expr.append(~excluded_csqs.contains(ht.vep.most_severe_consequence)) -def filter_to_lof_variants(ht: hl.Table) -> hl.Table: - """ - Filter to loss-of-function (LoF) variants. + if len(filter_expr) == 0: + raise ValueError( + "No valid consequence specified. Choose from 'lof', 'synonymous', 'missense', 'other'." + ) - :param ht: Input Table. - :return: Table with LoF variants. - """ - ht = filter_vep_transcript_csqs( - ht, - lof=True, - canonical=True, - ) - ht = get_most_severe_consequence_for_summary(ht) + # Combine filter expressions with logical OR + if len(filter_expr) == 1: + combined_filter = filter_expr[0] + else: + combined_filter = reduce(lambda acc, expr: acc | expr, filter_expr) - filter_expr = {} - filter_expr["lof"] = hl.any(lambda csq: ht.most_severe_csq == csq, CSQ_CODING) + ht = ht.filter(combined_filter) - ht = ht.filter(filter_expr["lof"]) + if pass_filters: + ht = ht.filter(hl.len(ht.filters) == 0) return ht From 34fab765cd35fb9f4ae23ff9600d36c630a7ca87 Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Fri, 1 Nov 2024 22:10:52 -0400 Subject: [PATCH 022/121] Correct small errors --- gnomad_toolbox/modules/filter_variant.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/gnomad_toolbox/modules/filter_variant.py b/gnomad_toolbox/modules/filter_variant.py index 6e3bd87..f73fe73 100644 --- a/gnomad_toolbox/modules/filter_variant.py +++ b/gnomad_toolbox/modules/filter_variant.py @@ -3,7 +3,7 @@ from functools import reduce import hail as hl -from gnomad.utils.vep import LOF_CSQ_SET +from gnomad.utils.vep import CSQ_CODING, LOF_CSQ_SET def get_variant_count( @@ -83,6 +83,9 @@ def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table: :param gene: Gene symbol. :return: Table with variants in the gene. """ + # Make gene symbol uppercase + gene = gene.upper() + if ht.locus.dtype.reference_genome.name == "GRCh37": gene_ht = hl.read_table( "gs://gcp-public-data--gnomad/resources/grch37/browser/gnomad" @@ -151,10 +154,15 @@ def filter_by_csqs( if "other" in csqs: excluded_csqs = hl.literal( - LOF_CSQ_SET + missense_inframe + ["synonymous_variant"] + list(LOF_CSQ_SET) + missense_inframe + ["synonymous_variant"] ) filter_expr.append(~excluded_csqs.contains(ht.vep.most_severe_consequence)) + if "coding" in csqs: + filter_expr.append( + hl.literal(CSQ_CODING).contains(ht.vep.most_severe_consequence) + ) + if len(filter_expr) == 0: raise ValueError( "No valid consequence specified. Choose from 'lof', 'synonymous', 'missense', 'other'." From 0ccdb5bb4b2eac13b5ff3b986da890bc3191c68e Mon Sep 17 00:00:00 2001 From: Qin He <44242118+KoalaQin@users.noreply.github.com> Date: Fri, 1 Nov 2024 22:12:25 -0400 Subject: [PATCH 023/121] Update notebook --- .../use_cases/toolbox_for_gnomad_users.ipynb | 1874 ++++++++++++++--- 1 file changed, 1593 insertions(+), 281 deletions(-) diff --git a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb index 6b42cfd..0e081fe 100644 --- a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb +++ b/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb @@ -8,14 +8,34 @@ }, "source": [ "

Table of Contents

\n", - "
" + "
" ] }, + { + "cell_type": "markdown", + "id": "853c94b9", + "metadata": {}, + "source": [ + "# README\n", + "\n", + "This toolbox is meant to use Hail tables of gnomAD releases on cloud computing, if you want to query variants for gene(s), you should use gnomAD API (https://gnomad.broadinstitute.org/api).\n", + "\n", + "If you want to import your own data to use other gnomAD notebooks, such as for ancestry inference (https://github.com/broadinstitute/gnomad_qc/blob/main/gnomad_qc/example_notebooks/ancestry_classification_using_gnomad_rf.ipynb), you may use Hail's `import_vcf` functions." + ] + }, + { + "cell_type": "markdown", + "id": "ff73954c", + "metadata": {}, + "source": [] + }, { "cell_type": "code", "execution_count": 1, "id": "e77d32b1", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -30,7 +50,7 @@ " \n", "
\n", " \n", - " Loading BokehJS ...\n", + " Loading BokehJS ...\n", "
\n" ] }, @@ -202,7 +222,7 @@ " \"\"}};\n", "\n", " function display_loaded() {\n", - " const el = document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\");\n", + " const el = document.getElementById(\"c9c511bf-e817-434d-8072-f4db620005e8\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", @@ -308,7 +328,7 @@ " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\")).parents('.cell').data().cell;\n", + " const cell = $(document.getElementById(\"c9c511bf-e817-434d-8072-f4db620005e8\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " }\n", @@ -324,7 +344,7 @@ " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"e95c853b-736c-43c1-b869-4fb671ab4b50\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"c9c511bf-e817-434d-8072-f4db620005e8\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"c9c511bf-e817-434d-8072-f4db620005e8\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -346,8 +366,8 @@ "output_type": "stream", "text": [ "SPARKMONITOR_LISTENER: Started SparkListener for Jupyter Notebook\n", - "SPARKMONITOR_LISTENER: Port obtained from environment: 49385\n", - "SPARKMONITOR_LISTENER: Application Started: application_1727797895584_0002 ...Start Time: 1727798616140\n" + "SPARKMONITOR_LISTENER: Port obtained from environment: 51311\n", + "SPARKMONITOR_LISTENER: Application Started: application_1730470703538_0002 ...Start Time: 1730485367380\n" ] }, { @@ -355,12 +375,12 @@ "output_type": "stream", "text": [ "Running on Apache Spark version 3.5.0\n", - "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:45603\n", + "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:39033\n", "Welcome to\n", " __ __ <>__\n", " / /_/ /__ __/ /\n", " / __ / _ `/ / /\n", - " /_/ /_/\\_,_/_/_/ version 0.2.132-678e1f52b999\n", + " /_/ /_/\\_,_/_/_/ version 0.2.133-4c60fddb171a\n", "LOGGING: writing to /test_toolbox.log\n" ] } @@ -384,17 +404,46 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "e69953f7", "metadata": {}, "outputs": [], "source": [ - "from gnomad_toolbox.modules.variant_filtering import get_variant_count\n", - "from gnomad.resources.grch38.gnomad import public_release as v4_public_release\n", - "from gnomad.utils.vep import filter_vep_transcript_csqs\n", - "from gnomad.resources.grch38.gnomad import coverage\n", - "from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX\n", - "from gnomad.utils.filtering import filter_arrays_by_meta" + "from gnomad_toolbox.modules.filter_variant import get_variant_count, filter_by_interval, filter_by_gene_symbol, filter_by_csqs\n", + "from gnomad_toolbox.modules.import_data import get_ht_by_datatype_and_version\n", + "from gnomad_toolbox.modules.filter_variant import \n", + "from gnomad_toolbox.modules.extract_freq import extract_callstats_for_1anc_1variant, extract_callstats_for_multiple_ancs\n", + "from gnomad.resources.grch38.gnomad import coverage" + ] + }, + { + "cell_type": "markdown", + "id": "5335a135", + "metadata": {}, + "source": [ + "# Import data\n", + "\n", + "You can choose which version of gnomAD release you want to look at, here we listed the available version per data type per reference build. \n", + "\n", + "Available versions for each data type are (as of 2024-10-29):\n", + "\n", + "| Data Type | GRCh38 Versions | GRCh37 Versions |\n", + "|-----------------|----------------------------------|----------------------|\n", + "| exomes | 4.0, 4.1 | 2.1, 2.1.1 |\n", + "| genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 |\n", + "| joint | 4.1 | N/A |\n", + "\n", + "We use gnomAD v4.1 exomes to demonstrate for examples below. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "100cf576", + "metadata": {}, + "outputs": [], + "source": [ + "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')" ] }, { @@ -415,16 +464,6 @@ "**Note: this will take long if your notebook is NOT using multiple nodes.**" ] }, - { - "cell_type": "code", - "execution_count": 3, - "id": "112c5065", - "metadata": {}, - "outputs": [], - "source": [ - "ht = v4_public_release(\"exomes\").ht()" - ] - }, { "cell_type": "code", "execution_count": 12, @@ -483,45 +522,205 @@ "print(get_variant_count(ht, singletons=True, doubletons=True))" ] }, + { + "cell_type": "markdown", + "id": "ec659eeb", + "metadata": {}, + "source": [ + "## Get variant count by AF for coding variants" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "d65b0ea8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 21:===================================================>(8784 + 5) / 8789]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'number of variants with AF < 0.01': 23762097, 'number of variants with AF < 0.001': 23643787, 'number of variants with AF < 0.0005': 23569893}\n" + ] + } + ], + "source": [ + "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n", + "\n", + "ht = filter_by_csqs(ht, ['coding'])\n", + "\n", + "print(get_variant_count(ht, afs=[0.01, 0.001, 0.0005]))" + ] + }, + { + "cell_type": "markdown", + "id": "f07ca88f", + "metadata": {}, + "source": [ + "## Get variant count by VEP consequence" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "b515bfc0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18231426" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# total number of missense variant in exomes data\n", + "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n", + "\n", + "ht.filter(\n", + " hl.any(\n", + " hl.map(\n", + " lambda x: (x.consequence_terms.contains(\"missense_variant\")),\n", + " ht.vep.transcript_consequences,\n", + " )\n", + " )\n", + ").count()" + ] + }, { "cell_type": "markdown", "id": "725f9a57", "metadata": {}, "source": [ - "## Get variant count by AF for a gene interval\n", + "## Get variant count by AF for a gene\n", + "\n", + "**Note: This isn't necessarily equal to the number of variants annotated to this gene by VEP.**\n", + "\n", + "Here we show two ways that you can load a variant table on the gnomAD browser, one is the [gene page](https://gnomad.broadinstitute.org/gene/ENSG00000149295?dataset=gnomad_r4) (filtered to MANE Select transcript of that gene, and only variants located in or within 75 base pairs of a coding exon (CDS)), the other is the [region view](https://gnomad.broadinstitute.org/region/11-113409605-113475691?dataset=gnomad_r4). We use *DRD2* gene as an example. " + ] + }, + { + "attachments": { + "Screenshot%202024-11-01%20at%209.31.00%E2%80%AFPM.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "164a07b7", + "metadata": {}, + "source": [ + "### On region view (the interval of a gene)\n", "\n", - "**Note: This isn't necessarily equal to the number of variants annotated to this gene by VEP.**" + "This is for 'DRD2' gene. \n", + "![Screenshot%202024-11-01%20at%209.31.00%E2%80%AFPM.png](attachment:Screenshot%202024-11-01%20at%209.31.00%E2%80%AFPM.png)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 58, "id": "9f8e1ba4", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The total number of variants in this gene interval is: 8126\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "[Stage 3:=======================================> (2 + 1) / 3]\r" + "[Stage 37:=============================> (1 + 1) / 2]\r" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "{'number of singletons': 5282, 'number of doubletons': 1616, 'number of variants with AF < 0.01': 10733, 'number of variants with AF < 0.001': 10656}\n" + "{'number of singletons': 1390, 'number of doubletons': 384, 'number of variants with AF < 0.01': 2711, 'number of variants with AF < 0.001': 2662}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "[Stage 38:=============================> (1 + 1) / 2]\r" ] } ], "source": [ - "# Filter to interval, e.g. for ASH1L.\n", - "gene_interval = \"chr1:155335268-155563162\"\n", + "# Filter to interval, e.g. for DRD2.\n", + "gene_interval = \"11:113409605-113475691\"\n", + "\n", + "gene_ht = filter_by_interval(ht, gene_interval)\n", "\n", "# Filter the exome release Hail Table to the ASH1L gene interval.\n", - "ht = hl.filter_intervals(ht, [hl.parse_locus_interval(gene_interval, reference_genome=\"GRCh38\")])\n", + "print(\"The total number of variants in this gene interval is: \", gene_ht.count())\n", "\n", - "print(get_variant_count(ht, singletons=True, doubletons=True))" + "print(get_variant_count(gene_ht, singletons=True, doubletons=True))" + ] + }, + { + "attachments": { + "Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "ac393319", + "metadata": {}, + "source": [ + "### On gene page\n", + "\n", + "To get the number of variants shown on the gene page, if you click 'all' and check 'exomes', 'SNVs', 'Indels', and 'Filtered variants' as this: \n", + "\n", + "![Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png](attachment:Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "e6bf7236", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 26:> (0 + 1) / 1]\r" + ] + }, + { + "data": { + "text/plain": [ + "1764" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# gene_symbol can be upper or lower case\n", + "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n", + "\n", + "drd2 = filter_by_gene_symbol(ht, 'drd2')\n", + "drd2.count()" ] }, { @@ -529,7 +728,1203 @@ "id": "7bff63bb", "metadata": {}, "source": [ - "# Filter to variants by VEP annotations" + "# Filter to variants by VEP annotations\n", + "\n", + "You can get the variant table either by gene_symbol or gene interval, we recommen you to get by gene_symbol because it's already filtered to MANE Select transcript of a gene. " + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "700582e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
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n_smaller
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n_smaller
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bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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showing top 5 rows

\n" + ], + "text/plain": [ + "+-----------------+------------+\n", + "| locus | alleles |\n", + "+-----------------+------------+\n", + "| locus | array |\n", + "+-----------------+------------+\n", + "| chr11:113410731 | [\"C\",\"A\"] |\n", + "| chr11:113410731 | [\"C\",\"T\"] |\n", + "| chr11:113410735 | [\"G\",\"A\"] |\n", + "| chr11:113410736 | [\"G\",\"A\"] |\n", + "| chr11:113410736 | [\"G\",\"T\"] |\n", + "+-----------------+------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| freq |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [(2,1.37e-06,1461866,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(2,4.... |\n", + "| [(1,6.84e-07,1461866,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n", + "| [(75,5.13e-05,1461876,0),(75,5.13e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n", + "| [(15,1.03e-05,1461882,0),(15,1.03e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n", + "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------+------------------+------------------+\n", + "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n", + "+------------------+------------------+------------------+\n", + "| int32 | float64 | int32 |\n", + "+------------------+------------------+------------------+\n", + "| 2 | 4.47e-05 | 44724 |\n", + "| 1 | 8.99e-07 | 1112010 |\n", + "| 8 | 7.19e-06 | 1112004 |\n", + "| 15 | 1.35e-05 | 1112010 |\n", + "| 1 | 8.99e-07 | 1112010 |\n", + "+------------------+------------------+------------------+\n", + "\n", + "+--------------------------------+-----------------------+-------------------+\n", + "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n", + "+--------------------------------+-----------------------+-------------------+\n", + "| int64 | str | int32 |\n", + "+--------------------------------+-----------------------+-------------------+\n", + "| 0 | \"amr\" | 2 |\n", + "| 0 | \"nfe\" | NA |\n", + "| 0 | \"nfe\" | 2 |\n", + "| 0 | \"nfe\" | 1 |\n", + "| 0 | \"nfe\" | NA |\n", + "+--------------------------------+-----------------------+-------------------+\n", + "\n", + "+-------------------+-------------------+---------------------------------+\n", + "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n", + "+-------------------+-------------------+---------------------------------+\n", + "| float64 | int32 | int64 |\n", + "+-------------------+-------------------+---------------------------------+\n", + "| 4.57e-05 | 43740 | 0 |\n", + "| NA | NA | NA |\n", + "| 5.71e-06 | 350102 | 0 |\n", + "| 2.86e-06 | 350106 | 0 |\n", + "| NA | NA | NA |\n", + "+-------------------+-------------------+---------------------------------+\n", + "\n", + "+------------------------+\n", + "| grpmax.non_ukb.gen_anc |\n", + "+------------------------+\n", + "| str |\n", + "+------------------------+\n", + "| \"amr\" |\n", + "| NA |\n", + "| \"nfe\" |\n", + "| \"nfe\" |\n", + "| NA |\n", + "+------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| faf |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(7.41e-06,2.77e-06),(0.00e+00,0.... |\n", + "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "| [(4.19e-05,3.82e-05),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "| [(6.16e-06,4.89e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-------------------------+---------------------------------+\n", + "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n", + "+-------------------------+---------------------------------+\n", + "| float64 | str |\n", + "+-------------------------+---------------------------------+\n", + "| 7.41e-06 | \"amr\" |\n", + "| NA | NA |\n", + "| 3.09e-06 | \"nfe\" |\n", + "| 8.10e-06 | \"nfe\" |\n", + "| NA | NA |\n", + "+-------------------------+---------------------------------+\n", + "\n", + "+-------------------------+---------------------------------+\n", + "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n", + "+-------------------------+---------------------------------+\n", + "| float64 | str |\n", + "+-------------------------+---------------------------------+\n", + "| 2.77e-06 | \"amr\" |\n", + "| NA | NA |\n", + "| 2.24e-06 | \"nfe\" |\n", + "| 6.42e-06 | \"nfe\" |\n", + "| NA | NA |\n", + "+-------------------------+---------------------------------+\n", + "\n", + "+--------------------------+----------------------------------+\n", + "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n", + "+--------------------------+----------------------------------+\n", + "| float64 | str |\n", + "+--------------------------+----------------------------------+\n", + "| 7.58e-06 | \"amr\" |\n", + "| NA | NA |\n", + "| 9.50e-07 | \"nfe\" |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+--------------------------+----------------------------------+\n", + "\n", + "+--------------------------+----------------------------------+---------+\n", + "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n", + 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{} | 5.49e-01 | 6.00e+01 |\n", + "+-----------+------------------+----------+----------+----------+\n", + "\n", + "+----------------+-----------------+----------+---------------------+\n", + "| info.MQRankSum | info.QUALapprox | info.QD | info.ReadPosRankSum |\n", + "+----------------+-----------------+----------+---------------------+\n", + "| float64 | int64 | float64 | float64 |\n", + "+----------------+-----------------+----------+---------------------+\n", + "| 0.00e+00 | 3862 | 1.02e+01 | -1.98e-01 |\n", + "| 0.00e+00 | 3862 | 1.02e+01 | -1.98e-01 |\n", + "| 0.00e+00 | 127501 | 1.23e+01 | 3.70e-02 |\n", + "| 0.00e+00 | 12307 | 1.16e+01 | 7.40e-02 |\n", + "| 0.00e+00 | 12307 | 1.16e+01 | 7.40e-02 |\n", + "+----------------+-----------------+----------+---------------------+\n", + "\n", + "+-----------------------+----------+------------+------------+------------+\n", + "| info.SB | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n", + 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[386,195,304,161] | 6.47e-01 | 1013 |\n", + "| -9.23e-01 | [386,195,12,5] | 7.90e-01 | 50 |\n", + "+------------------------+-----------------------+-------------+---------------+\n", + "\n", + "+----------------+----------------------------+------------------------+\n", + "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n", + "+----------------+----------------------------+------------------------+\n", + "| bool | bool | bool |\n", + "+----------------+----------------------------+------------------------+\n", + "| False | NA | NA |\n", + "| True | False | False |\n", + "| False | NA | NA |\n", + "| False | NA | NA |\n", + "| True | False | False |\n", + "+----------------+----------------------------+------------------------+\n", + "\n", + "+-----------+------------+------------------+---------------+----------------+\n", + "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n", + 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|\n", + "+-----------------------+-----------------------+---------------------+\n", + "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"A\"] |\n", + "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"T\"] |\n", + "| [113410734,113410734] | [113410735,113410735] | [\"G\",\"A\"] |\n", + "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"A\"] |\n", + "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"T\"] |\n", + "+-----------------------+-----------------------+---------------------+\n", + "\n", + "+-------------------+-----------+--------+--------------------------------+\n", + "| vep.allele_string | vep.end | vep.id | vep.input |\n", + "+-------------------+-----------+--------+--------------------------------+\n", + "| str | int32 | str | str |\n", + "+-------------------+-----------+--------+--------------------------------+\n", + "| \"C/A\" | 113410731 | \".\" | \"chr11\t113410731\t.\tC\tA\t.\t.\tGT\" |\n", + "| \"C/T\" | 113410731 | \".\" | \"chr11\t113410731\t.\tC\tT\t.\t.\tGT\" |\n", + "| \"G/A\" | 113410735 | \".\" | \"chr11\t113410735\t.\tG\tA\t.\t.\tGT\" |\n", + "| \"G/A\" | 113410736 | \".\" | \"chr11\t113410736\t.\tG\tA\t.\t.\tGT\" |\n", + "| \"G/T\" | 113410736 | \".\" | \"chr11\t113410736\t.\tG\tT\t.\t.\tGT\" |\n", + "+-------------------+-----------+--------+--------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| vep.intergenic_consequences |\n", + "+------------------------------------------------------------------------------+\n", + "| array, impact: st... |\n", + "+------------------------------------------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-----------------------------+\n", + "| vep.most_severe_consequence |\n", + "+-----------------------------+\n", + 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[0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n", + "| [0,0,0,0,12,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n", + "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n", + "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n", + "+---------------------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_all.n_smaller |\n", + "+---------------------------------------------+\n", + "| int64 |\n", + "+---------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_all.n_larger |\n", + "+--------------------------------------------+\n", + "| int64 |\n", + "+--------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + 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[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_alt.bin_freq |\n", + "+--------------------------------------------+\n", + "| array |\n", + "+--------------------------------------------+\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1] |\n", + "+--------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_alt.n_smaller |\n", + "+---------------------------------------------+\n", + "| int64 |\n", + "+---------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_alt.n_larger |\n", + "+--------------------------------------------+\n", + "| int64 |\n", + "+--------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+--------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_alt.bin_edges |\n", + 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[0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0] |\n", + "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2] |\n", + "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0] |\n", + "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0] |\n", + "+--------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_alt.n_smaller |\n", + "+---------------------------------------------+\n", + "| int64 |\n", + "+---------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_alt.n_larger |\n", + "+--------------------------------------------+\n", + "| int64 |\n", + "+--------------------------------------------+\n", + "| 1 |\n", + "| 0 |\n", + "| 32 |\n", + "| 1 |\n", + "| 0 |\n", + 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|\n", + "| 2.69e+00 | 2.67e-01 |\n", + "| 2.28e-01 | NA |\n", + "| 1.86e-01 | NA |\n", + "+-------------------------------------+--------------------------------+\n", + "\n", + "+--------------------------------------+\n", + "| in_silico_predictors.spliceai_ds_max |\n", + "+--------------------------------------+\n", + "| float32 |\n", + "+--------------------------------------+\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "| 0.00e+00 |\n", + "+--------------------------------------+\n", + "\n", + "+------------------------------------------+-----------------------------+\n", + "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n", + "+------------------------------------------+-----------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------------------+-----------------------------+\n", + "| 0.00e+00 | 8.78e+00 |\n", + "| 2.00e-02 | 8.78e+00 |\n", + "| 1.00e-02 | 8.67e+00 |\n", + "| 1.00e-02 | -2.55e-01 |\n", + "| 3.00e-02 | -2.55e-01 |\n", + "+------------------------------------------+-----------------------------+\n", + "\n", + "+-------------------------------+-----------------------------------+\n", + "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n", + "+-------------------------------+-----------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------+-----------------------------------+\n", + "| 0.00e+00 | 9.89e-01 |\n", + "| 0.00e+00 | 9.89e-01 |\n", + "| 1.30e-01 | 1.18e-01 |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+-------------------------------+-----------------------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# lof, missense, synonymous variants passing filters\n", + "variants_of_interest = filter_by_csqs(drd2,['lof','missense','synonymous'])\n", + "variants_of_interest.show(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "9887fdb0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "[Stage 27:> (0 + 1) / 1]\r" + ] + }, + { + "data": { + "text/plain": [ + "17" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# number of lof variants passing filters\n", + "filter_by_csqs(drd2,['lof']).count()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "86596aaf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "[Stage 28:> (0 + 1) / 1]\r" + ] + }, + { + "data": { + "text/plain": [ + "409" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# number of missense variants passing filters\n", + "filter_by_csqs(drd2,['missense']).count()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "b7e4368b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "[Stage 29:> (0 + 1) / 1]\r" + ] + }, + { + "data": { + "text/plain": [ + "238" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# number of synonymous variants passing filters\n", + "filter_by_csqs(drd2,['synonymous']).count()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "4141ccb3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "[Stage 30:> (0 + 1) / 1]\r" + ] + }, + { + "data": { + "text/plain": [ + "783" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# number of 'Other' variants passing filters\n", + "filter_by_csqs(drd2,['other']).count()" ] }, { @@ -537,7 +1932,7 @@ "id": "b1031947", "metadata": {}, "source": [ - "## Filter to LOF variants" + "## Filter to 'HC' LOF variants for certain genes" ] }, { @@ -580,12 +1975,12 @@ "id": "f9b2d921", "metadata": {}, "source": [ - "## Filter to pLOF variants that we used to compute constraint metrics\n", + "### Filter to pLOF variants that we used to compute constraint metrics\n", "pLOF variants meets the following requirements:\n", "* High-confidence LOFTEE variants (without any flags),\n", "* Only variants in the MANE Select transcript,\n", "* PASS variants that are SNVs with MAF ≤ 0.1%, and\n", - "* Exome median depth ≥ 30\n", + "* Exome median depth ≥ 30 (**This is changing in v4 constraint?**)\n", "\n", "**Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**\n", "\n", @@ -812,12 +2207,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 60, "id": "4f78166f", "metadata": {}, "outputs": [], "source": [ - "ht = v4_public_release(\"exomes\").ht()\n", + "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n", "\n", "# Filter to interval, e.g. for ASH1L.\n", "gene_interval = \"chr1:155335268-155563162\"\n", @@ -831,150 +2226,46 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "8f625a41", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'ami', 'asj', 'fin', 'oth', 'remaining'}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# For this example, we filter to the ancestry that we included in the FAF calculation\n", - "POPS_TO_REMOVE_FOR_POPMAX[\"v4\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "15729719", - "metadata": {}, - "outputs": [], - "source": [ - "# Remove unwanted stratifications\n", - "items_to_filter1 = ['sex','downsampling','subset']\n", - "freq_meta1, array_exprs1 = filter_arrays_by_meta(\n", - " ht.freq_meta,\n", - " {\n", - " **{a: ht[a] for a in ['freq']},\n", - " \"freq_meta_sample_count\": ht.index_globals().freq_meta_sample_count,\n", - " },\n", - " items_to_filter=items_to_filter1,\n", - " keep=False,\n", - " combine_operator=\"or\",\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "d2886179", - "metadata": {}, - "outputs": [], - "source": [ - "# Remove the genetic ancetries/group that you don't need \n", - "items_to_filter2 = {'gen_anc':['ami', 'asj', 'fin', 'oth', 'remaining'], 'group':['raw']}\n", - "\n", - "freq_meta2, array_exprs2 = filter_arrays_by_meta(\n", - " freq_meta1,\n", - " array_exprs1,\n", - " items_to_filter=items_to_filter2,\n", - " keep=False,\n", - " combine_operator=\"or\",\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "5471689c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[[{'group': 'adj'},\n", - " {'gen_anc': 'afr', 'group': 'adj'},\n", - " {'gen_anc': 'amr', 'group': 'adj'},\n", - " {'gen_anc': 'eas', 'group': 'adj'},\n", - " {'gen_anc': 'mid', 'group': 'adj'},\n", - " {'gen_anc': 'nfe', 'group': 'adj'},\n", - " {'gen_anc': 'sas', 'group': 'adj'}]]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# The order in the meta is order how AC, AF, AN and homozygote_count is stored in freq. \n", - "freq_meta2.collect()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "16170ed7", - "metadata": {}, - "outputs": [], - "source": [ - "ht = ht.annotate(**{a: array_exprs2[a] for a in ['freq']})\n", - "ht = ht.annotate_globals(\n", - " freq_meta=freq_meta2,\n", - " freq_meta_sample_count=array_exprs2[\"freq_meta_sample_count\"],\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "e3bcf7a0", + "execution_count": 61, + "id": "ce7a1e8c", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-10-01 16:07:07.655 Hail: WARN: Name collision: field 'all' already in object dict. \n", - " This field must be referenced with __getitem__ syntax: obj['all']\n" + "\r", + "[Stage 39:> (0 + 3) / 3]\r", + "\r", + "[Stage 39:======================================> (2 + 1) / 3]\r" ] }, { "data": { "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "
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chr1:155335497["A","C"]11.79e-0356000NA000NA0000.00e+0013800NA0000.00e+00200NA00
chr1:155335570["T","C"]35.26e-0357000NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335571["TA","T"]35.26e-0357000NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335746["G","C"]11.77e-0356400NA000NA0000.00e+0013200NA0000.00e+00200NA00
chr1:155335855["G","A"]11.74e-0357600NA000NA0017.25e-0313800NA0000.00e+00600NA00
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chr1:155335497["A","C"]0NA000NA0000.00e+0013800NA0000.00e+00200NA00
chr1:155335570["T","C"]0NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335571["TA","T"]0NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335746["G","C"]0NA000NA0000.00e+0013200NA0000.00e+00200NA00
chr1:155335855["G","A"]0NA000NA0017.25e-0313800NA0000.00e+00600NA00

showing top 5 rows

\n" ], "text/plain": [ - "+----------------+------------+--------+----------+--------+\n", - "| locus | alleles | all.AC | all.AF | all.AN |\n", - "+----------------+------------+--------+----------+--------+\n", - "| locus | array | int32 | float64 | int32 |\n", - "+----------------+------------+--------+----------+--------+\n", - "| chr1:155335497 | [\"A\",\"C\"] | 1 | 1.79e-03 | 560 |\n", - "| chr1:155335570 | [\"T\",\"C\"] | 3 | 5.26e-03 | 570 |\n", - "| chr1:155335571 | [\"TA\",\"T\"] | 3 | 5.26e-03 | 570 |\n", - "| chr1:155335746 | [\"G\",\"C\"] | 1 | 1.77e-03 | 564 |\n", - "| chr1:155335855 | [\"G\",\"A\"] | 1 | 1.74e-03 | 576 |\n", - "+----------------+------------+--------+----------+--------+\n", + "+----------------+------------+--------+---------+--------+\n", + "| locus | alleles | afr.AC | afr.AF | afr.AN |\n", + "+----------------+------------+--------+---------+--------+\n", + "| locus | array | int32 | float64 | int32 |\n", + "+----------------+------------+--------+---------+--------+\n", + "| chr1:155335497 | [\"A\",\"C\"] | 0 | NA | 0 |\n", + "| chr1:155335570 | [\"T\",\"C\"] | 0 | NA | 0 |\n", + "| chr1:155335571 | [\"TA\",\"T\"] | 0 | NA | 0 |\n", + "| chr1:155335746 | [\"G\",\"C\"] | 0 | NA | 0 |\n", + "| chr1:155335855 | [\"G\",\"A\"] | 0 | NA | 0 |\n", + "+----------------+------------+--------+---------+--------+\n", "\n", "+----------------------+--------+---------+--------+----------------------+\n", - "| all.homozygote_count | afr.AC | afr.AF | afr.AN | afr.homozygote_count |\n", + "| afr.homozygote_count | amr.AC | amr.AF | amr.AN | amr.homozygote_count |\n", "+----------------------+--------+---------+--------+----------------------+\n", "| int64 | int32 | float64 | int32 | int64 |\n", "+----------------------+--------+---------+--------+----------------------+\n", @@ -985,53 +2276,41 @@ "| 0 | 0 | NA | 0 | 0 |\n", "+----------------------+--------+---------+--------+----------------------+\n", "\n", - "+--------+---------+--------+----------------------+--------+----------+\n", - "| amr.AC | amr.AF | amr.AN | amr.homozygote_count | eas.AC | eas.AF |\n", - "+--------+---------+--------+----------------------+--------+----------+\n", - "| int32 | float64 | int32 | int64 | int32 | float64 |\n", - "+--------+---------+--------+----------------------+--------+----------+\n", - "| 0 | NA | 0 | 0 | 0 | 0.00e+00 |\n", - "| 0 | NA | 0 | 0 | 1 | 7.25e-03 |\n", - "| 0 | NA | 0 | 0 | 1 | 7.25e-03 |\n", - "| 0 | NA | 0 | 0 | 0 | 0.00e+00 |\n", - "| 0 | NA | 0 | 0 | 1 | 7.25e-03 |\n", - "+--------+---------+--------+----------------------+--------+----------+\n", - "\n", - "+--------+----------------------+--------+---------+--------+\n", - "| eas.AN | eas.homozygote_count | mid.AC | mid.AF | mid.AN |\n", - "+--------+----------------------+--------+---------+--------+\n", - "| int32 | int64 | int32 | float64 | int32 |\n", - "+--------+----------------------+--------+---------+--------+\n", - "| 138 | 0 | 0 | NA | 0 |\n", - "| 138 | 0 | 0 | NA | 0 |\n", - "| 138 | 0 | 0 | NA | 0 |\n", - "| 132 | 0 | 0 | NA | 0 |\n", - "| 138 | 0 | 0 | NA | 0 |\n", - "+--------+----------------------+--------+---------+--------+\n", - "\n", - "+----------------------+--------+----------+--------+----------------------+\n", - "| mid.homozygote_count | nfe.AC | nfe.AF | nfe.AN | nfe.homozygote_count |\n", - "+----------------------+--------+----------+--------+----------------------+\n", - "| int64 | int32 | float64 | int32 | int64 |\n", - "+----------------------+--------+----------+--------+----------------------+\n", - "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", - "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", - "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", - "| 0 | 0 | 0.00e+00 | 2 | 0 |\n", - "| 0 | 0 | 0.00e+00 | 6 | 0 |\n", - "+----------------------+--------+----------+--------+----------------------+\n", - "\n", - "+--------+---------+--------+----------------------+\n", - "| sas.AC | sas.AF | sas.AN | sas.homozygote_count |\n", - "+--------+---------+--------+----------------------+\n", - "| int32 | float64 | int32 | int64 |\n", - "+--------+---------+--------+----------------------+\n", - "| 0 | NA | 0 | 0 |\n", - "| 0 | NA | 0 | 0 |\n", - "| 0 | NA | 0 | 0 |\n", - "| 0 | NA | 0 | 0 |\n", - "| 0 | NA | 0 | 0 |\n", - "+--------+---------+--------+----------------------+\n", + "+--------+----------+--------+----------------------+--------+---------+\n", + "| eas.AC | eas.AF | eas.AN | eas.homozygote_count | mid.AC | mid.AF |\n", + "+--------+----------+--------+----------------------+--------+---------+\n", + "| int32 | float64 | int32 | int64 | int32 | float64 |\n", + "+--------+----------+--------+----------------------+--------+---------+\n", + "| 0 | 0.00e+00 | 138 | 0 | 0 | NA |\n", + "| 1 | 7.25e-03 | 138 | 0 | 0 | NA |\n", + "| 1 | 7.25e-03 | 138 | 0 | 0 | NA |\n", + "| 0 | 0.00e+00 | 132 | 0 | 0 | NA |\n", + "| 1 | 7.25e-03 | 138 | 0 | 0 | NA |\n", + "+--------+----------+--------+----------------------+--------+---------+\n", + "\n", + "+--------+----------------------+--------+----------+--------+\n", + "| mid.AN | mid.homozygote_count | nfe.AC | nfe.AF | nfe.AN |\n", + "+--------+----------------------+--------+----------+--------+\n", + "| int32 | int64 | int32 | float64 | int32 |\n", + "+--------+----------------------+--------+----------+--------+\n", + "| 0 | 0 | 0 | 0.00e+00 | 2 |\n", + "| 0 | 0 | 0 | 0.00e+00 | 2 |\n", + "| 0 | 0 | 0 | 0.00e+00 | 2 |\n", + "| 0 | 0 | 0 | 0.00e+00 | 2 |\n", + "| 0 | 0 | 0 | 0.00e+00 | 6 |\n", + "+--------+----------------------+--------+----------+--------+\n", + "\n", + "+----------------------+--------+---------+--------+----------------------+\n", + "| nfe.homozygote_count | sas.AC | sas.AF | sas.AN | sas.homozygote_count |\n", + "+----------------------+--------+---------+--------+----------------------+\n", + "| int64 | int32 | float64 | int32 | int64 |\n", + "+----------------------+--------+---------+--------+----------------------+\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "| 0 | 0 | NA | 0 | 0 |\n", + "+----------------------+--------+---------+--------+----------------------+\n", "showing top 5 rows" ] }, @@ -1040,8 +2319,9 @@ } ], "source": [ - "populations = ['all', 'afr', 'amr', 'eas', 'mid', 'nfe', 'sas']\n", - "ht.select(**{pop: ht.freq[i] for i, pop in enumerate(populations)}).show(5)" + "ht = extract_callstats_for_multiple_ancs(ht, gen_ancs=['afr', 'amr', 'eas', 'mid', 'nfe', 'sas'])\n", + "\n", + "ht.show(5)" ] }, { @@ -1054,84 +2334,79 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "9a4f26a2", + "execution_count": 62, + "id": "4846958a", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "\n", + "\n", + "
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chr22:15528692["C","G"]6351.90e-02333806
" + ], "text/plain": [ - "[[{'gen_anc': 'afr', 'group': 'adj'}]]" + "+----------------+------------+--------+----------+--------+\n", + "| locus | alleles | afr.AC | afr.AF | afr.AN |\n", + "+----------------+------------+--------+----------+--------+\n", + "| locus | array | int32 | float64 | int32 |\n", + "+----------------+------------+--------+----------+--------+\n", + "| chr22:15528692 | [\"C\",\"G\"] | 635 | 1.90e-02 | 33380 |\n", + "+----------------+------------+--------+----------+--------+\n", + "\n", + "+----------------------+\n", + "| afr.homozygote_count |\n", + "+----------------------+\n", + "| int64 |\n", + "+----------------------+\n", + "| 6 |\n", + "+----------------------+" ] }, - "execution_count": 14, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "ht = v4_public_release(\"exomes\").ht()\n", - "\n", - "# Filter by the location of the variant\n", - "ht = ht.filter((ht.locus.contig == \"chr22\") & (ht.locus.position==15528692))\n", - "\n", - "# Assign th\n", - "items_to_filter1 = {'gen_anc':['afr'], 'group':['adj']}\n", - "freq_meta1, array_exprs1 = filter_arrays_by_meta(\n", - " ht.freq_meta,\n", - " {\n", - " **{a: ht[a] for a in ['freq']},\n", - " \"freq_meta_sample_count\": ht.index_globals().freq_meta_sample_count,\n", - " },\n", - " items_to_filter=items_to_filter1,\n", - " keep=True,\n", - " combine_operator=\"and\",\n", - " )\n", + "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n", "\n", - "# if you want to further remove 'downsampling', 'sex', and 'subset'\n", - "items_to_filter2 = ['sex','downsampling','subset']\n", - "freq_meta2, array_exprs2 = filter_arrays_by_meta(\n", - " freq_meta1,\n", - " {\n", - " **{a: ht[a] for a in ['freq']},\n", - " \"freq_meta_sample_count\": ht.index_globals().freq_meta_sample_count,\n", - " },\n", - " items_to_filter=items_to_filter2,\n", - " keep=False,\n", - " combine_operator=\"or\",\n", - " )\n", - "freq_meta2.collect()" + "# When a variant exists...\n", + "extract_callstats_for_1anc_1variant(ht, gen_anc='AFR', contig='chr22', position=15528692, alleles=['C','G']).show(-1)" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "7e044201", + "execution_count": 64, + "id": "9f4c689b", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-02 02:02:23.969 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n" + ] + }, { "data": { "text/html": [ - "\n", - "\n", - "
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chr22:15528692["C","G"][(793,5.43e-04,1459438,7)]
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locus<GRCh38>array<str>int32float64int32int64
" ], "text/plain": [ - "+----------------+------------+\n", - "| locus | alleles |\n", - "+----------------+------------+\n", - "| locus | array |\n", - "+----------------+------------+\n", - "| chr22:15528692 | [\"C\",\"G\"] |\n", - "+----------------+------------+\n", - "\n", - "+---------------------------------------------------------------------------+\n", - "| freq |\n", - "+---------------------------------------------------------------------------+\n", - "| array |\n", - "+---------------------------------------------------------------------------+\n", - "| [(793,5.43e-04,1459438,7)] |\n", - "+---------------------------------------------------------------------------+" + "+---------------+------------+--------+---------+--------+\n", + "| locus | alleles | afr.AC | afr.AF | afr.AN |\n", + "+---------------+------------+--------+---------+--------+\n", + "| locus | array | int32 | float64 | int32 |\n", + "+---------------+------------+--------+---------+--------+\n", + "+---------------+------------+--------+---------+--------+\n", + "\n", + "+----------------------+\n", + "| afr.homozygote_count |\n", + "+----------------------+\n", + "| int64 |\n", + "+----------------------+\n", + "+----------------------+" ] }, "metadata": {}, @@ -1139,19 +2414,56 @@ } ], "source": [ - "ht = ht.annotate(**{a: array_exprs2[a] for a in ['freq']})\n", - "ht = ht.annotate_globals(\n", - " freq_meta=freq_meta2,\n", - " freq_meta_sample_count=array_exprs2[\"freq_meta_sample_count\"],\n", - " )\n", + "# When a variant doesn't exist...\n", + "extract_callstats_for_1anc_1variant(ht, gen_anc='AFR', contig='chr22', position=15528692, alleles=['C','A']).show(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "6fc82c5c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Copying gs://gnomad-qin/qin_notebooks/toolbox_for_gnomad_users.ipynb...\n", + "/ [1 files][747.1 KiB/747.1 KiB] \n", + "Operation completed over 1 objects/747.1 KiB. \n", + "[NbConvertApp] WARNING | Config option `extra_template_paths` not recognized by `EmbedHTMLExporter`. Did you mean `template_path`?\n", + "[NbConvertApp] Converting notebook toolbox_for_gnomad_users.ipynb to html_embed\n", + "[NbConvertApp] Writing 943138 bytes to toolbox_for_gnomad_users.html\n", + "Copying file://toolbox_for_gnomad_users.html [Content-Type=text/html]...\n", + "/ [1 files][921.1 KiB/921.1 KiB] \n", + "Operation completed over 1 objects/921.1 KiB. \n" + ] + } + ], + "source": [ + "notebook_name='toolbox_for_gnomad_users'\n", + "\n", + "#Uncomment top lines for the first time exporting on a cluster\n", + "#!/opt/conda/default/bin/conda create -n save-html-env --clone /opt/conda/default\n", + "#!/opt/conda/miniconda3/envs/save-html-env/bin/pip install \"nbconvert<6\" jinja2==3.0.3 jupyter_contrib_nbextensions\n", "\n", - "ht.select('freq').show(-1)" + "# Download the notebook from Google Cloud Storage\n", + "!gsutil -u broad-mpg-gnomad cp gs://gnomad-qin/qin_notebooks/{notebook_name}.ipynb .\n", + "\n", + "# Convert the notebook to HTML with embedded resources\n", + "! /opt/conda/miniconda3/envs/save-html-env/bin/jupyter nbconvert \\\n", + " --CodeFoldingPreprocessor.remove_folded_code=True --to html_embed \\\n", + " --template \"/opt/conda/miniconda3/envs/save-html-env/lib/python3.11/site-packages/jupyter_contrib_nbextensions/templates/toc2.tpl\" \\\n", + " {notebook_name}.ipynb\n", + "\n", + "# Upload the converted HTML back to Google Cloud Storage\n", + "!gsutil -u broad-mpg-gnomad cp {notebook_name}.html gs://gnomad-qin/qin_notebooks/{notebook_name}.html" ] }, { "cell_type": "code", "execution_count": null, - "id": "6fc82c5c", + "id": "a9e4c9bb", "metadata": {}, "outputs": [], "source": [] From c1834707ed35fbcd7ed326bc95ff2e412591b0bc Mon Sep 17 00:00:00 2001 From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com> Date: Mon, 9 Dec 2024 10:49:50 -0700 Subject: [PATCH 024/121] - Restructure files - Add a description of the repo structure to the README - Add some potential requirements - Update the documentation and make sure it works - Create a notebook specific to loading gnomAD release data and just showing what each dataset looks like. --- README.md | 30 + docs/generate_api_reference.py | 45 +- gnomad_toolbox/analysis/general.py | 42 + gnomad_toolbox/filtering/__init__.py | 1 + gnomad_toolbox/filtering/constraint.py | 1 + gnomad_toolbox/filtering/coverage.py | 1 + .../frequency.py} | 5 +- gnomad_toolbox/filtering/interval.py | 84 + gnomad_toolbox/filtering/pext.py | 1 + gnomad_toolbox/filtering/vep.py | 64 + gnomad_toolbox/load_data.py | 111 + gnomad_toolbox/modules/filter_variant.py | 182 - gnomad_toolbox/modules/import_data.py | 60 - .../notebooks/intro_to_release_data.ipynb | 6622 +++++++++++++++++ .../toolbox_for_gnomad_users.ipynb | 1963 ++++- requirements.txt | 7 + 16 files changed, 8590 insertions(+), 629 deletions(-) create mode 100644 gnomad_toolbox/analysis/general.py create mode 100644 gnomad_toolbox/filtering/__init__.py create mode 100644 gnomad_toolbox/filtering/constraint.py create mode 100644 gnomad_toolbox/filtering/coverage.py rename gnomad_toolbox/{modules/extract_freq.py => filtering/frequency.py} (95%) create mode 100644 gnomad_toolbox/filtering/interval.py create mode 100644 gnomad_toolbox/filtering/pext.py create mode 100644 gnomad_toolbox/filtering/vep.py create mode 100644 gnomad_toolbox/load_data.py delete mode 100644 gnomad_toolbox/modules/filter_variant.py delete mode 100644 gnomad_toolbox/modules/import_data.py create mode 100644 gnomad_toolbox/notebooks/intro_to_release_data.ipynb rename gnomad_toolbox/{use_cases => notebooks}/toolbox_for_gnomad_users.ipynb (72%) diff --git a/README.md b/README.md index 10ead05..52bdd94 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,32 @@ # gnomad-toolbox This repository provides a set of Python functions to simplify working with gnomAD Hail Tables. It includes tools for data access, filtering, and analysis. + +## Repository structure +``` +ggnomad_toolbox/ +│ +├── load_data.py # Functions to load gnomAD release Hail Tables. +│ +├── filtering/ +│ ├── __init__.py +│ ├── constraint.py # Functions to filter constraint metrics (e.g., observed/expected ratios). +│ ├── coverage.py # Functions to filter variants or regions based on coverage thresholds. +│ ├── frequency.py # Functions to filter variants by allele frequency thresholds. +│ ├── interval.py # Functions to filter variants within specified genomic intervals and genes. +│ ├── pext.py # Functions to filter variants using predicted expression (pext) scores. +│ ├── vep.py # Functions to filter variants based on VEP (Variant Effect Predictor) annotations. +│ +├── analysis/ +│ ├── __init__.py +│ ├── general.py # General analysis functions, such as summarizing variant statistics. +│ +├── notebooks/ +│ ├── intro_to_release_data.ipynb # Jupyter notebook introducing the loading of gnomAD release data. +``` + +## Getting started +### Install +pip install -r requirements.txt + +### Opening the notebooks +jupyter lab diff --git a/docs/generate_api_reference.py b/docs/generate_api_reference.py index a38ba3e..34930cf 100755 --- a/docs/generate_api_reference.py +++ b/docs/generate_api_reference.py @@ -21,16 +21,6 @@ EXCLUDE_PACKAGES = ["tests"] -EXCLUDE_TOP_LEVEL_PACKAGES = [] -""" -List of packages/modules to exclude from API reference documentation. - -This should be a list of strings where each string is the full name (from the top level) -of a package or module to exclude. For example, if 'gnomad_toolbox' includes a -'example_notebooks' that you want to exclude, you would add -'gnomad_toolbox.example_notebooks' to this list. -""" - PACKAGE_DOC_TEMPLATE = """{title} {package_doc} @@ -119,7 +109,11 @@ def write_module_doc(module_name): write_file(doc_path, doc) -def write_package_doc(package_name): +def write_package_doc( + package_name, + package_doc = None, + doc_path = None, +): """Write API reference documentation file for a package.""" package = importlib.import_module(package_name) @@ -139,32 +133,21 @@ def write_package_doc(package_name): doc = PACKAGE_DOC_TEMPLATE.format( title=format_title(package_name), - package_doc=package.__doc__ or "", + package_doc= package_doc or package.__doc__ or "", module_links="\n ".join(module_links), ) - doc_path = package_doc_path(package) + doc_path = doc_path or package_doc_path(package) write_file(doc_path, doc) if __name__ == "__main__": - packages = setuptools.find_namespace_packages( - where=REPOSITORY_ROOT_PATH, include=["gnomad_toolbox.*"] - ) - top_level_packages = [ - pkg for pkg in packages if pkg.count(".") == 1 and pkg not in EXCLUDE_TOP_LEVEL_PACKAGES - ] - - for pkg in top_level_packages: - write_package_doc(pkg) - - root_doc = PACKAGE_DOC_TEMPLATE.format( - title=format_title("gnomad_toolbox"), - package_doc="", - module_links="\n ".join( - f"{pkg.split('.')[1]} <{pkg.split('.')[1]}/index>" - for pkg in top_level_packages + write_package_doc( + "gnomad_toolbox", + package_doc=( + "This repository provides a set of Python functions to simplify working " + "with gnomAD Hail Tables. It includes tools for data access, filtering, " + "and analysis." ), + doc_path=os.path.join(DOCS_DIRECTORY, "api_reference", "index.rst"), ) - - write_file(os.path.join(DOCS_DIRECTORY, "api_reference", "index.rst"), root_doc) diff --git a/gnomad_toolbox/analysis/general.py b/gnomad_toolbox/analysis/general.py new file mode 100644 index 0000000..7334f43 --- /dev/null +++ b/gnomad_toolbox/analysis/general.py @@ -0,0 +1,42 @@ +"""Set of general functions for gnomAD analysis.""" + +import hail as hl + + +def get_variant_count( + ht: hl.Table, + afs: list[float] = [0.01, 0.001], + singletons: bool = False, + doubletons: bool = False, +) -> dict: + """ + Count variants with frequency <1%, <0.1%, and singletons (AC == 1). + + .. note:: + + This function works for gnomAD exomes and genomes datasets, not yet for gnomAD + joint dataset, since the HT schema is slightly different. + + :param ht: Input Table. + :param afs: List of allele frequencies cutoffs. + :param singletons: Include singletons. + :param doubletons: Include doubletons. + :return: Dictionary with counts. + """ + counts = {} + + # Filter to PASS variants. + ht = ht.filter(hl.len(ht.filters) == 0) + if singletons: + n_singletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 1)) + counts["number of singletons"] = n_singletons + if doubletons: + n_doubletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 2)) + counts["number of doubletons"] = n_doubletons + + for af in afs: + n_variants = ht.aggregate(hl.agg.count_where(ht.freq[0].AF < af)) + counts[f"number of variants with AF < {af}"] = n_variants + + # Count variants with frequency <1%, <0.1%, and singletons (AC == 1). + return counts diff --git a/gnomad_toolbox/filtering/__init__.py b/gnomad_toolbox/filtering/__init__.py new file mode 100644 index 0000000..6e03199 --- /dev/null +++ b/gnomad_toolbox/filtering/__init__.py @@ -0,0 +1 @@ +# noqa: D104 diff --git a/gnomad_toolbox/filtering/constraint.py b/gnomad_toolbox/filtering/constraint.py new file mode 100644 index 0000000..edbb4ee --- /dev/null +++ b/gnomad_toolbox/filtering/constraint.py @@ -0,0 +1 @@ +# noqa: D104, D100 diff --git a/gnomad_toolbox/filtering/coverage.py b/gnomad_toolbox/filtering/coverage.py new file mode 100644 index 0000000..edbb4ee --- /dev/null +++ b/gnomad_toolbox/filtering/coverage.py @@ -0,0 +1 @@ +# noqa: D104, D100 diff --git a/gnomad_toolbox/modules/extract_freq.py b/gnomad_toolbox/filtering/frequency.py similarity index 95% rename from gnomad_toolbox/modules/extract_freq.py rename to gnomad_toolbox/filtering/frequency.py index ee051e7..1c028a3 100644 --- a/gnomad_toolbox/modules/extract_freq.py +++ b/gnomad_toolbox/filtering/frequency.py @@ -1,9 +1,8 @@ -"""Extract callstats from 'freq' of gnomAD HTs.""" +"""Functions for filtering the gnomAD sites HT frequency data.""" -from typing import List, Optional +from typing import List import hail as hl -from gnomad.resources.grch38.gnomad import POPS_TO_REMOVE_FOR_POPMAX from gnomad.utils.filtering import filter_arrays_by_meta diff --git a/gnomad_toolbox/filtering/interval.py b/gnomad_toolbox/filtering/interval.py new file mode 100644 index 0000000..0a3b810 --- /dev/null +++ b/gnomad_toolbox/filtering/interval.py @@ -0,0 +1,84 @@ +"""Functions to filter the gnmoAD sites HT by interval.""" + +import hail as hl + + +def filter_by_interval(ht: hl.Table, interval: str) -> hl.Table: + """ + Filter variants by interval. + + :param ht: Input Table. + :param interval: Interval string. Format has to be "chr:start-end", e.g., "1:1000-2000". + :return: Table with variants in the interval. + """ + if ht.locus.dtype.reference_genome.name == "GRCh38": + interval = "chr" + interval + ht = hl.filter_intervals( + ht, + [ + hl.parse_locus_interval( + interval, + reference_genome=( + "GRCh38" + if ht.locus.dtype.reference_genome.name == "GRCh38" + else "GRCh37" + ), + ) + ], + ) + return ht + + +def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table: + """ + Filter variants in a gene. + + .. note:: + This function is to match the number of variants that you will get in the + gnomAD browser, which only focus on variants in "CDS" regions plus 75bp + up- and downstream. This is not the same as filtering by gene symbol with + our `filter_vep_transcript_csqs` function, which will include all variants. + + :param ht: Input Table. + :param gene: Gene symbol. + :return: Table with variants in the gene. + """ + # Make gene symbol uppercase + gene = gene.upper() + + if ht.locus.dtype.reference_genome.name == "GRCh37": + gene_ht = hl.read_table( + "gs://gcp-public-data--gnomad/resources/grch37/browser/gnomad" + ".genes.GRCh37.GENCODEv19.ht" + ) + else: + gene_ht = hl.read_table( + "gs://gcp-public-data--gnomad/resources/grch38/browser/gnomad" + ".genes.GRCh38.GENCODEv39.ht" + ) + + gene_ht = gene_ht.annotate( + cds_intervals=hl.array( + gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS") + ).map( + lambda exon: hl.locus_interval( + hl.if_else( + gene_ht.interval.start.dtype.reference_genome.name == "GRCh38", + "chr" + gene_ht.chrom, + gene_ht.chrom, + ), + exon.start - 75, + exon.stop + 75, + reference_genome=gene_ht.interval.start.dtype.reference_genome, + includes_end=True, + ) + ) + ) + + intervals = gene_ht.filter(gene_ht.gencode_symbol == gene).cds_intervals.collect()[ + 0 + ] + + ht = hl.filter_intervals(ht, intervals) + + return ht diff --git a/gnomad_toolbox/filtering/pext.py b/gnomad_toolbox/filtering/pext.py new file mode 100644 index 0000000..edbb4ee --- /dev/null +++ b/gnomad_toolbox/filtering/pext.py @@ -0,0 +1 @@ +# noqa: D104, D100 diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py new file mode 100644 index 0000000..f851598 --- /dev/null +++ b/gnomad_toolbox/filtering/vep.py @@ -0,0 +1,64 @@ +"""Functions to filter gnomAD sites HT by VEP annotations.""" + +from functools import reduce + +import hail as hl +from gnomad.utils.vep import CSQ_CODING, LOF_CSQ_SET + + +def filter_by_csqs( + ht: hl.Table, csqs: list[str], pass_filters: bool = True +) -> hl.Table: + """ + Filter variants by consequences. + + :param ht: Input Table. + :param csqs: List of consequences to filter by. It can be specified as the + categories on the browser: pLoF, Missense / Inframe indel, Synonymous, Other. + :param pass_filters: Boolean if the variants pass the filters. + :return: Table with variants with the specified consequences. + """ + missense_inframe = ["missense_variant", "inframe_insertion", "inframe_deletion"] + + filter_expr = [] + if "lof" in csqs: + filter_expr.append( + hl.literal(LOF_CSQ_SET).contains(ht.vep.most_severe_consequence) + ) + + if "synonymous" in csqs: + filter_expr.append(ht.vep.most_severe_consequence == "synonymous_variant") + + if "missense" in csqs: + filter_expr.append( + hl.literal(missense_inframe).contains(ht.vep.most_severe_consequence) + ) + + if "other" in csqs: + excluded_csqs = hl.literal( + list(LOF_CSQ_SET) + missense_inframe + ["synonymous_variant"] + ) + filter_expr.append(~excluded_csqs.contains(ht.vep.most_severe_consequence)) + + if "coding" in csqs: + filter_expr.append( + hl.literal(CSQ_CODING).contains(ht.vep.most_severe_consequence) + ) + + if len(filter_expr) == 0: + raise ValueError( + "No valid consequence specified. Choose from 'lof', 'synonymous', 'missense', 'other'." + ) + + # Combine filter expressions with logical OR + if len(filter_expr) == 1: + combined_filter = filter_expr[0] + else: + combined_filter = reduce(lambda acc, expr: acc | expr, filter_expr) + + ht = ht.filter(combined_filter) + + if pass_filters: + ht = ht.filter(hl.len(ht.filters) == 0) + + return ht diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py new file mode 100644 index 0000000..b40dc10 --- /dev/null +++ b/gnomad_toolbox/load_data.py @@ -0,0 +1,111 @@ +"""Functions to import gnomAD data.""" + +import gnomad.resources.grch37.gnomad as grch37_gnomad +import gnomad.resources.grch38.gnomad as grch38_gnomad +import hail as hl + +GNOMAD_BY_BUILD = { + "GRCh37": grch37_gnomad, + "GRCh38": grch38_gnomad, +} +DATASETS = { + "variant": "public_release", + "all_sites_an": "all_sites_an", + "coverage": "coverage", +} +DATA_TYPES = ["exomes", "genomes", "joint"] +RELEASES_GLOBAL = { + "variant": { + "exomes": "EXOME_RELEASES", + "genomes": "GENOME_RELEASES", + "joint": "JOINT_RELEASES", + }, + "all_sites_an": { + "exomes": "EXOME_AN_RELEASES", + "genomes": "GENOME_AN_RELEASES", + }, + "coverage": { + "exomes": "EXOME_COVERAGE_RELEASES", + "genomes": "GENOME_COVERAGE_RELEASES", + }, +} +RELEASES = { + dataset: { + data_type: { + build: getattr(res, release_global, None) + for build, res in GNOMAD_BY_BUILD.items() + } + for data_type, release_global in data_types.items() + } + for dataset, data_types in RELEASES_GLOBAL.items() +} + + +def get_gnomad_release( + data_type: str = "exomes", + version: str = grch38_gnomad.CURRENT_EXOME_RELEASE, + dataset: str = "variant", +) -> hl.Table: + """ + Get gnomAD HT by dataset, data type, and version. + + .. table:: Available versions for each dataset and data type are (as of 2024-10-29) + :widths: auto + + +--------------+-----------------+----------------------------------+----------------------+ + | Dataset | Data Type | GRCh38 Versions | GRCh37 Versions | + +==============+=================+==================================+======================+ + | variant | exomes | 4.0, 4.1 | 2.1, 2.1.1 | + | +-----------------+----------------------------------+----------------------+ + | | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | + | +-----------------+----------------------------------+----------------------+ + | | joint | 4.1 | N/A | + +--------------+-----------------+----------------------------------+----------------------+ + | coverage | exomes | 4.0 | 2.1 | + | +-----------------+----------------------------------+----------------------+ + | | genomes | 3.0.1 | 2.1 | + +--------------+-----------------+----------------------------------+----------------------+ + | all_sites_an | exomes | 4.1 | N/A | + | +-----------------+----------------------------------+----------------------+ + | | genomes | 4.1 | N/A | + +--------------+-----------------+----------------------------------+----------------------+ + + :param data_type: Data type (exomes, genomes, or joint). Default is "exomes". + :param version: gnomAD version. Default is the current exome release. + :param dataset: Dataset type. One of "variant", "all_sites_an", "coverage". Default + is "variant". + :return: Hail Table for requested dataset, data type, and version. + """ + # Get all releases for the given dataset. + releases = RELEASES.get(dataset) + + # Validate dataset. + if releases is None: + raise ValueError(f"{dataset} is invalid. Choose from {RELEASES.keys()}") + + # Get all releases for the given dataset and data_type. + data_type_releases = releases.get(data_type) + + # Validate data type. + if data_type_releases is None: + raise ValueError( + f"Data type {data_type} is invalid. Choose from 'exomes', 'genomes', or " + "'joint'." + ) + + # Check version availability for GRCh38 and GRCh37. + if data_type_releases["GRCh38"] and version in data_type_releases["GRCh38"]: + return ( + getattr(grch38_gnomad, DATASETS[dataset])(data_type).versions[version].ht() + ) + elif data_type_releases["GRCh37"] and version in data_type_releases["GRCh37"]: + return ( + getattr(grch37_gnomad, DATASETS[dataset])(data_type).versions[version].ht() + ) + else: + raise ValueError( + f"Version {version} is not available for { + data_type} in the {dataset} dataset. " + f"Available versions: GRCh38 - {data_type_releases['GRCh38']}, " + f"GRCh37 - {data_type_releases['GRCh37']}." + ) diff --git a/gnomad_toolbox/modules/filter_variant.py b/gnomad_toolbox/modules/filter_variant.py deleted file mode 100644 index f73fe73..0000000 --- a/gnomad_toolbox/modules/filter_variant.py +++ /dev/null @@ -1,182 +0,0 @@ -"""Small functions to filter variants in gnomAD datasets, such as by allele frequency or by variant type.""" - -from functools import reduce - -import hail as hl -from gnomad.utils.vep import CSQ_CODING, LOF_CSQ_SET - - -def get_variant_count( - ht: hl.Table, - afs: list[float] = [0.01, 0.001], - singletons: bool = False, - doubletons: bool = False, -) -> dict: - """ - Count variants with frequency <1%, <0.1%, and singletons (AC == 1). - - .. note:: This function works for gnomAD exomes and genomes datasets, not yet for - gnomAD joint dataset, since the HT schema is slightly different. - - :param ht: Input Table. - :param afs: List of allele frequencies cutoffs. - :param singletons: Include singletons. - :param doubletons: Include doubletons. - :return: Dictionary with counts. - """ - counts = {} - - # Filter to PASS variants. - ht = ht.filter(hl.len(ht.filters) == 0) - if singletons: - n_singletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 1)) - counts["number of singletons"] = n_singletons - if doubletons: - n_doubletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 2)) - counts["number of doubletons"] = n_doubletons - - for af in afs: - n_variants = ht.aggregate(hl.agg.count_where(ht.freq[0].AF < af)) - counts[f"number of variants with AF < {af}"] = n_variants - - # Count variants with frequency <1%, <0.1%, and singletons (AC == 1). - return counts - - -def filter_by_interval(ht: hl.Table, interval: str) -> hl.Table: - """ - Filter variants by interval. - - :param ht: Input Table. - :param interval: Interval string. Format has to be "chr:start-end", e.g., "1:1000-2000". - :return: Table with variants in the interval. - """ - if ht.locus.dtype.reference_genome.name == "GRCh38": - interval = "chr" + interval - ht = hl.filter_intervals( - ht, - [ - hl.parse_locus_interval( - interval, - reference_genome=( - "GRCh38" - if ht.locus.dtype.reference_genome.name == "GRCh38" - else "GRCh37" - ), - ) - ], - ) - return ht - - -def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table: - """ - Filter variants in a gene. - - .. note:: - This function is to match the number of variants that you will get in the - gnomAD browser, which only focus on variants in "CDS" regions plus 75bp - up- and downstream. This is not the same as filtering by gene symbol with - our `filter_vep_transcript_csqs` function, which will include all variants. - - :param ht: Input Table. - :param gene: Gene symbol. - :return: Table with variants in the gene. - """ - # Make gene symbol uppercase - gene = gene.upper() - - if ht.locus.dtype.reference_genome.name == "GRCh37": - gene_ht = hl.read_table( - "gs://gcp-public-data--gnomad/resources/grch37/browser/gnomad" - ".genes.GRCh37.GENCODEv19.ht" - ) - else: - gene_ht = hl.read_table( - "gs://gcp-public-data--gnomad/resources/grch38/browser/gnomad" - ".genes.GRCh38.GENCODEv39.ht" - ) - - gene_ht = gene_ht.annotate( - cds_intervals=hl.array( - gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS") - ).map( - lambda exon: hl.locus_interval( - hl.if_else( - gene_ht.interval.start.dtype.reference_genome.name == "GRCh38", - "chr" + gene_ht.chrom, - gene_ht.chrom, - ), - exon.start - 75, - exon.stop + 75, - reference_genome=gene_ht.interval.start.dtype.reference_genome, - includes_end=True, - ) - ) - ) - - intervals = gene_ht.filter(gene_ht.gencode_symbol == gene).cds_intervals.collect()[ - 0 - ] - - ht = hl.filter_intervals(ht, intervals) - - return ht - - -def filter_by_csqs( - ht: hl.Table, csqs: list[str], pass_filters: bool = True -) -> hl.Table: - """ - Filter variants by consequences. - - :param ht: Input Table. - :param csqs: List of consequences to filter by. It can be specified as the - categories on the browser: pLoF, Missense / Inframe indel, Synonymous, Other. - :param pass_filters: Boolean if the variants pass the filters. - :return: Table with variants with the specified consequences. - """ - missense_inframe = ["missense_variant", "inframe_insertion", "inframe_deletion"] - - filter_expr = [] - if "lof" in csqs: - filter_expr.append( - hl.literal(LOF_CSQ_SET).contains(ht.vep.most_severe_consequence) - ) - - if "synonymous" in csqs: - filter_expr.append(ht.vep.most_severe_consequence == "synonymous_variant") - - if "missense" in csqs: - filter_expr.append( - hl.literal(missense_inframe).contains(ht.vep.most_severe_consequence) - ) - - if "other" in csqs: - excluded_csqs = hl.literal( - list(LOF_CSQ_SET) + missense_inframe + ["synonymous_variant"] - ) - filter_expr.append(~excluded_csqs.contains(ht.vep.most_severe_consequence)) - - if "coding" in csqs: - filter_expr.append( - hl.literal(CSQ_CODING).contains(ht.vep.most_severe_consequence) - ) - - if len(filter_expr) == 0: - raise ValueError( - "No valid consequence specified. Choose from 'lof', 'synonymous', 'missense', 'other'." - ) - - # Combine filter expressions with logical OR - if len(filter_expr) == 1: - combined_filter = filter_expr[0] - else: - combined_filter = reduce(lambda acc, expr: acc | expr, filter_expr) - - ht = ht.filter(combined_filter) - - if pass_filters: - ht = ht.filter(hl.len(ht.filters) == 0) - - return ht diff --git a/gnomad_toolbox/modules/import_data.py b/gnomad_toolbox/modules/import_data.py deleted file mode 100644 index 8616917..0000000 --- a/gnomad_toolbox/modules/import_data.py +++ /dev/null @@ -1,60 +0,0 @@ -"""Functions to import gnomAD data.""" - -import hail as hl -from gnomad.resources.grch37.gnomad import EXOME_RELEASES as GRCh37_EXOME_RELEASES -from gnomad.resources.grch37.gnomad import GENOME_RELEASES as GRCh37_GENOME_RELEASES -from gnomad.resources.grch37.gnomad import public_release as grch37_public_release -from gnomad.resources.grch38.gnomad import EXOME_RELEASES as GRCh38_EXOME_RELEASES -from gnomad.resources.grch38.gnomad import GENOME_RELEASES as GRCh38_GENOME_RELEASES -from gnomad.resources.grch38.gnomad import JOINT_RELEASES as GRCh38_JOINT_RELEASES -from gnomad.resources.grch38.gnomad import public_release as grch38_public_release - - -def get_ht_by_datatype_and_version( - data_type: str = "exomes", version: str = "4.1" -) -> hl.Table: - """ - Get gnomAD HT by data type and version. - - .. note:: - - Available versions for each data type are (as of 2024-10-29): - - :: - - | Data Type | GRCh38 Versions | GRCh37 Versions | - |-----------------|----------------------------------|----------------------| - | exomes | 4.0, 4.1 | 2.1, 2.1.1 | - | genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 | - | joint | 4.1 | N/A | - - :param data_type: Data type (exomes, genomes, or joint). - :param version: gnomAD version. - :return: Hail Table. - """ - # Mapping data types to version sets for GRCh38 and GRCh37 - versions_by_type = { - "exomes": (GRCh38_EXOME_RELEASES, GRCh37_EXOME_RELEASES), - "genomes": (GRCh38_GENOME_RELEASES, GRCh37_GENOME_RELEASES), - "joint": (GRCh38_JOINT_RELEASES, []), - } - - # Validate data type - if data_type not in versions_by_type: - raise ValueError( - f"Data type {data_type} is invalid. Choose from 'exomes', 'genomes', or 'joint'." - ) - - # Get GRCh38 and GRCh37 versions for the given data type - grch38_versions, grch37_versions = versions_by_type[data_type] - - # Check version availability for GRCh38 and GRCh37 - if version in grch38_versions: - return grch38_public_release(data_type).ht() - elif version in grch37_versions: - return grch37_public_release(data_type).ht() - else: - raise ValueError( - f"Version {version} is not available for {data_type}. " - f"Available versions: GRCh38 - {grch38_versions}, GRCh37 - {grch37_versions}." - ) diff --git a/gnomad_toolbox/notebooks/intro_to_release_data.ipynb b/gnomad_toolbox/notebooks/intro_to_release_data.ipynb new file mode 100644 index 0000000..c84c0d7 --- /dev/null +++ b/gnomad_toolbox/notebooks/intro_to_release_data.ipynb @@ -0,0 +1,6622 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "853c94b9", + "metadata": {}, + "source": [ + "# Introduction to gnomAD Hail release files\n" + ] + }, + { + "cell_type": "markdown", + "id": "5cf83cfe-0fce-40ae-add7-c9f2c20c1e85", + "metadata": {}, + "source": [ + "In this notebook we will explore all of the available [gnomAD v4 release files](https://gnomad.broadinstitute.org/data#v4) that are in Hail formats." + ] + }, + { + "attachments": { + "afcd4ecc-4e90-464b-91c7-9cd80b0e92ba.png": { + "image/png": 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" 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" + } + }, + "cell_type": "markdown", + "id": "f97431a4-78a0-473f-99a0-67eeefb8bbc6", + "metadata": {}, + "source": [ + "![image.png](attachment:2f957256-5d08-40e1-b77c-5faa4f771fb2.png)" + ] + }, + { + "cell_type": "markdown", + "id": "8e609a46", + "metadata": { + "tags": [], + "toc": true + }, + "source": [ + "

Table of Contents

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "8e713032", + "metadata": {}, + "source": [ + "## Import modules" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e69953f7", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:40.044647Z", + "start_time": "2024-12-06T20:08:37.758061Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " function drop(id) {\n", + " const view = Bokeh.index.get_by_id(id)\n", + " if (view != null) {\n", + " view.model.document.clear()\n", + " Bokeh.index.delete(view)\n", + " }\n", + " }\n", + "\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + "\n", + " // Clean up Bokeh references\n", + " if (id != null) {\n", + " drop(id)\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim()\n", + " drop(id)\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import hail as hl\n", + "from gnomad_toolbox.load_data import get_gnomad_release" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8649f215-0afc-4f66-920a-53b707f41c4a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Welcome to\n", + " __ __ <>__\n", + " / /_/ /__ __/ /\n", + " / __ / _ `/ / /\n", + " /_/ /_/\\_,_/_/_/ version 0.2.132-678e1f52b999\n", + "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241208-2218-0.2.132-678e1f52b999.log\n" + ] + } + ], + "source": [ + "hl.init(backend=\"local\")" + ] + }, + { + "cell_type": "markdown", + "id": "5335a135", + "metadata": { + "tags": [] + }, + "source": [ + "## Variant data\n", + "\n", + "Available versions for each data type and reference build are (as of 2024-10-29):\n", + "\n", + "| Data Type | GRCh38 Versions | GRCh37 Versions |\n", + "|-----------------|----------------------------------|----------------------|\n", + "| exomes | 4.0, 4.1 | 2.1, 2.1.1 |\n", + "| genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 |\n", + "| joint | 4.1 | N/A |\n", + "\n", + "For a description of all fields the the HT, see the [Help/FAQ](https://gnomad.broadinstitute.org/help/v4-hts) page." + ] + }, + { + "cell_type": "markdown", + "id": "d1a4ae8933ba6421", + "metadata": { + "tags": [] + }, + "source": [ + "### v4.1 exomes Hail Table" + ] + }, + { + "cell_type": "markdown", + "id": "318a034c-ac84-4147-9f25-e5e8783e9b91", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "100cf576", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + } + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='exomes', version='4.1')" + ] + }, + { + "cell_type": "markdown", + "id": "77d7a05e31c1f37a", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "95c14f2c8cc3e699", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'freq_meta': array> \n", + " 'freq_index_dict': dict \n", + " 'freq_meta_sample_count': array \n", + " 'faf_meta': array> \n", + " 'faf_index_dict': dict \n", + " 'age_distribution': struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int32, \n", + " n_larger: int32\n", + " } \n", + " 'downsamplings': dict> \n", + " 'filtering_model': struct {\n", + " filter_name: str, \n", + " score_name: str, \n", + " snv_cutoff: struct {\n", + " bin: int32, \n", + " min_score: float64\n", + " }, \n", + " indel_cutoff: struct {\n", + " bin: int32, \n", + " min_score: float64\n", + " }, \n", + " snv_training_variables: array, \n", + " indel_training_variables: array\n", + " } \n", + " 'inbreeding_coeff_cutoff': float64 \n", + " 'interval_qc_parameters': struct {\n", + " per_platform: bool, \n", + " all_platforms: bool, \n", + " high_qual_cutoffs: dict>, \n", + " min_platform_size: int32\n", + " } \n", + " 'tool_versions': struct {\n", + " cadd_version: str, \n", + " revel_version: str, \n", + " spliceai_version: str, \n", + " pangolin_version: array, \n", + " phylop_version: str, \n", + " dbsnp_version: str, \n", + " sift_version: str, \n", + " polyphen_version: str\n", + " } \n", + " 'vrs_versions': struct {\n", + " vrs_schema_version: str, \n", + " vrs_python_version: str, \n", + " seqrepo_version: str\n", + " } \n", + " 'vep_globals': struct {\n", + " vep_version: str, \n", + " vep_help: str, \n", + " vep_config: str, \n", + " gencode_version: str, \n", + " mane_select_version: str\n", + " } \n", + " 'frequency_README': str \n", + " 'date': str \n", + " 'version': str \n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'alleles': array \n", + " 'freq': array \n", + " 'grpmax': struct {\n", + " gnomad: struct {\n", + " AC: int32, \n", + " AF: float64, \n", + " AN: int32, \n", + " homozygote_count: int64, \n", + " gen_anc: str\n", + " }, \n", + " non_ukb: struct {\n", + " AC: int32, \n", + " AF: float64, \n", + " AN: int32, \n", + " homozygote_count: int64, \n", + " gen_anc: str\n", + " }\n", + " } \n", + " 'faf': array \n", + " 'fafmax': struct {\n", + " gnomad: struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " }, \n", + " non_ukb: struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " }\n", + " } \n", + " 'a_index': int32 \n", + " 'was_split': bool \n", + " 'rsid': set \n", + " 'filters': set \n", + " 'info': struct {\n", + " FS: float64, \n", + " MQ: float64, \n", + " MQRankSum: float64, \n", + " QUALapprox: int64, \n", + " QD: float64, \n", + " ReadPosRankSum: float64, \n", + " SB: array, \n", + " SOR: float64, \n", + " VarDP: int32, \n", + " AS_FS: float64, \n", + " AS_MQ: float64, \n", + " AS_MQRankSum: float64, \n", + " AS_pab_max: float64, \n", + " AS_QUALapprox: int64, \n", + " AS_QD: float64, \n", + " AS_ReadPosRankSum: float64, \n", + " AS_SB_TABLE: array, \n", + " AS_SOR: float64, \n", + " AS_VarDP: int32, \n", + " singleton: bool, \n", + " transmitted_singleton: bool, \n", + " sibling_singleton: bool, \n", + " omni: bool, \n", + " mills: bool, \n", + " monoallelic: bool, \n", + " only_het: bool, \n", + " AS_VQSLOD: float64, \n", + " inbreeding_coeff: float64, \n", + " vrs: struct {\n", + " VRS_Allele_IDs: array, \n", + " VRS_Starts: array, \n", + " VRS_Ends: array, \n", + " VRS_States: array\n", + " }\n", + " } \n", + " 'vep': struct {\n", + " allele_string: str, \n", + " end: int32, \n", + " id: str, \n", + " input: str, \n", + " intergenic_consequences: array, \n", + " impact: str, \n", + " variant_allele: str\n", + " }>, \n", + " most_severe_consequence: str, \n", + " motif_feature_consequences: array, \n", + " high_inf_pos: str, \n", + " impact: str, \n", + " motif_feature_id: str, \n", + " motif_name: str, \n", + " motif_pos: int32, \n", + " motif_score_change: float64, \n", + " transcription_factors: array, \n", + " strand: int32, \n", + " variant_allele: str\n", + " }>, \n", + " regulatory_feature_consequences: array, \n", + " impact: str, \n", + " regulatory_feature_id: str, \n", + " variant_allele: str\n", + " }>, \n", + " seq_region_name: str, \n", + " start: int32, \n", + " strand: int32, \n", + " transcript_consequences: array, \n", + " distance: int32, \n", + " domains: array, \n", + " exon: str, \n", + " flags: str, \n", + " gene_id: str, \n", + " gene_pheno: int32, \n", + " gene_symbol: str, \n", + " gene_symbol_source: str, \n", + " hgnc_id: str, \n", + " hgvsc: str, \n", + " hgvsp: str, \n", + " hgvs_offset: int32, \n", + " impact: str, \n", + " intron: str, \n", + " lof: str, \n", + " lof_flags: str, \n", + " lof_filter: str, \n", + " lof_info: str, \n", + " mane_select: str, \n", + " mane_plus_clinical: str, \n", + " mirna: array, \n", + " protein_end: int32, \n", + " protein_start: int32, \n", + " protein_id: str, \n", + " source: str, \n", + " strand: int32, \n", + " transcript_id: str, \n", + " tsl: int32, \n", + " uniprot_isoform: array, \n", + " variant_allele: str\n", + " }>, \n", + " variant_class: str\n", + " } \n", + " 'vqsr_results': struct {\n", + " AS_VQSLOD: float64, \n", + " AS_culprit: str, \n", + " positive_train_site: bool, \n", + " negative_train_site: bool\n", + " } \n", + " 'region_flags': struct {\n", + " non_par: bool, \n", + " lcr: bool, \n", + " segdup: bool, \n", + " fail_interval_qc: bool, \n", + " outside_ukb_capture_region: bool, \n", + " outside_broad_capture_region: bool\n", + " } \n", + " 'allele_info': struct {\n", + " variant_type: str, \n", + " n_alt_alleles: int32, \n", + " has_star: bool, \n", + " allele_type: str, \n", + " was_mixed: bool\n", + " } \n", + " 'histograms': struct {\n", + " qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " raw_qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " age_hists: struct {\n", + " age_hist_het: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " age_hist_hom: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }\n", + " } \n", + " 'in_silico_predictors': struct {\n", + " cadd: struct {\n", + " phred: float32, \n", + " raw_score: float32\n", + " }, \n", + " revel_max: float64, \n", + " spliceai_ds_max: float32, \n", + " pangolin_largest_ds: float64, \n", + " phylop: float64, \n", + " sift_max: float64, \n", + " polyphen_max: float64\n", + " } \n", + "----------------------------------------\n", + "Key: ['locus', 'alleles']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "a071f738b2c888e", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "222de580c305d72a", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
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bin_edges
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bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
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showing top 5 rows

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[(0,0.00e+00,4,0),(1,1.59e-06,628784,0),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n", + "| [(0,0.00e+00,4,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n", + "| [(0,0.00e+00,32,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0)... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------+------------------+------------------+\n", + "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n", + "+------------------+------------------+------------------+\n", + "| int32 | float64 | int32 |\n", + "+------------------+------------------+------------------+\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "+------------------+------------------+------------------+\n", + "\n", + "+--------------------------------+-----------------------+-------------------+\n", + "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n", + "+--------------------------------+-----------------------+-------------------+\n", + "| int64 | str | int32 |\n", + "+--------------------------------+-----------------------+-------------------+\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "+--------------------------------+-----------------------+-------------------+\n", + "\n", + "+-------------------+-------------------+---------------------------------+\n", + "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n", + "+-------------------+-------------------+---------------------------------+\n", + "| float64 | int32 | int64 |\n", + "+-------------------+-------------------+---------------------------------+\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + 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NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+-------------------------+---------------------------------+\n", + "\n", + "+--------------------------+----------------------------------+\n", + "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n", + "+--------------------------+----------------------------------+\n", + "| float64 | str |\n", + "+--------------------------+----------------------------------+\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+--------------------------+----------------------------------+\n", + "\n", + "+--------------------------+----------------------------------+---------+\n", + "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n", + "+--------------------------+----------------------------------+---------+\n", + "| float64 | str | int32 |\n", + "+--------------------------+----------------------------------+---------+\n", + "| NA | NA | 1 |\n", + "| NA 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[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_all.bin_freq |\n", + "+--------------------------------------------+\n", + "| array |\n", + "+--------------------------------------------+\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "+--------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| 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[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "+--------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_alt.n_smaller |\n", + "+---------------------------------------------+\n", + "| int64 |\n", + "+---------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_alt.n_larger |\n", + "+--------------------------------------------+\n", + "| int64 |\n", + "+--------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + 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|\n", + "| 0 | 1.52e+01 |\n", + "| 0 | 4.42e+00 |\n", + "+--------------------------------------------+---------------------------------+\n", + "\n", + "+-------------------------------------+--------------------------------+\n", + "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n", + "+-------------------------------------+--------------------------------+\n", + "| float32 | float64 |\n", + "+-------------------------------------+--------------------------------+\n", + "| 1.08e+00 | NA |\n", + "| 1.54e+00 | NA |\n", + "| 7.07e-01 | NA |\n", + "| 1.41e+00 | NA |\n", + "| 3.11e-01 | NA |\n", + "+-------------------------------------+--------------------------------+\n", + "\n", + "+--------------------------------------+\n", + "| in_silico_predictors.spliceai_ds_max |\n", + "+--------------------------------------+\n", + "| float32 |\n", + "+--------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+--------------------------------------+\n", + "\n", + "+------------------------------------------+-----------------------------+\n", + "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n", + "+------------------------------------------+-----------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------------------+-----------------------------+\n", + "| -1.10e-01 | 1.09e+00 |\n", + "| -7.00e-02 | 6.55e+00 |\n", + "| -9.00e-02 | -4.41e+00 |\n", + "| -4.00e-02 | 6.01e+00 |\n", + "| -8.00e-02 | 1.38e+00 |\n", + "+------------------------------------------+-----------------------------+\n", + "\n", + "+-------------------------------+-----------------------------------+\n", + "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n", + "+-------------------------------+-----------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------+-----------------------------------+\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+-------------------------------+-----------------------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "b7a158a3-f21a-4f87-9596-1f918156d713", + "metadata": { + "tags": [] + }, + "source": [ + "### v4.1 genomes Hail Table" + ] + }, + { + "cell_type": "markdown", + "id": "30f86500-afc5-419e-ae2e-f944dc461fee", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "62ca9934-20dd-437e-898b-86a056e2606e", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + } + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='genomes', version='4.1')" + ] + }, + { + "cell_type": "markdown", + "id": "9cf4b782-f289-47b6-9123-d08ca761b074", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "09de90df-0b03-4a54-817c-c8a0606026f6", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'freq_meta': array> \n", + " 'freq_index_dict': dict \n", + " 'freq_meta_sample_count': array \n", + " 'faf_meta': array> \n", + " 'faf_index_dict': dict \n", + " 'age_distribution': struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int32, \n", + " n_larger: int32\n", + " } \n", + " 'filtering_model': struct {\n", + " filter_name: str, \n", + " score_name: str, \n", + " snv_cutoff: struct {\n", + " bin: int32, \n", + " min_score: float64\n", + " }, \n", + " indel_cutoff: struct {\n", + " bin: int32, \n", + " min_score: float64\n", + " }, \n", + " snv_training_variables: array, \n", + " indel_training_variables: array\n", + " } \n", + " 'inbreeding_coeff_cutoff': float64 \n", + " 'tool_versions': struct {\n", + " cadd_version: str, \n", + " revel_version: str, \n", + " spliceai_version: str, \n", + " pangolin_version: array, \n", + " phylop_version: str, \n", + " dbsnp_version: str, \n", + " sift_version: str, \n", + " polyphen_version: str\n", + " } \n", + " 'vrs_versions': struct {\n", + " vrs_schema_version: str, \n", + " vrs_python_version: str, \n", + " seqrepo_version: str\n", + " } \n", + " 'vep_globals': struct {\n", + " vep_version: str, \n", + " vep_help: str, \n", + " vep_config: str, \n", + " gencode_version: str, \n", + " mane_select_version: str\n", + " } \n", + " 'frequency_README': str \n", + " 'date': str \n", + " 'version': str \n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'alleles': array \n", + " 'freq': array \n", + " 'grpmax': struct {\n", + " AC: int32, \n", + " AF: float64, \n", + " AN: int32, \n", + " homozygote_count: int32, \n", + " gen_anc: str\n", + " } \n", + " 'faf': array \n", + " 'fafmax': struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " } \n", + " 'a_index': int32 \n", + " 'was_split': bool \n", + " 'rsid': set \n", + " 'filters': set \n", + " 'info': struct {\n", + " FS: float64, \n", + " MQ: float64, \n", + " MQRankSum: float64, \n", + " QUALapprox: int64, \n", + " QD: float32, \n", + " ReadPosRankSum: float64, \n", + " SB: array, \n", + " SOR: float64, \n", + " VarDP: int32, \n", + " AS_FS: float64, \n", + " AS_MQ: float64, \n", + " AS_MQRankSum: float64, \n", + " AS_pab_max: float64, \n", + " AS_QUALapprox: int64, \n", + " AS_QD: float32, \n", + " AS_ReadPosRankSum: float64, \n", + " AS_SB_TABLE: array, \n", + " AS_SOR: float64, \n", + " AS_VarDP: int32, \n", + " singleton: bool, \n", + " transmitted_singleton: bool, \n", + " omni: bool, \n", + " mills: bool, \n", + " monoallelic: bool, \n", + " only_het: bool, \n", + " AS_VQSLOD: float64, \n", + " inbreeding_coeff: float64, \n", + " vrs: struct {\n", + " VRS_Allele_IDs: array, \n", + " VRS_Starts: array, \n", + " VRS_Ends: array, \n", + " VRS_States: array\n", + " }\n", + " } \n", + " 'vep': struct {\n", + " allele_string: str, \n", + " end: int32, \n", + " id: str, \n", + " input: str, \n", + " intergenic_consequences: array, \n", + " impact: str, \n", + " variant_allele: str\n", + " }>, \n", + " most_severe_consequence: str, \n", + " motif_feature_consequences: array, \n", + " high_inf_pos: str, \n", + " impact: str, \n", + " motif_feature_id: str, \n", + " motif_name: str, \n", + " motif_pos: int32, \n", + " motif_score_change: float64, \n", + " transcription_factors: array, \n", + " strand: int32, \n", + " variant_allele: str\n", + " }>, \n", + " regulatory_feature_consequences: array, \n", + " impact: str, \n", + " regulatory_feature_id: str, \n", + " variant_allele: str\n", + " }>, \n", + " seq_region_name: str, \n", + " start: int32, \n", + " strand: int32, \n", + " transcript_consequences: array, \n", + " distance: int32, \n", + " domains: array, \n", + " exon: str, \n", + " flags: str, \n", + " gene_id: str, \n", + " gene_pheno: int32, \n", + " gene_symbol: str, \n", + " gene_symbol_source: str, \n", + " hgnc_id: str, \n", + " hgvsc: str, \n", + " hgvsp: str, \n", + " hgvs_offset: int32, \n", + " impact: str, \n", + " intron: str, \n", + " lof: str, \n", + " lof_flags: str, \n", + " lof_filter: str, \n", + " lof_info: str, \n", + " mane_select: str, \n", + " mane_plus_clinical: str, \n", + " mirna: array, \n", + " protein_end: int32, \n", + " protein_start: int32, \n", + " protein_id: str, \n", + " source: str, \n", + " strand: int32, \n", + " transcript_id: str, \n", + " tsl: int32, \n", + " uniprot_isoform: array, \n", + " variant_allele: str\n", + " }>, \n", + " variant_class: str\n", + " } \n", + " 'vqsr_results': struct {\n", + " AS_VQSLOD: float64, \n", + " AS_culprit: str, \n", + " positive_train_site: bool, \n", + " negative_train_site: bool\n", + " } \n", + " 'region_flags': struct {\n", + " non_par: bool, \n", + " lcr: bool, \n", + " segdup: bool\n", + " } \n", + " 'allele_info': struct {\n", + " allele_type: str, \n", + " n_alt_alleles: int32, \n", + " variant_type: str, \n", + " was_mixed: bool\n", + " } \n", + " 'histograms': struct {\n", + " qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " raw_qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " age_hists: struct {\n", + " age_hist_het: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " age_hist_hom: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }\n", + " } \n", + " 'in_silico_predictors': struct {\n", + " cadd: struct {\n", + " phred: float32, \n", + " raw_score: float32\n", + " }, \n", + " revel_max: float64, \n", + " spliceai_ds_max: float32, \n", + " pangolin_largest_ds: float64, \n", + " phylop: float64, \n", + " sift_max: float64, \n", + " polyphen_max: float64\n", + " } \n", + "----------------------------------------\n", + "Key: ['locus', 'alleles']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "ed916197-3b0e-45dc-bacd-a13cb66d70ee", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "00b0ea2f-5685-4bae-886a-b9ea31866818", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
histograms
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
grpmax
fafmax
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
allele_type
n_alt_alleles
variant_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>int32float64int32int32strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strint32boolset<str>set<str>float64float64float64int64float32float64array<int32>float64int32float64float64float64float64int64float32float64array<int32>float64int32boolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolstrint32strboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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639542312"}{"AC0","AS_VQSR"}7.30e+003.48e+016.70e-02962.74e+00-1.07e+00[21,6,4,4]9.60e-02355.10e+003.51e+01-5.72e-016.87e-01772.96e+00-1.38e+00[21,6,3,3]9.64e-0226FalseNANANAFalseFalse-4.57e+00-1.65e-05["ga4gh:VA.oTAtTrgYxm81O9fu6Mrhfo1t3eHsgg4L","ga4gh:VA.Y283OnlLjyi1T1IT_JzvW255rC6YJsW6"][10030,10030][10031,10031]["T","C"]"T/C"10031".""chr1\t10031\t.\tT\tC\t.\t.\tGT"NA"upstream_gene_variant"NANA"chr1"100311[(1,NA,NA,"transcribed_unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1979,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000450305",NA,NA,"C"),(1,NA,NA,"processed_transcript",NA,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1838,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000456328",1,NA,"C"),(1,NA,NA,"unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4373,NA,NA,NA,"ENSG00000227232",NA,"WASH7P","HGNC","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000488147",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4331,NA,NA,NA,"653635",NA,"WASH7P","EntrezGene","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",-1,"NR_024540.1",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1843,NA,NA,NA,"100287102",NA,"DDX11L1","EntrezGene","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",1,"NR_046018.2",NA,NA,"C")]"SNV"-4.57e+00"AS_QD"FalseFalseFalseTrueTrue"snv"2"multi-snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]008.97e+007.57e-01NANANANANANA
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showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+------------+\n", + "| locus | alleles |\n", + "+---------------+------------+\n", + "| locus | array |\n", + "+---------------+------------+\n", + "| chr1:10031 | [\"T\",\"C\"] |\n", + "| chr1:10037 | [\"T\",\"C\"] |\n", + "| chr1:10043 | [\"T\",\"C\"] |\n", + "| chr1:10055 | [\"T\",\"C\"] |\n", + "| chr1:10057 | [\"A\",\"C\"] |\n", + "+---------------+------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| freq |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e... |\n", + "| [(2,2.60e-05,76882,0),(4,3.15e-05,126902,0),(0,0.00e+00,34670,0),(1,1.80e... |\n", + "| [(1,1.17e-05,85634,0),(1,8.24e-06,121430,0),(0,0.00e+00,39236,0),(0,0.00e... |\n", + "| [(1,1.06e-05,94224,0),(5,4.64e-05,107704,0),(0,0.00e+00,44382,0),(1,1.80e... |\n", + "| [(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-----------+-----------+-----------+-------------------------+----------------+\n", + "| grpmax.AC | grpmax.AF | grpmax.AN | grpmax.homozygote_count | grpmax.gen_anc |\n", + "+-----------+-----------+-----------+-------------------------+----------------+\n", + "| int32 | float64 | int32 | int32 | str |\n", + "+-----------+-----------+-----------+-------------------------+----------------+\n", + "| NA | NA | NA | NA | NA |\n", + "| 1 | 4.07e-04 | 2456 | 0 | \"eas\" |\n", + "| 1 | 4.39e-05 | 22760 | 0 | \"afr\" |\n", + "| NA | NA | NA | NA | NA |\n", + "| 2 | 3.78e-05 | 52912 | 0 | \"nfe\" |\n", + "+-----------+-----------+-----------+-------------------------+----------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| faf |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------+--------------------------+------------------+\n", + "| fafmax.faf95_max | fafmax.faf95_max_gen_anc | fafmax.faf99_max |\n", + "+------------------+--------------------------+------------------+\n", + "| float64 | str | float64 |\n", + "+------------------+--------------------------+------------------+\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| 6.27e-06 | \"nfe\" | 2.35e-06 |\n", + "+------------------+--------------------------+------------------+\n", + "\n", + "+--------------------------+---------+-----------+------------------+\n", + "| fafmax.faf99_max_gen_anc | a_index | was_split | rsid |\n", + "+--------------------------+---------+-----------+------------------+\n", + "| str | int32 | bool | set |\n", + "+--------------------------+---------+-----------+------------------+\n", + "| NA | 2 | True | {\"rs1639542312\"} |\n", + "| NA | 1 | False | {\"rs1639542418\"} |\n", + "| NA | 1 | False | NA |\n", + "| NA | 2 | True | {\"rs892501864\"} |\n", + "| \"nfe\" | 1 | True | {\"rs1570391741\"} |\n", + 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info.ReadPosRankSum | info.SB | info.SOR | info.VarDP |\n", + "+----------+---------------------+---------------+----------+------------+\n", + "| float32 | float64 | array | float64 | int32 |\n", + "+----------+---------------------+---------------+----------+------------+\n", + "| 2.74e+00 | -1.07e+00 | [21,6,4,4] | 9.60e-02 | 35 |\n", + "| 2.20e+00 | -4.80e-01 | [49,12,13,8] | 1.51e-01 | 82 |\n", + "| 2.77e+00 | -8.96e-01 | [25,0,5,5] | 1.00e-03 | 35 |\n", + "| 2.12e+00 | -1.16e+00 | [51,29,15,9] | 6.16e-01 | 104 |\n", + "| 1.79e+00 | -6.84e-01 | [97,29,17,20] | 3.75e-01 | 163 |\n", + "+----------+---------------------+---------------+----------+------------+\n", + "\n", + "+------------+------------+-------------------+-----------------+\n", + "| info.AS_FS | info.AS_MQ | info.AS_MQRankSum | info.AS_pab_max |\n", + "+------------+------------+-------------------+-----------------+\n", + "| float64 | float64 | float64 | float64 |\n", + 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[51,29,7,8] |\n", + "| 264 | 2.06e+00 | -6.84e-01 | [97,29,13,19] |\n", + "+--------------------+------------+------------------------+------------------+\n", + "\n", + "+-------------+---------------+----------------+----------------------------+\n", + "| info.AS_SOR | info.AS_VarDP | info.singleton | info.transmitted_singleton |\n", + "+-------------+---------------+----------------+----------------------------+\n", + "| float64 | int32 | bool | bool |\n", + "+-------------+---------------+----------------+----------------------------+\n", + "| 9.64e-02 | 26 | False | NA |\n", + "| 1.51e-01 | 82 | False | NA |\n", + "| 1.48e-03 | 35 | True | False |\n", + "| 4.69e-01 | 75 | False | NA |\n", + "| 7.58e-01 | 128 | False | NA |\n", + "+-------------+---------------+----------------+----------------------------+\n", + "\n", + "+-----------+------------+------------------+---------------+----------------+\n", + "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n", + "+-----------+------------+------------------+---------------+----------------+\n", + "| bool | bool | bool | bool | float64 |\n", + "+-----------+------------+------------------+---------------+----------------+\n", + "| NA | NA | False | False | -4.57e+00 |\n", + "| NA | NA | False | False | -3.18e+00 |\n", + "| NA | NA | False | False | -5.79e+00 |\n", + "| NA | NA | False | False | -3.72e+00 |\n", + "| NA | NA | False | False | -3.31e+00 |\n", + "+-----------+------------+------------------+---------------+----------------+\n", + "\n", + "+-----------------------+\n", + "| info.inbreeding_coeff |\n", + "+-----------------------+\n", + "| float64 |\n", + "+-----------------------+\n", + "| -1.65e-05 |\n", + "| -3.15e-05 |\n", + "| -8.24e-06 |\n", + "| -4.64e-05 |\n", + "| -2.41e-05 |\n", + "+-----------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| info.vrs.VRS_Allele_IDs |\n", + 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"+---------------------+-------------------+---------------------+\n", + "| [10030,10030] | [10031,10031] | [\"T\",\"C\"] |\n", + "| [10036,10036] | [10037,10037] | [\"T\",\"C\"] |\n", + "| [10042,10042] | [10043,10043] | [\"T\",\"C\"] |\n", + "| [10054,10054] | [10055,10055] | [\"T\",\"C\"] |\n", + "| [10056,10056] | [10057,10057] | [\"A\",\"C\"] |\n", + "+---------------------+-------------------+---------------------+\n", + "\n", + "+-------------------+---------+--------+---------------------------+\n", + "| vep.allele_string | vep.end | vep.id | vep.input |\n", + "+-------------------+---------+--------+---------------------------+\n", + "| str | int32 | str | str |\n", + "+-------------------+---------+--------+---------------------------+\n", + "| \"T/C\" | 10031 | \".\" | \"chr1\t10031\t.\tT\tC\t.\t.\tGT\" |\n", + "| \"T/C\" | 10037 | \".\" | \"chr1\t10037\t.\tT\tC\t.\t.\tGT\" |\n", + "| \"T/C\" | 10043 | \".\" | \"chr1\t10043\t.\tT\tC\t.\t.\tGT\" |\n", + "| \"T/C\" | 10055 | \".\" | \"chr1\t10055\t.\tT\tC\t.\t.\tGT\" |\n", + "| \"A/C\" | 10057 | \".\" | \"chr1\t10057\t.\tA\tC\t.\t.\tGT\" |\n", + "+-------------------+---------+--------+---------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| vep.intergenic_consequences |\n", + "+------------------------------------------------------------------------------+\n", + "| array, impact: st... |\n", + "+------------------------------------------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-----------------------------+\n", + "| vep.most_severe_consequence |\n", + "+-----------------------------+\n", + "| str |\n", + "+-----------------------------+\n", + "| \"upstream_gene_variant\" |\n", + "| \"upstream_gene_variant\" |\n", + "| \"upstream_gene_variant\" |\n", + "| \"upstream_gene_variant\" |\n", + "| \"upstream_gene_variant\" |\n", + "+-----------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| vep.motif_feature_consequences |\n", + "+------------------------------------------------------------------------------+\n", + "| array, high_inf_p... |\n", + "+------------------------------------------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| vep.regulatory_feature_consequences |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_all.bin_freq |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2] |\n", + "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15] |\n", + "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9] |\n", + "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n", + "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_all.n_smaller |\n", + "+---------------------------------------------+\n", + "| int64 |\n", + "+---------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_all.n_larger |\n", + "+--------------------------------------------+\n", + "| int64 |\n", + 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"+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_all.bin_freq |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n", + "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n", + "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n", + "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n", + "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + 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[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0] |\n", + "+--------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_alt.n_smaller |\n", + "+---------------------------------------------+\n", + "| int64 |\n", + "+---------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.gq_hist_alt.n_larger |\n", + "+--------------------------------------------+\n", + "| int64 |\n", + "+--------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+--------------------------------------------+\n", + "\n", + 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"+--------------------------------------------+\n", + "| array |\n", + "+--------------------------------------------+\n", + "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n", + "+--------------------------------------------+\n", + "\n", + "+---------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_alt.n_smaller |\n", + "+---------------------------------------------+\n", + "| int64 |\n", + "+---------------------------------------------+\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "| 0 |\n", + "+---------------------------------------------+\n", + "\n", + "+--------------------------------------------+\n", + "| histograms.qual_hists.dp_hist_alt.n_larger |\n", + "+--------------------------------------------+\n", + "| int64 |\n", + 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in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n", + "+-------------------------------------+--------------------------------+\n", + "| float32 | float64 |\n", + "+-------------------------------------+--------------------------------+\n", + "| 7.57e-01 | NA |\n", + "| 7.49e-01 | NA |\n", + "| 7.48e-01 | NA |\n", + "| 7.46e-01 | NA |\n", + "| 7.09e-01 | NA |\n", + "+-------------------------------------+--------------------------------+\n", + "\n", + "+--------------------------------------+\n", + "| in_silico_predictors.spliceai_ds_max |\n", + "+--------------------------------------+\n", + "| float32 |\n", + "+--------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+--------------------------------------+\n", + "\n", + "+------------------------------------------+-----------------------------+\n", + "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n", + "+------------------------------------------+-----------------------------+\n", + "| float64 | float64 |\n", + "+------------------------------------------+-----------------------------+\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+------------------------------------------+-----------------------------+\n", + "\n", + "+-------------------------------+-----------------------------------+\n", + "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n", + "+-------------------------------+-----------------------------------+\n", + "| float64 | float64 |\n", + "+-------------------------------+-----------------------------------+\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+-------------------------------+-----------------------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "22e6f759-a0ee-4e9c-8ca4-eb154cb08763", + "metadata": { + "tags": [] + }, + "source": [ + "### v4.1 Joint Frequency Hail Table" + ] + }, + { + "cell_type": "markdown", + "id": "0a569b77-d3d2-45a4-803a-1214c77e46f2", + "metadata": {}, + "source": [ + "The joint frequency Hail table includes frequency for the exomes, genomes, and the exomes+genomes. We have also added statistics for the combined exomes and genomes frequencies, more details on these stats can be found on our [Help](https://gnomad.broadinstitute.org/help/combined-freq-stats) page." + ] + }, + { + "cell_type": "markdown", + "id": "46d4fc43-609d-4a16-8a0a-ab1e870b5d3d", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c1c1fbb0-4ef9-4892-bd91-aae9985317a7", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + } + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='joint', version='4.1')" + ] + }, + { + "cell_type": "markdown", + "id": "163df47b-70de-4e1e-91be-a65c90cf2db5", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "750c0111-4566-4b86-8c08-18c504ff1a79", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'exomes_globals': struct {\n", + " freq_meta: array>, \n", + " freq_index_dict: dict, \n", + " freq_meta_sample_count: array, \n", + " faf_meta: array>, \n", + " faf_index_dict: dict, \n", + " age_distribution: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int32, \n", + " n_larger: int32\n", + " }\n", + " } \n", + " 'genomes_globals': struct {\n", + " freq_meta: array>, \n", + " freq_index_dict: dict, \n", + " freq_meta_sample_count: array, \n", + " faf_meta: array>, \n", + " faf_index_dict: dict, \n", + " age_distribution: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int32, \n", + " n_larger: int32\n", + " }\n", + " } \n", + " 'joint_globals': struct {\n", + " freq_meta: array>, \n", + " freq_index_dict: dict, \n", + " faf_meta: array>, \n", + " faf_index_dict: dict, \n", + " freq_meta_sample_count: array, \n", + " age_distribution: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int32, \n", + " n_larger: int32\n", + " }\n", + " } \n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'alleles': array \n", + " 'region_flags': struct {\n", + " fail_interval_qc: bool, \n", + " outside_broad_capture_region: bool, \n", + " outside_ukb_capture_region: bool, \n", + " outside_broad_calling_region: bool, \n", + " outside_ukb_calling_region: bool, \n", + " not_called_in_exomes: bool, \n", + " not_called_in_genomes: bool\n", + " } \n", + " 'exomes': struct {\n", + " filters: set, \n", + " freq: array, \n", + " faf: array, \n", + " grpmax: struct {\n", + " AC: int32, \n", + " AF: float64, \n", + " AN: int32, \n", + " homozygote_count: int64, \n", + " gen_anc: str\n", + " }, \n", + " fafmax: struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " }, \n", + " histograms: struct {\n", + " qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " raw_qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " age_hists: struct {\n", + " age_hist_het: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " age_hist_hom: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }\n", + " }\n", + " } \n", + " 'genomes': struct {\n", + " filters: set, \n", + " freq: array, \n", + " faf: array, \n", + " grpmax: struct {\n", + " AC: int32, \n", + " AF: float64, \n", + " AN: int32, \n", + " homozygote_count: int32, \n", + " gen_anc: str\n", + " }, \n", + " fafmax: struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " }, \n", + " histograms: struct {\n", + " qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " raw_qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " age_hists: struct {\n", + " age_hist_het: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " age_hist_hom: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }\n", + " }\n", + " } \n", + " 'joint': struct {\n", + " freq: array, \n", + " faf: array, \n", + " fafmax: struct {\n", + " faf95_max: float64, \n", + " faf95_max_gen_anc: str, \n", + " faf99_max: float64, \n", + " faf99_max_gen_anc: str\n", + " }, \n", + " grpmax: struct {\n", + " AC: int32, \n", + " AF: float64, \n", + " AN: int32, \n", + " homozygote_count: int32, \n", + " gen_anc: str\n", + " }, \n", + " histograms: struct {\n", + " qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " raw_qual_hists: struct {\n", + " gq_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_all: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " gq_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " dp_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " ab_hist_alt: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }, \n", + " age_hists: struct {\n", + " age_hist_het: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }, \n", + " age_hist_hom: struct {\n", + " bin_edges: array, \n", + " bin_freq: array, \n", + " n_smaller: int64, \n", + " n_larger: int64\n", + " }\n", + " }\n", + " }\n", + " } \n", + " 'freq_comparison_stats': struct {\n", + " contingency_table_test: array, \n", + " cochran_mantel_haenszel_test: struct {\n", + " p_value: float64, \n", + " chisq: float64\n", + " }, \n", + " stat_union: struct {\n", + " p_value: float64, \n", + " stat_test_name: str, \n", + " gen_ancs: array\n", + " }\n", + " } \n", + "----------------------------------------\n", + "Key: ['locus', 'alleles']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "d6843db4-9e8f-42f4-9178-fc945f61a827", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "477ed281-4ee9-4799-b5b6-e6a0c528fa9a", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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gen_ancs
locus<GRCh38>array<str>boolboolboolboolboolboolboolset<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>int32float64int32int64strfloat64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64set<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>int32float64int32int32strfloat64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>float64strfloat64strint32float64int32int32strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{p_value: float64, odds_ratio: float64}>float64float64float64strarray<str>
chr1:10031["T","C"]NATrueTrueTrueTrueTrueFalseNA[(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA)]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA{"AC0","AS_VQSR"}[(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e+00,4326,0),(0,0.00e+00,192,0),(0,0.00e+00,782,0),(0,0.00e+00,352,0),(0,0.00e+00,1550,0),(0,0.00e+00,14642,0),(0,0.00e+00,1712,0),(0,0.00e+00,1120,0),(0,0.00e+00,6420,0),(0,0.00e+00,29308,0),(0,0.00e+00,27334,0),(0,0.00e+00,14998,0),(0,0.00e+00,1060,0),(0,0.00e+00,102,0),(0,0.00e+00,402,0),(0,0.00e+00,154,0),(0,0.00e+00,814,0),(0,0.00e+00,7978,0),(0,0.00e+00,770,0),(0,0.00e+00,260,0),(0,0.00e+00,2770,0),(0,0.00e+00,10548,0),(0,0.00e+00,3266,0),(0,0.00e+00,90,0),(0,0.00e+00,380,0),(0,0.00e+00,198,0),(0,0.00e+00,736,0),(0,0.00e+00,6664,0),(0,0.00e+00,942,0),(0,0.00e+00,860,0),(0,0.00e+00,3650,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANANANANANA[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e+00,4326,0),(0,0.00e+00,192,0),(0,0.00e+00,782,0),(0,0.00e+00,352,0),(0,0.00e+00,1550,0),(0,0.00e+00,14642,0),(0,0.00e+00,1712,0),(0,0.00e+00,1120,0),(0,0.00e+00,6420,0),(0,0.00e+00,29308,0),(0,0.00e+00,27334,0),(0,0.00e+00,14998,0),(0,0.00e+00,1060,0),(0,0.00e+00,102,0),(0,0.00e+00,402,0),(0,0.00e+00,154,0),(0,0.00e+00,814,0),(0,0.00e+00,7978,0),(0,0.00e+00,770,0),(0,0.00e+00,260,0),(0,0.00e+00,2770,0),(0,0.00e+00,10548,0),(0,0.00e+00,3266,0),(0,0.00e+00,90,0),(0,0.00e+00,380,0),(0,0.00e+00,198,0),(0,0.00e+00,736,0),(0,0.00e+00,6664,0),(0,0.00e+00,942,0),(0,0.00e+00,860,0),(0,0.00e+00,3650,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,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showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+------------+-------------------------------+\n", + "| locus | alleles | region_flags.fail_interval_qc |\n", + "+---------------+------------+-------------------------------+\n", + "| locus | array | bool |\n", + "+---------------+------------+-------------------------------+\n", + "| chr1:10031 | [\"T\",\"C\"] | NA |\n", + "| chr1:10037 | [\"T\",\"C\"] | NA |\n", + "| chr1:10043 | [\"T\",\"C\"] | NA |\n", + "| chr1:10055 | [\"T\",\"C\"] | NA |\n", + "| chr1:10057 | [\"A\",\"C\"] | NA |\n", + "+---------------+------------+-------------------------------+\n", + "\n", + "+-------------------------------------------+\n", + "| region_flags.outside_broad_capture_region |\n", + "+-------------------------------------------+\n", + "| bool |\n", + "+-------------------------------------------+\n", + "| True |\n", + "| True |\n", + "| True |\n", + "| True |\n", + "| True |\n", + "+-------------------------------------------+\n", + "\n", + "+-----------------------------------------+\n", + "| region_flags.outside_ukb_capture_region |\n", + "+-----------------------------------------+\n", + "| bool |\n", + "+-----------------------------------------+\n", + "| True |\n", + "| True |\n", + "| True |\n", + "| True |\n", + "| True |\n", + "+-----------------------------------------+\n", + "\n", + "+-------------------------------------------+\n", + "| region_flags.outside_broad_calling_region |\n", + "+-------------------------------------------+\n", + "| bool |\n", + "+-------------------------------------------+\n", + "| True |\n", + "| True |\n", + "| True |\n", + "| True |\n", + "| True |\n", + "+-------------------------------------------+\n", + "\n", + "+-----------------------------------------+-----------------------------------+\n", + "| region_flags.outside_ukb_calling_region | region_flags.not_called_in_exomes |\n", + "+-----------------------------------------+-----------------------------------+\n", + "| bool | bool |\n", + "+-----------------------------------------+-----------------------------------+\n", + "| True | True |\n", + "| True | True |\n", + "| True | True |\n", + "| True | True |\n", + "| True | True |\n", + "+-----------------------------------------+-----------------------------------+\n", + "\n", + "+------------------------------------+----------------+\n", + "| region_flags.not_called_in_genomes | exomes.filters |\n", + "+------------------------------------+----------------+\n", + "| bool | set |\n", + "+------------------------------------+----------------+\n", + "| False | NA |\n", + "| False | NA |\n", + "| False | NA |\n", + "| False | NA |\n", + "| False | NA |\n", + "+------------------------------------+----------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| exomes.freq |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n", + "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n", + "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n", + "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n", + "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+-----------------------------------------------+------------------+\n", + "| exomes.faf | exomes.grpmax.AC |\n", + "+-----------------------------------------------+------------------+\n", + "| array | int32 |\n", + "+-----------------------------------------------+------------------+\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+-----------------------------------------------+------------------+\n", + "\n", + "+------------------+------------------+--------------------------------+\n", + "| exomes.grpmax.AF | exomes.grpmax.AN | exomes.grpmax.homozygote_count |\n", + "+------------------+------------------+--------------------------------+\n", + "| float64 | int32 | int64 |\n", + "+------------------+------------------+--------------------------------+\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "| NA | NA | NA |\n", + "+------------------+------------------+--------------------------------+\n", + "\n", + "+-----------------------+-------------------------+\n", + "| exomes.grpmax.gen_anc | exomes.fafmax.faf95_max |\n", + "+-----------------------+-------------------------+\n", + "| str | float64 |\n", + "+-----------------------+-------------------------+\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+-----------------------+-------------------------+\n", + "\n", + "+---------------------------------+-------------------------+\n", + "| exomes.fafmax.faf95_max_gen_anc | exomes.fafmax.faf99_max |\n", + "+---------------------------------+-------------------------+\n", + "| str | float64 |\n", + "+---------------------------------+-------------------------+\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "| NA | NA |\n", + "+---------------------------------+-------------------------+\n", + "\n", + "+---------------------------------+\n", + "| exomes.fafmax.faf99_max_gen_anc |\n", + "+---------------------------------+\n", + "| str |\n", + "+---------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+---------------------------------+\n", + "\n", + "+----------------------------------------------------+\n", + "| exomes.histograms.qual_hists.gq_hist_all.bin_edges |\n", + 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"+------------------------------------------+\n", + "| float64 |\n", + "+------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+------------------------------------------+\n", + "\n", + "+-------------------------------------------------+\n", + "| freq_comparison_stats.stat_union.stat_test_name |\n", + "+-------------------------------------------------+\n", + "| str |\n", + "+-------------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+-------------------------------------------------+\n", + "\n", + "+-------------------------------------------+\n", + "| freq_comparison_stats.stat_union.gen_ancs |\n", + "+-------------------------------------------+\n", + "| array |\n", + "+-------------------------------------------+\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "| NA |\n", + "+-------------------------------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "354d7a5e-07a2-4f33-a830-970877cd4d63", + "metadata": { + "tags": [] + }, + "source": [ + "## All sites allele numbers\n", + "\n", + "As part of gnomAD v4.1, we [released](https://gnomad.broadinstitute.org/data#v4-all-sites-allele-number) allele number across all callable sites in the gnomAD exomes and genomes. For more information, see our [v4.1 blog post](https://gnomad.broadinstitute.org/news/2024-04-gnomad-v4-1/#allele-numbers-across-all-possible-sites)." + ] + }, + { + "cell_type": "markdown", + "id": "81008401-eec4-4e95-9709-4781db066f7f", + "metadata": { + "tags": [] + }, + "source": [ + "### Exomes all sites allele number Hail Table" + ] + }, + { + "cell_type": "markdown", + "id": "f7f1c013-a013-4fde-a7e6-fcb18d8d8a5c", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "3b7c75d0-1eec-4b92-883e-410337b09c92", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + } + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='exomes', version='4.1', dataset=\"all_sites_an\")" + ] + }, + { + "cell_type": "markdown", + "id": "6868a2d1-6e62-492a-8086-822c910e8608", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f9c6d73b-7683-47fe-bf7d-2f5bef1d23d3", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'strata_meta': array> \n", + " 'strata_sample_count': array \n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'AN': array \n", + " 'outside_broad_capture_region': bool \n", + " 'outside_ukb_capture_region': bool \n", + " 'outside_broad_calling_region': bool \n", + " 'outside_ukb_calling_region': bool \n", + "----------------------------------------\n", + "Key: ['locus']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "626f20d9-43c1-4687-9b05-01d53115c168", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ee361058-54ae-4793-951b-1e0a6df6f685", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
locus
AN
outside_broad_capture_region
outside_ukb_capture_region
outside_broad_calling_region
outside_ukb_calling_region
locus<GRCh38>array<int64>boolboolboolbool
chr1:11719[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11720[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11721[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11722[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11723[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue

showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+\n", + "| locus |\n", + "+---------------+\n", + "| locus |\n", + "+---------------+\n", + "| chr1:11719 |\n", + "| chr1:11720 |\n", + "| chr1:11721 |\n", + "| chr1:11722 |\n", + "| chr1:11723 |\n", + "+---------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| AN |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n", + "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n", + "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n", + "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n", + "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+------------------------------+----------------------------+\n", + "| outside_broad_capture_region | outside_ukb_capture_region |\n", + "+------------------------------+----------------------------+\n", + "| bool | bool |\n", + "+------------------------------+----------------------------+\n", + "| True | True |\n", + "| True | True |\n", + "| True | True |\n", + "| True | True |\n", + "| True | True |\n", + "+------------------------------+----------------------------+\n", + "\n", + "+------------------------------+----------------------------+\n", + "| outside_broad_calling_region | outside_ukb_calling_region |\n", + "+------------------------------+----------------------------+\n", + "| bool | bool |\n", + "+------------------------------+----------------------------+\n", + "| False | True |\n", + "| False | True |\n", + "| False | True |\n", + "| False | True |\n", + "| False | True |\n", + "+------------------------------+----------------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "dc4a4f23-d754-4e31-8e59-f62f9be65942", + "metadata": { + "tags": [] + }, + "source": [ + "### Genomes all sites allele number Hail Table" + ] + }, + { + "cell_type": "markdown", + "id": "65cb8d93-c5ef-409b-9c51-7c282a63bdc2", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0bb38926-f803-4be5-852c-782023b387bb", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='genomes', version='4.1', dataset=\"all_sites_an\")" + ] + }, + { + "cell_type": "markdown", + "id": "7d5c2549-151c-4b99-bac3-23fd9024f114", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "64d2c64c-b533-433a-89e6-72d473bd6464", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " 'strata_meta': array> \n", + " 'strata_sample_count': array \n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'AN': array \n", + "----------------------------------------\n", + "Key: ['locus']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "140f66aa-83d4-4752-abcf-c674bf208194", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e2271a6e-16f6-48ca-805f-735e17a8f711", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
locus
AN
locus<GRCh38>array<int64>
chr1:10001[16,18232,0,0,4,0,0,6,0,6,0,0,8,8,0,0,0,0,4,0,0,0,0,0,2,4,0,0,2,4,0,0,0,0]
chr1:10002[78,32090,12,0,22,2,0,26,0,14,2,0,30,48,8,4,0,0,4,18,2,0,0,0,12,14,0,0,2,12,2,0,0,0]
chr1:10003[200,38154,48,0,48,6,4,28,6,48,6,6,102,98,38,10,0,0,8,40,6,0,0,4,14,14,2,4,26,22,6,0,2,4]
chr1:10004[948,62380,202,0,248,18,12,172,10,258,18,10,400,548,112,90,0,0,66,182,14,4,2,10,70,102,4,6,118,140,10,8,4,6]
chr1:10005[1774,70720,356,6,444,30,18,288,18,552,32,30,782,992,206,150,2,4,134,310,18,12,6,12,116,172,6,12,268,284,16,16,10,20]

showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+\n", + "| locus |\n", + "+---------------+\n", + "| locus |\n", + "+---------------+\n", + "| chr1:10001 |\n", + "| chr1:10002 |\n", + "| chr1:10003 |\n", + "| chr1:10004 |\n", + "| chr1:10005 |\n", + "+---------------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| AN |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [16,18232,0,0,4,0,0,6,0,6,0,0,8,8,0,0,0,0,4,0,0,0,0,0,2,4,0,0,2,4,0,0,0,0] |\n", + "| [78,32090,12,0,22,2,0,26,0,14,2,0,30,48,8,4,0,0,4,18,2,0,0,0,12,14,0,0,2,... |\n", + "| [200,38154,48,0,48,6,4,28,6,48,6,6,102,98,38,10,0,0,8,40,6,0,0,4,14,14,2,... |\n", + "| [948,62380,202,0,248,18,12,172,10,258,18,10,400,548,112,90,0,0,66,182,14,... |\n", + "| [1774,70720,356,6,444,30,18,288,18,552,32,30,782,992,206,150,2,4,134,310,... |\n", + "+------------------------------------------------------------------------------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + }, + { + "cell_type": "markdown", + "id": "1bf9f31f-34ff-4385-a80e-985cbb0acfe8", + "metadata": { + "tags": [] + }, + "source": [ + "## Coverage\n" + ] + }, + { + "cell_type": "markdown", + "id": "de70c319-787b-4d6c-9058-255a1137d81f", + "metadata": { + "tags": [] + }, + "source": [ + "### Exomes coverage Hail Table" + ] + }, + { + "cell_type": "markdown", + "id": "3278430c-4279-4d89-85e7-276184ec42b8", + "metadata": {}, + "source": [ + "#### Load the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "7f005af0-df6d-4a0a-a2e0-9cb4ff6782c2", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:08:59.197890Z", + "start_time": "2024-12-06T20:08:50.859108Z" + } + }, + "outputs": [], + "source": [ + "ht = get_gnomad_release(data_type='exomes', version='4.0', dataset=\"coverage\")" + ] + }, + { + "cell_type": "markdown", + "id": "128e58ce-c219-472a-88be-6babc2ba5a15", + "metadata": {}, + "source": [ + "#### Print the schema of the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6058bf38-4ce9-4d37-89a7-d11af0e4d9f1", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:00.032524Z", + "start_time": "2024-12-06T20:09:00.029271Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "Global fields:\n", + " None\n", + "----------------------------------------\n", + "Row fields:\n", + " 'locus': locus \n", + " 'mean': float64 \n", + " 'median_approx': int32 \n", + " 'total_DP': int64 \n", + " 'over_1': float64 \n", + " 'over_5': float64 \n", + " 'over_10': float64 \n", + " 'over_15': float64 \n", + " 'over_20': float64 \n", + " 'over_25': float64 \n", + " 'over_30': float64 \n", + " 'over_50': float64 \n", + " 'over_100': float64 \n", + "----------------------------------------\n", + "Key: ['locus']\n", + "----------------------------------------\n" + ] + } + ], + "source": [ + "ht.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "5969ab0c-7cee-4061-8740-8b82366ae806", + "metadata": { + "tags": [] + }, + "source": [ + "#### Show the first 5 variants in the Hail Table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "49fcf64b-1a91-4052-bb98-b329a0e8031b", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T20:09:20.949958Z", + "start_time": "2024-12-06T20:09:05.622171Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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chr1:118221.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118231.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00

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chr1:100022.10e+0118[0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,435,417,366,346,320,437,415,405,359,333,308,283,266,272,248,218,231,241,184,176,162,138,119,127,137,118,63,82,87,66,66,46,33,39,43,22,25,26,19,19,11,7,6,7,5,3,5,2,4,2,6,2,3,2,0,1,1,1,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]2.20e-012.15e-011.79e-011.39e-011.02e-017.60e-025.06e-024.83e-032.79e-05
chr1:100032.44e+0123[0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,658,649,590,548,486,679,656,640,571,533,491,439,398,412,404,349,383,360,298,263,242,207,182,186,194,159,118,123,116,96,96,67,59,61,64,34,33,34,31,30,15,12,11,13,10,7,7,3,7,5,10,3,3,5,0,2,2,1,0,0,2,1,1,1,4,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,4]2.62e-012.59e-012.41e-012.07e-011.59e-011.18e-017.77e-027.61e-035.58e-05
chr1:100042.43e+0123[0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1242,1181,1162,1083,966,845,1149,1088,1047,922,857,804,725,645,633,658,525,610,537,451,411,369,343,285,290,260,235,184,190,174,151,152,96,83,96,91,52,52,56,43,47,30,20,19,22,16,13,9,9,10,10,14,8,7,8,0,5,3,3,2,1,2,1,1,1,5,3,3,2,1,0,2,0,0,1,0,1,0,1,1,0,0,12]4.27e-014.24e-013.99e-013.43e-012.60e-011.87e-011.21e-011.20e-021.67e-04
chr1:100052.45e+0123[0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1401,1341,1300,1266,1105,966,1288,1243,1198,1068,976,905,842,725,728,740,600,678,613,515,464,414,396,338,324,300,268,213,210,198,175,165,113,100,108,102,61,58,61,50,53,35,22,22,27,22,15,11,12,10,13,14,10,8,9,1,6,4,6,5,3,3,4,2,2,5,3,7,3,1,0,2,0,1,2,2,2,0,1,1,0,0,17]4.83e-014.80e-014.56e-013.92e-012.96e-012.14e-011.38e-011.42e-022.37e-04

showing top 5 rows

\n" + ], + "text/plain": [ + "+---------------+----------+--------+\n", + "| locus | mean | median |\n", + "+---------------+----------+--------+\n", + "| locus | float64 | int32 |\n", + "+---------------+----------+--------+\n", + "| chr1:10001 | 1.93e+01 | 16 |\n", + "| chr1:10002 | 2.10e+01 | 18 |\n", + "| chr1:10003 | 2.44e+01 | 23 |\n", + "| chr1:10004 | 2.43e+01 | 23 |\n", + "| chr1:10005 | 2.45e+01 | 23 |\n", + "+---------------+----------+--------+\n", + "\n", + "+------------------------------------------------------------------------------+\n", + "| count_array |\n", + "+------------------------------------------------------------------------------+\n", + "| array |\n", + "+------------------------------------------------------------------------------+\n", + "| [0,20,65,141,226,328,386,442,415,385,292,198,362,367,337,273,289,266,241,... |\n", + "| [0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,... |\n", + "| [0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,65... |\n", + "| [0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1... |\n", + "| [0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1... |\n", + "+------------------------------------------------------------------------------+\n", + "\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "| over_1 | over_5 | over_10 | over_15 | over_20 | over_25 | over_30 |\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "| float32 | float32 | float32 | float32 | float32 | float32 | float32 |\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "| 1.25e-01 | 1.19e-01 | 9.12e-02 | 6.95e-02 | 5.15e-02 | 3.88e-02 | 2.62e-02 |\n", + "| 2.20e-01 | 2.15e-01 | 1.79e-01 | 1.39e-01 | 1.02e-01 | 7.60e-02 | 5.06e-02 |\n", + "| 2.62e-01 | 2.59e-01 | 2.41e-01 | 2.07e-01 | 1.59e-01 | 1.18e-01 | 7.77e-02 |\n", + "| 4.27e-01 | 4.24e-01 | 3.99e-01 | 3.43e-01 | 2.60e-01 | 1.87e-01 | 1.21e-01 |\n", + "| 4.83e-01 | 4.80e-01 | 4.56e-01 | 3.92e-01 | 2.96e-01 | 2.14e-01 | 1.38e-01 |\n", + "+----------+----------+----------+----------+----------+----------+----------+\n", + "\n", + "+----------+----------+\n", + "| over_50 | over_100 |\n", + "+----------+----------+\n", + "| float32 | float32 |\n", + "+----------+----------+\n", + "| 2.27e-03 | 0.00e+00 |\n", + "| 4.83e-03 | 2.79e-05 |\n", + "| 7.61e-03 | 5.58e-05 |\n", + "| 1.20e-02 | 1.67e-04 |\n", + "| 1.42e-02 | 2.37e-04 |\n", + "+----------+----------+\n", + "showing top 5 rows" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ht.show(5)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + }, + "toc": { + "base_numbering": 1, + "nav_menu": { + "height": "613.99px", + "width": "526.312px" + }, + "number_sections": false, + "sideBar": true, + "skip_h1_title": true, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": true, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "202.438px" + }, + "toc_section_display": true, + "toc_window_display": true + }, + "toc-autonumbering": false, + "toc-showcode": false, + "toc-showmarkdowntxt": true, + "toc-showtags": false, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/notebooks/toolbox_for_gnomad_users.ipynb similarity index 72% rename from gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb rename to gnomad_toolbox/notebooks/toolbox_for_gnomad_users.ipynb index 0e081fe..326a2a3 100644 --- a/gnomad_toolbox/use_cases/toolbox_for_gnomad_users.ipynb +++ b/gnomad_toolbox/notebooks/toolbox_for_gnomad_users.ipynb @@ -23,19 +23,17 @@ "If you want to import your own data to use other gnomAD notebooks, such as for ancestry inference (https://github.com/broadinstitute/gnomad_qc/blob/main/gnomad_qc/example_notebooks/ancestry_classification_using_gnomad_rf.ipynb), you may use Hail's `import_vcf` functions." ] }, - { - "cell_type": "markdown", - "id": "ff73954c", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": 1, "id": "e77d32b1", "metadata": { - "scrolled": true + "scrolled": true, + "ExecuteTime": { + "end_time": "2024-12-06T18:02:57.909455Z", + "start_time": "2024-12-06T18:02:56.316003Z" + } }, + "source": "import hail as hl", "outputs": [ { "data": { @@ -50,7 +48,7 @@ " \n", "
\n", " \n", - " Loading BokehJS ...\n", + " Loading BokehJS ...\n", "
\n" ] }, @@ -59,340 +57,14 @@ }, { "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - "const JS_MIME_TYPE = 'application/javascript';\n", - " const HTML_MIME_TYPE = 'text/html';\n", - " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " const CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " const script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " function drop(id) {\n", - " const view = Bokeh.index.get_by_id(id)\n", - " if (view != null) {\n", - " view.model.document.clear()\n", - " Bokeh.index.delete(view)\n", - " }\n", - " }\n", - "\n", - " const cell = handle.cell;\n", - "\n", - " const id = cell.output_area._bokeh_element_id;\n", - " const server_id = cell.output_area._bokeh_server_id;\n", - "\n", - " // Clean up Bokeh references\n", - " if (id != null) {\n", - " drop(id)\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd_clean, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " const id = msg.content.text.trim()\n", - " drop(id)\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd_destroy);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " const output_area = handle.output_area;\n", - " const output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " const bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " const script_attrs = bk_div.children[0].attributes;\n", - " for (let i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " const toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? 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\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"c9c511bf-e817-434d-8072-f4db620005e8\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"c9c511bf-e817-434d-8072-f4db620005e8\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n function drop(id) {\n const view = Bokeh.index.get_by_id(id)\n if (view != null) {\n view.model.document.clear()\n Bokeh.index.delete(view)\n }\n }\n\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n\n // Clean up Bokeh references\n if (id != null) {\n drop(id)\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim()\n drop(id)\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/miniconda3/lib/python3.11/site-packages/hailtop/aiocloud/aiogoogle/user_config.py:43: UserWarning:\n", - "\n", - "Reading spark-defaults.conf to determine GCS requester pays configuration. This is deprecated. Please use `hailctl config set gcs_requester_pays/project` and `hailctl config set gcs_requester_pays/buckets`.\n", - "\n", - "Setting default log level to \"WARN\".\n", - "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SPARKMONITOR_LISTENER: Started SparkListener for Jupyter Notebook\n", - "SPARKMONITOR_LISTENER: Port obtained from environment: 51311\n", - "SPARKMONITOR_LISTENER: Application Started: application_1730470703538_0002 ...Start Time: 1730485367380\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Running on Apache Spark version 3.5.0\n", - "SparkUI available at http://qh1-m.c.broad-mpg-gnomad.internal:39033\n", - "Welcome to\n", - " __ __ <>__\n", - " / /_/ /__ __/ /\n", - " / __ / _ `/ / /\n", - " /_/ /_/\\_,_/_/_/ version 0.2.133-4c60fddb171a\n", - "LOGGING: writing to /test_toolbox.log\n" - ] } ], - "source": [ - "import hail as hl\n", - "\n", - "hl.init(\n", - " log=\"/test_toolbox.log\",\n", - " tmp_dir=\"gs://gnomad-tmp-30day\",\n", - " )" - ] + "execution_count": 1 }, { "cell_type": "markdown", @@ -404,17 +76,16 @@ }, { "cell_type": "code", - "execution_count": 3, "id": "e69953f7", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T18:04:56.165634Z", + "start_time": "2024-12-06T18:04:55.603516Z" + } + }, + "source": "from gnomad_toolbox.load_data import get_gnomad_release", "outputs": [], - "source": [ - "from gnomad_toolbox.modules.filter_variant import get_variant_count, filter_by_interval, filter_by_gene_symbol, filter_by_csqs\n", - "from gnomad_toolbox.modules.import_data import get_ht_by_datatype_and_version\n", - "from gnomad_toolbox.modules.filter_variant import \n", - "from gnomad_toolbox.modules.extract_freq import extract_callstats_for_1anc_1variant, extract_callstats_for_multiple_ancs\n", - "from gnomad.resources.grch38.gnomad import coverage" - ] + "execution_count": 3 }, { "cell_type": "markdown", @@ -433,18 +104,1604 @@ "| genomes | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1 |\n", "| joint | 4.1 | N/A |\n", "\n", - "We use gnomAD v4.1 exomes to demonstrate for examples below. " + "We use gnomAD v4.1 exomes to demonstrate for examples below." ] }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Loading gnomAD v4.1 exomes sites Hail Table\n", + "id": "d1a4ae8933ba6421" + }, { "cell_type": "code", - "execution_count": 30, "id": "100cf576", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-06T18:08:08.225100Z", + "start_time": "2024-12-06T18:07:55.852971Z" + } + }, + "source": "ht = get_gnomad_release(data_type='exomes', version='4.1')", + "outputs": [ + { + "data": { + "text/plain": [ + "\u001B[?25l" + ], + "text/html": [ + "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
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+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "4720d6aa643c489bb768ba33f92dbc45"
+      }
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "\u001B[?25h"
+      ],
+      "text/html": [
+       "
\n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "data": {
+      "text/plain": [
+       "\u001B[?25l"
+      ],
+      "text/html": [
+       "
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+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
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+     "data": {
+      "text/plain": [
+       "\n",
+       "\u001B[?25h"
+      ],
+      "text/html": [
+       "
\n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 5
+  },
+  {
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')"
-   ]
+   "cell_type": "markdown",
+   "source": "## Print the schema of the Hail Table\n",
+   "id": "77d7a05e31c1f37a"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T18:14:53.139250Z",
+     "start_time": "2024-12-06T18:14:53.136478Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "ht.describe()",
+   "id": "95c14f2c8cc3e699",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'freq_meta': array> \n",
+      "    'freq_index_dict': dict \n",
+      "    'freq_meta_sample_count': array \n",
+      "    'faf_meta': array> \n",
+      "    'faf_index_dict': dict \n",
+      "    'age_distribution': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int32, \n",
+      "        n_larger: int32\n",
+      "    } \n",
+      "    'downsamplings': dict> \n",
+      "    'filtering_model': struct {\n",
+      "        filter_name: str, \n",
+      "        score_name: str, \n",
+      "        snv_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }, \n",
+      "        indel_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }, \n",
+      "        snv_training_variables: array, \n",
+      "        indel_training_variables: array\n",
+      "    } \n",
+      "    'inbreeding_coeff_cutoff': float64 \n",
+      "    'interval_qc_parameters': struct {\n",
+      "        per_platform: bool, \n",
+      "        all_platforms: bool, \n",
+      "        high_qual_cutoffs: dict>, \n",
+      "        min_platform_size: int32\n",
+      "    } \n",
+      "    'tool_versions': struct {\n",
+      "        cadd_version: str, \n",
+      "        revel_version: str, \n",
+      "        spliceai_version: str, \n",
+      "        pangolin_version: array, \n",
+      "        phylop_version: str, \n",
+      "        dbsnp_version: str, \n",
+      "        sift_version: str, \n",
+      "        polyphen_version: str\n",
+      "    } \n",
+      "    'vrs_versions': struct {\n",
+      "        vrs_schema_version: str, \n",
+      "        vrs_python_version: str, \n",
+      "        seqrepo_version: str\n",
+      "    } \n",
+      "    'vep_globals': struct {\n",
+      "        vep_version: str, \n",
+      "        vep_help: str, \n",
+      "        vep_config: str, \n",
+      "        gencode_version: str, \n",
+      "        mane_select_version: str\n",
+      "    } \n",
+      "    'frequency_README': str \n",
+      "    'date': str \n",
+      "    'version': str \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'alleles': array \n",
+      "    'freq': array \n",
+      "    'grpmax': struct {\n",
+      "        gnomad: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int64, \n",
+      "            gen_anc: str\n",
+      "        }, \n",
+      "        non_ukb: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int64, \n",
+      "            gen_anc: str\n",
+      "        }\n",
+      "    } \n",
+      "    'faf': array \n",
+      "    'fafmax': struct {\n",
+      "        gnomad: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }, \n",
+      "        non_ukb: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }\n",
+      "    } \n",
+      "    'a_index': int32 \n",
+      "    'was_split': bool \n",
+      "    'rsid': set \n",
+      "    'filters': set \n",
+      "    'info': struct {\n",
+      "        FS: float64, \n",
+      "        MQ: float64, \n",
+      "        MQRankSum: float64, \n",
+      "        QUALapprox: int64, \n",
+      "        QD: float64, \n",
+      "        ReadPosRankSum: float64, \n",
+      "        SB: array, \n",
+      "        SOR: float64, \n",
+      "        VarDP: int32, \n",
+      "        AS_FS: float64, \n",
+      "        AS_MQ: float64, \n",
+      "        AS_MQRankSum: float64, \n",
+      "        AS_pab_max: float64, \n",
+      "        AS_QUALapprox: int64, \n",
+      "        AS_QD: float64, \n",
+      "        AS_ReadPosRankSum: float64, \n",
+      "        AS_SB_TABLE: array, \n",
+      "        AS_SOR: float64, \n",
+      "        AS_VarDP: int32, \n",
+      "        singleton: bool, \n",
+      "        transmitted_singleton: bool, \n",
+      "        sibling_singleton: bool, \n",
+      "        omni: bool, \n",
+      "        mills: bool, \n",
+      "        monoallelic: bool, \n",
+      "        only_het: bool, \n",
+      "        AS_VQSLOD: float64, \n",
+      "        inbreeding_coeff: float64, \n",
+      "        vrs: struct {\n",
+      "            VRS_Allele_IDs: array, \n",
+      "            VRS_Starts: array, \n",
+      "            VRS_Ends: array, \n",
+      "            VRS_States: array\n",
+      "        }\n",
+      "    } \n",
+      "    'vep': struct {\n",
+      "        allele_string: str, \n",
+      "        end: int32, \n",
+      "        id: str, \n",
+      "        input: str, \n",
+      "        intergenic_consequences: array, \n",
+      "            impact: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        most_severe_consequence: str, \n",
+      "        motif_feature_consequences: array, \n",
+      "            high_inf_pos: str, \n",
+      "            impact: str, \n",
+      "            motif_feature_id: str, \n",
+      "            motif_name: str, \n",
+      "            motif_pos: int32, \n",
+      "            motif_score_change: float64, \n",
+      "            transcription_factors: array, \n",
+      "            strand: int32, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        regulatory_feature_consequences: array, \n",
+      "            impact: str, \n",
+      "            regulatory_feature_id: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        seq_region_name: str, \n",
+      "        start: int32, \n",
+      "        strand: int32, \n",
+      "        transcript_consequences: array, \n",
+      "            distance: int32, \n",
+      "            domains: array, \n",
+      "            exon: str, \n",
+      "            flags: str, \n",
+      "            gene_id: str, \n",
+      "            gene_pheno: int32, \n",
+      "            gene_symbol: str, \n",
+      "            gene_symbol_source: str, \n",
+      "            hgnc_id: str, \n",
+      "            hgvsc: str, \n",
+      "            hgvsp: str, \n",
+      "            hgvs_offset: int32, \n",
+      "            impact: str, \n",
+      "            intron: str, \n",
+      "            lof: str, \n",
+      "            lof_flags: str, \n",
+      "            lof_filter: str, \n",
+      "            lof_info: str, \n",
+      "            mane_select: str, \n",
+      "            mane_plus_clinical: str, \n",
+      "            mirna: array, \n",
+      "            protein_end: int32, \n",
+      "            protein_start: int32, \n",
+      "            protein_id: str, \n",
+      "            source: str, \n",
+      "            strand: int32, \n",
+      "            transcript_id: str, \n",
+      "            tsl: int32, \n",
+      "            uniprot_isoform: array, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        variant_class: str\n",
+      "    } \n",
+      "    'vqsr_results': struct {\n",
+      "        AS_VQSLOD: float64, \n",
+      "        AS_culprit: str, \n",
+      "        positive_train_site: bool, \n",
+      "        negative_train_site: bool\n",
+      "    } \n",
+      "    'region_flags': struct {\n",
+      "        non_par: bool, \n",
+      "        lcr: bool, \n",
+      "        segdup: bool, \n",
+      "        fail_interval_qc: bool, \n",
+      "        outside_ukb_capture_region: bool, \n",
+      "        outside_broad_capture_region: bool\n",
+      "    } \n",
+      "    'allele_info': struct {\n",
+      "        variant_type: str, \n",
+      "        n_alt_alleles: int32, \n",
+      "        has_star: bool, \n",
+      "        allele_type: str, \n",
+      "        was_mixed: bool\n",
+      "    } \n",
+      "    'histograms': struct {\n",
+      "        qual_hists: struct {\n",
+      "            gq_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            gq_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            ab_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        raw_qual_hists: struct {\n",
+      "            gq_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            gq_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            ab_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        age_hists: struct {\n",
+      "            age_hist_het: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            age_hist_hom: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }\n",
+      "    } \n",
+      "    'in_silico_predictors': struct {\n",
+      "        cadd: struct {\n",
+      "            phred: float32, \n",
+      "            raw_score: float32\n",
+      "        }, \n",
+      "        revel_max: float64, \n",
+      "        spliceai_ds_max: float32, \n",
+      "        pangolin_largest_ds: float64, \n",
+      "        phylop: float64, \n",
+      "        sift_max: float64, \n",
+      "        polyphen_max: float64\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Key: ['locus', 'alleles']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Show the first 5 variants in the Hail Table\n",
+   "id": "a071f738b2c888e"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T18:16:28.722532Z",
+     "start_time": "2024-12-06T18:16:00.055352Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "ht.show(5)",
+   "id": "222de580c305d72a",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[?25l"
+      ],
+      "text/html": [
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Output()"
+      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "a239765de31846aab6def7bc857e326e"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "\u001B[?25h"
+      ],
+      "text/html": [
+       "
\n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "+---------------+----------------+\n",
+       "| locus         | alleles        |\n",
+       "+---------------+----------------+\n",
+       "| locus | array     |\n",
+       "+---------------+----------------+\n",
+       "| chr1:11994    | [\"T\",\"C\"]      |\n",
+       "| chr1:12016    | [\"G\",\"A\"]      |\n",
+       "| chr1:12060    | [\"CTGGAG\",\"C\"] |\n",
+       "| chr1:12074    | [\"T\",\"C\"]      |\n",
+       "| chr1:12102    | [\"G\",\"A\"]      |\n",
+       "+---------------+----------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0,NA,0,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
+       "| [(0,NA,0,0),(6,9.54e-06,628784,2),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
+       "| [(0,0.00e+00,4,0),(1,1.59e-06,628784,0),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
+       "| [(0,0.00e+00,4,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
+       "| [(0,0.00e+00,32,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0)... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "| was_split | rsid     | filters           | info.FS |  info.MQ |\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "|      bool | set | set          | float64 |  float64 |\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.60e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.56e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.45e+01 |\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|             NA |              60 | 3.00e+01 |                  NA |\n",
+       "|       0.00e+00 |             262 | 2.18e+01 |            6.74e-01 |\n",
+       "|       6.74e-01 |             123 | 3.08e+01 |           -1.15e+00 |\n",
+       "|       4.31e-01 |              99 | 1.24e+01 |           -2.53e-01 |\n",
+       "|             NA |              90 | 2.25e+01 |                  NA |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "| info.SB      | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "| array |  float64 |      int32 |    float64 |    float64 |\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "| [0,0,2,0]    | 2.30e+00 |          2 |         NA |   2.50e+01 |\n",
+       "| [2,0,10,0]   | 2.67e+00 |         12 |         NA |   2.50e+01 |\n",
+       "| [1,0,3,0]    | 1.61e+00 |          4 |         NA |   2.60e+01 |\n",
+       "| [4,0,4,0]    | 6.93e-01 |          8 |         NA |   2.56e+01 |\n",
+       "| [0,0,4,0]    | 3.26e+00 |          4 |         NA |   2.45e+01 |\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|                NA |              NA |                 60 |   3.00e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |                262 |   2.18e+01 |\n",
+       "|          6.74e-01 |        6.25e-01 |                123 |   3.08e+01 |\n",
+       "|          4.31e-01 |        6.87e-01 |                 99 |   1.24e+01 |\n",
+       "|                NA |              NA |                 90 |   2.25e+01 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE | info.AS_SOR | info.AS_VarDP |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "|                float64 | array     |     float64 |         int32 |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "|                     NA | [0,0,2,0]        |    2.30e+00 |             2 |\n",
+       "|               6.74e-01 | [2,0,10,0]       |    2.67e+00 |            12 |\n",
+       "|              -1.15e+00 | [1,0,3,0]        |    1.61e+00 |             4 |\n",
+       "|              -2.53e-01 | [4,0,4,0]        |    6.93e-01 |             8 |\n",
+       "|                     NA | [0,0,4,0]        |    3.26e+00 |             4 |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           bool |                       bool |                   bool |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|     False |      False |            False |         False |      -5.25e+00 |\n",
+       "|     False |      False |            False |         False |      -2.75e+00 |\n",
+       "|     False |      False |            False |         False |      -2.22e+00 |\n",
+       "|     False |      False |            False |         False |      -2.18e+00 |\n",
+       "|     False |      False |            False |         False |      -2.86e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|              1.00e+00 |\n",
+       "|              6.67e-01 |\n",
+       "|             -1.59e-06 |\n",
+       "|              1.00e+00 |\n",
+       "|              1.00e+00 |\n",
+       "+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.ps4-9woXy7o4rS39i8hDK_cUPBa-UcyP\",\"ga4gh:VA.nHlWYJXgiuvXrLAxQs... |\n",
+       "| [\"ga4gh:VA.VFxwcI4knOzk6SHzS2qowyDGnkG3mfEH\",\"ga4gh:VA.ctgP7qNjQAGjI2eTDo... |\n",
+       "| [\"ga4gh:VA.P573ZtUtAaRcceE7NLanEyynSefvcAPL\",\"ga4gh:VA.CHmk9uDiHW2LIHndZW... |\n",
+       "| [\"ga4gh:VA.neBeBT28ISe_1-yKPFsYxntP2jz1pj7E\",\"ga4gh:VA.1RPHSwBHNUwoECJ9VV... |\n",
+       "| [\"ga4gh:VA.ZXRhPWtip8HseOMWpCnaja0-ATo8hLE1\",\"ga4gh:VA.Qi95g6E8nt6DaqTGpH... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts | info.vrs.VRS_Ends | info.vrs.VRS_States |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| array        | array      | array          |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| [11993,11993]       | [11994,11994]     | [\"T\",\"C\"]           |\n",
+       "| [12015,12015]       | [12016,12016]     | [\"G\",\"A\"]           |\n",
+       "| [12059,12060]       | [12065,12071]     | [\"CTGGAG\",\"TGGAGT\"] |\n",
+       "| [12073,12073]       | [12074,12074]     | [\"T\",\"C\"]           |\n",
+       "| [12101,12101]       | [12102,12102]     | [\"G\",\"A\"]           |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "| vep.allele_string | vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "| str               |   int32 | str    | str                            |\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "| \"T/C\"             |   11994 | \".\"    | \"chr1\t11994\t.\tT\tC\t.\t.\tGT\"      |\n",
+       "| \"G/A\"             |   12016 | \".\"    | \"chr1\t12016\t.\tG\tA\t.\t.\tGT\"      |\n",
+       "| \"TGGAG/-\"         |   12065 | \".\"    | \"chr1\t12060\t.\tCTGGAG\tC\t.\t.\tGT\" |\n",
+       "| \"T/C\"             |   12074 | \".\"    | \"chr1\t12074\t.\tT\tC\t.\t.\tGT\"      |\n",
+       "| \"G/A\"             |   12102 | \".\"    | \"chr1\t12102\t.\tG\tA\t.\t.\tGT\"      |\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| vep.most_severe_consequence          |\n",
+       "+--------------------------------------+\n",
+       "| str                                  |\n",
+       "+--------------------------------------+\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "| \"splice_donor_5th_base_variant\"      |\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,2,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq   |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [314391,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [314388,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [314380,0,0,0,11,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]  |\n",
+       "| [314369,1,0,0,20,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [314323,1,0,0,54,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq     |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                       |\n",
+       "+----------------------------------------------------+\n",
+       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308833,5557,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308823,5567,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308789,5585,16,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]      |\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        1.26e+01 |\n",
+       "|                                          0 |                        1.60e+01 |\n",
+       "|                                          0 |                        8.52e+00 |\n",
+       "|                                          0 |                        1.52e+01 |\n",
+       "|                                          0 |                        4.42e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            1.08e+00 |                             NA |\n",
+       "|                            1.54e+00 |                             NA |\n",
+       "|                            7.07e-01 |                             NA |\n",
+       "|                            1.41e+00 |                             NA |\n",
+       "|                            3.11e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                -1.10e-01 |                    1.09e+00 |\n",
+       "|                                -7.00e-02 |                    6.55e+00 |\n",
+       "|                                -9.00e-02 |                   -4.41e+00 |\n",
+       "|                                -4.00e-02 |                    6.01e+00 |\n",
+       "|                                -8.00e-02 |                    1.38e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ],
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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showing top 5 rows
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 9
   },
   {
    "cell_type": "markdown",
diff --git a/requirements.txt b/requirements.txt
index 2900720..32706b5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,10 @@
 # We're using the main branch of gnomad_method on github rather than the pip version
 git+https://github.com/broadinstitute/gnomad_methods@main
 hail
+jupyter
+jupyter_contrib_nbextensions
+jupyter_nbextensions_configurator
+jupyterlab
+nodejs
+npm
+jupyter_bokeh

From 095a234b196f57ead2a0c6cb8d2ac5bbb102fc55 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Mon, 9 Dec 2024 10:50:03 -0700
Subject: [PATCH 025/121] - Restructure files - Add a description of the repo
 structure to the README - Add some potential requirements - Update the
 documentation and make sure it works - Create a notebook specific to loading
 gnomAD release data and just showing what each dataset looks like.

---
 gnomad_toolbox/{modules => analysis}/__init__.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename gnomad_toolbox/{modules => analysis}/__init__.py (100%)

diff --git a/gnomad_toolbox/modules/__init__.py b/gnomad_toolbox/analysis/__init__.py
similarity index 100%
rename from gnomad_toolbox/modules/__init__.py
rename to gnomad_toolbox/analysis/__init__.py

From 5fe3010853ccc1ec9cf730ade73c86ed508b151e Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Mon, 9 Dec 2024 14:58:29 -0700
Subject: [PATCH 026/121] - Modifications to support setting a default
 data_type and version - Addition of variant.py to store some functions that
 can also be used by frequency based filtering - Changes to frequency
 filtering to use new default settings if not passed a ht

---
 README.md                                     |   2 +-
 gnomad_toolbox/filtering/frequency.py         | 131 ++++++++-------
 gnomad_toolbox/filtering/interval.py          |  84 ---------
 gnomad_toolbox/filtering/variant.py           | 159 ++++++++++++++++++
 gnomad_toolbox/load_data.py                   | 159 ++++++++++++++----
 .../notebooks/intro_to_release_data.ipynb     |  22 +--
 6 files changed, 373 insertions(+), 184 deletions(-)
 delete mode 100644 gnomad_toolbox/filtering/interval.py
 create mode 100644 gnomad_toolbox/filtering/variant.py

diff --git a/README.md b/README.md
index 52bdd94..76ef6da 100644
--- a/README.md
+++ b/README.md
@@ -12,8 +12,8 @@ ggnomad_toolbox/
 │   ├── constraint.py    # Functions to filter constraint metrics (e.g., observed/expected ratios).
 │   ├── coverage.py      # Functions to filter variants or regions based on coverage thresholds.
 │   ├── frequency.py     # Functions to filter variants by allele frequency thresholds.
-│   ├── interval.py      # Functions to filter variants within specified genomic intervals and genes.
 │   ├── pext.py          # Functions to filter variants using predicted expression (pext) scores.
+|   ├── variant.py       # Functions to filter to a specific variant or set of variants.
 │   ├── vep.py           # Functions to filter variants based on VEP (Variant Effect Predictor) annotations.
 │
 ├── analysis/
diff --git a/gnomad_toolbox/filtering/frequency.py b/gnomad_toolbox/filtering/frequency.py
index 1c028a3..2adf1c9 100644
--- a/gnomad_toolbox/filtering/frequency.py
+++ b/gnomad_toolbox/filtering/frequency.py
@@ -5,35 +5,28 @@
 import hail as hl
 from gnomad.utils.filtering import filter_arrays_by_meta
 
+from gnomad_toolbox.filtering.variant import get_single_variant
+from gnomad_toolbox.load_data import _get_gnomad_release
 
-def extract_callstats_for_1anc_1variant(
-    ht: hl.Table, gen_anc: str, contig: str, position: int, alleles: List[str]
+
+def get_callstats_for_multiple_ancestries(
+    gen_ancs: List[str],
+    **kwargs,
 ) -> hl.Table:
     """
-    Extract callstats for a specific ancestry group and single variant.
+    Extract callstats for specified ancestry groups.
 
-    :param ht: Input Hail Table with variant data.
-    :param gen_anc: Genetic ancestry group (e.g., 'afr', 'nfe').
-    :param contig: Chromosome of the variant.
-    :param position: Variant position.
-    :param alleles: List of alleles for the variant (e.g., ['A', 'T']).
-    :return: Filtered Table with callstats for the specified group.
+    :param gen_ancs: List of genetic ancestry groups (e.g., 'afr', 'amr', 'asj', 'eas',
+        'fin', 'nfe', 'oth', 'sas').
+    :param kwargs: Keyword arguments to pass to _get_gnomad_release.
+    :return: Table with callstats for the given ancestry groups and variant.
     """
-    # Filter to the variant of interest
-    ht = ht.filter(
-        (ht.locus.contig == contig)
-        & (ht.locus.position == position)
-        & (ht.alleles == alleles)
-    )
-
-    # Check if the variant exists
-    if ht.count() == 0:
-        hl.utils.warning(
-            f"No variant found at {contig}:{position} with alleles {alleles}"
-        )
+    # Load the Hail Table if not provided
+    ht = _get_gnomad_release(dataset="variant", **kwargs)
 
-    # Format gen_anc to lowercase and filter arrays by metadata
-    items_to_filter = {"gen_anc": [gen_anc.lower()], "group": ["adj"]}
+    # Format gen_ancs to lowercase and filter arrays by metadata.
+    gen_ancs = [gen_anc.lower() for gen_anc in gen_ancs]
+    items_to_filter = {"gen_anc": gen_ancs, "group": ["adj"]}
     freq_meta, array_exprs = filter_arrays_by_meta(
         ht.freq_meta,
         {
@@ -45,51 +38,75 @@ def extract_callstats_for_1anc_1variant(
         combine_operator="and",
         exact_match=True,
     )
-    # Select frequency for ancestry group
     ht = ht.select(
-        **{
-            gen_anc: array_exprs["freq"][i]
-            for i, gen_anc in enumerate([gen_anc.lower()])
-        }
+        "filters",
+        **{gen_anc: array_exprs["freq"][i] for i, gen_anc in enumerate(gen_ancs)},
     )
-    ht = ht.annotate_globals(
+
+    # Select a subset of the globals.
+    ht = ht.select_globals(
+        "date",
+        "version",
         freq_meta=freq_meta,
         freq_meta_sample_count=array_exprs["freq_meta_sample_count"],
     )
+
     return ht
 
 
-def extract_callstats_for_multiple_ancs(
-    ht: hl.Table,
-    gen_ancs: List[str],
+def get_callstats_for_single_ancestry(
+    gen_anc: str,
+    **kwargs,
 ) -> hl.Table:
     """
-    Extract callstats for multiple genetic ancestry groups.
+    Extract callstats for a specific ancestry group and single variant.
 
-    :param ht: Input Table.
-    :param gen_ancs: List of Ancestry Groups (e.g., 'afr', 'amr', 'asj', 'eas', 'fin', 'nfe',
-        'oth', 'sas').
-    :return: Table with callstats for the given groups.
+    :param gen_anc: Genetic ancestry group (e.g., 'afr', 'amr', 'asj', 'eas', 'fin',
+        'nfe', 'oth', 'sas').
+    :param kwargs: Keyword arguments to pass to _get_gnomad_release.
+    :return: Table with callstats for the given ancestry group.
     """
-    # Format the gen_ancs to lowercase if they're fed in as uppercase
-    gen_ancs = [gen_anc.lower() for gen_anc in gen_ancs]
-    items_to_filter = {"gen_anc": gen_ancs, "group": ["adj"]}
-    freq_meta, array_exprs = filter_arrays_by_meta(
-        ht.freq_meta,
-        {
-            **{a: ht[a] for a in ["freq"]},
-            "freq_meta_sample_count": ht.index_globals().freq_meta_sample_count,
-        },
-        items_to_filter=items_to_filter,
-        keep=True,
-        combine_operator="and",
-        exact_match=True,
-    )
-    ht = ht.select(
-        **{gen_anc: array_exprs["freq"][i] for i, gen_anc in enumerate(gen_ancs)}
-    )
-    ht = ht.annotate_globals(
-        freq_meta=freq_meta,
-        freq_meta_sample_count=array_exprs["freq_meta_sample_count"],
+    ht = get_callstats_for_multiple_ancestries([gen_anc], **kwargs)
+
+    # Select a subset of the globals.
+    ht = ht.select_globals(
+        "date",
+        "version",
+        sample_count=ht.freq_meta_sample_count[0],
     )
+
     return ht
+
+
+def get_single_variant_callstats_for_multiple_ancestries(
+    gen_ancs: List[str],
+    **kwargs,
+) -> hl.Table:
+    """
+    Extract callstats for specified ancestry groups and a single variant.
+
+    :param gen_ancs: List of genetic ancestry groups (e.g., 'afr', 'amr', 'asj', 'eas',
+        'fin', 'nfe', 'oth', 'sas').
+    :param kwargs: Keyword arguments to pass to get_single_variant.
+    :return: Table with callstats for the given ancestry groups and variant.
+    """
+    ht = get_single_variant(**kwargs)
+
+    return get_callstats_for_multiple_ancestries(gen_ancs, ht=ht)
+
+
+def get_single_variant_callstats_for_single_ancestry(
+    gen_anc: str,
+    **kwargs,
+) -> hl.Table:
+    """
+    Extract callstats for a specific ancestry group and single variant.
+
+    :param gen_anc: Genetic ancestry group (e.g., 'afr', 'amr', 'asj', 'eas', 'fin',
+        'nfe', 'oth', 'sas').
+    :param kwargs: Keyword arguments to pass to get_single_variant.
+    :return: Table with callstats for the given ancestry group.
+    """
+    ht = get_single_variant(**kwargs)
+
+    return get_callstats_for_single_ancestry(gen_anc, ht=ht)
diff --git a/gnomad_toolbox/filtering/interval.py b/gnomad_toolbox/filtering/interval.py
deleted file mode 100644
index 0a3b810..0000000
--- a/gnomad_toolbox/filtering/interval.py
+++ /dev/null
@@ -1,84 +0,0 @@
-"""Functions to filter the gnmoAD sites HT by interval."""
-
-import hail as hl
-
-
-def filter_by_interval(ht: hl.Table, interval: str) -> hl.Table:
-    """
-    Filter variants by interval.
-
-    :param ht: Input Table.
-    :param interval: Interval string. Format has to be "chr:start-end", e.g., "1:1000-2000".
-    :return: Table with variants in the interval.
-    """
-    if ht.locus.dtype.reference_genome.name == "GRCh38":
-        interval = "chr" + interval
-    ht = hl.filter_intervals(
-        ht,
-        [
-            hl.parse_locus_interval(
-                interval,
-                reference_genome=(
-                    "GRCh38"
-                    if ht.locus.dtype.reference_genome.name == "GRCh38"
-                    else "GRCh37"
-                ),
-            )
-        ],
-    )
-    return ht
-
-
-def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table:
-    """
-    Filter variants in a gene.
-
-    .. note::
-           This function is to match the number of variants that you will get in the
-           gnomAD browser, which only focus on variants in "CDS" regions plus 75bp
-           up- and downstream. This is not the same as filtering by gene symbol with
-           our `filter_vep_transcript_csqs` function, which will include all variants.
-
-    :param ht: Input Table.
-    :param gene: Gene symbol.
-    :return: Table with variants in the gene.
-    """
-    # Make gene symbol uppercase
-    gene = gene.upper()
-
-    if ht.locus.dtype.reference_genome.name == "GRCh37":
-        gene_ht = hl.read_table(
-            "gs://gcp-public-data--gnomad/resources/grch37/browser/gnomad"
-            ".genes.GRCh37.GENCODEv19.ht"
-        )
-    else:
-        gene_ht = hl.read_table(
-            "gs://gcp-public-data--gnomad/resources/grch38/browser/gnomad"
-            ".genes.GRCh38.GENCODEv39.ht"
-        )
-
-    gene_ht = gene_ht.annotate(
-        cds_intervals=hl.array(
-            gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS")
-        ).map(
-            lambda exon: hl.locus_interval(
-                hl.if_else(
-                    gene_ht.interval.start.dtype.reference_genome.name == "GRCh38",
-                    "chr" + gene_ht.chrom,
-                    gene_ht.chrom,
-                ),
-                exon.start - 75,
-                exon.stop + 75,
-                reference_genome=gene_ht.interval.start.dtype.reference_genome,
-                includes_end=True,
-            )
-        )
-    )
-
-    intervals = gene_ht.filter(gene_ht.gencode_symbol == gene).cds_intervals.collect()[
-        0
-    ]
-
-    ht = hl.filter_intervals(ht, intervals)
-
-    return ht
diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
new file mode 100644
index 0000000..e6a1a9e
--- /dev/null
+++ b/gnomad_toolbox/filtering/variant.py
@@ -0,0 +1,159 @@
+"""Functions to filter the gnomAD sites HT to a specific set of variants."""
+
+from typing import Optional
+
+import hail as hl
+from gnomad.utils.reference_genome import get_reference_genome
+
+from gnomad_toolbox.load_data import _get_gnomad_release
+
+
+def get_single_variant(
+    variant: Optional[str] = None,
+    contig: Optional[str] = None,
+    position: Optional[int] = None,
+    ref: Optional[str] = None,
+    alt: Optional[str] = None,
+    **kwargs,
+) -> hl.Table:
+    """
+    Get a single variant from the gnomAD HT.
+
+    .. note::
+
+        One of `variant` or all of `contig`, `position`, `ref`, and `alt` must be
+        provided. If `variant` is provided, `contig`, `position`, `ref`, and `alt` are
+        ignored.
+
+    :param variant: Variant string in the format "chr12-235245-A-C" or
+        "chr12:235245:A:C". If provided, `contig`, `position`, `ref`, and `alt` are
+        ignored.
+    :param contig: Chromosome of the variant. Required if `variant` is not provided.
+    :param position: Variant position. Required if `variant` is not provided.
+    :param ref: Reference allele. Required if `variant` is not provided.
+    :param alt: Alternate allele. Required if `variant` is not provided.
+    :param kwargs: Additional arguments to pass to `_get_gnomad_release`.
+    :return: Table with the single variant.
+    """
+    if not variant and not all([contig, position, ref, alt]):
+        raise ValueError(
+            "Either `variant` must be provided or all of `contig`, `position`, `ref`, "
+            "and `alt`."
+        )
+
+    # Load the Hail Table if not provided
+    ht = _get_gnomad_release(dataset="variant", **kwargs)
+
+    # Determine the reference genome build for the ht.
+    build = get_reference_genome(ht.locus).name
+
+    # TODO: Move this to gnomad_methods.
+    # Parse the variant string if provided.
+    try:
+        if variant and ":" not in variant:
+            contig, position, ref, alt = variant.split("-")
+        if all([contig, position, ref, alt]):
+            variant = f"{contig}:{position}:{ref}:{alt}"
+        variant = hl.eval(hl.parse_variant(variant, reference_genome=build))
+    except ValueError:
+        raise ValueError(
+            f"Invalid variant format: {variant}. Expected format: chr12-235245-A-C "
+            f"or chr12:235245:A:C"
+        )
+
+    # Filter to the Locus of the variant of interest.
+    ht = hl.filter_intervals(
+        ht, [hl.interval(variant.locus, variant.locus, includes_end=True)]
+    )
+
+    # Filter to the variant of interest.
+    ht = ht.filter(ht.alleles == variant.alleles)
+
+    # Check if the variant exists.
+    if ht.count() == 0:
+        hl.utils.warning(
+            f"No variant found at {variant.locus} with alleles {variant.alleles}"
+        )
+
+    return ht
+
+
+def filter_by_interval(ht: hl.Table, interval: str) -> hl.Table:
+    """
+    Filter variants by interval.
+
+    :param ht: Input Table.
+    :param interval: Interval string. Format has to be "chr:start-end", e.g., "1:1000-2000".
+    :return: Table with variants in the interval.
+    """
+    if ht.locus.dtype.reference_genome.name == "GRCh38":
+        interval = "chr" + interval
+    ht = hl.filter_intervals(
+        ht,
+        [
+            hl.parse_locus_interval(
+                interval,
+                reference_genome=(
+                    "GRCh38"
+                    if ht.locus.dtype.reference_genome.name == "GRCh38"
+                    else "GRCh37"
+                ),
+            )
+        ],
+    )
+    return ht
+
+
+def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table:
+    """
+    Filter variants in a gene.
+
+    .. note::
+           This function is to match the number of variants that you will get in the
+           gnomAD browser, which only focus on variants in "CDS" regions plus 75bp
+           up- and downstream. This is not the same as filtering by gene symbol with
+           our `filter_vep_transcript_csqs` function, which will include all variants.
+
+    :param ht: Input Table.
+    :param gene: Gene symbol.
+    :return: Table with variants in the gene.
+    """
+    # Make gene symbol uppercase
+    gene = gene.upper()
+
+    if ht.locus.dtype.reference_genome.name == "GRCh37":
+        gene_ht = hl.read_table(
+            "gs://gcp-public-data--gnomad/resources/grch37/browser/gnomad"
+            ".genes.GRCh37.GENCODEv19.ht"
+        )
+    else:
+        gene_ht = hl.read_table(
+            "gs://gcp-public-data--gnomad/resources/grch38/browser/gnomad"
+            ".genes.GRCh38.GENCODEv39.ht"
+        )
+
+    gene_ht = gene_ht.annotate(
+        cds_intervals=hl.array(
+            gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS")
+        ).map(
+            lambda exon: hl.locus_interval(
+                hl.if_else(
+                    gene_ht.interval.start.dtype.reference_genome.name == "GRCh38",
+                    "chr" + gene_ht.chrom,
+                    gene_ht.chrom,
+                ),
+                exon.start - 75,
+                exon.stop + 75,
+                reference_genome=gene_ht.interval.start.dtype.reference_genome,
+                includes_end=True,
+            )
+        )
+    )
+
+    intervals = gene_ht.filter(gene_ht.gencode_symbol == gene).cds_intervals.collect()[
+        0
+    ]
+
+    ht = hl.filter_intervals(ht, intervals)
+
+    return ht
diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index b40dc10..5c39aff 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -1,5 +1,8 @@
 """Functions to import gnomAD data."""
 
+import functools
+from typing import Optional
+
 import gnomad.resources.grch37.gnomad as grch37_gnomad
 import gnomad.resources.grch38.gnomad as grch38_gnomad
 import hail as hl
@@ -32,50 +35,106 @@
 RELEASES = {
     dataset: {
         data_type: {
-            build: getattr(res, release_global, None)
+            build: (
+                None
+                if release_global.get(data_type) is None
+                else getattr(res, release_global.get(data_type), None)
+            )
             for build, res in GNOMAD_BY_BUILD.items()
         }
-        for data_type, release_global in data_types.items()
+        for data_type in DATA_TYPES
     }
-    for dataset, data_types in RELEASES_GLOBAL.items()
+    for dataset, release_global in RELEASES_GLOBAL.items()
 }
 
 
-def get_gnomad_release(
-    data_type: str = "exomes",
-    version: str = grch38_gnomad.CURRENT_EXOME_RELEASE,
+class GnomADSession:
+    """Class to manage the default data type and version for a gnomAD session."""
+
+    def __init__(self) -> None:
+        """
+        Initialize a gnomAD session.
+
+        The default data type is exomes and the default version is the current exome
+        release.
+
+        :return: None.
+        """
+        self.data_type = "exomes"
+        self.version = grch38_gnomad.CURRENT_EXOME_RELEASE
+
+    def set_default_data(
+        self,
+        data_type: Optional[str] = None,
+        version: Optional[str] = None,
+    ) -> None:
+        """
+        Set default data type and version.
+
+        :param data_type: Data type (exomes, genomes, or joint).
+        :param version: gnomAD version.
+        :return: None.
+        """
+        data_type = data_type or self.data_type
+        version = version or self.version
+
+        # Validate data type.
+        if data_type and data_type not in DATA_TYPES:
+            raise ValueError(
+                f"Data type {data_type} is invalid. Choose from 'exomes', 'genomes', "
+                f"or 'joint'."
+            )
+
+        # Get all possible versions.
+        possible_versions = functools.reduce(
+            lambda x, y: (x or []) + (y or []),
+            [
+                ds[dt][r]
+                for ds in RELEASES.values()
+                for dt in ([data_type] if data_type else DATA_TYPES)
+                for r in GNOMAD_BY_BUILD
+            ],
+        )
+
+        # Check version availability.
+        if version not in possible_versions:
+            raise ValueError(
+                f"Version {version} is not available"
+                f"{'' if data_type else f' for {data_type}'}. "
+            )
+
+        self.data_type = data_type
+        self.version = version
+
+
+# Global gnomad session object
+gnomad_session = GnomADSession()
+
+
+def _get_gnomad_release(
+    ht: hl.Table = None,
     dataset: str = "variant",
+    data_type: str = None,
+    version: str = None,
 ) -> hl.Table:
     """
-    Get gnomAD HT by dataset, data type,  and version.
-
-    .. table:: Available versions for each dataset and data type are (as of 2024-10-29)
-        :widths: auto
+    Get gnomAD HT using a Hail Table, specific parameters, or session defaults.
 
-        +--------------+-----------------+----------------------------------+----------------------+
-        | Dataset      | Data Type       | GRCh38 Versions                  | GRCh37 Versions      |
-        +==============+=================+==================================+======================+
-        | variant      | exomes          | 4.0, 4.1                         | 2.1, 2.1.1           |
-        |              +-----------------+----------------------------------+----------------------+
-        |              | genomes         | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1           |
-        |              +-----------------+----------------------------------+----------------------+
-        |              | joint           | 4.1                              | N/A                  |
-        +--------------+-----------------+----------------------------------+----------------------+
-        | coverage     | exomes          | 4.0                              | 2.1                  |
-        |              +-----------------+----------------------------------+----------------------+
-        |              | genomes         | 3.0.1                            | 2.1                  |
-        +--------------+-----------------+----------------------------------+----------------------+
-        | all_sites_an | exomes          | 4.1                              | N/A                  |
-        |              +-----------------+----------------------------------+----------------------+
-        |              | genomes         | 4.1                              | N/A                  |
-        +--------------+-----------------+----------------------------------+----------------------+
-
-    :param data_type: Data type (exomes, genomes, or joint). Default is "exomes".
-    :param version: gnomAD version. Default is the current exome release.
+    :param ht: Pre-loaded Hail Table. If provided, other parameters are ignored.
     :param dataset: Dataset type. One of "variant", "all_sites_an", "coverage". Default
-        is "variant".
+        is variant.
+    :param data_type: Data type (exomes, genomes, or joint). Default is session value.
+    :param version: gnomAD version. Default is session value.
     :return: Hail Table for requested dataset, data type, and version.
     """
+    # If a pre-loaded Hail Table is provided, return it directly.
+    if ht is not None:
+        return ht
+
+    # Use session defaults if parameters are not provided.
+    data_type = data_type or gnomad_session.data_type
+    version = version or gnomad_session.version
+
     # Get all releases for the given dataset.
     releases = RELEASES.get(dataset)
 
@@ -109,3 +168,41 @@ def get_gnomad_release(
             f"Available versions: GRCh38 - {data_type_releases['GRCh38']}, "
             f"GRCh37 - {data_type_releases['GRCh37']}."
         )
+
+
+def get_gnomad_release(
+    dataset: str = "variant",
+    data_type: Optional[str] = None,
+    version: Optional[str] = None,
+) -> hl.Table:
+    """
+    Get gnomAD HT by dataset, data type,  and version.
+
+    .. table:: Available versions for each dataset and data type are (as of 2024-10-29)
+        :widths: auto
+
+        +--------------+-----------------+----------------------------------+----------------------+
+        | Dataset      | Data Type       | GRCh38 Versions                  | GRCh37 Versions      |
+        +==============+=================+==================================+======================+
+        | variant      | exomes          | 4.0, 4.1                         | 2.1, 2.1.1           |
+        |              +-----------------+----------------------------------+----------------------+
+        |              | genomes         | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1           |
+        |              +-----------------+----------------------------------+----------------------+
+        |              | joint           | 4.1                              | N/A                  |
+        +--------------+-----------------+----------------------------------+----------------------+
+        | coverage     | exomes          | 4.0                              | 2.1                  |
+        |              +-----------------+----------------------------------+----------------------+
+        |              | genomes         | 3.0.1                            | 2.1                  |
+        +--------------+-----------------+----------------------------------+----------------------+
+        | all_sites_an | exomes          | 4.1                              | N/A                  |
+        |              +-----------------+----------------------------------+----------------------+
+        |              | genomes         | 4.1                              | N/A                  |
+        +--------------+-----------------+----------------------------------+----------------------+
+
+    :param data_type: Data type (exomes, genomes, or joint). Default is "exomes".
+    :param version: gnomAD version. Default is the current exome release.
+    :param dataset: Dataset type. One of "variant", "all_sites_an", "coverage". Default
+        is "variant".
+    :return: Hail Table for requested dataset, data type, and version.
+    """
+    return _get_gnomad_release(dataset=dataset, data_type=data_type, version=version)
diff --git a/gnomad_toolbox/notebooks/intro_to_release_data.ipynb b/gnomad_toolbox/notebooks/intro_to_release_data.ipynb
index c84c0d7..1e49925 100644
--- a/gnomad_toolbox/notebooks/intro_to_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/intro_to_release_data.ipynb
@@ -86,7 +86,7 @@
        "    \n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -258,7 +258,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\");\n",
+       "    const el = document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -364,7 +364,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -380,7 +380,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"e0412447-897b-4b15-8731-38893ba83dd5\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -406,7 +406,7 @@
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
       "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241208-2218-0.2.132-678e1f52b999.log\n"
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241209-1355-0.2.132-678e1f52b999.log\n"
      ]
     }
    ],
@@ -6254,7 +6254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "7f005af0-df6d-4a0a-a2e0-9cb4ff6782c2",
    "metadata": {
     "ExecuteTime": {
@@ -6277,7 +6277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "6058bf38-4ce9-4d37-89a7-d11af0e4d9f1",
    "metadata": {
     "ExecuteTime": {
@@ -6331,7 +6331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "49fcf64b-1a91-4052-bb98-b329a0e8031b",
    "metadata": {
     "ExecuteTime": {
@@ -6407,7 +6407,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "id": "a8d0be07-c35d-425a-b554-c86034e367fc",
    "metadata": {
     "ExecuteTime": {
@@ -6431,7 +6431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "id": "18afb20f-7429-4fe2-a6c5-73a22dcbdb76",
    "metadata": {
     "ExecuteTime": {
@@ -6485,7 +6485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 23,
    "id": "b27cb655-3abb-4501-bcc9-3f634db64591",
    "metadata": {
     "ExecuteTime": {

From 5330ea6a68009c11681f232f43403599c55d5f10 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 10 Dec 2024 22:38:45 -0700
Subject: [PATCH 027/121] More clean-up of notebooks and functions

---
 README.md                                     |    1 +
 gnomad_toolbox/analysis/general.py            |  124 +-
 gnomad_toolbox/filtering/frequency.py         |  100 +-
 gnomad_toolbox/filtering/variant.py           |  139 +-
 gnomad_toolbox/filtering/vep.py               |   39 +-
 gnomad_toolbox/load_data.py                   |    2 +
 .../notebooks/intro_to_release_data.ipynb     | 6622 -----------------
 .../notebooks/toolbox_for_gnomad_users.ipynb  | 3778 ----------
 8 files changed, 266 insertions(+), 10539 deletions(-)
 delete mode 100644 gnomad_toolbox/notebooks/intro_to_release_data.ipynb
 delete mode 100644 gnomad_toolbox/notebooks/toolbox_for_gnomad_users.ipynb

diff --git a/README.md b/README.md
index 76ef6da..08eecb8 100644
--- a/README.md
+++ b/README.md
@@ -24,6 +24,7 @@ ggnomad_toolbox/
 │   ├── intro_to_release_data.ipynb    # Jupyter notebook introducing the loading of gnomAD release data.
 ```
 
+# TODO: Add fully detailed info about how to install and open the notebooks.
 ## Getting started
 ### Install
 pip install -r requirements.txt
diff --git a/gnomad_toolbox/analysis/general.py b/gnomad_toolbox/analysis/general.py
index 7334f43..bcdde4a 100644
--- a/gnomad_toolbox/analysis/general.py
+++ b/gnomad_toolbox/analysis/general.py
@@ -1,42 +1,126 @@
 """Set of general functions for gnomAD analysis."""
 
+from typing import Dict, List, Optional, Tuple, Union
+
 import hail as hl
 
+from gnomad_toolbox.load_data import _get_gnomad_release
+
+
+# TODO: Modify this function in gnomad_methods.
+def freq_bin_expr(
+    freq_expr: Union[hl.expr.StructExpression, hl.expr.ArrayExpression],
+    index: int = 0,
+    ac_cutoffs: Optional[List[Union[int, Tuple[int, str]]]] = [
+        (0, "AC0"),
+        (1, "singleton"),
+        (2, "doubleton"),
+    ],
+    af_cutoffs: Optional[List[Union[float, Tuple[float, str]]]] = [
+        (1e-4, "0.01%"),
+        (1e-3, "0.1%"),
+        (1e-2, "1%"),
+        (1e-1, "10%"),
+    ],
+    upper_af: Optional[Union[float, Tuple[float, str]]] = (0.95, "95%"),
+) -> hl.expr.StringExpression:
+    """
+    Return frequency string annotations based on input AC or AF.
+
+    .. note::
+
+        - Default index is 0 because function assumes freq_expr was calculated with
+          `annotate_freq`.
+        - Frequency index 0 from `annotate_freq` is frequency for all pops calculated
+          on adj genotypes only.
+
+    :param freq_expr: Array of structs containing frequency information.
+    :param index: Which index of freq_expr to use for annotation. Default is 0.
+    :param ac_cutoffs:
+    :return: StringExpression containing bin name based on input AC or AF.
+    """
+    if isinstance(freq_expr, hl.expr.ArrayExpression):
+        freq_expr = freq_expr[index]
+
+    if ac_cutoffs and isinstance(ac_cutoffs[0], int):
+        ac_cutoffs = [(c, f"AC{c}") for c in ac_cutoffs]
+
+    if af_cutoffs and isinstance(af_cutoffs[0], float):
+        af_cutoffs = [(c, f"{c*100}%") for c in af_cutoffs]
 
-def get_variant_count(
-    ht: hl.Table,
-    afs: list[float] = [0.01, 0.001],
+    if isinstance(upper_af, float):
+        upper_af = (upper_af, f"{upper_af*100}%")
+
+    freq_bin_expr = hl.case().when(hl.is_missing(freq_expr.AC), "Missing")
+    prev_af = None
+    for ac, name in sorted(ac_cutoffs):
+        freq_bin_expr = freq_bin_expr.when(freq_expr.AC == ac, name)
+        prev_af = name
+
+    for af, name in sorted(af_cutoffs):
+        prev_af = "<" if prev_af is None else f"{prev_af} - "
+        freq_bin_expr = freq_bin_expr.when(freq_expr.AF < af, f"{prev_af}{name}")
+        prev_af = name
+
+    if upper_af:
+        freq_bin_expr = freq_bin_expr.when(
+            freq_expr.AF > upper_af[0], f">{upper_af[1]}"
+        )
+        default_af = "<" if prev_af is None else f"{prev_af} - "
+        default_af = f"{default_af}{upper_af[1]}"
+    else:
+        default_af = f">{prev_af}"
+
+    return freq_bin_expr.default(default_af)
+
+
+def get_variant_count_by_freq_bin(
+    af_cutoffs: List[float] = [0.001, 0.01],
     singletons: bool = False,
     doubletons: bool = False,
-) -> dict:
+    pass_only: bool = True,
+    **kwargs,
+) -> Dict[str, int]:
     """
-    Count variants with frequency <1%, <0.1%, and singletons (AC == 1).
+    Count variants by frequency bin.
+
+    By default, this function counts PASS variants that are AC0, AF < 0.01%, and
+    AF 0.01% - 0.1%.
+
+    The function can also include counts of singletons and doubletons, with or
+    without passing filters.
 
     .. note::
 
-        This function works for gnomAD exomes and genomes datasets, not yet for gnomAD
-        joint dataset, since the HT schema is slightly different.
+        This function works for gnomAD exomes and genomes data types, not yet for gnomAD
+        joint data type, since the HT schema is slightly different.
 
-    :param ht: Input Table.
-    :param afs: List of allele frequencies cutoffs.
+    :param af_cutoffs: List of allele frequencies cutoffs.
     :param singletons: Include singletons.
     :param doubletons: Include doubletons.
+    :param pass_only: Include only PASS variants.
+    :param kwargs: Keyword arguments to pass to _get_gnomad_release. Includes
+        'ht', 'data_type', and 'version'.
     :return: Dictionary with counts.
     """
-    counts = {}
+    # Load the Hail Table if not provided
+    ht = _get_gnomad_release(dataset="variant", **kwargs)
 
     # Filter to PASS variants.
-    ht = ht.filter(hl.len(ht.filters) == 0)
+    if pass_only:
+        ht = ht.filter(hl.len(ht.filters) == 0)
+
+    # Initialize allele count cutoffs with AC0.
+    ac_cutoffs = [(0, "AC0")]
+
     if singletons:
-        n_singletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 1))
-        counts["number of singletons"] = n_singletons
+        ac_cutoffs.append((1, "singletons"))
+
     if doubletons:
-        n_doubletons = ht.aggregate(hl.agg.count_where(ht.freq[0].AC == 2))
-        counts["number of doubletons"] = n_doubletons
+        ac_cutoffs.append((2, "doubletons"))
 
-    for af in afs:
-        n_variants = ht.aggregate(hl.agg.count_where(ht.freq[0].AF < af))
-        counts[f"number of variants with AF < {af}"] = n_variants
+    freq_expr = freq_bin_expr(
+        ht.freq, ac_cutoffs=ac_cutoffs, af_cutoffs=af_cutoffs, upper_af=None
+    )
 
-    # Count variants with frequency <1%, <0.1%, and singletons (AC == 1).
-    return counts
+    return ht.aggregate(hl.agg.counter(freq_expr))
diff --git a/gnomad_toolbox/filtering/frequency.py b/gnomad_toolbox/filtering/frequency.py
index 2adf1c9..c5a008b 100644
--- a/gnomad_toolbox/filtering/frequency.py
+++ b/gnomad_toolbox/filtering/frequency.py
@@ -1,6 +1,6 @@
 """Functions for filtering the gnomAD sites HT frequency data."""
 
-from typing import List
+from typing import List, Union
 
 import hail as hl
 from gnomad.utils.filtering import filter_arrays_by_meta
@@ -9,24 +9,34 @@
 from gnomad_toolbox.load_data import _get_gnomad_release
 
 
-def get_callstats_for_multiple_ancestries(
-    gen_ancs: List[str],
+def get_ancestry_callstats(
+    gen_ancs: Union[str, List[str]],
     **kwargs,
 ) -> hl.Table:
     """
-    Extract callstats for specified ancestry groups.
+    Extract callstats for specified ancestry group(s).
 
-    :param gen_ancs: List of genetic ancestry groups (e.g., 'afr', 'amr', 'asj', 'eas',
-        'fin', 'nfe', 'oth', 'sas').
+    :param gen_ancs: Genetic ancestry group(s) (e.g., 'afr', 'amr', 'asj', 'eas',
+        'fin', 'nfe', 'oth', 'sas'). Can be a single ancestry group or a list of
+        ancestry groups.
     :param kwargs: Keyword arguments to pass to _get_gnomad_release.
     :return: Table with callstats for the given ancestry groups and variant.
     """
     # Load the Hail Table if not provided
     ht = _get_gnomad_release(dataset="variant", **kwargs)
 
+    # Check if gen_ancs is a single ancestry group.
+    one_anc = isinstance(gen_ancs, str)
+
+    if one_anc:
+        gen_ancs = [gen_ancs]
+
     # Format gen_ancs to lowercase and filter arrays by metadata.
     gen_ancs = [gen_anc.lower() for gen_anc in gen_ancs]
-    items_to_filter = {"gen_anc": gen_ancs, "group": ["adj"]}
+    gen_anc_label = (
+        "gen_anc" if any(["gen_anc" in m for m in hl.eval(ht.freq_meta)]) else "pop"
+    )
+    items_to_filter = {gen_anc_label: gen_ancs, "group": ["adj"]}
     freq_meta, array_exprs = filter_arrays_by_meta(
         ht.freq_meta,
         {
@@ -40,73 +50,41 @@ def get_callstats_for_multiple_ancestries(
     )
     ht = ht.select(
         "filters",
-        **{gen_anc: array_exprs["freq"][i] for i, gen_anc in enumerate(gen_ancs)},
-    )
-
-    # Select a subset of the globals.
-    ht = ht.select_globals(
-        "date",
-        "version",
-        freq_meta=freq_meta,
-        freq_meta_sample_count=array_exprs["freq_meta_sample_count"],
+        **{
+            m[gen_anc_label]: array_exprs["freq"][i]
+            for i, m in enumerate(hl.eval(freq_meta))
+        },
     )
 
-    return ht
-
-
-def get_callstats_for_single_ancestry(
-    gen_anc: str,
-    **kwargs,
-) -> hl.Table:
-    """
-    Extract callstats for a specific ancestry group and single variant.
-
-    :param gen_anc: Genetic ancestry group (e.g., 'afr', 'amr', 'asj', 'eas', 'fin',
-        'nfe', 'oth', 'sas').
-    :param kwargs: Keyword arguments to pass to _get_gnomad_release.
-    :return: Table with callstats for the given ancestry group.
-    """
-    ht = get_callstats_for_multiple_ancestries([gen_anc], **kwargs)
-
     # Select a subset of the globals.
-    ht = ht.select_globals(
-        "date",
-        "version",
-        sample_count=ht.freq_meta_sample_count[0],
-    )
+    sample_count = array_exprs["freq_meta_sample_count"]
+    if one_anc:
+        sample_count = sample_count[0]
+    else:
+        sample_count = hl.struct(
+            **{
+                m[gen_anc_label]: sample_count[i]
+                for i, m in enumerate(hl.eval(freq_meta))
+            }
+        )
+    ht = ht.select_globals("date", "version", sample_count=sample_count)
 
     return ht
 
 
-def get_single_variant_callstats_for_multiple_ancestries(
-    gen_ancs: List[str],
+def get_single_variant_ancestry_callstats(
+    gen_ancs: Union[str, List[str]],
     **kwargs,
 ) -> hl.Table:
     """
-    Extract callstats for specified ancestry groups and a single variant.
+    Extract callstats for specified ancestry group(s) and a single variant.
 
-    :param gen_ancs: List of genetic ancestry groups (e.g., 'afr', 'amr', 'asj', 'eas',
-        'fin', 'nfe', 'oth', 'sas').
+    :param gen_ancs: Genetic ancestry group(s) (e.g., 'afr', 'amr', 'asj', 'eas',
+        'fin', 'nfe', 'oth', 'sas'). Can be a single ancestry group or a list of
+        ancestry groups.
     :param kwargs: Keyword arguments to pass to get_single_variant.
     :return: Table with callstats for the given ancestry groups and variant.
     """
     ht = get_single_variant(**kwargs)
 
-    return get_callstats_for_multiple_ancestries(gen_ancs, ht=ht)
-
-
-def get_single_variant_callstats_for_single_ancestry(
-    gen_anc: str,
-    **kwargs,
-) -> hl.Table:
-    """
-    Extract callstats for a specific ancestry group and single variant.
-
-    :param gen_anc: Genetic ancestry group (e.g., 'afr', 'amr', 'asj', 'eas', 'fin',
-        'nfe', 'oth', 'sas').
-    :param kwargs: Keyword arguments to pass to get_single_variant.
-    :return: Table with callstats for the given ancestry group.
-    """
-    ht = get_single_variant(**kwargs)
-
-    return get_callstats_for_single_ancestry(gen_anc, ht=ht)
+    return get_ancestry_callstats(gen_ancs, ht=ht)
diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index e6a1a9e..ec00bc7 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -1,6 +1,6 @@
 """Functions to filter the gnomAD sites HT to a specific set of variants."""
 
-from typing import Optional
+from typing import Optional, Union
 
 import hail as hl
 from gnomad.utils.reference_genome import get_reference_genome
@@ -78,81 +78,110 @@ def get_single_variant(
     return ht
 
 
-def filter_by_interval(ht: hl.Table, interval: str) -> hl.Table:
+def filter_by_intervals(
+    intervals: Union[str, list[str]],
+    **kwargs,
+) -> hl.Table:
     """
-    Filter variants by interval.
+    Filter variants by interval(s).
 
-    :param ht: Input Table.
-    :param interval: Interval string. Format has to be "chr:start-end", e.g., "1:1000-2000".
-    :return: Table with variants in the interval.
+    :param intervals: Interval string or list of interval strings. The interval string
+        format has to be "contig:start-end", e.g.,"1:1000-2000" (GRCh37) or
+        "chr1:1000-2000" (GRCh38).
+    :param kwargs: Arguments to pass to `_get_gnomad_release`.
+    :return: Table with variants in the interval(s).
     """
-    if ht.locus.dtype.reference_genome.name == "GRCh38":
-        interval = "chr" + interval
+    # Load the Hail Table if not provided
+    ht = _get_gnomad_release(dataset="variant", **kwargs)
+
+    # Determine the reference genome build for the ht.
+    build = get_reference_genome(ht.locus).name
+
+    if isinstance(intervals, str):
+        intervals = [intervals]
+
+    if build == "GRCh38" and any([not i.startswith("chr") for i in intervals]):
+        raise ValueError("Interval must start with 'chr' for GRCh38 reference genome.")
+
     ht = hl.filter_intervals(
-        ht,
-        [
-            hl.parse_locus_interval(
-                interval,
-                reference_genome=(
-                    "GRCh38"
-                    if ht.locus.dtype.reference_genome.name == "GRCh38"
-                    else "GRCh37"
-                ),
-            )
-        ],
+        ht, [hl.parse_locus_interval(i, reference_genome=build) for i in intervals]
     )
+
     return ht
 
 
-def filter_by_gene_symbol(ht: hl.Table, gene: str) -> hl.Table:
+def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.Table:
     """
     Filter variants in a gene.
 
     .. note::
+
            This function is to match the number of variants that you will get in the
-           gnomAD browser, which only focus on variants in "CDS" regions plus 75bp
-           up- and downstream. This is not the same as filtering by gene symbol with
-           our `filter_vep_transcript_csqs` function, which will include all variants.
+           gnomAD browser, which only focus on variants in "CDS" regions plus
+           75bp (default of `exon_padding_bp`) up- and downstream.
 
-    :param ht: Input Table.
     :param gene: Gene symbol.
+    :param exon_padding_bp: Number of base pairs to pad the CDS intervals. Default is
+        75bp.
+    :param kwargs: Arguments to pass to `_get_gnomad_release`.
     :return: Table with variants in the gene.
     """
+    # Load the Hail Table if not provided
+    ht = _get_gnomad_release(dataset="variant", **kwargs)
+
+    # Determine the reference genome build for the ht.
+    build = get_reference_genome(ht.locus).name
+
     # Make gene symbol uppercase
     gene = gene.upper()
 
-    if ht.locus.dtype.reference_genome.name == "GRCh37":
-        gene_ht = hl.read_table(
-            "gs://gcp-public-data--gnomad/resources/grch37/browser/gnomad"
-            ".genes.GRCh37.GENCODEv19.ht"
-        )
-    else:
-        gene_ht = hl.read_table(
-            "gs://gcp-public-data--gnomad/resources/grch38/browser/gnomad"
-            ".genes.GRCh38.GENCODEv39.ht"
-        )
-
-    gene_ht = gene_ht.annotate(
-        cds_intervals=hl.array(
-            gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS")
-        ).map(
-            lambda exon: hl.locus_interval(
-                hl.if_else(
-                    gene_ht.interval.start.dtype.reference_genome.name == "GRCh38",
-                    "chr" + gene_ht.chrom,
-                    gene_ht.chrom,
-                ),
-                exon.start - 75,
-                exon.stop + 75,
-                reference_genome=gene_ht.interval.start.dtype.reference_genome,
-                includes_end=True,
-            )
-        )
+    # TODO: Create a resource for this in gnomad_methods (is it different from our
+    #  current gencode resources?
+    # gene_ht = hl.read_table(
+    #    f"gs://gcp-public-data--gnomad/resources/{build.lower()}/browser/gnomad"
+    #    f".genes.{build}.GENCODEv{'19' if build == 'GRCh37' else '39'}.ht"
+    # )
+
+    # TODO: This actually takes a while to run locally for a single gene. Is there a
+    #  way to speed this up?
+
+    # Filter to the gene of interest.
+    # gene_ht = gene_ht.filter(gene_ht.gencode_symbol == gene)
+
+    # Get the CDS intervals for the gene.
+    # chrom_expr = hl.if_else(build == "GRCh38", "chr" + gene_ht.chrom, gene_ht.chrom)
+    # intervals = gene_ht.aggregate(
+    #    hl.agg.explode(
+    #        lambda exon: hl.agg.collect(
+    #            hl.locus_interval(
+    #                chrom_expr,
+    #                exon.start - 75,
+    #                exon.stop + 75,
+    #                reference_genome=build,
+    #                includes_end=True,
+    #            )
+    #        ),
+    #        gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS"),
+    #    )
+    # )
+
+    # TODO: Consider this alternative approach to get the intervals from gencode. That
+    #  is not too bad time wise
+
+    from gnomad.resources.grch38.reference_data import gencode
+
+    gencode_ht = gencode.ht()
+    gencode_ht = gencode_ht.filter(
+        (gencode_ht.gene_name == gene) & ((gencode_ht.feature == "CDS"))
     )
-
-    intervals = gene_ht.filter(gene_ht.gencode_symbol == gene).cds_intervals.collect()[
-        0
-    ]
+    intervals = hl.locus_interval(
+        gencode_ht.interval.start.contig,
+        gencode_ht.interval.start.position - exon_padding_bp,
+        gencode_ht.interval.end.position + exon_padding_bp,
+        includes_start=gencode_ht.interval.includes_start,
+        includes_end=gencode_ht.interval.includes_end,
+        reference_genome=build,
+    ).collect()
 
     ht = hl.filter_intervals(ht, intervals)
 
diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py
index f851598..a8b8793 100644
--- a/gnomad_toolbox/filtering/vep.py
+++ b/gnomad_toolbox/filtering/vep.py
@@ -5,19 +5,30 @@
 import hail as hl
 from gnomad.utils.vep import CSQ_CODING, LOF_CSQ_SET
 
+from gnomad_toolbox.load_data import _get_gnomad_release
+
+# TODO: I haven't looked over this function yet. Is there anything in gnomad_methods
+#  that could be used here? If not, is there anything here that should be moved to
+#  gnomad_methods?
+
 
 def filter_by_csqs(
-    ht: hl.Table, csqs: list[str], pass_filters: bool = True
+    csqs: list[str],
+    pass_filters: bool = True,
+    **kwargs,
 ) -> hl.Table:
     """
-    Filter variants by consequences.
+    Filter variants by VEP transcript consequences.
 
-    :param ht: Input Table.
     :param csqs: List of consequences to filter by. It can be specified as the
          categories on the browser: pLoF, Missense / Inframe indel, Synonymous, Other.
     :param pass_filters: Boolean if the variants pass the filters.
+    :param kwargs: Arguments to pass to _get_gnomad_release.
     :return: Table with variants with the specified consequences.
     """
+    # Load the Hail Table if not provided
+    ht = _get_gnomad_release(dataset="variant", **kwargs)
+
     missense_inframe = ["missense_variant", "inframe_insertion", "inframe_deletion"]
 
     filter_expr = []
@@ -62,3 +73,25 @@ def filter_by_csqs(
         ht = ht.filter(hl.len(ht.filters) == 0)
 
     return ht
+
+
+# TODO: The following was in one of the notebooks, and I think we should add a wrapper
+#  around this function to make it much simpler instead of using it in the notebook.
+
+# Filter to LOFTEE high-confidence variants for certain genes
+
+# In this example, we are filtering to variants in ASH1L that are LOFTEE high-confidence
+# (with no flags) in the MANE select transcript.
+
+# from gnomad.utils.vep import filter_vep_transcript_csqs
+# ht = get_gnomad_release(data_type='exomes', version='4.1')
+# ht = filter_vep_transcript_csqs(
+#    ht,
+#    synonymous=False,
+#    mane_select=True,
+#    genes=["ASH1L"],
+#    match_by_gene_symbol=True,
+#    additional_filtering_criteria=[lambda x: (x.lof == "HC") & hl.is_missing(x.lof_flags)],
+# )
+# ht.show()
+# ht.count()
diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index 5c39aff..d591bed 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -48,6 +48,8 @@
 }
 
 
+# TODO: Are there other things we want to store in a session? Like a PASS variants only
+#  variable?
 class GnomADSession:
     """Class to manage the default data type and version for a gnomAD session."""
 
diff --git a/gnomad_toolbox/notebooks/intro_to_release_data.ipynb b/gnomad_toolbox/notebooks/intro_to_release_data.ipynb
deleted file mode 100644
index 1e49925..0000000
--- a/gnomad_toolbox/notebooks/intro_to_release_data.ipynb
+++ /dev/null
@@ -1,6622 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "853c94b9",
-   "metadata": {},
-   "source": [
-    "# Introduction to gnomAD Hail release files\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5cf83cfe-0fce-40ae-add7-c9f2c20c1e85",
-   "metadata": {},
-   "source": [
-    "In this notebook we will explore all of the available [gnomAD v4 release files](https://gnomad.broadinstitute.org/data#v4) that are in Hail formats."
-   ]
-  },
-  {
-   "attachments": {
-    "afcd4ecc-4e90-464b-91c7-9cd80b0e92ba.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "88ddfe98-92b3-4c9c-906d-84d4835d2bf1",
-   "metadata": {},
-   "source": [
-    "![image.png](attachment:afcd4ecc-4e90-464b-91c7-9cd80b0e92ba.png)"
-   ]
-  },
-  {
-   "attachments": {
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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "f97431a4-78a0-473f-99a0-67eeefb8bbc6",
-   "metadata": {},
-   "source": [
-    "![image.png](attachment:2f957256-5d08-40e1-b77c-5faa4f771fb2.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e609a46",
-   "metadata": {
-    "tags": [],
-    "toc": true
-   },
-   "source": [
-    "
Table of Contents
\n",
-    "
"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e713032",
-   "metadata": {},
-   "source": [
-    "## Import modules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "e69953f7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:40.044647Z",
-     "start_time": "2024-12-06T20:08:37.758061Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "    \n",
-       "    
\n",
-       "        \n",
-       "        Loading BokehJS ...\n",
-       "    
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "(function(root) {\n",
-       "  function now() {\n",
-       "    return new Date();\n",
-       "  }\n",
-       "\n",
-       "  const force = true;\n",
-       "\n",
-       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
-       "    root._bokeh_onload_callbacks = [];\n",
-       "    root._bokeh_is_loading = undefined;\n",
-       "  }\n",
-       "\n",
-       "const JS_MIME_TYPE = 'application/javascript';\n",
-       "  const HTML_MIME_TYPE = 'text/html';\n",
-       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
-       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
-       "\n",
-       "  /**\n",
-       "   * Render data to the DOM node\n",
-       "   */\n",
-       "  function render(props, node) {\n",
-       "    const script = document.createElement(\"script\");\n",
-       "    node.appendChild(script);\n",
-       "  }\n",
-       "\n",
-       "  /**\n",
-       "   * Handle when an output is cleared or removed\n",
-       "   */\n",
-       "  function handleClearOutput(event, handle) {\n",
-       "    function drop(id) {\n",
-       "      const view = Bokeh.index.get_by_id(id)\n",
-       "      if (view != null) {\n",
-       "        view.model.document.clear()\n",
-       "        Bokeh.index.delete(view)\n",
-       "      }\n",
-       "    }\n",
-       "\n",
-       "    const cell = handle.cell;\n",
-       "\n",
-       "    const id = cell.output_area._bokeh_element_id;\n",
-       "    const server_id = cell.output_area._bokeh_server_id;\n",
-       "\n",
-       "    // Clean up Bokeh references\n",
-       "    if (id != null) {\n",
-       "      drop(id)\n",
-       "    }\n",
-       "\n",
-       "    if (server_id !== undefined) {\n",
-       "      // Clean up Bokeh references\n",
-       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
-       "      cell.notebook.kernel.execute(cmd_clean, {\n",
-       "        iopub: {\n",
-       "          output: function(msg) {\n",
-       "            const id = msg.content.text.trim()\n",
-       "            drop(id)\n",
-       "          }\n",
-       "        }\n",
-       "      });\n",
-       "      // Destroy server and session\n",
-       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
-       "      cell.notebook.kernel.execute(cmd_destroy);\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  /**\n",
-       "   * Handle when a new output is added\n",
-       "   */\n",
-       "  function handleAddOutput(event, handle) {\n",
-       "    const output_area = handle.output_area;\n",
-       "    const output = handle.output;\n",
-       "\n",
-       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
-       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
-       "      return\n",
-       "    }\n",
-       "\n",
-       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
-       "\n",
-       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
-       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
-       "      // store reference to embed id on output_area\n",
-       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
-       "    }\n",
-       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
-       "      const bk_div = document.createElement(\"div\");\n",
-       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
-       "      const script_attrs = bk_div.children[0].attributes;\n",
-       "      for (let i = 0; i < script_attrs.length; i++) {\n",
-       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
-       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
-       "      }\n",
-       "      // store reference to server id on output_area\n",
-       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  function register_renderer(events, OutputArea) {\n",
-       "\n",
-       "    function append_mime(data, metadata, element) {\n",
-       "      // create a DOM node to render to\n",
-       "      const toinsert = this.create_output_subarea(\n",
-       "        metadata,\n",
-       "        CLASS_NAME,\n",
-       "        EXEC_MIME_TYPE\n",
-       "      );\n",
-       "      this.keyboard_manager.register_events(toinsert);\n",
-       "      // Render to node\n",
-       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
-       "      render(props, toinsert[toinsert.length - 1]);\n",
-       "      element.append(toinsert);\n",
-       "      return toinsert\n",
-       "    }\n",
-       "\n",
-       "    /* Handle when an output is cleared or removed */\n",
-       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
-       "    events.on('delete.Cell', handleClearOutput);\n",
-       "\n",
-       "    /* Handle when a new output is added */\n",
-       "    events.on('output_added.OutputArea', handleAddOutput);\n",
-       "\n",
-       "    /**\n",
-       "     * Register the mime type and append_mime function with output_area\n",
-       "     */\n",
-       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
-       "      /* Is output safe? */\n",
-       "      safe: true,\n",
-       "      /* Index of renderer in `output_area.display_order` */\n",
-       "      index: 0\n",
-       "    });\n",
-       "  }\n",
-       "\n",
-       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
-       "  if (root.Jupyter !== undefined) {\n",
-       "    const events = require('base/js/events');\n",
-       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
-       "\n",
-       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
-       "      register_renderer(events, OutputArea);\n",
-       "    }\n",
-       "  }\n",
-       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
-       "    root._bokeh_timeout = Date.now() + 5000;\n",
-       "    root._bokeh_failed_load = false;\n",
-       "  }\n",
-       "\n",
-       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
-       "     \"
\\n\"+\n",
-       "     \"
\\n\"+\n",
-       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
-       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
-       "     \"
\\n\"+\n",
-       "     \"
    \\n\"+\n",
-       "     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n",
-       "     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n",
-       "     \"
\\n\"+\n",
-       "     \"\\n\"+\n",
-       "     \"from bokeh.resources import INLINE\\n\"+\n",
-       "     \"output_notebook(resources=INLINE)\\n\"+\n",
-       "     \"\\n\"+\n",
-       "     \"
\"}};\n",
-       "\n",
-       "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\");\n",
-       "    if (el != null) {\n",
-       "      el.textContent = \"BokehJS is loading...\";\n",
-       "    }\n",
-       "    if (root.Bokeh !== undefined) {\n",
-       "      if (el != null) {\n",
-       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
-       "      }\n",
-       "    } else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(display_loaded, 100)\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  function run_callbacks() {\n",
-       "    try {\n",
-       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
-       "        if (callback != null)\n",
-       "          callback();\n",
-       "      });\n",
-       "    } finally {\n",
-       "      delete root._bokeh_onload_callbacks\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
-       "  }\n",
-       "\n",
-       "  function load_libs(css_urls, js_urls, callback) {\n",
-       "    if (css_urls == null) css_urls = [];\n",
-       "    if (js_urls == null) js_urls = [];\n",
-       "\n",
-       "    root._bokeh_onload_callbacks.push(callback);\n",
-       "    if (root._bokeh_is_loading > 0) {\n",
-       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
-       "      return null;\n",
-       "    }\n",
-       "    if (js_urls == null || js_urls.length === 0) {\n",
-       "      run_callbacks();\n",
-       "      return null;\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
-       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
-       "\n",
-       "    function on_load() {\n",
-       "      root._bokeh_is_loading--;\n",
-       "      if (root._bokeh_is_loading === 0) {\n",
-       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
-       "        run_callbacks()\n",
-       "      }\n",
-       "    }\n",
-       "\n",
-       "    function on_error(url) {\n",
-       "      console.error(\"failed to load \" + url);\n",
-       "    }\n",
-       "\n",
-       "    for (let i = 0; i < css_urls.length; i++) {\n",
-       "      const url = css_urls[i];\n",
-       "      const element = document.createElement(\"link\");\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error.bind(null, url);\n",
-       "      element.rel = \"stylesheet\";\n",
-       "      element.type = \"text/css\";\n",
-       "      element.href = url;\n",
-       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
-       "      document.body.appendChild(element);\n",
-       "    }\n",
-       "\n",
-       "    for (let i = 0; i < js_urls.length; i++) {\n",
-       "      const url = js_urls[i];\n",
-       "      const element = document.createElement('script');\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error.bind(null, url);\n",
-       "      element.async = false;\n",
-       "      element.src = url;\n",
-       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
-       "      document.head.appendChild(element);\n",
-       "    }\n",
-       "  };\n",
-       "\n",
-       "  function inject_raw_css(css) {\n",
-       "    const element = document.createElement(\"style\");\n",
-       "    element.appendChild(document.createTextNode(css));\n",
-       "    document.body.appendChild(element);\n",
-       "  }\n",
-       "\n",
-       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
-       "  const css_urls = [];\n",
-       "\n",
-       "  const inline_js = [    function(Bokeh) {\n",
-       "      Bokeh.set_log_level(\"info\");\n",
-       "    },\n",
-       "function(Bokeh) {\n",
-       "    }\n",
-       "  ];\n",
-       "\n",
-       "  function run_inline_js() {\n",
-       "    if (root.Bokeh !== undefined || force === true) {\n",
-       "          for (let i = 0; i < inline_js.length; i++) {\n",
-       "      inline_js[i].call(root, root.Bokeh);\n",
-       "    }\n",
-       "if (force === true) {\n",
-       "        display_loaded();\n",
-       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(run_inline_js, 100);\n",
-       "    } else if (!root._bokeh_failed_load) {\n",
-       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
-       "      root._bokeh_failed_load = true;\n",
-       "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\")).parents('.cell').data().cell;\n",
-       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  if (root._bokeh_is_loading === 0) {\n",
-       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
-       "    run_inline_js();\n",
-       "  } else {\n",
-       "    load_libs(css_urls, js_urls, function() {\n",
-       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
-       "      run_inline_js();\n",
-       "    });\n",
-       "  }\n",
-       "}(window));"
-      ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"a8702bf8-b019-4dc5-b5ff-b8a2fe2976da\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import hail as hl\n",
-    "from gnomad_toolbox.load_data import  get_gnomad_release"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8649f215-0afc-4f66-920a-53b707f41c4a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Welcome to\n",
-      "     __  __     <>__\n",
-      "    / /_/ /__  __/ /\n",
-      "   / __  / _ `/ / /\n",
-      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241209-1355-0.2.132-678e1f52b999.log\n"
-     ]
-    }
-   ],
-   "source": [
-    "hl.init(backend=\"local\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5335a135",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Variant data\n",
-    "\n",
-    "Available versions for each data type and reference build are (as of 2024-10-29):\n",
-    "\n",
-    "| Data Type       | GRCh38 Versions                  | GRCh37 Versions      |\n",
-    "|-----------------|----------------------------------|----------------------|\n",
-    "| exomes          | 4.0, 4.1                         | 2.1, 2.1.1           |\n",
-    "| genomes         | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1           |\n",
-    "| joint           | 4.1                              | N/A                  |\n",
-    "\n",
-    "For a description of all fields the the HT, see the [Help/FAQ](https://gnomad.broadinstitute.org/help/v4-hts) page."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d1a4ae8933ba6421",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### v4.1 exomes Hail Table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "318a034c-ac84-4147-9f25-e5e8783e9b91",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "100cf576",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='exomes', version='4.1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77d7a05e31c1f37a",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "95c14f2c8cc3e699",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'freq_meta': array> \n",
-      "    'freq_index_dict': dict \n",
-      "    'freq_meta_sample_count': array \n",
-      "    'faf_meta': array> \n",
-      "    'faf_index_dict': dict \n",
-      "    'age_distribution': struct {\n",
-      "        bin_edges: array, \n",
-      "        bin_freq: array, \n",
-      "        n_smaller: int32, \n",
-      "        n_larger: int32\n",
-      "    } \n",
-      "    'downsamplings': dict> \n",
-      "    'filtering_model': struct {\n",
-      "        filter_name: str, \n",
-      "        score_name: str, \n",
-      "        snv_cutoff: struct {\n",
-      "            bin: int32, \n",
-      "            min_score: float64\n",
-      "        }, \n",
-      "        indel_cutoff: struct {\n",
-      "            bin: int32, \n",
-      "            min_score: float64\n",
-      "        }, \n",
-      "        snv_training_variables: array, \n",
-      "        indel_training_variables: array\n",
-      "    } \n",
-      "    'inbreeding_coeff_cutoff': float64 \n",
-      "    'interval_qc_parameters': struct {\n",
-      "        per_platform: bool, \n",
-      "        all_platforms: bool, \n",
-      "        high_qual_cutoffs: dict>, \n",
-      "        min_platform_size: int32\n",
-      "    } \n",
-      "    'tool_versions': struct {\n",
-      "        cadd_version: str, \n",
-      "        revel_version: str, \n",
-      "        spliceai_version: str, \n",
-      "        pangolin_version: array, \n",
-      "        phylop_version: str, \n",
-      "        dbsnp_version: str, \n",
-      "        sift_version: str, \n",
-      "        polyphen_version: str\n",
-      "    } \n",
-      "    'vrs_versions': struct {\n",
-      "        vrs_schema_version: str, \n",
-      "        vrs_python_version: str, \n",
-      "        seqrepo_version: str\n",
-      "    } \n",
-      "    'vep_globals': struct {\n",
-      "        vep_version: str, \n",
-      "        vep_help: str, \n",
-      "        vep_config: str, \n",
-      "        gencode_version: str, \n",
-      "        mane_select_version: str\n",
-      "    } \n",
-      "    'frequency_README': str \n",
-      "    'date': str \n",
-      "    'version': str \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'alleles': array \n",
-      "    'freq': array \n",
-      "    'grpmax': struct {\n",
-      "        gnomad: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int64, \n",
-      "            gen_anc: str\n",
-      "        }, \n",
-      "        non_ukb: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int64, \n",
-      "            gen_anc: str\n",
-      "        }\n",
-      "    } \n",
-      "    'faf': array \n",
-      "    'fafmax': struct {\n",
-      "        gnomad: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }, \n",
-      "        non_ukb: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }\n",
-      "    } \n",
-      "    'a_index': int32 \n",
-      "    'was_split': bool \n",
-      "    'rsid': set \n",
-      "    'filters': set \n",
-      "    'info': struct {\n",
-      "        FS: float64, \n",
-      "        MQ: float64, \n",
-      "        MQRankSum: float64, \n",
-      "        QUALapprox: int64, \n",
-      "        QD: float64, \n",
-      "        ReadPosRankSum: float64, \n",
-      "        SB: array, \n",
-      "        SOR: float64, \n",
-      "        VarDP: int32, \n",
-      "        AS_FS: float64, \n",
-      "        AS_MQ: float64, \n",
-      "        AS_MQRankSum: float64, \n",
-      "        AS_pab_max: float64, \n",
-      "        AS_QUALapprox: int64, \n",
-      "        AS_QD: float64, \n",
-      "        AS_ReadPosRankSum: float64, \n",
-      "        AS_SB_TABLE: array, \n",
-      "        AS_SOR: float64, \n",
-      "        AS_VarDP: int32, \n",
-      "        singleton: bool, \n",
-      "        transmitted_singleton: bool, \n",
-      "        sibling_singleton: bool, \n",
-      "        omni: bool, \n",
-      "        mills: bool, \n",
-      "        monoallelic: bool, \n",
-      "        only_het: bool, \n",
-      "        AS_VQSLOD: float64, \n",
-      "        inbreeding_coeff: float64, \n",
-      "        vrs: struct {\n",
-      "            VRS_Allele_IDs: array, \n",
-      "            VRS_Starts: array, \n",
-      "            VRS_Ends: array, \n",
-      "            VRS_States: array\n",
-      "        }\n",
-      "    } \n",
-      "    'vep': struct {\n",
-      "        allele_string: str, \n",
-      "        end: int32, \n",
-      "        id: str, \n",
-      "        input: str, \n",
-      "        intergenic_consequences: array, \n",
-      "            impact: str, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        most_severe_consequence: str, \n",
-      "        motif_feature_consequences: array, \n",
-      "            high_inf_pos: str, \n",
-      "            impact: str, \n",
-      "            motif_feature_id: str, \n",
-      "            motif_name: str, \n",
-      "            motif_pos: int32, \n",
-      "            motif_score_change: float64, \n",
-      "            transcription_factors: array, \n",
-      "            strand: int32, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        regulatory_feature_consequences: array, \n",
-      "            impact: str, \n",
-      "            regulatory_feature_id: str, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        seq_region_name: str, \n",
-      "        start: int32, \n",
-      "        strand: int32, \n",
-      "        transcript_consequences: array, \n",
-      "            distance: int32, \n",
-      "            domains: array, \n",
-      "            exon: str, \n",
-      "            flags: str, \n",
-      "            gene_id: str, \n",
-      "            gene_pheno: int32, \n",
-      "            gene_symbol: str, \n",
-      "            gene_symbol_source: str, \n",
-      "            hgnc_id: str, \n",
-      "            hgvsc: str, \n",
-      "            hgvsp: str, \n",
-      "            hgvs_offset: int32, \n",
-      "            impact: str, \n",
-      "            intron: str, \n",
-      "            lof: str, \n",
-      "            lof_flags: str, \n",
-      "            lof_filter: str, \n",
-      "            lof_info: str, \n",
-      "            mane_select: str, \n",
-      "            mane_plus_clinical: str, \n",
-      "            mirna: array, \n",
-      "            protein_end: int32, \n",
-      "            protein_start: int32, \n",
-      "            protein_id: str, \n",
-      "            source: str, \n",
-      "            strand: int32, \n",
-      "            transcript_id: str, \n",
-      "            tsl: int32, \n",
-      "            uniprot_isoform: array, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        variant_class: str\n",
-      "    } \n",
-      "    'vqsr_results': struct {\n",
-      "        AS_VQSLOD: float64, \n",
-      "        AS_culprit: str, \n",
-      "        positive_train_site: bool, \n",
-      "        negative_train_site: bool\n",
-      "    } \n",
-      "    'region_flags': struct {\n",
-      "        non_par: bool, \n",
-      "        lcr: bool, \n",
-      "        segdup: bool, \n",
-      "        fail_interval_qc: bool, \n",
-      "        outside_ukb_capture_region: bool, \n",
-      "        outside_broad_capture_region: bool\n",
-      "    } \n",
-      "    'allele_info': struct {\n",
-      "        variant_type: str, \n",
-      "        n_alt_alleles: int32, \n",
-      "        has_star: bool, \n",
-      "        allele_type: str, \n",
-      "        was_mixed: bool\n",
-      "    } \n",
-      "    'histograms': struct {\n",
-      "        qual_hists: struct {\n",
-      "            gq_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            gq_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            ab_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }, \n",
-      "        raw_qual_hists: struct {\n",
-      "            gq_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            gq_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            ab_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }, \n",
-      "        age_hists: struct {\n",
-      "            age_hist_het: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            age_hist_hom: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }\n",
-      "    } \n",
-      "    'in_silico_predictors': struct {\n",
-      "        cadd: struct {\n",
-      "            phred: float32, \n",
-      "            raw_score: float32\n",
-      "        }, \n",
-      "        revel_max: float64, \n",
-      "        spliceai_ds_max: float32, \n",
-      "        pangolin_largest_ds: float64, \n",
-      "        phylop: float64, \n",
-      "        sift_max: float64, \n",
-      "        polyphen_max: float64\n",
-      "    } \n",
-      "----------------------------------------\n",
-      "Key: ['locus', 'alleles']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a071f738b2c888e",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "222de580c305d72a",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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cessed_transcript",NA,NA,234,234,NA,NA,NA,["non_coding_transcript_exon_variant"],NA,NA,"1/3",NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102","ENST00000456328.2:n.234G>A",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000456328",1,NA,"A"),(1,NA,NA,"unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],2302,NA,NA,NA,"ENSG00000227232",NA,"WASH7P","HGNC","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000488147",NA,NA,"A"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],2260,NA,NA,NA,"653635",NA,"WASH7P","EntrezGene","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",-1,"NR_024540.1",NA,NA,"A"),(1,NA,NA,"transcribed_pseudogene",1,NA,229,229,NA,NA,NA,["non_coding_transcript_exon_variant"],NA,NA,"1/3",NA,"100287102",NA,"DDX11L1","EntrezGene","HGNC:37102","NR_046018.2:n.229G>A",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",1,"NR_046018.2",NA,NA,"A")]"SNV"-2.86e+00"AS_MQ"FalseFalseFalseFalseTrueNATrueFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][314323,1,0,0,54,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][308789,5585,16,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]004.42e+003.11e-01NANA-8.00e-021.38e+00NANA
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+----------------+\n",
-       "| locus         | alleles        |\n",
-       "+---------------+----------------+\n",
-       "| locus | array     |\n",
-       "+---------------+----------------+\n",
-       "| chr1:11994    | [\"T\",\"C\"]      |\n",
-       "| chr1:12016    | [\"G\",\"A\"]      |\n",
-       "| chr1:12060    | [\"CTGGAG\",\"C\"] |\n",
-       "| chr1:12074    | [\"T\",\"C\"]      |\n",
-       "| chr1:12102    | [\"G\",\"A\"]      |\n",
-       "+---------------+----------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq                                                                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0,NA,0,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
-       "| [(0,NA,0,0),(6,9.54e-06,628784,2),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
-       "| [(0,0.00e+00,4,0),(1,1.59e-06,628784,0),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
-       "| [(0,0.00e+00,4,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
-       "| [(0,0.00e+00,32,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0)... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+\n",
-       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
-       "+------------------+------------------+------------------+\n",
-       "|            int32 |          float64 |            int32 |\n",
-       "+------------------+------------------+------------------+\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "+------------------+------------------+------------------+\n",
-       "\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                          int64 | str                   |             int32 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|           float64 |             int32 |                           int64 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "\n",
-       "+------------------------+\n",
-       "| grpmax.non_ukb.gen_anc |\n",
-       "+------------------------+\n",
-       "| str                    |\n",
-       "+------------------------+\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "+------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| faf                                                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                  float64 | str                              |   int32 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "| was_split | rsid     | filters           | info.FS |  info.MQ |\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "|      bool | set | set          | float64 |  float64 |\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.60e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.56e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.45e+01 |\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|        float64 |           int64 |  float64 |             float64 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|             NA |              60 | 3.00e+01 |                  NA |\n",
-       "|       0.00e+00 |             262 | 2.18e+01 |            6.74e-01 |\n",
-       "|       6.74e-01 |             123 | 3.08e+01 |           -1.15e+00 |\n",
-       "|       4.31e-01 |              99 | 1.24e+01 |           -2.53e-01 |\n",
-       "|             NA |              90 | 2.25e+01 |                  NA |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "| info.SB      | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "| array |  float64 |      int32 |    float64 |    float64 |\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "| [0,0,2,0]    | 2.30e+00 |          2 |         NA |   2.50e+01 |\n",
-       "| [2,0,10,0]   | 2.67e+00 |         12 |         NA |   2.50e+01 |\n",
-       "| [1,0,3,0]    | 1.61e+00 |          4 |         NA |   2.60e+01 |\n",
-       "| [4,0,4,0]    | 6.93e-01 |          8 |         NA |   2.56e+01 |\n",
-       "| [0,0,4,0]    | 3.26e+00 |          4 |         NA |   2.45e+01 |\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|           float64 |         float64 |              int64 |    float64 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|                NA |              NA |                 60 |   3.00e+01 |\n",
-       "|          0.00e+00 |        1.00e+00 |                262 |   2.18e+01 |\n",
-       "|          6.74e-01 |        6.25e-01 |                123 |   3.08e+01 |\n",
-       "|          4.31e-01 |        6.87e-01 |                 99 |   1.24e+01 |\n",
-       "|                NA |              NA |                 90 |   2.25e+01 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE | info.AS_SOR | info.AS_VarDP |\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "|                float64 | array     |     float64 |         int32 |\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "|                     NA | [0,0,2,0]        |    2.30e+00 |             2 |\n",
-       "|               6.74e-01 | [2,0,10,0]       |    2.67e+00 |            12 |\n",
-       "|              -1.15e+00 | [1,0,3,0]        |    1.61e+00 |             4 |\n",
-       "|              -2.53e-01 | [4,0,4,0]        |    6.93e-01 |             8 |\n",
-       "|                     NA | [0,0,4,0]        |    3.26e+00 |             4 |\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|           bool |                       bool |                   bool |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|           True |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|      bool |       bool |             bool |          bool |        float64 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|     False |      False |            False |         False |      -5.25e+00 |\n",
-       "|     False |      False |            False |         False |      -2.75e+00 |\n",
-       "|     False |      False |            False |         False |      -2.22e+00 |\n",
-       "|     False |      False |            False |         False |      -2.18e+00 |\n",
-       "|     False |      False |            False |         False |      -2.86e+00 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "\n",
-       "+-----------------------+\n",
-       "| info.inbreeding_coeff |\n",
-       "+-----------------------+\n",
-       "|               float64 |\n",
-       "+-----------------------+\n",
-       "|              1.00e+00 |\n",
-       "|              6.67e-01 |\n",
-       "|             -1.59e-06 |\n",
-       "|              1.00e+00 |\n",
-       "|              1.00e+00 |\n",
-       "+-----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| info.vrs.VRS_Allele_IDs                                                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.ps4-9woXy7o4rS39i8hDK_cUPBa-UcyP\",\"ga4gh:VA.nHlWYJXgiuvXrLAxQs... |\n",
-       "| [\"ga4gh:VA.VFxwcI4knOzk6SHzS2qowyDGnkG3mfEH\",\"ga4gh:VA.ctgP7qNjQAGjI2eTDo... |\n",
-       "| [\"ga4gh:VA.P573ZtUtAaRcceE7NLanEyynSefvcAPL\",\"ga4gh:VA.CHmk9uDiHW2LIHndZW... |\n",
-       "| [\"ga4gh:VA.neBeBT28ISe_1-yKPFsYxntP2jz1pj7E\",\"ga4gh:VA.1RPHSwBHNUwoECJ9VV... |\n",
-       "| [\"ga4gh:VA.ZXRhPWtip8HseOMWpCnaja0-ATo8hLE1\",\"ga4gh:VA.Qi95g6E8nt6DaqTGpH... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| info.vrs.VRS_Starts | info.vrs.VRS_Ends | info.vrs.VRS_States |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| array        | array      | array          |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| [11993,11993]       | [11994,11994]     | [\"T\",\"C\"]           |\n",
-       "| [12015,12015]       | [12016,12016]     | [\"G\",\"A\"]           |\n",
-       "| [12059,12060]       | [12065,12071]     | [\"CTGGAG\",\"TGGAGT\"] |\n",
-       "| [12073,12073]       | [12074,12074]     | [\"T\",\"C\"]           |\n",
-       "| [12101,12101]       | [12102,12102]     | [\"G\",\"A\"]           |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "| vep.allele_string | vep.end | vep.id | vep.input                      |\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "| str               |   int32 | str    | str                            |\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "| \"T/C\"             |   11994 | \".\"    | \"chr1\t11994\t.\tT\tC\t.\t.\tGT\"      |\n",
-       "| \"G/A\"             |   12016 | \".\"    | \"chr1\t12016\t.\tG\tA\t.\t.\tGT\"      |\n",
-       "| \"TGGAG/-\"         |   12065 | \".\"    | \"chr1\t12060\t.\tCTGGAG\tC\t.\t.\tGT\" |\n",
-       "| \"T/C\"             |   12074 | \".\"    | \"chr1\t12074\t.\tT\tC\t.\t.\tGT\"      |\n",
-       "| \"G/A\"             |   12102 | \".\"    | \"chr1\t12102\t.\tG\tA\t.\t.\tGT\"      |\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.intergenic_consequences                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, impact: st... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| vep.most_severe_consequence          |\n",
-       "+--------------------------------------+\n",
-       "| str                                  |\n",
-       "+--------------------------------------+\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "| \"splice_donor_5th_base_variant\"      |\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.motif_feature_consequences                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, high_inf_p... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.regulatory_feature_consequences                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,2,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq   |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [314391,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [314388,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [314380,0,0,0,11,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]  |\n",
-       "| [314369,1,0,0,20,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [314323,1,0,0,54,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq     |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                       |\n",
-       "+----------------------------------------------------+\n",
-       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308833,5557,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308823,5567,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308789,5585,16,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]      |\n",
-       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                      int64 |                         float32 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        1.26e+01 |\n",
-       "|                                          0 |                        1.60e+01 |\n",
-       "|                                          0 |                        8.52e+00 |\n",
-       "|                                          0 |                        1.52e+01 |\n",
-       "|                                          0 |                        4.42e+00 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                            1.08e+00 |                             NA |\n",
-       "|                            1.54e+00 |                             NA |\n",
-       "|                            7.07e-01 |                             NA |\n",
-       "|                            1.41e+00 |                             NA |\n",
-       "|                            3.11e-01 |                             NA |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                -1.10e-01 |                    1.09e+00 |\n",
-       "|                                -7.00e-02 |                    6.55e+00 |\n",
-       "|                                -9.00e-02 |                   -4.41e+00 |\n",
-       "|                                -4.00e-02 |                    6.01e+00 |\n",
-       "|                                -8.00e-02 |                    1.38e+00 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b7a158a3-f21a-4f87-9596-1f918156d713",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### v4.1 genomes Hail Table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "30f86500-afc5-419e-ae2e-f944dc461fee",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "62ca9934-20dd-437e-898b-86a056e2606e",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='genomes', version='4.1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9cf4b782-f289-47b6-9123-d08ca761b074",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "09de90df-0b03-4a54-817c-c8a0606026f6",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'freq_meta': array> \n",
-      "    'freq_index_dict': dict \n",
-      "    'freq_meta_sample_count': array \n",
-      "    'faf_meta': array> \n",
-      "    'faf_index_dict': dict \n",
-      "    'age_distribution': struct {\n",
-      "        bin_edges: array, \n",
-      "        bin_freq: array, \n",
-      "        n_smaller: int32, \n",
-      "        n_larger: int32\n",
-      "    } \n",
-      "    'filtering_model': struct {\n",
-      "        filter_name: str, \n",
-      "        score_name: str, \n",
-      "        snv_cutoff: struct {\n",
-      "            bin: int32, \n",
-      "            min_score: float64\n",
-      "        }, \n",
-      "        indel_cutoff: struct {\n",
-      "            bin: int32, \n",
-      "            min_score: float64\n",
-      "        }, \n",
-      "        snv_training_variables: array, \n",
-      "        indel_training_variables: array\n",
-      "    } \n",
-      "    'inbreeding_coeff_cutoff': float64 \n",
-      "    'tool_versions': struct {\n",
-      "        cadd_version: str, \n",
-      "        revel_version: str, \n",
-      "        spliceai_version: str, \n",
-      "        pangolin_version: array, \n",
-      "        phylop_version: str, \n",
-      "        dbsnp_version: str, \n",
-      "        sift_version: str, \n",
-      "        polyphen_version: str\n",
-      "    } \n",
-      "    'vrs_versions': struct {\n",
-      "        vrs_schema_version: str, \n",
-      "        vrs_python_version: str, \n",
-      "        seqrepo_version: str\n",
-      "    } \n",
-      "    'vep_globals': struct {\n",
-      "        vep_version: str, \n",
-      "        vep_help: str, \n",
-      "        vep_config: str, \n",
-      "        gencode_version: str, \n",
-      "        mane_select_version: str\n",
-      "    } \n",
-      "    'frequency_README': str \n",
-      "    'date': str \n",
-      "    'version': str \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'alleles': array \n",
-      "    'freq': array \n",
-      "    'grpmax': struct {\n",
-      "        AC: int32, \n",
-      "        AF: float64, \n",
-      "        AN: int32, \n",
-      "        homozygote_count: int32, \n",
-      "        gen_anc: str\n",
-      "    } \n",
-      "    'faf': array \n",
-      "    'fafmax': struct {\n",
-      "        faf95_max: float64, \n",
-      "        faf95_max_gen_anc: str, \n",
-      "        faf99_max: float64, \n",
-      "        faf99_max_gen_anc: str\n",
-      "    } \n",
-      "    'a_index': int32 \n",
-      "    'was_split': bool \n",
-      "    'rsid': set \n",
-      "    'filters': set \n",
-      "    'info': struct {\n",
-      "        FS: float64, \n",
-      "        MQ: float64, \n",
-      "        MQRankSum: float64, \n",
-      "        QUALapprox: int64, \n",
-      "        QD: float32, \n",
-      "        ReadPosRankSum: float64, \n",
-      "        SB: array, \n",
-      "        SOR: float64, \n",
-      "        VarDP: int32, \n",
-      "        AS_FS: float64, \n",
-      "        AS_MQ: float64, \n",
-      "        AS_MQRankSum: float64, \n",
-      "        AS_pab_max: float64, \n",
-      "        AS_QUALapprox: int64, \n",
-      "        AS_QD: float32, \n",
-      "        AS_ReadPosRankSum: float64, \n",
-      "        AS_SB_TABLE: array, \n",
-      "        AS_SOR: float64, \n",
-      "        AS_VarDP: int32, \n",
-      "        singleton: bool, \n",
-      "        transmitted_singleton: bool, \n",
-      "        omni: bool, \n",
-      "        mills: bool, \n",
-      "        monoallelic: bool, \n",
-      "        only_het: bool, \n",
-      "        AS_VQSLOD: float64, \n",
-      "        inbreeding_coeff: float64, \n",
-      "        vrs: struct {\n",
-      "            VRS_Allele_IDs: array, \n",
-      "            VRS_Starts: array, \n",
-      "            VRS_Ends: array, \n",
-      "            VRS_States: array\n",
-      "        }\n",
-      "    } \n",
-      "    'vep': struct {\n",
-      "        allele_string: str, \n",
-      "        end: int32, \n",
-      "        id: str, \n",
-      "        input: str, \n",
-      "        intergenic_consequences: array, \n",
-      "            impact: str, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        most_severe_consequence: str, \n",
-      "        motif_feature_consequences: array, \n",
-      "            high_inf_pos: str, \n",
-      "            impact: str, \n",
-      "            motif_feature_id: str, \n",
-      "            motif_name: str, \n",
-      "            motif_pos: int32, \n",
-      "            motif_score_change: float64, \n",
-      "            transcription_factors: array, \n",
-      "            strand: int32, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        regulatory_feature_consequences: array, \n",
-      "            impact: str, \n",
-      "            regulatory_feature_id: str, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        seq_region_name: str, \n",
-      "        start: int32, \n",
-      "        strand: int32, \n",
-      "        transcript_consequences: array, \n",
-      "            distance: int32, \n",
-      "            domains: array, \n",
-      "            exon: str, \n",
-      "            flags: str, \n",
-      "            gene_id: str, \n",
-      "            gene_pheno: int32, \n",
-      "            gene_symbol: str, \n",
-      "            gene_symbol_source: str, \n",
-      "            hgnc_id: str, \n",
-      "            hgvsc: str, \n",
-      "            hgvsp: str, \n",
-      "            hgvs_offset: int32, \n",
-      "            impact: str, \n",
-      "            intron: str, \n",
-      "            lof: str, \n",
-      "            lof_flags: str, \n",
-      "            lof_filter: str, \n",
-      "            lof_info: str, \n",
-      "            mane_select: str, \n",
-      "            mane_plus_clinical: str, \n",
-      "            mirna: array, \n",
-      "            protein_end: int32, \n",
-      "            protein_start: int32, \n",
-      "            protein_id: str, \n",
-      "            source: str, \n",
-      "            strand: int32, \n",
-      "            transcript_id: str, \n",
-      "            tsl: int32, \n",
-      "            uniprot_isoform: array, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        variant_class: str\n",
-      "    } \n",
-      "    'vqsr_results': struct {\n",
-      "        AS_VQSLOD: float64, \n",
-      "        AS_culprit: str, \n",
-      "        positive_train_site: bool, \n",
-      "        negative_train_site: bool\n",
-      "    } \n",
-      "    'region_flags': struct {\n",
-      "        non_par: bool, \n",
-      "        lcr: bool, \n",
-      "        segdup: bool\n",
-      "    } \n",
-      "    'allele_info': struct {\n",
-      "        allele_type: str, \n",
-      "        n_alt_alleles: int32, \n",
-      "        variant_type: str, \n",
-      "        was_mixed: bool\n",
-      "    } \n",
-      "    'histograms': struct {\n",
-      "        qual_hists: struct {\n",
-      "            gq_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            gq_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            ab_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }, \n",
-      "        raw_qual_hists: struct {\n",
-      "            gq_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            gq_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            ab_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }, \n",
-      "        age_hists: struct {\n",
-      "            age_hist_het: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            age_hist_hom: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }\n",
-      "    } \n",
-      "    'in_silico_predictors': struct {\n",
-      "        cadd: struct {\n",
-      "            phred: float32, \n",
-      "            raw_score: float32\n",
-      "        }, \n",
-      "        revel_max: float64, \n",
-      "        spliceai_ds_max: float32, \n",
-      "        pangolin_largest_ds: float64, \n",
-      "        phylop: float64, \n",
-      "        sift_max: float64, \n",
-      "        polyphen_max: float64\n",
-      "    } \n",
-      "----------------------------------------\n",
-      "Key: ['locus', 'alleles']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed916197-3b0e-45dc-bacd-a13cb66d70ee",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "00b0ea2f-5685-4bae-886a-b9ea31866818",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
histograms
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
grpmax
fafmax
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
allele_type
n_alt_alleles
variant_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>int32float64int32int32strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strint32boolset<str>set<str>float64float64float64int64float32float64array<int32>float64int32float64float64float64float64int64float32float64array<int32>float64int32boolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolstrint32strboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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639542312"}{"AC0","AS_VQSR"}7.30e+003.48e+016.70e-02962.74e+00-1.07e+00[21,6,4,4]9.60e-02355.10e+003.51e+01-5.72e-016.87e-01772.96e+00-1.38e+00[21,6,3,3]9.64e-0226FalseNANANAFalseFalse-4.57e+00-1.65e-05["ga4gh:VA.oTAtTrgYxm81O9fu6Mrhfo1t3eHsgg4L","ga4gh:VA.Y283OnlLjyi1T1IT_JzvW255rC6YJsW6"][10030,10030][10031,10031]["T","C"]"T/C"10031".""chr1\t10031\t.\tT\tC\t.\t.\tGT"NA"upstream_gene_variant"NANA"chr1"100311[(1,NA,NA,"transcribed_unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1979,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000450305",NA,NA,"C"),(1,NA,NA,"processed_transcript",NA,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1838,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000456328",1,NA,"C"),(1,NA,NA,"unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4373,NA,NA,NA,"ENSG00000227232",NA,"WASH7P","HGNC","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000488147",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4331,NA,NA,NA,"653635",NA,"WASH7P","EntrezGene","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",-1,"NR_024540.1",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1843,NA,NA,NA,"100287102",NA,"DDX11L1","EntrezGene","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",1,"NR_046018.2",NA,NA,"C")]"SNV"-4.57e+00"AS_QD"FalseFalseFalseTrueTrue"snv"2"multi-snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]008.97e+007.57e-01NANANANANANA
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[(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]6.27e-06"nfe"2.35e-06"nfe"1True{"rs1570391741"}{"AS_VQSR"}3.30e+013.60e+017.88e-012921.79e+00-6.84e-01[97,29,17,20]3.75e-011633.79e+013.61e+017.88e-011.00e+002642.06e+00-6.84e-01[97,29,13,19]7.58e-01128FalseNANANAFalseFalse-3.31e+00-2.41e-05["ga4gh:VA.TV0UyS-jCvsVkEOMbipUnzTc4hYuVNTg","ga4gh:VA.33zGTKcHyj5wWpTfc3OfD8-UlPQ-YOfx"][10056,10056][10057,10057]["A","C"]"A/C"10057".""chr1\t10057\t.\tA\tC\t.\t.\tGT"NA"upstream_gene_variant"NANA"chr1"100571[(1,NA,NA,"transcribed_unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1953,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000450305",NA,NA,"C"),(1,NA,NA,"processed_transcript",NA,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1812,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000456328",1,NA,"C"),(1,NA,NA,"unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4347,NA,NA,NA,"ENSG00000227232",NA,"WASH7P","HGNC","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000488147",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4305,NA,NA,NA,"653635",NA,"WASH7P","EntrezGene","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",-1,"NR_024540.1",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1817,NA,NA,NA,"100287102",NA,"DDX11L1","EntrezGene","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",1,"NR_046018.2",NA,NA,"C")]"SNV"-3.31e+00"AS_FS"FalseFalseFalseTrueTrue"snv"2"multi-snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]008.53e+007.09e-01NANANANANANA
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+------------+\n",
-       "| locus         | alleles    |\n",
-       "+---------------+------------+\n",
-       "| locus | array |\n",
-       "+---------------+------------+\n",
-       "| chr1:10031    | [\"T\",\"C\"]  |\n",
-       "| chr1:10037    | [\"T\",\"C\"]  |\n",
-       "| chr1:10043    | [\"T\",\"C\"]  |\n",
-       "| chr1:10055    | [\"T\",\"C\"]  |\n",
-       "| chr1:10057    | [\"A\",\"C\"]  |\n",
-       "+---------------+------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq                                                                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e... |\n",
-       "| [(2,2.60e-05,76882,0),(4,3.15e-05,126902,0),(0,0.00e+00,34670,0),(1,1.80e... |\n",
-       "| [(1,1.17e-05,85634,0),(1,8.24e-06,121430,0),(0,0.00e+00,39236,0),(0,0.00e... |\n",
-       "| [(1,1.06e-05,94224,0),(5,4.64e-05,107704,0),(0,0.00e+00,44382,0),(1,1.80e... |\n",
-       "| [(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------+-----------+-----------+-------------------------+----------------+\n",
-       "| grpmax.AC | grpmax.AF | grpmax.AN | grpmax.homozygote_count | grpmax.gen_anc |\n",
-       "+-----------+-----------+-----------+-------------------------+----------------+\n",
-       "|     int32 |   float64 |     int32 |                   int32 | str            |\n",
-       "+-----------+-----------+-----------+-------------------------+----------------+\n",
-       "|        NA |        NA |        NA |                      NA | NA             |\n",
-       "|         1 |  4.07e-04 |      2456 |                       0 | \"eas\"          |\n",
-       "|         1 |  4.39e-05 |     22760 |                       0 | \"afr\"          |\n",
-       "|        NA |        NA |        NA |                      NA | NA             |\n",
-       "|         2 |  3.78e-05 |     52912 |                       0 | \"nfe\"          |\n",
-       "+-----------+-----------+-----------+-------------------------+----------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| faf                                                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------+--------------------------+------------------+\n",
-       "| fafmax.faf95_max | fafmax.faf95_max_gen_anc | fafmax.faf99_max |\n",
-       "+------------------+--------------------------+------------------+\n",
-       "|          float64 | str                      |          float64 |\n",
-       "+------------------+--------------------------+------------------+\n",
-       "|               NA | NA                       |               NA |\n",
-       "|               NA | NA                       |               NA |\n",
-       "|               NA | NA                       |               NA |\n",
-       "|               NA | NA                       |               NA |\n",
-       "|         6.27e-06 | \"nfe\"                    |         2.35e-06 |\n",
-       "+------------------+--------------------------+------------------+\n",
-       "\n",
-       "+--------------------------+---------+-----------+------------------+\n",
-       "| fafmax.faf99_max_gen_anc | a_index | was_split | rsid             |\n",
-       "+--------------------------+---------+-----------+------------------+\n",
-       "| str                      |   int32 |      bool | set         |\n",
-       "+--------------------------+---------+-----------+------------------+\n",
-       "| NA                       |       2 |      True | {\"rs1639542312\"} |\n",
-       "| NA                       |       1 |     False | {\"rs1639542418\"} |\n",
-       "| NA                       |       1 |     False | NA               |\n",
-       "| NA                       |       2 |      True | {\"rs892501864\"}  |\n",
-       "| \"nfe\"                    |       1 |      True | {\"rs1570391741\"} |\n",
-       "+--------------------------+---------+-----------+------------------+\n",
-       "\n",
-       "+-------------------+----------+----------+----------------+-----------------+\n",
-       "| filters           |  info.FS |  info.MQ | info.MQRankSum | info.QUALapprox |\n",
-       "+-------------------+----------+----------+----------------+-----------------+\n",
-       "| set          |  float64 |  float64 |        float64 |           int64 |\n",
-       "+-------------------+----------+----------+----------------+-----------------+\n",
-       "| {\"AC0\",\"AS_VQSR\"} | 7.30e+00 | 3.48e+01 |       6.70e-02 |              96 |\n",
-       "| {\"AS_VQSR\"}       | 8.58e+00 | 3.83e+01 |       1.37e+00 |             180 |\n",
-       "| {\"AS_VQSR\"}       | 3.11e+01 | 3.52e+01 |       1.23e+00 |              97 |\n",
-       "| {\"AS_VQSR\"}       | 0.00e+00 | 3.55e+01 |       1.07e-01 |             220 |\n",
-       "| {\"AS_VQSR\"}       | 3.30e+01 | 3.60e+01 |       7.88e-01 |             292 |\n",
-       "+-------------------+----------+----------+----------------+-----------------+\n",
-       "\n",
-       "+----------+---------------------+---------------+----------+------------+\n",
-       "|  info.QD | info.ReadPosRankSum | info.SB       | info.SOR | info.VarDP |\n",
-       "+----------+---------------------+---------------+----------+------------+\n",
-       "|  float32 |             float64 | array  |  float64 |      int32 |\n",
-       "+----------+---------------------+---------------+----------+------------+\n",
-       "| 2.74e+00 |           -1.07e+00 | [21,6,4,4]    | 9.60e-02 |         35 |\n",
-       "| 2.20e+00 |           -4.80e-01 | [49,12,13,8]  | 1.51e-01 |         82 |\n",
-       "| 2.77e+00 |           -8.96e-01 | [25,0,5,5]    | 1.00e-03 |         35 |\n",
-       "| 2.12e+00 |           -1.16e+00 | [51,29,15,9]  | 6.16e-01 |        104 |\n",
-       "| 1.79e+00 |           -6.84e-01 | [97,29,17,20] | 3.75e-01 |        163 |\n",
-       "+----------+---------------------+---------------+----------+------------+\n",
-       "\n",
-       "+------------+------------+-------------------+-----------------+\n",
-       "| info.AS_FS | info.AS_MQ | info.AS_MQRankSum | info.AS_pab_max |\n",
-       "+------------+------------+-------------------+-----------------+\n",
-       "|    float64 |    float64 |           float64 |         float64 |\n",
-       "+------------+------------+-------------------+-----------------+\n",
-       "|   5.10e+00 |   3.51e+01 |         -5.72e-01 |        6.87e-01 |\n",
-       "|   8.58e+00 |   3.83e+01 |          1.37e+00 |        1.00e+00 |\n",
-       "|   3.11e+01 |   3.52e+01 |          1.23e+00 |        1.00e+00 |\n",
-       "|   5.94e+00 |   3.48e+01 |          7.15e-01 |        2.27e-01 |\n",
-       "|   3.79e+01 |   3.61e+01 |          7.88e-01 |        1.00e+00 |\n",
-       "+------------+------------+-------------------+-----------------+\n",
-       "\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "| info.AS_QUALapprox | info.AS_QD | info.AS_ReadPosRankSum | info.AS_SB_TABLE |\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "|              int64 |    float32 |                float64 | array     |\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "|                 77 |   2.96e+00 |              -1.38e+00 | [21,6,3,3]       |\n",
-       "|                180 |   2.20e+00 |              -4.80e-01 | [49,12,13,8]     |\n",
-       "|                 97 |   2.77e+00 |              -8.96e-01 | [25,0,5,5]       |\n",
-       "|                 91 |   1.21e+00 |              -1.16e+00 | [51,29,7,8]      |\n",
-       "|                264 |   2.06e+00 |              -6.84e-01 | [97,29,13,19]    |\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "| info.AS_SOR | info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "|     float64 |         int32 |           bool |                       bool |\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "|    9.64e-02 |            26 |          False |                         NA |\n",
-       "|    1.51e-01 |            82 |          False |                         NA |\n",
-       "|    1.48e-03 |            35 |           True |                      False |\n",
-       "|    4.69e-01 |            75 |          False |                         NA |\n",
-       "|    7.58e-01 |           128 |          False |                         NA |\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|      bool |       bool |             bool |          bool |        float64 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|        NA |         NA |            False |         False |      -4.57e+00 |\n",
-       "|        NA |         NA |            False |         False |      -3.18e+00 |\n",
-       "|        NA |         NA |            False |         False |      -5.79e+00 |\n",
-       "|        NA |         NA |            False |         False |      -3.72e+00 |\n",
-       "|        NA |         NA |            False |         False |      -3.31e+00 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "\n",
-       "+-----------------------+\n",
-       "| info.inbreeding_coeff |\n",
-       "+-----------------------+\n",
-       "|               float64 |\n",
-       "+-----------------------+\n",
-       "|             -1.65e-05 |\n",
-       "|             -3.15e-05 |\n",
-       "|             -8.24e-06 |\n",
-       "|             -4.64e-05 |\n",
-       "|             -2.41e-05 |\n",
-       "+-----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| info.vrs.VRS_Allele_IDs                                                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.oTAtTrgYxm81O9fu6Mrhfo1t3eHsgg4L\",\"ga4gh:VA.Y283OnlLjyi1T1IT_J... |\n",
-       "| [\"ga4gh:VA.6pIoPAYDsphCprRcyrititenuEWlZaxV\",\"ga4gh:VA.iHyQ1mh_iQX6WcqXcx... |\n",
-       "| [\"ga4gh:VA.HdrkFJS16zwJPcGSKX9___fJcdqSATlQ\",\"ga4gh:VA.LSHuFx03sqw8WJGN2-... |\n",
-       "| [\"ga4gh:VA.xis4Nhtveh7q75mtkipQAUqZfWUfboWB\",\"ga4gh:VA.Ciou33oFTNEilAEegb... |\n",
-       "| [\"ga4gh:VA.TV0UyS-jCvsVkEOMbipUnzTc4hYuVNTg\",\"ga4gh:VA.33zGTKcHyj5wWpTfc3... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| info.vrs.VRS_Starts | info.vrs.VRS_Ends | info.vrs.VRS_States |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| array        | array      | array          |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| [10030,10030]       | [10031,10031]     | [\"T\",\"C\"]           |\n",
-       "| [10036,10036]       | [10037,10037]     | [\"T\",\"C\"]           |\n",
-       "| [10042,10042]       | [10043,10043]     | [\"T\",\"C\"]           |\n",
-       "| [10054,10054]       | [10055,10055]     | [\"T\",\"C\"]           |\n",
-       "| [10056,10056]       | [10057,10057]     | [\"A\",\"C\"]           |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "\n",
-       "+-------------------+---------+--------+---------------------------+\n",
-       "| vep.allele_string | vep.end | vep.id | vep.input                 |\n",
-       "+-------------------+---------+--------+---------------------------+\n",
-       "| str               |   int32 | str    | str                       |\n",
-       "+-------------------+---------+--------+---------------------------+\n",
-       "| \"T/C\"             |   10031 | \".\"    | \"chr1\t10031\t.\tT\tC\t.\t.\tGT\" |\n",
-       "| \"T/C\"             |   10037 | \".\"    | \"chr1\t10037\t.\tT\tC\t.\t.\tGT\" |\n",
-       "| \"T/C\"             |   10043 | \".\"    | \"chr1\t10043\t.\tT\tC\t.\t.\tGT\" |\n",
-       "| \"T/C\"             |   10055 | \".\"    | \"chr1\t10055\t.\tT\tC\t.\t.\tGT\" |\n",
-       "| \"A/C\"             |   10057 | \".\"    | \"chr1\t10057\t.\tA\tC\t.\t.\tGT\" |\n",
-       "+-------------------+---------+--------+---------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.intergenic_consequences                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, impact: st... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------+\n",
-       "| vep.most_severe_consequence |\n",
-       "+-----------------------------+\n",
-       "| str                         |\n",
-       "+-----------------------------+\n",
-       "| \"upstream_gene_variant\"     |\n",
-       "| \"upstream_gene_variant\"     |\n",
-       "| \"upstream_gene_variant\"     |\n",
-       "| \"upstream_gene_variant\"     |\n",
-       "| \"upstream_gene_variant\"     |\n",
-       "+-----------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.motif_feature_consequences                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, high_inf_p... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.regulatory_feature_consequences                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.bin_freq                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
-       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
-       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
-       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
-       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
-       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
-       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
-       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
-       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                        527 |\n",
-       "|                                        888 |\n",
-       "|                                       1232 |\n",
-       "|                                       2440 |\n",
-       "|                                       3622 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
-       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
-       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
-       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
-       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
-       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
-       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
-       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
-       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                            744 |\n",
-       "|                                           1130 |\n",
-       "|                                           1487 |\n",
-       "|                                           2622 |\n",
-       "|                                           3743 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,1,0,0,1,0,0,0,0,0]                      |\n",
-       "| [0,0,1,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,1,0,1,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                      int64 |                         float32 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        8.97e+00 |\n",
-       "|                                          0 |                        8.90e+00 |\n",
-       "|                                          0 |                        8.90e+00 |\n",
-       "|                                          0 |                        8.88e+00 |\n",
-       "|                                          0 |                        8.53e+00 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                            7.57e-01 |                             NA |\n",
-       "|                            7.49e-01 |                             NA |\n",
-       "|                            7.48e-01 |                             NA |\n",
-       "|                            7.46e-01 |                             NA |\n",
-       "|                            7.09e-01 |                             NA |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                       NA |                          NA |\n",
-       "|                                       NA |                          NA |\n",
-       "|                                       NA |                          NA |\n",
-       "|                                       NA |                          NA |\n",
-       "|                                       NA |                          NA |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22e6f759-a0ee-4e9c-8ca4-eb154cb08763",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### v4.1 Joint Frequency Hail Table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a569b77-d3d2-45a4-803a-1214c77e46f2",
-   "metadata": {},
-   "source": [
-    "The joint frequency Hail table includes frequency for the exomes, genomes, and the exomes+genomes. We have also added statistics for the combined exomes and genomes frequencies, more details on these stats can be found on our [Help](https://gnomad.broadinstitute.org/help/combined-freq-stats) page."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "46d4fc43-609d-4a16-8a0a-ab1e870b5d3d",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "c1c1fbb0-4ef9-4892-bd91-aae9985317a7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='joint', version='4.1')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "163df47b-70de-4e1e-91be-a65c90cf2db5",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "750c0111-4566-4b86-8c08-18c504ff1a79",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'exomes_globals': struct {\n",
-      "        freq_meta: array>, \n",
-      "        freq_index_dict: dict, \n",
-      "        freq_meta_sample_count: array, \n",
-      "        faf_meta: array>, \n",
-      "        faf_index_dict: dict, \n",
-      "        age_distribution: struct {\n",
-      "            bin_edges: array, \n",
-      "            bin_freq: array, \n",
-      "            n_smaller: int32, \n",
-      "            n_larger: int32\n",
-      "        }\n",
-      "    } \n",
-      "    'genomes_globals': struct {\n",
-      "        freq_meta: array>, \n",
-      "        freq_index_dict: dict, \n",
-      "        freq_meta_sample_count: array, \n",
-      "        faf_meta: array>, \n",
-      "        faf_index_dict: dict, \n",
-      "        age_distribution: struct {\n",
-      "            bin_edges: array, \n",
-      "            bin_freq: array, \n",
-      "            n_smaller: int32, \n",
-      "            n_larger: int32\n",
-      "        }\n",
-      "    } \n",
-      "    'joint_globals': struct {\n",
-      "        freq_meta: array>, \n",
-      "        freq_index_dict: dict, \n",
-      "        faf_meta: array>, \n",
-      "        faf_index_dict: dict, \n",
-      "        freq_meta_sample_count: array, \n",
-      "        age_distribution: struct {\n",
-      "            bin_edges: array, \n",
-      "            bin_freq: array, \n",
-      "            n_smaller: int32, \n",
-      "            n_larger: int32\n",
-      "        }\n",
-      "    } \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'alleles': array \n",
-      "    'region_flags': struct {\n",
-      "        fail_interval_qc: bool, \n",
-      "        outside_broad_capture_region: bool, \n",
-      "        outside_ukb_capture_region: bool, \n",
-      "        outside_broad_calling_region: bool, \n",
-      "        outside_ukb_calling_region: bool, \n",
-      "        not_called_in_exomes: bool, \n",
-      "        not_called_in_genomes: bool\n",
-      "    } \n",
-      "    'exomes': struct {\n",
-      "        filters: set, \n",
-      "        freq: array, \n",
-      "        faf: array, \n",
-      "        grpmax: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int64, \n",
-      "            gen_anc: str\n",
-      "        }, \n",
-      "        fafmax: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }, \n",
-      "        histograms: struct {\n",
-      "            qual_hists: struct {\n",
-      "                gq_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                gq_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                ab_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            raw_qual_hists: struct {\n",
-      "                gq_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                gq_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                ab_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            age_hists: struct {\n",
-      "                age_hist_het: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                age_hist_hom: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }\n",
-      "        }\n",
-      "    } \n",
-      "    'genomes': struct {\n",
-      "        filters: set, \n",
-      "        freq: array, \n",
-      "        faf: array, \n",
-      "        grpmax: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int32, \n",
-      "            gen_anc: str\n",
-      "        }, \n",
-      "        fafmax: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }, \n",
-      "        histograms: struct {\n",
-      "            qual_hists: struct {\n",
-      "                gq_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                gq_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                ab_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            raw_qual_hists: struct {\n",
-      "                gq_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                gq_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                ab_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            age_hists: struct {\n",
-      "                age_hist_het: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                age_hist_hom: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }\n",
-      "        }\n",
-      "    } \n",
-      "    'joint': struct {\n",
-      "        freq: array, \n",
-      "        faf: array, \n",
-      "        fafmax: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }, \n",
-      "        grpmax: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int32, \n",
-      "            gen_anc: str\n",
-      "        }, \n",
-      "        histograms: struct {\n",
-      "            qual_hists: struct {\n",
-      "                gq_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                gq_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                ab_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            raw_qual_hists: struct {\n",
-      "                gq_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                gq_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                ab_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            age_hists: struct {\n",
-      "                age_hist_het: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                age_hist_hom: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }\n",
-      "        }\n",
-      "    } \n",
-      "    'freq_comparison_stats': struct {\n",
-      "        contingency_table_test: array, \n",
-      "        cochran_mantel_haenszel_test: struct {\n",
-      "            p_value: float64, \n",
-      "            chisq: float64\n",
-      "        }, \n",
-      "        stat_union: struct {\n",
-      "            p_value: float64, \n",
-      "            stat_test_name: str, \n",
-      "            gen_ancs: array\n",
-      "        }\n",
-      "    } \n",
-      "----------------------------------------\n",
-      "Key: ['locus', 'alleles']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6843db4-9e8f-42f4-9178-fc945f61a827",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "477ed281-4ee9-4799-b5b6-e6a0c528fa9a",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
exomes
genomes
joint
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stat_union
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faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
AC
AF
AN
homozygote_count
gen_anc
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
contingency_table_test
p_value
chisq
p_value
stat_test_name
gen_ancs
locus<GRCh38>array<str>boolboolboolboolboolboolboolset<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>int32float64int32int64strfloat64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64set<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>int32float64int32int32strfloat64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>float64strfloat64strint32float64int32int32strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{p_value: float64, odds_ratio: float64}>float64float64float64strarray<str>
chr1:10031["T","C"]NATrueTrueTrueTrueTrueFalseNA[(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA)]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA{"AC0","AS_VQSR"}[(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e+00,4326,0),(0,0.00e+00,192,0),(0,0.00e+00,782,0),(0,0.00e+00,352,0),(0,0.00e+00,1550,0),(0,0.00e+00,14642,0),(0,0.00e+00,1712,0),(0,0.00e+00,1120,0),(0,0.00e+00,6420,0),(0,0.00e+00,29308,0),(0,0.00e+00,27334,0),(0,0.00e+00,14998,0),(0,0.00e+00,1060,0),(0,0.00e+00,102,0),(0,0.00e+00,402,0),(0,0.00e+00,154,0),(0,0.00e+00,814,0),(0,0.00e+00,7978,0),(0,0.00e+00,770,0),(0,0.00e+00,260,0),(0,0.00e+00,2770,0),(0,0.00e+00,10548,0),(0,0.00e+00,3266,0),(0,0.00e+00,90,0),(0,0.00e+00,380,0),(0,0.00e+00,198,0),(0,0.00e+00,736,0),(0,0.00e+00,6664,0),(0,0.00e+00,942,0),(0,0.00e+00,860,0),(0,0.00e+00,3650,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANANANANANA[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e+00,4326,0),(0,0.00e+00,192,0),(0,0.00e+00,782,0),(0,0.00e+00,352,0),(0,0.00e+00,1550,0),(0,0.00e+00,14642,0),(0,0.00e+00,1712,0),(0,0.00e+00,1120,0),(0,0.00e+00,6420,0),(0,0.00e+00,29308,0),(0,0.00e+00,27334,0),(0,0.00e+00,14998,0),(0,0.00e+00,1060,0),(0,0.00e+00,102,0),(0,0.00e+00,402,0),(0,0.00e+00,154,0),(0,0.00e+00,814,0),(0,0.00e+00,7978,0),(0,0.00e+00,770,0),(0,0.00e+00,260,0),(0,0.00e+00,2770,0),(0,0.00e+00,10548,0),(0,0.00e+00,3266,0),(0,0.00e+00,90,0),(0,0.00e+00,380,0),(0,0.00e+00,198,0),(0,0.00e+00,736,0),(0,0.00e+00,6664,0),(0,0.00e+00,942,0),(0,0.00e+00,860,0),(0,0.00e+00,3650,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,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chr1:10057["A","C"]NATrueTrueTrueTrueTrueFalseNA[(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA)]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA{"AS_VQSR"}[(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00e+00,6886,0),(0,0.00e+00,266,0),(0,0.00e+00,1504,0),(0,0.00e+00,742,0),(0,0.00e+00,2768,0),(1,3.25e-05,30736,0),(0,0.00e+00,3850,0),(0,0.00e+00,3424,0),(0,0.00e+00,10448,0),(2,3.37e-05,59380,0),(1,1.85e-05,54156,0),(2,6.43e-05,31106,0),(0,0.00e+00,1472,0),(0,0.00e+00,132,0),(0,0.00e+00,764,0),(0,0.00e+00,378,0),(0,0.00e+00,1532,0),(0,0.00e+00,16598,0),(0,0.00e+00,1670,0),(0,0.00e+00,784,0),(0,0.00e+00,4944,0),(0,0.00e+00,21806,0),(0,0.00e+00,5414,0),(0,0.00e+00,134,0),(0,0.00e+00,740,0),(0,0.00e+00,364,0),(0,0.00e+00,1236,0),(1,7.07e-05,14138,0),(0,0.00e+00,2180,0),(0,0.00e+00,2640,0),(0,0.00e+00,5504,0)][(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]23.78e-05529120"nfe"6.27e-06"nfe"2.35e-06"nfe"[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00e+00,6886,0),(0,0.00e+00,266,0),(0,0.00e+00,1504,0),(0,0.00e+00,742,0),(0,0.00e+00,2768,0),(1,3.25e-05,30736,0),(0,0.00e+00,3850,0),(0,0.00e+00,3424,0),(0,0.00e+00,10448,0),(2,3.37e-05,59380,0),(1,1.85e-05,54156,0),(2,6.43e-05,31106,0),(0,0.00e+00,1472,0),(0,0.00e+00,132,0),(0,0.00e+00,764,0),(0,0.00e+00,378,0),(0,0.00e+00,1532,0),(0,0.00e+00,16598,0),(0,0.00e+00,1670,0),(0,0.00e+00,784,0),(0,0.00e+00,4944,0),(0,0.00e+00,21806,0),(0,0.00e+00,5414,0),(0,0.00e+00,134,0),(0,0.00e+00,740,0),(0,0.00e+00,364,0),(0,0.00e+00,1236,0),(1,7.07e-05,14138,0),(0,0.00e+00,2180,0),(0,0.00e+00,2640,0),(0,0.00e+00,5504,0)][(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]6.27e-06"nfe"2.35e-06"nfe"23.78e-05529120"nfe"[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANA
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+------------+-------------------------------+\n",
-       "| locus         | alleles    | region_flags.fail_interval_qc |\n",
-       "+---------------+------------+-------------------------------+\n",
-       "| locus | array |                          bool |\n",
-       "+---------------+------------+-------------------------------+\n",
-       "| chr1:10031    | [\"T\",\"C\"]  |                            NA |\n",
-       "| chr1:10037    | [\"T\",\"C\"]  |                            NA |\n",
-       "| chr1:10043    | [\"T\",\"C\"]  |                            NA |\n",
-       "| chr1:10055    | [\"T\",\"C\"]  |                            NA |\n",
-       "| chr1:10057    | [\"A\",\"C\"]  |                            NA |\n",
-       "+---------------+------------+-------------------------------+\n",
-       "\n",
-       "+-------------------------------------------+\n",
-       "| region_flags.outside_broad_capture_region |\n",
-       "+-------------------------------------------+\n",
-       "|                                      bool |\n",
-       "+-------------------------------------------+\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "+-------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------+\n",
-       "| region_flags.outside_ukb_capture_region |\n",
-       "+-----------------------------------------+\n",
-       "|                                    bool |\n",
-       "+-----------------------------------------+\n",
-       "|                                    True |\n",
-       "|                                    True |\n",
-       "|                                    True |\n",
-       "|                                    True |\n",
-       "|                                    True |\n",
-       "+-----------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------+\n",
-       "| region_flags.outside_broad_calling_region |\n",
-       "+-------------------------------------------+\n",
-       "|                                      bool |\n",
-       "+-------------------------------------------+\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "|                                      True |\n",
-       "+-------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------+-----------------------------------+\n",
-       "| region_flags.outside_ukb_calling_region | region_flags.not_called_in_exomes |\n",
-       "+-----------------------------------------+-----------------------------------+\n",
-       "|                                    bool |                              bool |\n",
-       "+-----------------------------------------+-----------------------------------+\n",
-       "|                                    True |                              True |\n",
-       "|                                    True |                              True |\n",
-       "|                                    True |                              True |\n",
-       "|                                    True |                              True |\n",
-       "|                                    True |                              True |\n",
-       "+-----------------------------------------+-----------------------------------+\n",
-       "\n",
-       "+------------------------------------+----------------+\n",
-       "| region_flags.not_called_in_genomes | exomes.filters |\n",
-       "+------------------------------------+----------------+\n",
-       "|                               bool | set       |\n",
-       "+------------------------------------+----------------+\n",
-       "|                              False | NA             |\n",
-       "|                              False | NA             |\n",
-       "|                              False | NA             |\n",
-       "|                              False | NA             |\n",
-       "|                              False | NA             |\n",
-       "+------------------------------------+----------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| exomes.freq                                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
-       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
-       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
-       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
-       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------+------------------+\n",
-       "| exomes.faf                                    | exomes.grpmax.AC |\n",
-       "+-----------------------------------------------+------------------+\n",
-       "| array |            int32 |\n",
-       "+-----------------------------------------------+------------------+\n",
-       "| NA                                            |               NA |\n",
-       "| NA                                            |               NA |\n",
-       "| NA                                            |               NA |\n",
-       "| NA                                            |               NA |\n",
-       "| NA                                            |               NA |\n",
-       "+-----------------------------------------------+------------------+\n",
-       "\n",
-       "+------------------+------------------+--------------------------------+\n",
-       "| exomes.grpmax.AF | exomes.grpmax.AN | exomes.grpmax.homozygote_count |\n",
-       "+------------------+------------------+--------------------------------+\n",
-       "|          float64 |            int32 |                          int64 |\n",
-       "+------------------+------------------+--------------------------------+\n",
-       "|               NA |               NA |                             NA |\n",
-       "|               NA |               NA |                             NA |\n",
-       "|               NA |               NA |                             NA |\n",
-       "|               NA |               NA |                             NA |\n",
-       "|               NA |               NA |                             NA |\n",
-       "+------------------+------------------+--------------------------------+\n",
-       "\n",
-       "+-----------------------+-------------------------+\n",
-       "| exomes.grpmax.gen_anc | exomes.fafmax.faf95_max |\n",
-       "+-----------------------+-------------------------+\n",
-       "| str                   |                 float64 |\n",
-       "+-----------------------+-------------------------+\n",
-       "| NA                    |                      NA |\n",
-       "| NA                    |                      NA |\n",
-       "| NA                    |                      NA |\n",
-       "| NA                    |                      NA |\n",
-       "| NA                    |                      NA |\n",
-       "+-----------------------+-------------------------+\n",
-       "\n",
-       "+---------------------------------+-------------------------+\n",
-       "| exomes.fafmax.faf95_max_gen_anc | exomes.fafmax.faf99_max |\n",
-       "+---------------------------------+-------------------------+\n",
-       "| str                             |                 float64 |\n",
-       "+---------------------------------+-------------------------+\n",
-       "| NA                              |                      NA |\n",
-       "| NA                              |                      NA |\n",
-       "| NA                              |                      NA |\n",
-       "| NA                              |                      NA |\n",
-       "| NA                              |                      NA |\n",
-       "+---------------------------------+-------------------------+\n",
-       "\n",
-       "+---------------------------------+\n",
-       "| exomes.fafmax.faf99_max_gen_anc |\n",
-       "+---------------------------------+\n",
-       "| str                             |\n",
-       "+---------------------------------+\n",
-       "| NA                              |\n",
-       "| NA                              |\n",
-       "| NA                              |\n",
-       "| NA                              |\n",
-       "| NA                              |\n",
-       "+---------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_all.bin_edges |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+----------------------------------------------------+\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_all.bin_freq |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_all.bin_edges |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+----------------------------------------------------+\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_all.bin_freq |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_alt.bin_edges |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+----------------------------------------------------+\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_alt.bin_edges |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+----------------------------------------------------+\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.ab_hist_alt.bin_edges |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+----------------------------------------------------+\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_all.bin_edges |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_all.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_all.bin_edges |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_all.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_alt.bin_edges |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_alt.bin_edges |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.ab_hist_alt.bin_edges |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| exomes.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_het.bin_edges |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+----------------------------------------------------+\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_het.n_larger |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "|                                                NA |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_hom.bin_edges |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+----------------------------------------------------+\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "| NA                                                 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "| NA                                                |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "|                                                 NA |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+-------------------+\n",
-       "| exomes.histograms.age_hists.age_hist_hom.n_larger | genomes.filters   |\n",
-       "+---------------------------------------------------+-------------------+\n",
-       "|                                             int64 | set          |\n",
-       "+---------------------------------------------------+-------------------+\n",
-       "|                                                NA | {\"AC0\",\"AS_VQSR\"} |\n",
-       "|                                                NA | {\"AS_VQSR\"}       |\n",
-       "|                                                NA | {\"AS_VQSR\"}       |\n",
-       "|                                                NA | {\"AS_VQSR\"}       |\n",
-       "|                                                NA | {\"AS_VQSR\"}       |\n",
-       "+---------------------------------------------------+-------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.freq                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e... |\n",
-       "| [(2,2.60e-05,76882,0),(4,3.15e-05,126902,0),(0,0.00e+00,34670,0),(1,1.80e... |\n",
-       "| [(1,1.17e-05,85634,0),(1,8.24e-06,121430,0),(0,0.00e+00,39236,0),(0,0.00e... |\n",
-       "| [(1,1.06e-05,94224,0),(5,4.64e-05,107704,0),(0,0.00e+00,44382,0),(1,1.80e... |\n",
-       "| [(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.faf                                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------+-------------------+-------------------+\n",
-       "| genomes.grpmax.AC | genomes.grpmax.AF | genomes.grpmax.AN |\n",
-       "+-------------------+-------------------+-------------------+\n",
-       "|             int32 |           float64 |             int32 |\n",
-       "+-------------------+-------------------+-------------------+\n",
-       "|                NA |                NA |                NA |\n",
-       "|                 1 |          4.07e-04 |              2456 |\n",
-       "|                 1 |          4.39e-05 |             22760 |\n",
-       "|                NA |                NA |                NA |\n",
-       "|                 2 |          3.78e-05 |             52912 |\n",
-       "+-------------------+-------------------+-------------------+\n",
-       "\n",
-       "+---------------------------------+------------------------+\n",
-       "| genomes.grpmax.homozygote_count | genomes.grpmax.gen_anc |\n",
-       "+---------------------------------+------------------------+\n",
-       "|                           int32 | str                    |\n",
-       "+---------------------------------+------------------------+\n",
-       "|                              NA | NA                     |\n",
-       "|                               0 | \"eas\"                  |\n",
-       "|                               0 | \"afr\"                  |\n",
-       "|                              NA | NA                     |\n",
-       "|                               0 | \"nfe\"                  |\n",
-       "+---------------------------------+------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| genomes.fafmax.faf95_max | genomes.fafmax.faf95_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                 6.27e-06 | \"nfe\"                            |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| genomes.fafmax.faf99_max | genomes.fafmax.faf99_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                 2.35e-06 | \"nfe\"                            |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_all.bin_edges                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_all.bin_freq                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
-       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
-       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
-       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
-       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                               int64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_all.bin_edges                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_all.bin_freq                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
-       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
-       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
-       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
-       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                               int64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                527 |\n",
-       "|                                                888 |\n",
-       "|                                               1232 |\n",
-       "|                                               2440 |\n",
-       "|                                               3622 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_alt.bin_edges                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                       |\n",
-       "+----------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]          |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                               int64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_alt.bin_edges                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                       |\n",
-       "+----------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                               int64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.ab_hist_alt.bin_edges                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                       |\n",
-       "+----------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                               int64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_all.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_all.bin_freq                       |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
-       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
-       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
-       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
-       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_all.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_all.bin_freq                       |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
-       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
-       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
-       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
-       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                    744 |\n",
-       "|                                                   1130 |\n",
-       "|                                                   1487 |\n",
-       "|                                                   2622 |\n",
-       "|                                                   3743 |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_alt.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+--------------------------------------------------------+\n",
-       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]              |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_alt.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+--------------------------------------------------------+\n",
-       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.ab_hist_alt.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+--------------------------------------------------------+\n",
-       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| genomes.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_het.bin_edges                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                       |\n",
-       "+----------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
-       "| [0,1,0,0,1,0,0,0,0,0]                              |\n",
-       "| [0,0,1,0,0,0,0,0,0,0]                              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
-       "| [0,0,1,0,1,0,0,0,0,0]                              |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                               int64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_het.n_larger |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_hom.bin_edges                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                       |\n",
-       "+----------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                               int64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "|                                                   0 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| genomes.histograms.age_hists.age_hist_hom.n_larger |\n",
-       "+----------------------------------------------------+\n",
-       "|                                              int64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "|                                                  0 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.freq                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e... |\n",
-       "| [(2,2.60e-05,76882,0),(4,3.15e-05,126902,0),(0,0.00e+00,34670,0),(1,1.80e... |\n",
-       "| [(1,1.17e-05,85634,0),(1,8.24e-06,121430,0),(0,0.00e+00,39236,0),(0,0.00e... |\n",
-       "| [(1,1.06e-05,94224,0),(5,4.64e-05,107704,0),(0,0.00e+00,44382,0),(1,1.80e... |\n",
-       "| [(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.faf                                                                    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------+--------------------------------+\n",
-       "| joint.fafmax.faf95_max | joint.fafmax.faf95_max_gen_anc |\n",
-       "+------------------------+--------------------------------+\n",
-       "|                float64 | str                            |\n",
-       "+------------------------+--------------------------------+\n",
-       "|                     NA | NA                             |\n",
-       "|                     NA | NA                             |\n",
-       "|                     NA | NA                             |\n",
-       "|                     NA | NA                             |\n",
-       "|               6.27e-06 | \"nfe\"                          |\n",
-       "+------------------------+--------------------------------+\n",
-       "\n",
-       "+------------------------+--------------------------------+-----------------+\n",
-       "| joint.fafmax.faf99_max | joint.fafmax.faf99_max_gen_anc | joint.grpmax.AC |\n",
-       "+------------------------+--------------------------------+-----------------+\n",
-       "|                float64 | str                            |           int32 |\n",
-       "+------------------------+--------------------------------+-----------------+\n",
-       "|                     NA | NA                             |              NA |\n",
-       "|                     NA | NA                             |               1 |\n",
-       "|                     NA | NA                             |               1 |\n",
-       "|                     NA | NA                             |              NA |\n",
-       "|               2.35e-06 | \"nfe\"                          |               2 |\n",
-       "+------------------------+--------------------------------+-----------------+\n",
-       "\n",
-       "+-----------------+-----------------+-------------------------------+\n",
-       "| joint.grpmax.AF | joint.grpmax.AN | joint.grpmax.homozygote_count |\n",
-       "+-----------------+-----------------+-------------------------------+\n",
-       "|         float64 |           int32 |                         int32 |\n",
-       "+-----------------+-----------------+-------------------------------+\n",
-       "|              NA |              NA |                            NA |\n",
-       "|        4.07e-04 |            2456 |                             0 |\n",
-       "|        4.39e-05 |           22760 |                             0 |\n",
-       "|              NA |              NA |                            NA |\n",
-       "|        3.78e-05 |           52912 |                             0 |\n",
-       "+-----------------+-----------------+-------------------------------+\n",
-       "\n",
-       "+----------------------+\n",
-       "| joint.grpmax.gen_anc |\n",
-       "+----------------------+\n",
-       "| str                  |\n",
-       "+----------------------+\n",
-       "| NA                   |\n",
-       "| \"eas\"                |\n",
-       "| \"afr\"                |\n",
-       "| NA                   |\n",
-       "| \"nfe\"                |\n",
-       "+----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_all.bin_edges                            |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_all.bin_freq                             |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
-       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
-       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
-       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
-       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_all.bin_edges                            |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_all.bin_freq                             |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
-       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
-       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
-       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
-       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                              527 |\n",
-       "|                                              888 |\n",
-       "|                                             1232 |\n",
-       "|                                             2440 |\n",
-       "|                                             3622 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_alt.bin_edges                            |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]        |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_alt.bin_edges                            |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.ab_hist_alt.bin_edges                            |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_all.bin_edges                        |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_all.bin_freq                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
-       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
-       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
-       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
-       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_all.bin_edges                        |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_all.bin_freq                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
-       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
-       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
-       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
-       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                  744 |\n",
-       "|                                                 1130 |\n",
-       "|                                                 1487 |\n",
-       "|                                                 2622 |\n",
-       "|                                                 3743 |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_alt.bin_edges                        |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+------------------------------------------------------+\n",
-       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]            |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_alt.bin_edges                        |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+------------------------------------------------------+\n",
-       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.ab_hist_alt.bin_edges                        |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+------------------------------------------------------+\n",
-       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_het.bin_edges                            |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,1,0,0,1,0,0,0,0,0]                            |\n",
-       "| [0,0,1,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,1,0,1,0,0,0,0,0]                            |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_hom.bin_edges                            |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_hom.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq_comparison_stats.contingency_table_test                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------+\n",
-       "| freq_comparison_stats.cochran_mantel_haenszel_test.p_value |\n",
-       "+------------------------------------------------------------+\n",
-       "|                                                    float64 |\n",
-       "+------------------------------------------------------------+\n",
-       "|                                                         NA |\n",
-       "|                                                         NA |\n",
-       "|                                                         NA |\n",
-       "|                                                         NA |\n",
-       "|                                                         NA |\n",
-       "+------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------------+\n",
-       "| freq_comparison_stats.cochran_mantel_haenszel_test.chisq |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                  float64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "+----------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+\n",
-       "| freq_comparison_stats.stat_union.p_value |\n",
-       "+------------------------------------------+\n",
-       "|                                  float64 |\n",
-       "+------------------------------------------+\n",
-       "|                                       NA |\n",
-       "|                                       NA |\n",
-       "|                                       NA |\n",
-       "|                                       NA |\n",
-       "|                                       NA |\n",
-       "+------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| freq_comparison_stats.stat_union.stat_test_name |\n",
-       "+-------------------------------------------------+\n",
-       "| str                                             |\n",
-       "+-------------------------------------------------+\n",
-       "| NA                                              |\n",
-       "| NA                                              |\n",
-       "| NA                                              |\n",
-       "| NA                                              |\n",
-       "| NA                                              |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------+\n",
-       "| freq_comparison_stats.stat_union.gen_ancs |\n",
-       "+-------------------------------------------+\n",
-       "| array                                |\n",
-       "+-------------------------------------------+\n",
-       "| NA                                        |\n",
-       "| NA                                        |\n",
-       "| NA                                        |\n",
-       "| NA                                        |\n",
-       "| NA                                        |\n",
-       "+-------------------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "354d7a5e-07a2-4f33-a830-970877cd4d63",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## All sites allele numbers\n",
-    "\n",
-    "As part of gnomAD v4.1, we [released](https://gnomad.broadinstitute.org/data#v4-all-sites-allele-number) allele number across all callable sites in the gnomAD exomes and genomes. For more information, see our [v4.1 blog post](https://gnomad.broadinstitute.org/news/2024-04-gnomad-v4-1/#allele-numbers-across-all-possible-sites)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81008401-eec4-4e95-9709-4781db066f7f",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Exomes all sites allele number Hail Table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f7f1c013-a013-4fde-a7e6-fcb18d8d8a5c",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "3b7c75d0-1eec-4b92-883e-410337b09c92",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='exomes', version='4.1', dataset=\"all_sites_an\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6868a2d1-6e62-492a-8086-822c910e8608",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "f9c6d73b-7683-47fe-bf7d-2f5bef1d23d3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'strata_meta': array> \n",
-      "    'strata_sample_count': array \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'AN': array \n",
-      "    'outside_broad_capture_region': bool \n",
-      "    'outside_ukb_capture_region': bool \n",
-      "    'outside_broad_calling_region': bool \n",
-      "    'outside_ukb_calling_region': bool \n",
-      "----------------------------------------\n",
-      "Key: ['locus']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "626f20d9-43c1-4687-9b05-01d53115c168",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "ee361058-54ae-4793-951b-1e0a6df6f685",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
locus
AN
outside_broad_capture_region
outside_ukb_capture_region
outside_broad_calling_region
outside_ukb_calling_region
locus<GRCh38>array<int64>boolboolboolbool
chr1:11719[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11720[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11721[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11722[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11723[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+\n",
-       "| locus         |\n",
-       "+---------------+\n",
-       "| locus |\n",
-       "+---------------+\n",
-       "| chr1:11719    |\n",
-       "| chr1:11720    |\n",
-       "| chr1:11721    |\n",
-       "| chr1:11722    |\n",
-       "| chr1:11723    |\n",
-       "+---------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| AN                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
-       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
-       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
-       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
-       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------+----------------------------+\n",
-       "| outside_broad_capture_region | outside_ukb_capture_region |\n",
-       "+------------------------------+----------------------------+\n",
-       "|                         bool |                       bool |\n",
-       "+------------------------------+----------------------------+\n",
-       "|                         True |                       True |\n",
-       "|                         True |                       True |\n",
-       "|                         True |                       True |\n",
-       "|                         True |                       True |\n",
-       "|                         True |                       True |\n",
-       "+------------------------------+----------------------------+\n",
-       "\n",
-       "+------------------------------+----------------------------+\n",
-       "| outside_broad_calling_region | outside_ukb_calling_region |\n",
-       "+------------------------------+----------------------------+\n",
-       "|                         bool |                       bool |\n",
-       "+------------------------------+----------------------------+\n",
-       "|                        False |                       True |\n",
-       "|                        False |                       True |\n",
-       "|                        False |                       True |\n",
-       "|                        False |                       True |\n",
-       "|                        False |                       True |\n",
-       "+------------------------------+----------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc4a4f23-d754-4e31-8e59-f62f9be65942",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Genomes all sites allele number Hail Table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "65cb8d93-c5ef-409b-9c51-7c282a63bdc2",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "0bb38926-f803-4be5-852c-782023b387bb",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='genomes', version='4.1', dataset=\"all_sites_an\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7d5c2549-151c-4b99-bac3-23fd9024f114",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "64d2c64c-b533-433a-89e6-72d473bd6464",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'strata_meta': array> \n",
-      "    'strata_sample_count': array \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'AN': array \n",
-      "----------------------------------------\n",
-      "Key: ['locus']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "140f66aa-83d4-4752-abcf-c674bf208194",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "e2271a6e-16f6-48ca-805f-735e17a8f711",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
locus
AN
locus<GRCh38>array<int64>
chr1:10001[16,18232,0,0,4,0,0,6,0,6,0,0,8,8,0,0,0,0,4,0,0,0,0,0,2,4,0,0,2,4,0,0,0,0]
chr1:10002[78,32090,12,0,22,2,0,26,0,14,2,0,30,48,8,4,0,0,4,18,2,0,0,0,12,14,0,0,2,12,2,0,0,0]
chr1:10003[200,38154,48,0,48,6,4,28,6,48,6,6,102,98,38,10,0,0,8,40,6,0,0,4,14,14,2,4,26,22,6,0,2,4]
chr1:10004[948,62380,202,0,248,18,12,172,10,258,18,10,400,548,112,90,0,0,66,182,14,4,2,10,70,102,4,6,118,140,10,8,4,6]
chr1:10005[1774,70720,356,6,444,30,18,288,18,552,32,30,782,992,206,150,2,4,134,310,18,12,6,12,116,172,6,12,268,284,16,16,10,20]
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+\n",
-       "| locus         |\n",
-       "+---------------+\n",
-       "| locus |\n",
-       "+---------------+\n",
-       "| chr1:10001    |\n",
-       "| chr1:10002    |\n",
-       "| chr1:10003    |\n",
-       "| chr1:10004    |\n",
-       "| chr1:10005    |\n",
-       "+---------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| AN                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [16,18232,0,0,4,0,0,6,0,6,0,0,8,8,0,0,0,0,4,0,0,0,0,0,2,4,0,0,2,4,0,0,0,0]   |\n",
-       "| [78,32090,12,0,22,2,0,26,0,14,2,0,30,48,8,4,0,0,4,18,2,0,0,0,12,14,0,0,2,... |\n",
-       "| [200,38154,48,0,48,6,4,28,6,48,6,6,102,98,38,10,0,0,8,40,6,0,0,4,14,14,2,... |\n",
-       "| [948,62380,202,0,248,18,12,172,10,258,18,10,400,548,112,90,0,0,66,182,14,... |\n",
-       "| [1774,70720,356,6,444,30,18,288,18,552,32,30,782,992,206,150,2,4,134,310,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1bf9f31f-34ff-4385-a80e-985cbb0acfe8",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Coverage\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "de70c319-787b-4d6c-9058-255a1137d81f",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Exomes coverage Hail Table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3278430c-4279-4d89-85e7-276184ec42b8",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "7f005af0-df6d-4a0a-a2e0-9cb4ff6782c2",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='exomes', version='4.0', dataset=\"coverage\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "128e58ce-c219-472a-88be-6babc2ba5a15",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "6058bf38-4ce9-4d37-89a7-d11af0e4d9f1",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    None\n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'mean': float64 \n",
-      "    'median_approx': int32 \n",
-      "    'total_DP': int64 \n",
-      "    'over_1': float64 \n",
-      "    'over_5': float64 \n",
-      "    'over_10': float64 \n",
-      "    'over_15': float64 \n",
-      "    'over_20': float64 \n",
-      "    'over_25': float64 \n",
-      "    'over_30': float64 \n",
-      "    'over_50': float64 \n",
-      "    'over_100': float64 \n",
-      "----------------------------------------\n",
-      "Key: ['locus']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5969ab0c-7cee-4061-8740-8b82366ae806",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "49fcf64b-1a91-4052-bb98-b329a0e8031b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
locus
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32int64float64float64float64float64float64float64float64float64float64
chr1:118191.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118201.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118211.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118221.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118231.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "| locus         |     mean | median_approx | total_DP |   over_1 |   over_5 |\n",
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "| locus |  float64 |         int32 |    int64 |  float64 |  float64 |\n",
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "| chr1:11819    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "| chr1:11820    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "| chr1:11821    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "| chr1:11822    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "| chr1:11823    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "|  over_10 |  over_15 |  over_20 |  over_25 |  over_30 |  over_50 | over_100 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "|  float64 |  float64 |  float64 |  float64 |  float64 |  float64 |  float64 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "19b2e9be-48fd-4859-af3d-a0775656d24c",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Genomes coverage Hail Table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5918f8e0-a723-439a-a11f-558d5ed13be8",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "a8d0be07-c35d-425a-b554-c86034e367fc",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='genomes', version='3.0', dataset=\"coverage\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d129b898-d642-44d1-8243-66a9cca8d1b1",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "18afb20f-7429-4fe2-a6c5-73a22dcbdb76",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    None\n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'mean': float64 \n",
-      "    'median': int32 \n",
-      "    'count_array': array \n",
-      "    'over_1': float32 \n",
-      "    'over_5': float32 \n",
-      "    'over_10': float32 \n",
-      "    'over_15': float32 \n",
-      "    'over_20': float32 \n",
-      "    'over_25': float32 \n",
-      "    'over_30': float32 \n",
-      "    'over_50': float32 \n",
-      "    'over_100': float32 \n",
-      "----------------------------------------\n",
-      "Key: ['locus']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05d8e22c-93d6-4ecf-b9e7-711945268c82",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "b27cb655-3abb-4501-bcc9-3f634db64591",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
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locus
mean
median
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over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32array<int32>float32float32float32float32float32float32float32float32float32
chr1:100011.93e+0116[0,20,65,141,226,328,386,442,415,385,292,198,362,367,337,273,289,266,241,226,191,169,168,151,227,194,179,190,177,166,143,131,150,130,125,121,136,94,93,83,67,60,68,68,59,33,39,39,39,38,20,18,21,25,10,9,16,8,7,6,2,3,2,4,1,2,2,2,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]1.25e-011.19e-019.12e-026.95e-025.15e-023.88e-022.62e-022.27e-030.00e+00
chr1:100022.10e+0118[0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,435,417,366,346,320,437,415,405,359,333,308,283,266,272,248,218,231,241,184,176,162,138,119,127,137,118,63,82,87,66,66,46,33,39,43,22,25,26,19,19,11,7,6,7,5,3,5,2,4,2,6,2,3,2,0,1,1,1,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]2.20e-012.15e-011.79e-011.39e-011.02e-017.60e-025.06e-024.83e-032.79e-05
chr1:100032.44e+0123[0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,658,649,590,548,486,679,656,640,571,533,491,439,398,412,404,349,383,360,298,263,242,207,182,186,194,159,118,123,116,96,96,67,59,61,64,34,33,34,31,30,15,12,11,13,10,7,7,3,7,5,10,3,3,5,0,2,2,1,0,0,2,1,1,1,4,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,4]2.62e-012.59e-012.41e-012.07e-011.59e-011.18e-017.77e-027.61e-035.58e-05
chr1:100042.43e+0123[0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1242,1181,1162,1083,966,845,1149,1088,1047,922,857,804,725,645,633,658,525,610,537,451,411,369,343,285,290,260,235,184,190,174,151,152,96,83,96,91,52,52,56,43,47,30,20,19,22,16,13,9,9,10,10,14,8,7,8,0,5,3,3,2,1,2,1,1,1,5,3,3,2,1,0,2,0,0,1,0,1,0,1,1,0,0,12]4.27e-014.24e-013.99e-013.43e-012.60e-011.87e-011.21e-011.20e-021.67e-04
chr1:100052.45e+0123[0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1401,1341,1300,1266,1105,966,1288,1243,1198,1068,976,905,842,725,728,740,600,678,613,515,464,414,396,338,324,300,268,213,210,198,175,165,113,100,108,102,61,58,61,50,53,35,22,22,27,22,15,11,12,10,13,14,10,8,9,1,6,4,6,5,3,3,4,2,2,5,3,7,3,1,0,2,0,1,2,2,2,0,1,1,0,0,17]4.83e-014.80e-014.56e-013.92e-012.96e-012.14e-011.38e-011.42e-022.37e-04
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+----------+--------+\n",
-       "| locus         |     mean | median |\n",
-       "+---------------+----------+--------+\n",
-       "| locus |  float64 |  int32 |\n",
-       "+---------------+----------+--------+\n",
-       "| chr1:10001    | 1.93e+01 |     16 |\n",
-       "| chr1:10002    | 2.10e+01 |     18 |\n",
-       "| chr1:10003    | 2.44e+01 |     23 |\n",
-       "| chr1:10004    | 2.43e+01 |     23 |\n",
-       "| chr1:10005    | 2.45e+01 |     23 |\n",
-       "+---------------+----------+--------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| count_array                                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,20,65,141,226,328,386,442,415,385,292,198,362,367,337,273,289,266,241,... |\n",
-       "| [0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,... |\n",
-       "| [0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,65... |\n",
-       "| [0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1... |\n",
-       "| [0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "|   over_1 |   over_5 |  over_10 |  over_15 |  over_20 |  over_25 |  over_30 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "|  float32 |  float32 |  float32 |  float32 |  float32 |  float32 |  float32 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "| 1.25e-01 | 1.19e-01 | 9.12e-02 | 6.95e-02 | 5.15e-02 | 3.88e-02 | 2.62e-02 |\n",
-       "| 2.20e-01 | 2.15e-01 | 1.79e-01 | 1.39e-01 | 1.02e-01 | 7.60e-02 | 5.06e-02 |\n",
-       "| 2.62e-01 | 2.59e-01 | 2.41e-01 | 2.07e-01 | 1.59e-01 | 1.18e-01 | 7.77e-02 |\n",
-       "| 4.27e-01 | 4.24e-01 | 3.99e-01 | 3.43e-01 | 2.60e-01 | 1.87e-01 | 1.21e-01 |\n",
-       "| 4.83e-01 | 4.80e-01 | 4.56e-01 | 3.92e-01 | 2.96e-01 | 2.14e-01 | 1.38e-01 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "\n",
-       "+----------+----------+\n",
-       "|  over_50 | over_100 |\n",
-       "+----------+----------+\n",
-       "|  float32 |  float32 |\n",
-       "+----------+----------+\n",
-       "| 2.27e-03 | 0.00e+00 |\n",
-       "| 4.83e-03 | 2.79e-05 |\n",
-       "| 7.61e-03 | 5.58e-05 |\n",
-       "| 1.20e-02 | 1.67e-04 |\n",
-       "| 1.42e-02 | 2.37e-04 |\n",
-       "+----------+----------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.2"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {
-    "height": "613.99px",
-    "width": "526.312px"
-   },
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": true,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "202.438px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "toc-autonumbering": false,
-  "toc-showcode": false,
-  "toc-showmarkdowntxt": true,
-  "toc-showtags": false,
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/gnomad_toolbox/notebooks/toolbox_for_gnomad_users.ipynb b/gnomad_toolbox/notebooks/toolbox_for_gnomad_users.ipynb
deleted file mode 100644
index 326a2a3..0000000
--- a/gnomad_toolbox/notebooks/toolbox_for_gnomad_users.ipynb
+++ /dev/null
@@ -1,3778 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "8e609a46",
-   "metadata": {
-    "toc": true
-   },
-   "source": [
-    "
Table of Contents
\n",
-    "
"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "853c94b9",
-   "metadata": {},
-   "source": [
-    "# README\n",
-    "\n",
-    "This toolbox is meant to use Hail tables of gnomAD releases on cloud computing, if you want to query variants for gene(s), you should use gnomAD API (https://gnomad.broadinstitute.org/api).\n",
-    "\n",
-    "If you want to import your own data to use other gnomAD notebooks, such as for ancestry inference (https://github.com/broadinstitute/gnomad_qc/blob/main/gnomad_qc/example_notebooks/ancestry_classification_using_gnomad_rf.ipynb), you may use Hail's `import_vcf` functions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "id": "e77d32b1",
-   "metadata": {
-    "scrolled": true,
-    "ExecuteTime": {
-     "end_time": "2024-12-06T18:02:57.909455Z",
-     "start_time": "2024-12-06T18:02:56.316003Z"
-    }
-   },
-   "source": "import hail as hl",
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "    \n",
-       "    
\n",
-       "        \n",
-       "        Loading BokehJS ...\n",
-       "    
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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*/\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"e603302b-40d4-4f8c-aa3c-1000266b48fa\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 1
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e713032",
-   "metadata": {},
-   "source": [
-    "# Import modules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "id": "e69953f7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T18:04:56.165634Z",
-     "start_time": "2024-12-06T18:04:55.603516Z"
-    }
-   },
-   "source": "from gnomad_toolbox.load_data import  get_gnomad_release",
-   "outputs": [],
-   "execution_count": 3
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5335a135",
-   "metadata": {},
-   "source": [
-    "# Import data\n",
-    "\n",
-    "You can choose which version of gnomAD release you want to look at, here we listed the available version per data type per reference build. \n",
-    "\n",
-    "Available versions for each data type are (as of 2024-10-29):\n",
-    "\n",
-    "| Data Type       | GRCh38 Versions                  | GRCh37 Versions      |\n",
-    "|-----------------|----------------------------------|----------------------|\n",
-    "| exomes          | 4.0, 4.1                         | 2.1, 2.1.1           |\n",
-    "| genomes         | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1           |\n",
-    "| joint           | 4.1                              | N/A                  |\n",
-    "\n",
-    "We use gnomAD v4.1 exomes to demonstrate for examples below."
-   ]
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "## Loading gnomAD v4.1 exomes sites Hail Table\n",
-   "id": "d1a4ae8933ba6421"
-  },
-  {
-   "cell_type": "code",
-   "id": "100cf576",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T18:08:08.225100Z",
-     "start_time": "2024-12-06T18:07:55.852971Z"
-    }
-   },
-   "source": "ht = get_gnomad_release(data_type='exomes', version='4.1')",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[?25l"
-      ],
-      "text/html": [
-       "
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Output()"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "4720d6aa643c489bb768ba33f92dbc45"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\n",
-       "\u001B[?25h"
-      ],
-      "text/html": [
-       "
\n",
-       "
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[?25l"
-      ],
-      "text/html": [
-       "
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Output()"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "782f39c8b7904334873519d3c7da2b37"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\n",
-       "\u001B[?25h"
-      ],
-      "text/html": [
-       "
\n",
-       "
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 5
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "## Print the schema of the Hail Table\n",
-   "id": "77d7a05e31c1f37a"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T18:14:53.139250Z",
-     "start_time": "2024-12-06T18:14:53.136478Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "ht.describe()",
-   "id": "95c14f2c8cc3e699",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'freq_meta': array> \n",
-      "    'freq_index_dict': dict \n",
-      "    'freq_meta_sample_count': array \n",
-      "    'faf_meta': array> \n",
-      "    'faf_index_dict': dict \n",
-      "    'age_distribution': struct {\n",
-      "        bin_edges: array, \n",
-      "        bin_freq: array, \n",
-      "        n_smaller: int32, \n",
-      "        n_larger: int32\n",
-      "    } \n",
-      "    'downsamplings': dict> \n",
-      "    'filtering_model': struct {\n",
-      "        filter_name: str, \n",
-      "        score_name: str, \n",
-      "        snv_cutoff: struct {\n",
-      "            bin: int32, \n",
-      "            min_score: float64\n",
-      "        }, \n",
-      "        indel_cutoff: struct {\n",
-      "            bin: int32, \n",
-      "            min_score: float64\n",
-      "        }, \n",
-      "        snv_training_variables: array, \n",
-      "        indel_training_variables: array\n",
-      "    } \n",
-      "    'inbreeding_coeff_cutoff': float64 \n",
-      "    'interval_qc_parameters': struct {\n",
-      "        per_platform: bool, \n",
-      "        all_platforms: bool, \n",
-      "        high_qual_cutoffs: dict>, \n",
-      "        min_platform_size: int32\n",
-      "    } \n",
-      "    'tool_versions': struct {\n",
-      "        cadd_version: str, \n",
-      "        revel_version: str, \n",
-      "        spliceai_version: str, \n",
-      "        pangolin_version: array, \n",
-      "        phylop_version: str, \n",
-      "        dbsnp_version: str, \n",
-      "        sift_version: str, \n",
-      "        polyphen_version: str\n",
-      "    } \n",
-      "    'vrs_versions': struct {\n",
-      "        vrs_schema_version: str, \n",
-      "        vrs_python_version: str, \n",
-      "        seqrepo_version: str\n",
-      "    } \n",
-      "    'vep_globals': struct {\n",
-      "        vep_version: str, \n",
-      "        vep_help: str, \n",
-      "        vep_config: str, \n",
-      "        gencode_version: str, \n",
-      "        mane_select_version: str\n",
-      "    } \n",
-      "    'frequency_README': str \n",
-      "    'date': str \n",
-      "    'version': str \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'alleles': array \n",
-      "    'freq': array \n",
-      "    'grpmax': struct {\n",
-      "        gnomad: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int64, \n",
-      "            gen_anc: str\n",
-      "        }, \n",
-      "        non_ukb: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int64, \n",
-      "            gen_anc: str\n",
-      "        }\n",
-      "    } \n",
-      "    'faf': array \n",
-      "    'fafmax': struct {\n",
-      "        gnomad: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }, \n",
-      "        non_ukb: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }\n",
-      "    } \n",
-      "    'a_index': int32 \n",
-      "    'was_split': bool \n",
-      "    'rsid': set \n",
-      "    'filters': set \n",
-      "    'info': struct {\n",
-      "        FS: float64, \n",
-      "        MQ: float64, \n",
-      "        MQRankSum: float64, \n",
-      "        QUALapprox: int64, \n",
-      "        QD: float64, \n",
-      "        ReadPosRankSum: float64, \n",
-      "        SB: array, \n",
-      "        SOR: float64, \n",
-      "        VarDP: int32, \n",
-      "        AS_FS: float64, \n",
-      "        AS_MQ: float64, \n",
-      "        AS_MQRankSum: float64, \n",
-      "        AS_pab_max: float64, \n",
-      "        AS_QUALapprox: int64, \n",
-      "        AS_QD: float64, \n",
-      "        AS_ReadPosRankSum: float64, \n",
-      "        AS_SB_TABLE: array, \n",
-      "        AS_SOR: float64, \n",
-      "        AS_VarDP: int32, \n",
-      "        singleton: bool, \n",
-      "        transmitted_singleton: bool, \n",
-      "        sibling_singleton: bool, \n",
-      "        omni: bool, \n",
-      "        mills: bool, \n",
-      "        monoallelic: bool, \n",
-      "        only_het: bool, \n",
-      "        AS_VQSLOD: float64, \n",
-      "        inbreeding_coeff: float64, \n",
-      "        vrs: struct {\n",
-      "            VRS_Allele_IDs: array, \n",
-      "            VRS_Starts: array, \n",
-      "            VRS_Ends: array, \n",
-      "            VRS_States: array\n",
-      "        }\n",
-      "    } \n",
-      "    'vep': struct {\n",
-      "        allele_string: str, \n",
-      "        end: int32, \n",
-      "        id: str, \n",
-      "        input: str, \n",
-      "        intergenic_consequences: array, \n",
-      "            impact: str, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        most_severe_consequence: str, \n",
-      "        motif_feature_consequences: array, \n",
-      "            high_inf_pos: str, \n",
-      "            impact: str, \n",
-      "            motif_feature_id: str, \n",
-      "            motif_name: str, \n",
-      "            motif_pos: int32, \n",
-      "            motif_score_change: float64, \n",
-      "            transcription_factors: array, \n",
-      "            strand: int32, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        regulatory_feature_consequences: array, \n",
-      "            impact: str, \n",
-      "            regulatory_feature_id: str, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        seq_region_name: str, \n",
-      "        start: int32, \n",
-      "        strand: int32, \n",
-      "        transcript_consequences: array, \n",
-      "            distance: int32, \n",
-      "            domains: array, \n",
-      "            exon: str, \n",
-      "            flags: str, \n",
-      "            gene_id: str, \n",
-      "            gene_pheno: int32, \n",
-      "            gene_symbol: str, \n",
-      "            gene_symbol_source: str, \n",
-      "            hgnc_id: str, \n",
-      "            hgvsc: str, \n",
-      "            hgvsp: str, \n",
-      "            hgvs_offset: int32, \n",
-      "            impact: str, \n",
-      "            intron: str, \n",
-      "            lof: str, \n",
-      "            lof_flags: str, \n",
-      "            lof_filter: str, \n",
-      "            lof_info: str, \n",
-      "            mane_select: str, \n",
-      "            mane_plus_clinical: str, \n",
-      "            mirna: array, \n",
-      "            protein_end: int32, \n",
-      "            protein_start: int32, \n",
-      "            protein_id: str, \n",
-      "            source: str, \n",
-      "            strand: int32, \n",
-      "            transcript_id: str, \n",
-      "            tsl: int32, \n",
-      "            uniprot_isoform: array, \n",
-      "            variant_allele: str\n",
-      "        }>, \n",
-      "        variant_class: str\n",
-      "    } \n",
-      "    'vqsr_results': struct {\n",
-      "        AS_VQSLOD: float64, \n",
-      "        AS_culprit: str, \n",
-      "        positive_train_site: bool, \n",
-      "        negative_train_site: bool\n",
-      "    } \n",
-      "    'region_flags': struct {\n",
-      "        non_par: bool, \n",
-      "        lcr: bool, \n",
-      "        segdup: bool, \n",
-      "        fail_interval_qc: bool, \n",
-      "        outside_ukb_capture_region: bool, \n",
-      "        outside_broad_capture_region: bool\n",
-      "    } \n",
-      "    'allele_info': struct {\n",
-      "        variant_type: str, \n",
-      "        n_alt_alleles: int32, \n",
-      "        has_star: bool, \n",
-      "        allele_type: str, \n",
-      "        was_mixed: bool\n",
-      "    } \n",
-      "    'histograms': struct {\n",
-      "        qual_hists: struct {\n",
-      "            gq_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            gq_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            ab_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }, \n",
-      "        raw_qual_hists: struct {\n",
-      "            gq_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_all: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            gq_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            dp_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            ab_hist_alt: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }, \n",
-      "        age_hists: struct {\n",
-      "            age_hist_het: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            age_hist_hom: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }\n",
-      "    } \n",
-      "    'in_silico_predictors': struct {\n",
-      "        cadd: struct {\n",
-      "            phred: float32, \n",
-      "            raw_score: float32\n",
-      "        }, \n",
-      "        revel_max: float64, \n",
-      "        spliceai_ds_max: float32, \n",
-      "        pangolin_largest_ds: float64, \n",
-      "        phylop: float64, \n",
-      "        sift_max: float64, \n",
-      "        polyphen_max: float64\n",
-      "    } \n",
-      "----------------------------------------\n",
-      "Key: ['locus', 'alleles']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "execution_count": 8
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "## Show the first 5 variants in the Hail Table\n",
-   "id": "a071f738b2c888e"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T18:16:28.722532Z",
-     "start_time": "2024-12-06T18:16:00.055352Z"
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-   "cell_type": "code",
-   "source": "ht.show(5)",
-   "id": "222de580c305d72a",
-   "outputs": [
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-      "text/plain": [
-       "+---------------+----------------+\n",
-       "| locus         | alleles        |\n",
-       "+---------------+----------------+\n",
-       "| locus | array     |\n",
-       "+---------------+----------------+\n",
-       "| chr1:11994    | [\"T\",\"C\"]      |\n",
-       "| chr1:12016    | [\"G\",\"A\"]      |\n",
-       "| chr1:12060    | [\"CTGGAG\",\"C\"] |\n",
-       "| chr1:12074    | [\"T\",\"C\"]      |\n",
-       "| chr1:12102    | [\"G\",\"A\"]      |\n",
-       "+---------------+----------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq                                                                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0,NA,0,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
-       "| [(0,NA,0,0),(6,9.54e-06,628784,2),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
-       "| [(0,0.00e+00,4,0),(1,1.59e-06,628784,0),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
-       "| [(0,0.00e+00,4,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
-       "| [(0,0.00e+00,32,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0)... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+\n",
-       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
-       "+------------------+------------------+------------------+\n",
-       "|            int32 |          float64 |            int32 |\n",
-       "+------------------+------------------+------------------+\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "|               NA |               NA |               NA |\n",
-       "+------------------+------------------+------------------+\n",
-       "\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                          int64 | str                   |             int32 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|           float64 |             int32 |                           int64 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "\n",
-       "+------------------------+\n",
-       "| grpmax.non_ukb.gen_anc |\n",
-       "+------------------------+\n",
-       "| str                    |\n",
-       "+------------------------+\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "+------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| faf                                                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                  float64 | str                              |   int32 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "| was_split | rsid     | filters           | info.FS |  info.MQ |\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "|      bool | set | set          | float64 |  float64 |\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.60e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.56e+01 |\n",
-       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.45e+01 |\n",
-       "+-----------+----------+-------------------+---------+----------+\n",
-       "\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|        float64 |           int64 |  float64 |             float64 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|             NA |              60 | 3.00e+01 |                  NA |\n",
-       "|       0.00e+00 |             262 | 2.18e+01 |            6.74e-01 |\n",
-       "|       6.74e-01 |             123 | 3.08e+01 |           -1.15e+00 |\n",
-       "|       4.31e-01 |              99 | 1.24e+01 |           -2.53e-01 |\n",
-       "|             NA |              90 | 2.25e+01 |                  NA |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "| info.SB      | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "| array |  float64 |      int32 |    float64 |    float64 |\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "| [0,0,2,0]    | 2.30e+00 |          2 |         NA |   2.50e+01 |\n",
-       "| [2,0,10,0]   | 2.67e+00 |         12 |         NA |   2.50e+01 |\n",
-       "| [1,0,3,0]    | 1.61e+00 |          4 |         NA |   2.60e+01 |\n",
-       "| [4,0,4,0]    | 6.93e-01 |          8 |         NA |   2.56e+01 |\n",
-       "| [0,0,4,0]    | 3.26e+00 |          4 |         NA |   2.45e+01 |\n",
-       "+--------------+----------+------------+------------+------------+\n",
-       "\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|           float64 |         float64 |              int64 |    float64 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|                NA |              NA |                 60 |   3.00e+01 |\n",
-       "|          0.00e+00 |        1.00e+00 |                262 |   2.18e+01 |\n",
-       "|          6.74e-01 |        6.25e-01 |                123 |   3.08e+01 |\n",
-       "|          4.31e-01 |        6.87e-01 |                 99 |   1.24e+01 |\n",
-       "|                NA |              NA |                 90 |   2.25e+01 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE | info.AS_SOR | info.AS_VarDP |\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "|                float64 | array     |     float64 |         int32 |\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "|                     NA | [0,0,2,0]        |    2.30e+00 |             2 |\n",
-       "|               6.74e-01 | [2,0,10,0]       |    2.67e+00 |            12 |\n",
-       "|              -1.15e+00 | [1,0,3,0]        |    1.61e+00 |             4 |\n",
-       "|              -2.53e-01 | [4,0,4,0]        |    6.93e-01 |             8 |\n",
-       "|                     NA | [0,0,4,0]        |    3.26e+00 |             4 |\n",
-       "+------------------------+------------------+-------------+---------------+\n",
-       "\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|           bool |                       bool |                   bool |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|           True |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|      bool |       bool |             bool |          bool |        float64 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|     False |      False |            False |         False |      -5.25e+00 |\n",
-       "|     False |      False |            False |         False |      -2.75e+00 |\n",
-       "|     False |      False |            False |         False |      -2.22e+00 |\n",
-       "|     False |      False |            False |         False |      -2.18e+00 |\n",
-       "|     False |      False |            False |         False |      -2.86e+00 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "\n",
-       "+-----------------------+\n",
-       "| info.inbreeding_coeff |\n",
-       "+-----------------------+\n",
-       "|               float64 |\n",
-       "+-----------------------+\n",
-       "|              1.00e+00 |\n",
-       "|              6.67e-01 |\n",
-       "|             -1.59e-06 |\n",
-       "|              1.00e+00 |\n",
-       "|              1.00e+00 |\n",
-       "+-----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| info.vrs.VRS_Allele_IDs                                                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.ps4-9woXy7o4rS39i8hDK_cUPBa-UcyP\",\"ga4gh:VA.nHlWYJXgiuvXrLAxQs... |\n",
-       "| [\"ga4gh:VA.VFxwcI4knOzk6SHzS2qowyDGnkG3mfEH\",\"ga4gh:VA.ctgP7qNjQAGjI2eTDo... |\n",
-       "| [\"ga4gh:VA.P573ZtUtAaRcceE7NLanEyynSefvcAPL\",\"ga4gh:VA.CHmk9uDiHW2LIHndZW... |\n",
-       "| [\"ga4gh:VA.neBeBT28ISe_1-yKPFsYxntP2jz1pj7E\",\"ga4gh:VA.1RPHSwBHNUwoECJ9VV... |\n",
-       "| [\"ga4gh:VA.ZXRhPWtip8HseOMWpCnaja0-ATo8hLE1\",\"ga4gh:VA.Qi95g6E8nt6DaqTGpH... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| info.vrs.VRS_Starts | info.vrs.VRS_Ends | info.vrs.VRS_States |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| array        | array      | array          |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "| [11993,11993]       | [11994,11994]     | [\"T\",\"C\"]           |\n",
-       "| [12015,12015]       | [12016,12016]     | [\"G\",\"A\"]           |\n",
-       "| [12059,12060]       | [12065,12071]     | [\"CTGGAG\",\"TGGAGT\"] |\n",
-       "| [12073,12073]       | [12074,12074]     | [\"T\",\"C\"]           |\n",
-       "| [12101,12101]       | [12102,12102]     | [\"G\",\"A\"]           |\n",
-       "+---------------------+-------------------+---------------------+\n",
-       "\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "| vep.allele_string | vep.end | vep.id | vep.input                      |\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "| str               |   int32 | str    | str                            |\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "| \"T/C\"             |   11994 | \".\"    | \"chr1\t11994\t.\tT\tC\t.\t.\tGT\"      |\n",
-       "| \"G/A\"             |   12016 | \".\"    | \"chr1\t12016\t.\tG\tA\t.\t.\tGT\"      |\n",
-       "| \"TGGAG/-\"         |   12065 | \".\"    | \"chr1\t12060\t.\tCTGGAG\tC\t.\t.\tGT\" |\n",
-       "| \"T/C\"             |   12074 | \".\"    | \"chr1\t12074\t.\tT\tC\t.\t.\tGT\"      |\n",
-       "| \"G/A\"             |   12102 | \".\"    | \"chr1\t12102\t.\tG\tA\t.\t.\tGT\"      |\n",
-       "+-------------------+---------+--------+--------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.intergenic_consequences                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, impact: st... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| vep.most_severe_consequence          |\n",
-       "+--------------------------------------+\n",
-       "| str                                  |\n",
-       "+--------------------------------------+\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "| \"splice_donor_5th_base_variant\"      |\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "| \"non_coding_transcript_exon_variant\" |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.motif_feature_consequences                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, high_inf_p... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.regulatory_feature_consequences                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,2,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq   |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [314391,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [314388,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [314380,0,0,0,11,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]  |\n",
-       "| [314369,1,0,0,20,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [314323,1,0,0,54,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq     |\n",
-       "+----------------------------------------------------+\n",
-       "| array                                       |\n",
-       "+----------------------------------------------------+\n",
-       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308833,5557,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308823,5567,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [308789,5585,16,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]      |\n",
-       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                      int64 |                         float32 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        1.26e+01 |\n",
-       "|                                          0 |                        1.60e+01 |\n",
-       "|                                          0 |                        8.52e+00 |\n",
-       "|                                          0 |                        1.52e+01 |\n",
-       "|                                          0 |                        4.42e+00 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                            1.08e+00 |                             NA |\n",
-       "|                            1.54e+00 |                             NA |\n",
-       "|                            7.07e-01 |                             NA |\n",
-       "|                            1.41e+00 |                             NA |\n",
-       "|                            3.11e-01 |                             NA |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                -1.10e-01 |                    1.09e+00 |\n",
-       "|                                -7.00e-02 |                    6.55e+00 |\n",
-       "|                                -9.00e-02 |                   -4.41e+00 |\n",
-       "|                                -4.00e-02 |                    6.01e+00 |\n",
-       "|                                -8.00e-02 |                    1.38e+00 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "showing top 5 rows"
-      ],
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
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AF
AN
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gen_anc
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AF
AN
homozygote_count
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faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
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revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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showing top 5 rows
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 9
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ba4bfaf",
-   "metadata": {},
-   "source": [
-    "# Get variant count"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "df28f17d",
-   "metadata": {},
-   "source": [
-    "## Get variant count by AF for a release\n",
-    "\n",
-    "**Note: this will take long if your notebook is NOT using multiple nodes.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "c276fb7e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 1:====================================================>(8782 + 7) / 8789]\r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'number of variants with AF < 0.01': 68398090, 'number of variants with AF < 0.001': 67709028}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(get_variant_count(ht))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "c0243c4b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 5:====================================================>(8781 + 8) / 8789]\r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'number of singletons': 34047562, 'number of doubletons': 10161819, 'number of variants with AF < 0.01': 68398090, 'number of variants with AF < 0.001': 67709028}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[Stage 5:====================================================>(8786 + 3) / 8789]\r"
-     ]
-    }
-   ],
-   "source": [
-    "print(get_variant_count(ht, singletons=True, doubletons=True))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec659eeb",
-   "metadata": {},
-   "source": [
-    "## Get variant count by AF for coding variants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "d65b0ea8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 21:===================================================>(8784 + 5) / 8789]\r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'number of variants with AF < 0.01': 23762097, 'number of variants with AF < 0.001': 23643787, 'number of variants with AF < 0.0005': 23569893}\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n",
-    "\n",
-    "ht = filter_by_csqs(ht, ['coding'])\n",
-    "\n",
-    "print(get_variant_count(ht, afs=[0.01, 0.001, 0.0005]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f07ca88f",
-   "metadata": {},
-   "source": [
-    "## Get variant count by VEP consequence"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "b515bfc0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18231426"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# total number of missense variant in exomes data\n",
-    "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n",
-    "\n",
-    "ht.filter(\n",
-    "    hl.any(\n",
-    "        hl.map(\n",
-    "            lambda x: (x.consequence_terms.contains(\"missense_variant\")),\n",
-    "            ht.vep.transcript_consequences,\n",
-    "        )\n",
-    "    )\n",
-    ").count()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "725f9a57",
-   "metadata": {},
-   "source": [
-    "## Get variant count by AF for a gene\n",
-    "\n",
-    "**Note: This isn't necessarily equal to the number of variants annotated to this gene by VEP.**\n",
-    "\n",
-    "Here we show two ways that you can load a variant table on the gnomAD browser, one is the [gene page](https://gnomad.broadinstitute.org/gene/ENSG00000149295?dataset=gnomad_r4) (filtered to MANE Select transcript of that gene, and only variants located in or within 75 base pairs of a coding exon (CDS)), the other is the [region view](https://gnomad.broadinstitute.org/region/11-113409605-113475691?dataset=gnomad_r4). We use *DRD2* gene as an example. "
-   ]
-  },
-  {
-   "attachments": {
-    "Screenshot%202024-11-01%20at%209.31.00%E2%80%AFPM.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "164a07b7",
-   "metadata": {},
-   "source": [
-    "### On region view (the interval of a gene)\n",
-    "\n",
-    "This is for 'DRD2' gene. \n",
-    "![Screenshot%202024-11-01%20at%209.31.00%E2%80%AFPM.png](attachment:Screenshot%202024-11-01%20at%209.31.00%E2%80%AFPM.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "9f8e1ba4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The total number of variants in this gene interval is:  8126\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 37:=============================>                            (1 + 1) / 2]\r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'number of singletons': 1390, 'number of doubletons': 384, 'number of variants with AF < 0.01': 2711, 'number of variants with AF < 0.001': 2662}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[Stage 38:=============================>                            (1 + 1) / 2]\r"
-     ]
-    }
-   ],
-   "source": [
-    "# Filter to interval, e.g. for DRD2.\n",
-    "gene_interval = \"11:113409605-113475691\"\n",
-    "\n",
-    "gene_ht = filter_by_interval(ht, gene_interval)\n",
-    "\n",
-    "# Filter the exome release Hail Table to the ASH1L gene interval.\n",
-    "print(\"The total number of variants in this gene interval is: \", gene_ht.count())\n",
-    "\n",
-    "print(get_variant_count(gene_ht, singletons=True, doubletons=True))"
-   ]
-  },
-  {
-   "attachments": {
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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "ac393319",
-   "metadata": {},
-   "source": [
-    "### On gene page\n",
-    "\n",
-    "To get the number of variants shown on the gene page, if you click 'all' and check 'exomes', 'SNVs', 'Indels', and 'Filtered variants' as this: \n",
-    "\n",
-    "![Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png](attachment:Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "e6bf7236",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 26:>                                                         (0 + 1) / 1]\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "1764"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# gene_symbol can be upper or lower case\n",
-    "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n",
-    "\n",
-    "drd2 = filter_by_gene_symbol(ht, 'drd2')\n",
-    "drd2.count()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7bff63bb",
-   "metadata": {},
-   "source": [
-    "# Filter to variants by VEP annotations\n",
-    "\n",
-    "You can get the variant table either by gene_symbol or gene interval, we recommen you to get by gene_symbol because it's already filtered to MANE Select transcript of a gene. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "700582e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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,"1"),("ENSP_mappings","5aer"),("ENSP_mappings","5aer"),("ENSP_mappings","6cm4"),("ENSP_mappings","6luq"),("ENSP_mappings","6vms"),("ENSP_mappings","7dfp"),("ENSP_mappings","7jvr"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000542968.5:c.1323C>A","ENSP00000442172.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,441,441,"ENSP00000442172","Ensembl",-1,"ENST00000542968",1,["P14416-1"],"T"),(1,"L","A1","protein_coding",NA,NA,1353,1353,1320,1320,"ctC/ctA",["synonymous_variant"],NA,[("Gene3D","1"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000544518.5:c.1320C>A","ENSP00000441068.1:p.Leu440=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,440,440,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"T"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["intron_variant","non_coding_transcript_variant"],NA,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,"ENST00000546284.1:n.245-811G>T",NA,NA,"MODIFIER","2/3",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"T"),(1,"L",NA,"protein_coding",1,NA,1677,1677,1323,1323,"ctC/ctA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.1323C>A","NP_000786.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,441,441,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"T"),(1,"L",NA,"protein_coding",NA,NA,1590,1590,1236,1236,"ctC/ctA",["synonymous_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.1236C>A","NP_057658.2:p.Leu412=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,412,412,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"T"),(1,"L",NA,"protein_coding",NA,NA,1405,1405,1323,1323,"ctC/ctA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.1323C>A","XP_016872785.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,441,441,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"T")]"SNV"5.00e+00"AS_MQ"FalseFalseFalseFalseFalseTrueFalseFalse"multi-snv"2False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,2955,2914,2974,2923,2564,2680]07685[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,2955,2914,2974,2923,2564,2680]07685[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,1,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]002.96e+001.86e-01NA0.00e+003.00e-02-2.55e-01NANA
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+-----------------+------------+\n",
-       "| locus           | alleles    |\n",
-       "+-----------------+------------+\n",
-       "| locus   | array |\n",
-       "+-----------------+------------+\n",
-       "| chr11:113410731 | [\"C\",\"A\"]  |\n",
-       "| chr11:113410731 | [\"C\",\"T\"]  |\n",
-       "| chr11:113410735 | [\"G\",\"A\"]  |\n",
-       "| chr11:113410736 | [\"G\",\"A\"]  |\n",
-       "| chr11:113410736 | [\"G\",\"T\"]  |\n",
-       "+-----------------+------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq                                                                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(2,1.37e-06,1461866,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(2,4.... |\n",
-       "| [(1,6.84e-07,1461866,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
-       "| [(75,5.13e-05,1461876,0),(75,5.13e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
-       "| [(15,1.03e-05,1461882,0),(15,1.03e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
-       "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+\n",
-       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
-       "+------------------+------------------+------------------+\n",
-       "|            int32 |          float64 |            int32 |\n",
-       "+------------------+------------------+------------------+\n",
-       "|                2 |         4.47e-05 |            44724 |\n",
-       "|                1 |         8.99e-07 |          1112010 |\n",
-       "|                8 |         7.19e-06 |          1112004 |\n",
-       "|               15 |         1.35e-05 |          1112010 |\n",
-       "|                1 |         8.99e-07 |          1112010 |\n",
-       "+------------------+------------------+------------------+\n",
-       "\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                          int64 | str                   |             int32 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                              0 | \"amr\"                 |                 2 |\n",
-       "|                              0 | \"nfe\"                 |                NA |\n",
-       "|                              0 | \"nfe\"                 |                 2 |\n",
-       "|                              0 | \"nfe\"                 |                 1 |\n",
-       "|                              0 | \"nfe\"                 |                NA |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|           float64 |             int32 |                           int64 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|          4.57e-05 |             43740 |                               0 |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|          5.71e-06 |            350102 |                               0 |\n",
-       "|          2.86e-06 |            350106 |                               0 |\n",
-       "|                NA |                NA |                              NA |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "\n",
-       "+------------------------+\n",
-       "| grpmax.non_ukb.gen_anc |\n",
-       "+------------------------+\n",
-       "| str                    |\n",
-       "+------------------------+\n",
-       "| \"amr\"                  |\n",
-       "| NA                     |\n",
-       "| \"nfe\"                  |\n",
-       "| \"nfe\"                  |\n",
-       "| NA                     |\n",
-       "+------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| faf                                                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(7.41e-06,2.77e-06),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(4.19e-05,3.82e-05),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(6.16e-06,4.89e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                7.41e-06 | \"amr\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "|                3.09e-06 | \"nfe\"                           |\n",
-       "|                8.10e-06 | \"nfe\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                2.77e-06 | \"amr\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "|                2.24e-06 | \"nfe\"                           |\n",
-       "|                6.42e-06 | \"nfe\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                 7.58e-06 | \"amr\"                            |\n",
-       "|                       NA | NA                               |\n",
-       "|                 9.50e-07 | \"nfe\"                            |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                  float64 | str                              |   int32 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                 2.84e-06 | \"amr\"                            |       1 |\n",
-       "|                       NA | NA                               |       2 |\n",
-       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       2 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "|      bool | set         | set |  float64 |  float64 |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "|      True | {\"rs140938110\"}  | {}       | 5.56e+00 | 6.00e+01 |\n",
-       "|      True | NA               | {}       | 5.56e+00 | 6.00e+01 |\n",
-       "|      True | {\"rs201801648\"}  | {}       | 0.00e+00 | 6.00e+01 |\n",
-       "|      True | {\"rs1159504221\"} | {}       | 5.49e-01 | 6.00e+01 |\n",
-       "|      True | NA               | {}       | 5.49e-01 | 6.00e+01 |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|        float64 |           int64 |  float64 |             float64 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
-       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
-       "|       0.00e+00 |          127501 | 1.23e+01 |            3.70e-02 |\n",
-       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
-       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "| info.SB               | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "| array          |  float64 |      int32 |    float64 |    float64 |\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "| [134,83,109,52]       | 9.81e-01 |        378 |   1.31e+01 |   6.00e+01 |\n",
-       "| [134,83,109,52]       | 9.81e-01 |        378 |   0.00e+00 |   6.00e+01 |\n",
-       "| [3240,2211,2993,1962] | 7.34e-01 |      10404 |   0.00e+00 |   6.00e+01 |\n",
-       "| [386,195,316,166]     | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
-       "| [386,195,316,166]     | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|           float64 |         float64 |              int64 |    float64 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|          0.00e+00 |        2.94e-01 |               1895 |   1.06e+01 |\n",
-       "|          0.00e+00 |        9.04e-01 |               1967 |   9.88e+00 |\n",
-       "|          0.00e+00 |        1.00e+00 |             127480 |   1.23e+01 |\n",
-       "|          0.00e+00 |        9.11e-01 |              11899 |   1.17e+01 |\n",
-       "|          0.00e+00 |        3.28e-02 |                408 |   8.16e+00 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE      | info.AS_SOR | info.AS_VarDP |\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "|                float64 | array          |     float64 |         int32 |\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "|               2.52e-01 | [134,83,56,19]        |    1.42e+00 |           179 |\n",
-       "|              -5.01e-01 | [134,83,53,33]        |    6.81e-01 |           199 |\n",
-       "|               1.60e-02 | [3240,2211,2991,1961] |    7.34e-01 |         10383 |\n",
-       "|               9.70e-02 | [386,195,304,161]     |    6.47e-01 |          1013 |\n",
-       "|              -9.23e-01 | [386,195,12,5]        |    7.90e-01 |            50 |\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|           bool |                       bool |                   bool |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|          False |                         NA |                     NA |\n",
-       "|           True |                      False |                  False |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|           True |                      False |                  False |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|      bool |       bool |             bool |          bool |        float64 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|     False |      False |            False |         False |       5.27e+00 |\n",
-       "|     False |      False |            False |         False |       5.40e+00 |\n",
-       "|     False |      False |            False |         False |       8.85e+00 |\n",
-       "|     False |      False |            False |         False |       7.50e+00 |\n",
-       "|     False |      False |            False |         False |       5.00e+00 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "\n",
-       "+-----------------------+\n",
-       "| info.inbreeding_coeff |\n",
-       "+-----------------------+\n",
-       "|               float64 |\n",
-       "+-----------------------+\n",
-       "|             -1.37e-06 |\n",
-       "|             -6.84e-07 |\n",
-       "|             -5.13e-05 |\n",
-       "|             -1.03e-05 |\n",
-       "|             -6.84e-07 |\n",
-       "+-----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| info.vrs.VRS_Allele_IDs                                                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.ZonDIhdwyghm7SuWkX... |\n",
-       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.Hp20SlFHUdM7jlWmVV... |\n",
-       "| [\"ga4gh:VA.SBSoboxxiC0V-mhjjHg2geRlf3I0Ib2d\",\"ga4gh:VA.GzUD_86k_EDbtk-R58... |\n",
-       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.OXPDvbuulFZ9CSk4Xt... |\n",
-       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.4qhkDG1qrfqiD2TeOA... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| array          | array          | array          |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"A\"]           |\n",
-       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"T\"]           |\n",
-       "| [113410734,113410734] | [113410735,113410735] | [\"G\",\"A\"]           |\n",
-       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"A\"]           |\n",
-       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"T\"]           |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| str               |     int32 | str    | str                            |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| \"C/A\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tA\t.\t.\tGT\" |\n",
-       "| \"C/T\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tT\t.\t.\tGT\" |\n",
-       "| \"G/A\"             | 113410735 | \".\"    | \"chr11\t113410735\t.\tG\tA\t.\t.\tGT\" |\n",
-       "| \"G/A\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tA\t.\t.\tGT\" |\n",
-       "| \"G/T\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tT\t.\t.\tGT\" |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.intergenic_consequences                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, impact: st... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------+\n",
-       "| vep.most_severe_consequence |\n",
-       "+-----------------------------+\n",
-       "| str                         |\n",
-       "+-----------------------------+\n",
-       "| \"missense_variant\"          |\n",
-       "| \"missense_variant\"          |\n",
-       "| \"missense_variant\"          |\n",
-       "| \"synonymous_variant\"        |\n",
-       "| \"synonymous_variant\"        |\n",
-       "+-----------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.motif_feature_consequences                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, high_inf_p... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.regulatory_feature_consequences                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.bin_freq              |\n",
-       "+---------------------------------------------------------+\n",
-       "| array                                            |\n",
-       "+---------------------------------------------------------+\n",
-       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [0,0,0,0,12,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
-       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
-       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,0,65,332,11308,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
-       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                       7715 |\n",
-       "|                                       7715 |\n",
-       "|                                       7717 |\n",
-       "|                                       7685 |\n",
-       "|                                       7685 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75] |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15] |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]  |\n",
-       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          1 |\n",
-       "|                                          0 |\n",
-       "|                                         32 |\n",
-       "|                                          1 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq   |\n",
-       "+----------------------------------------------+\n",
-       "| array                                 |\n",
-       "+----------------------------------------------+\n",
-       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]    |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]    |\n",
-       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0] |\n",
-       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]    |\n",
-       "+----------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq          |\n",
-       "+---------------------------------------------------------+\n",
-       "| array                                            |\n",
-       "+---------------------------------------------------------+\n",
-       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [6,0,1,0,14,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
-       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
-       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,2,67,335,11310,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
-       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                           7715 |\n",
-       "|                                           7715 |\n",
-       "|                                           7717 |\n",
-       "|                                           7685 |\n",
-       "|                                           7685 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75]     |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15]     |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]      |\n",
-       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              1 |\n",
-       "|                                              0 |\n",
-       "|                                             32 |\n",
-       "|                                              1 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]     |\n",
-       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
-       "| [1,0,0,2,3,3,8,4,0,0]                      |\n",
-       "| [0,0,1,2,4,2,4,1,0,0]                      |\n",
-       "| [0,0,0,0,0,1,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           1 |\n",
-       "|                                           0 |\n",
-       "|                                           3 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          1 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                      int64 |                         float32 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        2.77e+01 |\n",
-       "|                                          0 |                        2.75e+01 |\n",
-       "|                                          0 |                        2.28e+01 |\n",
-       "|                                          0 |                        3.44e+00 |\n",
-       "|                                          0 |                        2.96e+00 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                            4.10e+00 |                       6.02e-01 |\n",
-       "|                            4.07e+00 |                       7.19e-01 |\n",
-       "|                            2.69e+00 |                       2.67e-01 |\n",
-       "|                            2.28e-01 |                             NA |\n",
-       "|                            1.86e-01 |                             NA |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                 0.00e+00 |                    8.78e+00 |\n",
-       "|                                 2.00e-02 |                    8.78e+00 |\n",
-       "|                                 1.00e-02 |                    8.67e+00 |\n",
-       "|                                 1.00e-02 |                   -2.55e-01 |\n",
-       "|                                 3.00e-02 |                   -2.55e-01 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                      0.00e+00 |                          9.89e-01 |\n",
-       "|                      0.00e+00 |                          9.89e-01 |\n",
-       "|                      1.30e-01 |                          1.18e-01 |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# lof, missense, synonymous variants passing filters\n",
-    "variants_of_interest = filter_by_csqs(drd2,['lof','missense','synonymous'])\n",
-    "variants_of_interest.show(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "9887fdb0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[Stage 27:>                                                         (0 + 1) / 1]\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "17"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# number of lof variants passing filters\n",
-    "filter_by_csqs(drd2,['lof']).count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "86596aaf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[Stage 28:>                                                         (0 + 1) / 1]\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "409"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# number of missense variants passing filters\n",
-    "filter_by_csqs(drd2,['missense']).count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "b7e4368b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[Stage 29:>                                                         (0 + 1) / 1]\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "238"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# number of synonymous variants passing filters\n",
-    "filter_by_csqs(drd2,['synonymous']).count()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "4141ccb3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[Stage 30:>                                                         (0 + 1) / 1]\r"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "783"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# number of 'Other' variants passing filters\n",
-    "filter_by_csqs(drd2,['other']).count()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b1031947",
-   "metadata": {},
-   "source": [
-    "## Filter to 'HC' LOF variants for certain genes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5b08a706",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO (gnomad.utils.vep 928): Filtering to canonical transcripts\n",
-      "INFO (gnomad.utils.vep 931): Filtering to MANE Select transcripts...\n",
-      "INFO (gnomad.utils.vep 934): Filtering to Ensembl transcripts...\n",
-      "INFO (gnomad.utils.vep 940): Filtering to genes of interest...\n",
-      "INFO (gnomad.utils.vep 948): Filtering to variants with additional criteria...\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Filter to variants in ASH1L that are LOFTEE high-confidence (with no flags) in the MANE select transcript.\n",
-    "ht = filter_vep_transcript_csqs(\n",
-    "    ht, \n",
-    "    synonymous=False, \n",
-    "    mane_select=True,\n",
-    "    genes=[\"ASH1L\"],\n",
-    "    match_by_gene_symbol=True,\n",
-    "    additional_filtering_criteria=[lambda x: (x.lof == \"HC\") & hl.is_missing(x.lof_flags)],\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {
-    "Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "f9b2d921",
-   "metadata": {},
-   "source": [
-    "### Filter to pLOF variants that we used to compute constraint metrics\n",
-    "pLOF variants meets the following requirements:\n",
-    "* High-confidence LOFTEE variants (without any flags),\n",
-    "* Only variants in the MANE Select transcript,\n",
-    "* PASS variants that are SNVs with MAF ≤ 0.1%, and\n",
-    "* Exome median depth ≥ 30 (**This is changing in v4 constraint?**)\n",
-    "\n",
-    "**Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**\n",
-    "\n",
-    "![Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png](attachment:Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "6ce87a77",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 4:=======================================>                   (2 + 1) / 3]\r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of variants: 18\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 5:=======================================>                   (2 + 1) / 3]\r"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "
freq
coverage
locus
alleles
AC
AF
AN
homozygote_count
csq
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>array<str>int32float64int32int64array<str>float64int32int64float64float64float64float64float64float64float64float64float64
chr1:155337668["G","A"]4593.14e-0414612700["stop_gained"]2.99e+0130218875281.00e+001.00e+001.00e+009.97e-019.86e-019.32e-016.07e-018.96e-046.02e-05
chr1:155337704["G","A"]64.10e-0614617140["stop_gained"]3.02e+0130220423721.00e+001.00e+001.00e+009.99e-019.94e-019.48e-016.21e-018.39e-044.93e-05
chr1:155337735["G","C"]16.84e-0714616940["stop_gained"]3.00e+0130219569141.00e+001.00e+001.00e+009.99e-019.92e-019.37e-016.12e-018.29e-044.51e-05
chr1:155338087["A","T"]16.85e-0714596480["splice_donor_variant"]3.16e+0131231113191.00e+001.00e+009.99e-019.95e-019.89e-019.59e-017.28e-011.35e-027.59e-04
chr1:155338161["G","A"]16.84e-0714618840["stop_gained"]3.19e+0131233084341.00e+001.00e+001.00e+001.00e+001.00e+009.76e-017.42e-011.35e-027.59e-04
chr1:155349380["T","A"]16.84e-0714617680["stop_gained"]3.21e+0132234449991.00e+001.00e+001.00e+001.00e+009.99e-019.76e-017.69e-015.01e-031.85e-04
chr1:155354631["C","T"]16.88e-0714534160["splice_acceptor_variant"]3.04e+0131222097141.00e+001.00e+009.95e-019.83e-019.70e-019.28e-016.53e-014.38e-044.93e-05
chr1:155357583["A","T"]16.84e-0714616800["splice_donor_variant"]3.13e+0131228588871.00e+001.00e+001.00e+009.99e-019.97e-019.79e-017.47e-018.14e-046.29e-05
chr1:155370984["C","T"]32.06e-0614557260["splice_acceptor_variant"]3.12e+0131228021811.00e+001.00e+001.00e+009.99e-019.97e-019.54e-016.97e-011.14e-033.69e-05
chr1:155415924["C","A"]21.48e-0613520320["splice_acceptor_variant"]2.73e+0130199323751.00e+009.84e-019.41e-018.72e-018.17e-017.64e-015.70e-011.35e-031.76e-04
chr1:155478020["G","C"]16.84e-0714618880["stop_gained"]3.26e+0133238300811.00e+001.00e+001.00e+001.00e+001.00e+009.72e-017.53e-013.11e-031.40e-03
chr1:155478203["C","T"]16.84e-0714618740["stop_gained"]3.27e+0133239248711.00e+001.00e+001.00e+001.00e+001.00e+009.72e-017.55e-014.07e-032.20e-03
chr1:155478439["C","T"]16.84e-0714618900["stop_gained"]3.28e+0133239948001.00e+001.00e+001.00e+001.00e+001.00e+009.73e-017.59e-014.64e-032.58e-03
chr1:155478528["G","A"]16.84e-0714618760["stop_gained"]3.28e+0133239977761.00e+001.00e+001.00e+001.00e+001.00e+009.73e-017.59e-014.67e-032.60e-03
chr1:155479767["G","A"]16.84e-0714618580["stop_gained"]3.30e+0133241434591.00e+001.00e+001.00e+001.00e+001.00e+009.74e-017.66e-016.55e-033.89e-03
chr1:155479862["G","T"]16.84e-0714611600["stop_gained"]3.24e+0132236512031.00e+001.00e+001.00e+009.98e-019.91e-019.42e-017.13e-016.42e-033.91e-03
chr1:155521291["C","A"]16.84e-0714618620["stop_gained"]3.30e+0132241144561.00e+001.00e+001.00e+001.00e+001.00e+009.81e-017.72e-011.94e-023.68e-03
chr1:155521474["C","A"]16.84e-0714617240["stop_gained"]3.29e+0132240775511.00e+001.00e+001.00e+009.99e-019.98e-019.79e-017.69e-011.94e-023.67e-03
"
-      ],
-      "text/plain": [
-       "+----------------+------------+---------+----------+---------+\n",
-       "| locus          | alleles    | freq.AC |  freq.AF | freq.AN |\n",
-       "+----------------+------------+---------+----------+---------+\n",
-       "| locus  | array |   int32 |  float64 |   int32 |\n",
-       "+----------------+------------+---------+----------+---------+\n",
-       "| chr1:155337668 | [\"G\",\"A\"]  |     459 | 3.14e-04 | 1461270 |\n",
-       "| chr1:155337704 | [\"G\",\"A\"]  |       6 | 4.10e-06 | 1461714 |\n",
-       "| chr1:155337735 | [\"G\",\"C\"]  |       1 | 6.84e-07 | 1461694 |\n",
-       "| chr1:155338087 | [\"A\",\"T\"]  |       1 | 6.85e-07 | 1459648 |\n",
-       "| chr1:155338161 | [\"G\",\"A\"]  |       1 | 6.84e-07 | 1461884 |\n",
-       "| chr1:155349380 | [\"T\",\"A\"]  |       1 | 6.84e-07 | 1461768 |\n",
-       "| chr1:155354631 | [\"C\",\"T\"]  |       1 | 6.88e-07 | 1453416 |\n",
-       "| chr1:155357583 | [\"A\",\"T\"]  |       1 | 6.84e-07 | 1461680 |\n",
-       "| chr1:155370984 | [\"C\",\"T\"]  |       3 | 2.06e-06 | 1455726 |\n",
-       "| chr1:155415924 | [\"C\",\"A\"]  |       2 | 1.48e-06 | 1352032 |\n",
-       "| chr1:155478020 | [\"G\",\"C\"]  |       1 | 6.84e-07 | 1461888 |\n",
-       "| chr1:155478203 | [\"C\",\"T\"]  |       1 | 6.84e-07 | 1461874 |\n",
-       "| chr1:155478439 | [\"C\",\"T\"]  |       1 | 6.84e-07 | 1461890 |\n",
-       "| chr1:155478528 | [\"G\",\"A\"]  |       1 | 6.84e-07 | 1461876 |\n",
-       "| chr1:155479767 | [\"G\",\"A\"]  |       1 | 6.84e-07 | 1461858 |\n",
-       "| chr1:155479862 | [\"G\",\"T\"]  |       1 | 6.84e-07 | 1461160 |\n",
-       "| chr1:155521291 | [\"C\",\"A\"]  |       1 | 6.84e-07 | 1461862 |\n",
-       "| chr1:155521474 | [\"C\",\"A\"]  |       1 | 6.84e-07 | 1461724 |\n",
-       "+----------------+------------+---------+----------+---------+\n",
-       "\n",
-       "+-----------------------+-----------------------------+---------------+\n",
-       "| freq.homozygote_count | csq                         | coverage.mean |\n",
-       "+-----------------------+-----------------------------+---------------+\n",
-       "|                 int64 | array                  |       float64 |\n",
-       "+-----------------------+-----------------------------+---------------+\n",
-       "|                     0 | [\"stop_gained\"]             |      2.99e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.02e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.00e+01 |\n",
-       "|                     0 | [\"splice_donor_variant\"]    |      3.16e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.19e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.21e+01 |\n",
-       "|                     0 | [\"splice_acceptor_variant\"] |      3.04e+01 |\n",
-       "|                     0 | [\"splice_donor_variant\"]    |      3.13e+01 |\n",
-       "|                     0 | [\"splice_acceptor_variant\"] |      3.12e+01 |\n",
-       "|                     0 | [\"splice_acceptor_variant\"] |      2.73e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.26e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.27e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.28e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.28e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.30e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.24e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.30e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.29e+01 |\n",
-       "+-----------------------+-----------------------------+---------------+\n",
-       "\n",
-       "+------------------------+-------------------+-----------------+\n",
-       "| coverage.median_approx | coverage.total_DP | coverage.over_1 |\n",
-       "+------------------------+-------------------+-----------------+\n",
-       "|                  int32 |             int64 |         float64 |\n",
-       "+------------------------+-------------------+-----------------+\n",
-       "|                     30 |          21887528 |        1.00e+00 |\n",
-       "|                     30 |          22042372 |        1.00e+00 |\n",
-       "|                     30 |          21956914 |        1.00e+00 |\n",
-       "|                     31 |          23111319 |        1.00e+00 |\n",
-       "|                     31 |          23308434 |        1.00e+00 |\n",
-       "|                     32 |          23444999 |        1.00e+00 |\n",
-       "|                     31 |          22209714 |        1.00e+00 |\n",
-       "|                     31 |          22858887 |        1.00e+00 |\n",
-       "|                     31 |          22802181 |        1.00e+00 |\n",
-       "|                     30 |          19932375 |        1.00e+00 |\n",
-       "|                     33 |          23830081 |        1.00e+00 |\n",
-       "|                     33 |          23924871 |        1.00e+00 |\n",
-       "|                     33 |          23994800 |        1.00e+00 |\n",
-       "|                     33 |          23997776 |        1.00e+00 |\n",
-       "|                     33 |          24143459 |        1.00e+00 |\n",
-       "|                     32 |          23651203 |        1.00e+00 |\n",
-       "|                     32 |          24114456 |        1.00e+00 |\n",
-       "|                     32 |          24077551 |        1.00e+00 |\n",
-       "+------------------------+-------------------+-----------------+\n",
-       "\n",
-       "+-----------------+------------------+------------------+------------------+\n",
-       "| coverage.over_5 | coverage.over_10 | coverage.over_15 | coverage.over_20 |\n",
-       "+-----------------+------------------+------------------+------------------+\n",
-       "|         float64 |          float64 |          float64 |          float64 |\n",
-       "+-----------------+------------------+------------------+------------------+\n",
-       "|        1.00e+00 |         1.00e+00 |         9.97e-01 |         9.86e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.94e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.92e-01 |\n",
-       "|        1.00e+00 |         9.99e-01 |         9.95e-01 |         9.89e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         9.99e-01 |\n",
-       "|        1.00e+00 |         9.95e-01 |         9.83e-01 |         9.70e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.97e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.97e-01 |\n",
-       "|        9.84e-01 |         9.41e-01 |         8.72e-01 |         8.17e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.98e-01 |         9.91e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.98e-01 |\n",
-       "+-----------------+------------------+------------------+------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+-------------------+\n",
-       "| coverage.over_25 | coverage.over_30 | coverage.over_50 | coverage.over_100 |\n",
-       "+------------------+------------------+------------------+-------------------+\n",
-       "|          float64 |          float64 |          float64 |           float64 |\n",
-       "+------------------+------------------+------------------+-------------------+\n",
-       "|         9.32e-01 |         6.07e-01 |         8.96e-04 |          6.02e-05 |\n",
-       "|         9.48e-01 |         6.21e-01 |         8.39e-04 |          4.93e-05 |\n",
-       "|         9.37e-01 |         6.12e-01 |         8.29e-04 |          4.51e-05 |\n",
-       "|         9.59e-01 |         7.28e-01 |         1.35e-02 |          7.59e-04 |\n",
-       "|         9.76e-01 |         7.42e-01 |         1.35e-02 |          7.59e-04 |\n",
-       "|         9.76e-01 |         7.69e-01 |         5.01e-03 |          1.85e-04 |\n",
-       "|         9.28e-01 |         6.53e-01 |         4.38e-04 |          4.93e-05 |\n",
-       "|         9.79e-01 |         7.47e-01 |         8.14e-04 |          6.29e-05 |\n",
-       "|         9.54e-01 |         6.97e-01 |         1.14e-03 |          3.69e-05 |\n",
-       "|         7.64e-01 |         5.70e-01 |         1.35e-03 |          1.76e-04 |\n",
-       "|         9.72e-01 |         7.53e-01 |         3.11e-03 |          1.40e-03 |\n",
-       "|         9.72e-01 |         7.55e-01 |         4.07e-03 |          2.20e-03 |\n",
-       "|         9.73e-01 |         7.59e-01 |         4.64e-03 |          2.58e-03 |\n",
-       "|         9.73e-01 |         7.59e-01 |         4.67e-03 |          2.60e-03 |\n",
-       "|         9.74e-01 |         7.66e-01 |         6.55e-03 |          3.89e-03 |\n",
-       "|         9.42e-01 |         7.13e-01 |         6.42e-03 |          3.91e-03 |\n",
-       "|         9.81e-01 |         7.72e-01 |         1.94e-02 |          3.68e-03 |\n",
-       "|         9.79e-01 |         7.69e-01 |         1.94e-02 |          3.67e-03 |\n",
-       "+------------------+------------------+------------------+-------------------+"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "coverage_ht = coverage(\"exomes\").ht()\n",
-    "\n",
-    "#Filter to PASS SNVs with AF <= 0.1% and median exome depth ≥ 30.\n",
-    "ht = ht.filter(\n",
-    "    (hl.len(ht.filters) == 0) \n",
-    "    & (ht.allele_info.allele_type == \"snv\")\n",
-    "    & (ht.freq[0].AF <= 0.001)\n",
-    "    & (coverage_ht[ht.locus].median_approx >= 30)\n",
-    ")\n",
-    "\n",
-    "\n",
-    "print(f\"Number of variants: {ht.count()}\")\n",
-    "ht.select(\n",
-    "    freq=ht.freq[0],\n",
-    "    csq=ht.vep.transcript_consequences[0].consequence_terms,\n",
-    "    coverage=coverage_ht[ht.locus],\n",
-    ").show(-1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b104a39b",
-   "metadata": {},
-   "source": [
-    "# Get 'freq' for specific genetic ancestry groups"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "135565fe",
-   "metadata": {},
-   "source": [
-    "## Get 'freq' for multiple groups for an (gene) interval"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "4f78166f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n",
-    "\n",
-    "# Filter to interval, e.g. for ASH1L.\n",
-    "gene_interval = \"chr1:155335268-155563162\"\n",
-    "\n",
-    "# Filter the exome release Hail Table to the ASH1L gene interval.\n",
-    "ht = hl.filter_intervals(ht, [hl.parse_locus_interval(gene_interval, reference_genome=\"GRCh38\")])\n",
-    "\n",
-    "# Filter to variants with adj.AC > 0 \n",
-    "ht = ht.filter(ht.freq[0].AC>0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "ce7a1e8c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "[Stage 39:>                                                         (0 + 3) / 3]\r",
-      "\r",
-      "[Stage 39:======================================>                   (2 + 1) / 3]\r"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
afr
amr
eas
mid
nfe
sas
locus
alleles
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64
chr1:155335497["A","C"]0NA000NA0000.00e+0013800NA0000.00e+00200NA00
chr1:155335570["T","C"]0NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335571["TA","T"]0NA000NA0017.25e-0313800NA0000.00e+00200NA00
chr1:155335746["G","C"]0NA000NA0000.00e+0013200NA0000.00e+00200NA00
chr1:155335855["G","A"]0NA000NA0017.25e-0313800NA0000.00e+00600NA00
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+----------------+------------+--------+---------+--------+\n",
-       "| locus          | alleles    | afr.AC |  afr.AF | afr.AN |\n",
-       "+----------------+------------+--------+---------+--------+\n",
-       "| locus  | array |  int32 | float64 |  int32 |\n",
-       "+----------------+------------+--------+---------+--------+\n",
-       "| chr1:155335497 | [\"A\",\"C\"]  |      0 |      NA |      0 |\n",
-       "| chr1:155335570 | [\"T\",\"C\"]  |      0 |      NA |      0 |\n",
-       "| chr1:155335571 | [\"TA\",\"T\"] |      0 |      NA |      0 |\n",
-       "| chr1:155335746 | [\"G\",\"C\"]  |      0 |      NA |      0 |\n",
-       "| chr1:155335855 | [\"G\",\"A\"]  |      0 |      NA |      0 |\n",
-       "+----------------+------------+--------+---------+--------+\n",
-       "\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "| afr.homozygote_count | amr.AC |  amr.AF | amr.AN | amr.homozygote_count |\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "|                int64 |  int32 | float64 |  int32 |                int64 |\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "\n",
-       "+--------+----------+--------+----------------------+--------+---------+\n",
-       "| eas.AC |   eas.AF | eas.AN | eas.homozygote_count | mid.AC |  mid.AF |\n",
-       "+--------+----------+--------+----------------------+--------+---------+\n",
-       "|  int32 |  float64 |  int32 |                int64 |  int32 | float64 |\n",
-       "+--------+----------+--------+----------------------+--------+---------+\n",
-       "|      0 | 0.00e+00 |    138 |                    0 |      0 |      NA |\n",
-       "|      1 | 7.25e-03 |    138 |                    0 |      0 |      NA |\n",
-       "|      1 | 7.25e-03 |    138 |                    0 |      0 |      NA |\n",
-       "|      0 | 0.00e+00 |    132 |                    0 |      0 |      NA |\n",
-       "|      1 | 7.25e-03 |    138 |                    0 |      0 |      NA |\n",
-       "+--------+----------+--------+----------------------+--------+---------+\n",
-       "\n",
-       "+--------+----------------------+--------+----------+--------+\n",
-       "| mid.AN | mid.homozygote_count | nfe.AC |   nfe.AF | nfe.AN |\n",
-       "+--------+----------------------+--------+----------+--------+\n",
-       "|  int32 |                int64 |  int32 |  float64 |  int32 |\n",
-       "+--------+----------------------+--------+----------+--------+\n",
-       "|      0 |                    0 |      0 | 0.00e+00 |      2 |\n",
-       "|      0 |                    0 |      0 | 0.00e+00 |      2 |\n",
-       "|      0 |                    0 |      0 | 0.00e+00 |      2 |\n",
-       "|      0 |                    0 |      0 | 0.00e+00 |      2 |\n",
-       "|      0 |                    0 |      0 | 0.00e+00 |      6 |\n",
-       "+--------+----------------------+--------+----------+--------+\n",
-       "\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "| nfe.homozygote_count | sas.AC |  sas.AF | sas.AN | sas.homozygote_count |\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "|                int64 |  int32 | float64 |  int32 |                int64 |\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "|                    0 |      0 |      NA |      0 |                    0 |\n",
-       "+----------------------+--------+---------+--------+----------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht = extract_callstats_for_multiple_ancs(ht, gen_ancs=['afr', 'amr', 'eas', 'mid', 'nfe', 'sas'])\n",
-    "\n",
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fe2e98b8",
-   "metadata": {},
-   "source": [
-    "## Get 'freq' for a specific group and a specific variant"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "4846958a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "
afr
locus
alleles
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>int32float64int32int64
chr22:15528692["C","G"]6351.90e-02333806
"
-      ],
-      "text/plain": [
-       "+----------------+------------+--------+----------+--------+\n",
-       "| locus          | alleles    | afr.AC |   afr.AF | afr.AN |\n",
-       "+----------------+------------+--------+----------+--------+\n",
-       "| locus  | array |  int32 |  float64 |  int32 |\n",
-       "+----------------+------------+--------+----------+--------+\n",
-       "| chr22:15528692 | [\"C\",\"G\"]  |    635 | 1.90e-02 |  33380 |\n",
-       "+----------------+------------+--------+----------+--------+\n",
-       "\n",
-       "+----------------------+\n",
-       "| afr.homozygote_count |\n",
-       "+----------------------+\n",
-       "|                int64 |\n",
-       "+----------------------+\n",
-       "|                    6 |\n",
-       "+----------------------+"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht = get_ht_by_datatype_and_version(data_type='exomes', version='4.1')\n",
-    "\n",
-    "# When a variant exists...\n",
-    "extract_callstats_for_1anc_1variant(ht, gen_anc='AFR', contig='chr22', position=15528692, alleles=['C','G']).show(-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "9f4c689b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-11-02 02:02:23.969 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "
afr
locus
alleles
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>int32float64int32int64
"
-      ],
-      "text/plain": [
-       "+---------------+------------+--------+---------+--------+\n",
-       "| locus         | alleles    | afr.AC |  afr.AF | afr.AN |\n",
-       "+---------------+------------+--------+---------+--------+\n",
-       "| locus | array |  int32 | float64 |  int32 |\n",
-       "+---------------+------------+--------+---------+--------+\n",
-       "+---------------+------------+--------+---------+--------+\n",
-       "\n",
-       "+----------------------+\n",
-       "| afr.homozygote_count |\n",
-       "+----------------------+\n",
-       "|                int64 |\n",
-       "+----------------------+\n",
-       "+----------------------+"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# When a variant doesn't exist...\n",
-    "extract_callstats_for_1anc_1variant(ht, gen_anc='AFR', contig='chr22', position=15528692, alleles=['C','A']).show(-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "6fc82c5c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Copying gs://gnomad-qin/qin_notebooks/toolbox_for_gnomad_users.ipynb...\n",
-      "/ [1 files][747.1 KiB/747.1 KiB]                                                \n",
-      "Operation completed over 1 objects/747.1 KiB.                                    \n",
-      "[NbConvertApp] WARNING | Config option `extra_template_paths` not recognized by `EmbedHTMLExporter`.  Did you mean `template_path`?\n",
-      "[NbConvertApp] Converting notebook toolbox_for_gnomad_users.ipynb to html_embed\n",
-      "[NbConvertApp] Writing 943138 bytes to toolbox_for_gnomad_users.html\n",
-      "Copying file://toolbox_for_gnomad_users.html [Content-Type=text/html]...\n",
-      "/ [1 files][921.1 KiB/921.1 KiB]                                                \n",
-      "Operation completed over 1 objects/921.1 KiB.                                    \n"
-     ]
-    }
-   ],
-   "source": [
-    "notebook_name='toolbox_for_gnomad_users'\n",
-    "\n",
-    "#Uncomment top lines for the first time exporting on a cluster\n",
-    "#!/opt/conda/default/bin/conda create -n save-html-env --clone /opt/conda/default\n",
-    "#!/opt/conda/miniconda3/envs/save-html-env/bin/pip install \"nbconvert<6\" jinja2==3.0.3 jupyter_contrib_nbextensions\n",
-    "\n",
-    "# Download the notebook from Google Cloud Storage\n",
-    "!gsutil -u broad-mpg-gnomad cp gs://gnomad-qin/qin_notebooks/{notebook_name}.ipynb .\n",
-    "\n",
-    "# Convert the notebook to HTML with embedded resources\n",
-    "! /opt/conda/miniconda3/envs/save-html-env/bin/jupyter nbconvert \\\n",
-    "    --CodeFoldingPreprocessor.remove_folded_code=True --to html_embed \\\n",
-    "    --template \"/opt/conda/miniconda3/envs/save-html-env/lib/python3.11/site-packages/jupyter_contrib_nbextensions/templates/toc2.tpl\" \\\n",
-    "    {notebook_name}.ipynb\n",
-    "\n",
-    "# Upload the converted HTML back to Google Cloud Storage\n",
-    "!gsutil -u broad-mpg-gnomad cp {notebook_name}.html gs://gnomad-qin/qin_notebooks/{notebook_name}.html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a9e4c9bb",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {
-    "height": "613.99px",
-    "width": "526.312px"
-   },
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": true,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "219.438px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From b853fc20d2a78b6d6d533b8f00ea209f13c93e7a Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 11 Dec 2024 08:40:57 -0700
Subject: [PATCH 028/121] Add notebooks to git

---
 .../notebooks/explore_release_data.ipynb      | 6622 +++++++++++++++++
 .../intro_to_filtering_variant_data.ipynb     | 6288 ++++++++++++++++
 gnomad_toolbox/notebooks/needs_a_name.ipynb   |  602 ++
 3 files changed, 13512 insertions(+)
 create mode 100644 gnomad_toolbox/notebooks/explore_release_data.ipynb
 create mode 100644 gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
 create mode 100644 gnomad_toolbox/notebooks/needs_a_name.ipynb

diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
new file mode 100644
index 0000000..1801596
--- /dev/null
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -0,0 +1,6622 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "853c94b9",
+   "metadata": {},
+   "source": [
+    "# Introduction to gnomAD Hail release files\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cf83cfe-0fce-40ae-add7-c9f2c20c1e85",
+   "metadata": {},
+   "source": [
+    "In this notebook we will explore all of the available [gnomAD v4 release files](https://gnomad.broadinstitute.org/data#v4) that are in Hail formats."
+   ]
+  },
+  {
+   "attachments": {
+    "afcd4ecc-4e90-464b-91c7-9cd80b0e92ba.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "88ddfe98-92b3-4c9c-906d-84d4835d2bf1",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:afcd4ecc-4e90-464b-91c7-9cd80b0e92ba.png)"
+   ]
+  },
+  {
+   "attachments": {
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+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "f97431a4-78a0-473f-99a0-67eeefb8bbc6",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:2f957256-5d08-40e1-b77c-5faa4f771fb2.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e609a46",
+   "metadata": {
+    "tags": [],
+    "toc": true
+   },
+   "source": [
+    "
Table of Contents
\n",
+    "
"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e713032",
+   "metadata": {},
+   "source": [
+    "## Import modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e69953f7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:40.044647Z",
+     "start_time": "2024-12-06T20:08:37.758061Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "    \n",
+       "    
\n",
+       "        \n",
+       "        Loading BokehJS ...\n",
+       "    
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  const force = true;\n",
+       "\n",
+       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+       "    root._bokeh_onload_callbacks = [];\n",
+       "    root._bokeh_is_loading = undefined;\n",
+       "  }\n",
+       "\n",
+       "const JS_MIME_TYPE = 'application/javascript';\n",
+       "  const HTML_MIME_TYPE = 'text/html';\n",
+       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
+       "\n",
+       "  /**\n",
+       "   * Render data to the DOM node\n",
+       "   */\n",
+       "  function render(props, node) {\n",
+       "    const script = document.createElement(\"script\");\n",
+       "    node.appendChild(script);\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when an output is cleared or removed\n",
+       "   */\n",
+       "  function handleClearOutput(event, handle) {\n",
+       "    function drop(id) {\n",
+       "      const view = Bokeh.index.get_by_id(id)\n",
+       "      if (view != null) {\n",
+       "        view.model.document.clear()\n",
+       "        Bokeh.index.delete(view)\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    const cell = handle.cell;\n",
+       "\n",
+       "    const id = cell.output_area._bokeh_element_id;\n",
+       "    const server_id = cell.output_area._bokeh_server_id;\n",
+       "\n",
+       "    // Clean up Bokeh references\n",
+       "    if (id != null) {\n",
+       "      drop(id)\n",
+       "    }\n",
+       "\n",
+       "    if (server_id !== undefined) {\n",
+       "      // Clean up Bokeh references\n",
+       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+       "      cell.notebook.kernel.execute(cmd_clean, {\n",
+       "        iopub: {\n",
+       "          output: function(msg) {\n",
+       "            const id = msg.content.text.trim()\n",
+       "            drop(id)\n",
+       "          }\n",
+       "        }\n",
+       "      });\n",
+       "      // Destroy server and session\n",
+       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+       "      cell.notebook.kernel.execute(cmd_destroy);\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when a new output is added\n",
+       "   */\n",
+       "  function handleAddOutput(event, handle) {\n",
+       "    const output_area = handle.output_area;\n",
+       "    const output = handle.output;\n",
+       "\n",
+       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
+       "      return\n",
+       "    }\n",
+       "\n",
+       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+       "      // store reference to embed id on output_area\n",
+       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "    }\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "      const bk_div = document.createElement(\"div\");\n",
+       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "      const script_attrs = bk_div.children[0].attributes;\n",
+       "      for (let i = 0; i < script_attrs.length; i++) {\n",
+       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
+       "      }\n",
+       "      // store reference to server id on output_area\n",
+       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function register_renderer(events, OutputArea) {\n",
+       "\n",
+       "    function append_mime(data, metadata, element) {\n",
+       "      // create a DOM node to render to\n",
+       "      const toinsert = this.create_output_subarea(\n",
+       "        metadata,\n",
+       "        CLASS_NAME,\n",
+       "        EXEC_MIME_TYPE\n",
+       "      );\n",
+       "      this.keyboard_manager.register_events(toinsert);\n",
+       "      // Render to node\n",
+       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "      render(props, toinsert[toinsert.length - 1]);\n",
+       "      element.append(toinsert);\n",
+       "      return toinsert\n",
+       "    }\n",
+       "\n",
+       "    /* Handle when an output is cleared or removed */\n",
+       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
+       "    events.on('delete.Cell', handleClearOutput);\n",
+       "\n",
+       "    /* Handle when a new output is added */\n",
+       "    events.on('output_added.OutputArea', handleAddOutput);\n",
+       "\n",
+       "    /**\n",
+       "     * Register the mime type and append_mime function with output_area\n",
+       "     */\n",
+       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "      /* Is output safe? */\n",
+       "      safe: true,\n",
+       "      /* Index of renderer in `output_area.display_order` */\n",
+       "      index: 0\n",
+       "    });\n",
+       "  }\n",
+       "\n",
+       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+       "  if (root.Jupyter !== undefined) {\n",
+       "    const events = require('base/js/events');\n",
+       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  }\n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
+       "     \"
\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"
    \\n\"+\n",
+       "     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n",
+       "     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"from bokeh.resources import INLINE\\n\"+\n",
+       "     \"output_notebook(resources=INLINE)\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"
\"}};\n",
+       "\n",
+       "  function display_loaded() {\n",
+       "    const el = document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\");\n",
+       "    if (el != null) {\n",
+       "      el.textContent = \"BokehJS is loading...\";\n",
+       "    }\n",
+       "    if (root.Bokeh !== undefined) {\n",
+       "      if (el != null) {\n",
+       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+       "      }\n",
+       "    } else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(display_loaded, 100)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls == null || js_urls.length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    function on_error(url) {\n",
+       "      console.error(\"failed to load \" + url);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < css_urls.length; i++) {\n",
+       "      const url = css_urls[i];\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < js_urls.length; i++) {\n",
+       "      const url = js_urls[i];\n",
+       "      const element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
+       "  const css_urls = [];\n",
+       "\n",
+       "  const inline_js = [    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
+       "    },\n",
+       "function(Bokeh) {\n",
+       "    }\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    if (root.Bokeh !== undefined || force === true) {\n",
+       "          for (let i = 0; i < inline_js.length; i++) {\n",
+       "      inline_js[i].call(root, root.Bokeh);\n",
+       "    }\n",
+       "if (force === true) {\n",
+       "        display_loaded();\n",
+       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    } else if (force !== true) {\n",
+       "      const cell = $(document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\")).parents('.cell').data().cell;\n",
+       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  if (root._bokeh_is_loading === 0) {\n",
+       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+       "    run_inline_js();\n",
+       "  } else {\n",
+       "    load_libs(css_urls, js_urls, function() {\n",
+       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "      run_inline_js();\n",
+       "    });\n",
+       "  }\n",
+       "}(window));"
+      ],
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import hail as hl\n",
+    "from gnomad_toolbox.load_data import  get_gnomad_release"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8649f215-0afc-4f66-920a-53b707f41c4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Welcome to\n",
+      "     __  __     <>__\n",
+      "    / /_/ /__  __/ /\n",
+      "   / __  / _ `/ / /\n",
+      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241210-1438-0.2.132-678e1f52b999.log\n"
+     ]
+    }
+   ],
+   "source": [
+    "hl.init(backend=\"local\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5335a135",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Variant data\n",
+    "\n",
+    "Available versions for each data type and reference build are (as of 2024-10-29):\n",
+    "\n",
+    "| Data Type       | GRCh38 Versions                  | GRCh37 Versions      |\n",
+    "|-----------------|----------------------------------|----------------------|\n",
+    "| exomes          | 4.0, 4.1                         | 2.1, 2.1.1           |\n",
+    "| genomes         | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1           |\n",
+    "| joint           | 4.1                              | N/A                  |\n",
+    "\n",
+    "For a description of all fields the the HT, see the [Help/FAQ](https://gnomad.broadinstitute.org/help/v4-hts) page."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1a4ae8933ba6421",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### v4.1 exomes Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "318a034c-ac84-4147-9f25-e5e8783e9b91",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "100cf576",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='exomes', version='4.1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77d7a05e31c1f37a",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "95c14f2c8cc3e699",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'freq_meta': array> \n",
+      "    'freq_index_dict': dict \n",
+      "    'freq_meta_sample_count': array \n",
+      "    'faf_meta': array> \n",
+      "    'faf_index_dict': dict \n",
+      "    'age_distribution': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int32, \n",
+      "        n_larger: int32\n",
+      "    } \n",
+      "    'downsamplings': dict> \n",
+      "    'filtering_model': struct {\n",
+      "        filter_name: str, \n",
+      "        score_name: str, \n",
+      "        snv_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }, \n",
+      "        indel_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }, \n",
+      "        snv_training_variables: array, \n",
+      "        indel_training_variables: array\n",
+      "    } \n",
+      "    'inbreeding_coeff_cutoff': float64 \n",
+      "    'interval_qc_parameters': struct {\n",
+      "        per_platform: bool, \n",
+      "        all_platforms: bool, \n",
+      "        high_qual_cutoffs: dict>, \n",
+      "        min_platform_size: int32\n",
+      "    } \n",
+      "    'tool_versions': struct {\n",
+      "        cadd_version: str, \n",
+      "        revel_version: str, \n",
+      "        spliceai_version: str, \n",
+      "        pangolin_version: array, \n",
+      "        phylop_version: str, \n",
+      "        dbsnp_version: str, \n",
+      "        sift_version: str, \n",
+      "        polyphen_version: str\n",
+      "    } \n",
+      "    'vrs_versions': struct {\n",
+      "        vrs_schema_version: str, \n",
+      "        vrs_python_version: str, \n",
+      "        seqrepo_version: str\n",
+      "    } \n",
+      "    'vep_globals': struct {\n",
+      "        vep_version: str, \n",
+      "        vep_help: str, \n",
+      "        vep_config: str, \n",
+      "        gencode_version: str, \n",
+      "        mane_select_version: str\n",
+      "    } \n",
+      "    'frequency_README': str \n",
+      "    'date': str \n",
+      "    'version': str \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'alleles': array \n",
+      "    'freq': array \n",
+      "    'grpmax': struct {\n",
+      "        gnomad: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int64, \n",
+      "            gen_anc: str\n",
+      "        }, \n",
+      "        non_ukb: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int64, \n",
+      "            gen_anc: str\n",
+      "        }\n",
+      "    } \n",
+      "    'faf': array \n",
+      "    'fafmax': struct {\n",
+      "        gnomad: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }, \n",
+      "        non_ukb: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }\n",
+      "    } \n",
+      "    'a_index': int32 \n",
+      "    'was_split': bool \n",
+      "    'rsid': set \n",
+      "    'filters': set \n",
+      "    'info': struct {\n",
+      "        FS: float64, \n",
+      "        MQ: float64, \n",
+      "        MQRankSum: float64, \n",
+      "        QUALapprox: int64, \n",
+      "        QD: float64, \n",
+      "        ReadPosRankSum: float64, \n",
+      "        SB: array, \n",
+      "        SOR: float64, \n",
+      "        VarDP: int32, \n",
+      "        AS_FS: float64, \n",
+      "        AS_MQ: float64, \n",
+      "        AS_MQRankSum: float64, \n",
+      "        AS_pab_max: float64, \n",
+      "        AS_QUALapprox: int64, \n",
+      "        AS_QD: float64, \n",
+      "        AS_ReadPosRankSum: float64, \n",
+      "        AS_SB_TABLE: array, \n",
+      "        AS_SOR: float64, \n",
+      "        AS_VarDP: int32, \n",
+      "        singleton: bool, \n",
+      "        transmitted_singleton: bool, \n",
+      "        sibling_singleton: bool, \n",
+      "        omni: bool, \n",
+      "        mills: bool, \n",
+      "        monoallelic: bool, \n",
+      "        only_het: bool, \n",
+      "        AS_VQSLOD: float64, \n",
+      "        inbreeding_coeff: float64, \n",
+      "        vrs: struct {\n",
+      "            VRS_Allele_IDs: array, \n",
+      "            VRS_Starts: array, \n",
+      "            VRS_Ends: array, \n",
+      "            VRS_States: array\n",
+      "        }\n",
+      "    } \n",
+      "    'vep': struct {\n",
+      "        allele_string: str, \n",
+      "        end: int32, \n",
+      "        id: str, \n",
+      "        input: str, \n",
+      "        intergenic_consequences: array, \n",
+      "            impact: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        most_severe_consequence: str, \n",
+      "        motif_feature_consequences: array, \n",
+      "            high_inf_pos: str, \n",
+      "            impact: str, \n",
+      "            motif_feature_id: str, \n",
+      "            motif_name: str, \n",
+      "            motif_pos: int32, \n",
+      "            motif_score_change: float64, \n",
+      "            transcription_factors: array, \n",
+      "            strand: int32, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        regulatory_feature_consequences: array, \n",
+      "            impact: str, \n",
+      "            regulatory_feature_id: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        seq_region_name: str, \n",
+      "        start: int32, \n",
+      "        strand: int32, \n",
+      "        transcript_consequences: array, \n",
+      "            distance: int32, \n",
+      "            domains: array, \n",
+      "            exon: str, \n",
+      "            flags: str, \n",
+      "            gene_id: str, \n",
+      "            gene_pheno: int32, \n",
+      "            gene_symbol: str, \n",
+      "            gene_symbol_source: str, \n",
+      "            hgnc_id: str, \n",
+      "            hgvsc: str, \n",
+      "            hgvsp: str, \n",
+      "            hgvs_offset: int32, \n",
+      "            impact: str, \n",
+      "            intron: str, \n",
+      "            lof: str, \n",
+      "            lof_flags: str, \n",
+      "            lof_filter: str, \n",
+      "            lof_info: str, \n",
+      "            mane_select: str, \n",
+      "            mane_plus_clinical: str, \n",
+      "            mirna: array, \n",
+      "            protein_end: int32, \n",
+      "            protein_start: int32, \n",
+      "            protein_id: str, \n",
+      "            source: str, \n",
+      "            strand: int32, \n",
+      "            transcript_id: str, \n",
+      "            tsl: int32, \n",
+      "            uniprot_isoform: array, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        variant_class: str\n",
+      "    } \n",
+      "    'vqsr_results': struct {\n",
+      "        AS_VQSLOD: float64, \n",
+      "        AS_culprit: str, \n",
+      "        positive_train_site: bool, \n",
+      "        negative_train_site: bool\n",
+      "    } \n",
+      "    'region_flags': struct {\n",
+      "        non_par: bool, \n",
+      "        lcr: bool, \n",
+      "        segdup: bool, \n",
+      "        fail_interval_qc: bool, \n",
+      "        outside_ukb_capture_region: bool, \n",
+      "        outside_broad_capture_region: bool\n",
+      "    } \n",
+      "    'allele_info': struct {\n",
+      "        variant_type: str, \n",
+      "        n_alt_alleles: int32, \n",
+      "        has_star: bool, \n",
+      "        allele_type: str, \n",
+      "        was_mixed: bool\n",
+      "    } \n",
+      "    'histograms': struct {\n",
+      "        qual_hists: struct {\n",
+      "            gq_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            gq_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            ab_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        raw_qual_hists: struct {\n",
+      "            gq_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            gq_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            ab_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        age_hists: struct {\n",
+      "            age_hist_het: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            age_hist_hom: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }\n",
+      "    } \n",
+      "    'in_silico_predictors': struct {\n",
+      "        cadd: struct {\n",
+      "            phred: float32, \n",
+      "            raw_score: float32\n",
+      "        }, \n",
+      "        revel_max: float64, \n",
+      "        spliceai_ds_max: float32, \n",
+      "        pangolin_largest_ds: float64, \n",
+      "        phylop: float64, \n",
+      "        sift_max: float64, \n",
+      "        polyphen_max: float64\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Key: ['locus', 'alleles']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a071f738b2c888e",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "222de580c305d72a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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cessed_transcript",NA,NA,234,234,NA,NA,NA,["non_coding_transcript_exon_variant"],NA,NA,"1/3",NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102","ENST00000456328.2:n.234G>A",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000456328",1,NA,"A"),(1,NA,NA,"unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],2302,NA,NA,NA,"ENSG00000227232",NA,"WASH7P","HGNC","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000488147",NA,NA,"A"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],2260,NA,NA,NA,"653635",NA,"WASH7P","EntrezGene","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",-1,"NR_024540.1",NA,NA,"A"),(1,NA,NA,"transcribed_pseudogene",1,NA,229,229,NA,NA,NA,["non_coding_transcript_exon_variant"],NA,NA,"1/3",NA,"100287102",NA,"DDX11L1","EntrezGene","HGNC:37102","NR_046018.2:n.229G>A",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",1,"NR_046018.2",NA,NA,"A")]"SNV"-2.86e+00"AS_MQ"FalseFalseFalseFalseTrueNATrueFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][314323,1,0,0,54,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][308789,5585,16,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]004.42e+003.11e-01NANA-8.00e-021.38e+00NANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+----------------+\n",
+       "| locus         | alleles        |\n",
+       "+---------------+----------------+\n",
+       "| locus | array     |\n",
+       "+---------------+----------------+\n",
+       "| chr1:11994    | [\"T\",\"C\"]      |\n",
+       "| chr1:12016    | [\"G\",\"A\"]      |\n",
+       "| chr1:12060    | [\"CTGGAG\",\"C\"] |\n",
+       "| chr1:12074    | [\"T\",\"C\"]      |\n",
+       "| chr1:12102    | [\"G\",\"A\"]      |\n",
+       "+---------------+----------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0,NA,0,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
+       "| [(0,NA,0,0),(6,9.54e-06,628784,2),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,... |\n",
+       "| [(0,0.00e+00,4,0),(1,1.59e-06,628784,0),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
+       "| [(0,0.00e+00,4,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),... |\n",
+       "| [(0,0.00e+00,32,0),(2,3.18e-06,628784,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0)... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "|               NA |               NA |               NA |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "| was_split | rsid     | filters           | info.FS |  info.MQ |\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "|      bool | set | set          | float64 |  float64 |\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.50e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.60e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.56e+01 |\n",
+       "|     False | NA       | {\"AC0\",\"AS_VQSR\"} |      NA | 2.45e+01 |\n",
+       "+-----------+----------+-------------------+---------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|             NA |              60 | 3.00e+01 |                  NA |\n",
+       "|       0.00e+00 |             262 | 2.18e+01 |            6.74e-01 |\n",
+       "|       6.74e-01 |             123 | 3.08e+01 |           -1.15e+00 |\n",
+       "|       4.31e-01 |              99 | 1.24e+01 |           -2.53e-01 |\n",
+       "|             NA |              90 | 2.25e+01 |                  NA |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "| info.SB      | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "| array |  float64 |      int32 |    float64 |    float64 |\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "| [0,0,2,0]    | 2.30e+00 |          2 |         NA |   2.50e+01 |\n",
+       "| [2,0,10,0]   | 2.67e+00 |         12 |         NA |   2.50e+01 |\n",
+       "| [1,0,3,0]    | 1.61e+00 |          4 |         NA |   2.60e+01 |\n",
+       "| [4,0,4,0]    | 6.93e-01 |          8 |         NA |   2.56e+01 |\n",
+       "| [0,0,4,0]    | 3.26e+00 |          4 |         NA |   2.45e+01 |\n",
+       "+--------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|                NA |              NA |                 60 |   3.00e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |                262 |   2.18e+01 |\n",
+       "|          6.74e-01 |        6.25e-01 |                123 |   3.08e+01 |\n",
+       "|          4.31e-01 |        6.87e-01 |                 99 |   1.24e+01 |\n",
+       "|                NA |              NA |                 90 |   2.25e+01 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE | info.AS_SOR | info.AS_VarDP |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "|                float64 | array     |     float64 |         int32 |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "|                     NA | [0,0,2,0]        |    2.30e+00 |             2 |\n",
+       "|               6.74e-01 | [2,0,10,0]       |    2.67e+00 |            12 |\n",
+       "|              -1.15e+00 | [1,0,3,0]        |    1.61e+00 |             4 |\n",
+       "|              -2.53e-01 | [4,0,4,0]        |    6.93e-01 |             8 |\n",
+       "|                     NA | [0,0,4,0]        |    3.26e+00 |             4 |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           bool |                       bool |                   bool |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|     False |      False |            False |         False |      -5.25e+00 |\n",
+       "|     False |      False |            False |         False |      -2.75e+00 |\n",
+       "|     False |      False |            False |         False |      -2.22e+00 |\n",
+       "|     False |      False |            False |         False |      -2.18e+00 |\n",
+       "|     False |      False |            False |         False |      -2.86e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|              1.00e+00 |\n",
+       "|              6.67e-01 |\n",
+       "|             -1.59e-06 |\n",
+       "|              1.00e+00 |\n",
+       "|              1.00e+00 |\n",
+       "+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.ps4-9woXy7o4rS39i8hDK_cUPBa-UcyP\",\"ga4gh:VA.nHlWYJXgiuvXrLAxQs... |\n",
+       "| [\"ga4gh:VA.VFxwcI4knOzk6SHzS2qowyDGnkG3mfEH\",\"ga4gh:VA.ctgP7qNjQAGjI2eTDo... |\n",
+       "| [\"ga4gh:VA.P573ZtUtAaRcceE7NLanEyynSefvcAPL\",\"ga4gh:VA.CHmk9uDiHW2LIHndZW... |\n",
+       "| [\"ga4gh:VA.neBeBT28ISe_1-yKPFsYxntP2jz1pj7E\",\"ga4gh:VA.1RPHSwBHNUwoECJ9VV... |\n",
+       "| [\"ga4gh:VA.ZXRhPWtip8HseOMWpCnaja0-ATo8hLE1\",\"ga4gh:VA.Qi95g6E8nt6DaqTGpH... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts | info.vrs.VRS_Ends | info.vrs.VRS_States |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| array        | array      | array          |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| [11993,11993]       | [11994,11994]     | [\"T\",\"C\"]           |\n",
+       "| [12015,12015]       | [12016,12016]     | [\"G\",\"A\"]           |\n",
+       "| [12059,12060]       | [12065,12071]     | [\"CTGGAG\",\"TGGAGT\"] |\n",
+       "| [12073,12073]       | [12074,12074]     | [\"T\",\"C\"]           |\n",
+       "| [12101,12101]       | [12102,12102]     | [\"G\",\"A\"]           |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "| vep.allele_string | vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "| str               |   int32 | str    | str                            |\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "| \"T/C\"             |   11994 | \".\"    | \"chr1\t11994\t.\tT\tC\t.\t.\tGT\"      |\n",
+       "| \"G/A\"             |   12016 | \".\"    | \"chr1\t12016\t.\tG\tA\t.\t.\tGT\"      |\n",
+       "| \"TGGAG/-\"         |   12065 | \".\"    | \"chr1\t12060\t.\tCTGGAG\tC\t.\t.\tGT\" |\n",
+       "| \"T/C\"             |   12074 | \".\"    | \"chr1\t12074\t.\tT\tC\t.\t.\tGT\"      |\n",
+       "| \"G/A\"             |   12102 | \".\"    | \"chr1\t12102\t.\tG\tA\t.\t.\tGT\"      |\n",
+       "+-------------------+---------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| vep.most_severe_consequence          |\n",
+       "+--------------------------------------+\n",
+       "| str                                  |\n",
+       "+--------------------------------------+\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "| \"splice_donor_5th_base_variant\"      |\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,2,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,14,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq   |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [314391,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [314388,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [314380,0,0,0,11,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]  |\n",
+       "| [314369,1,0,0,20,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [314323,1,0,0,54,0,12,0,2,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq     |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                       |\n",
+       "+----------------------------------------------------+\n",
+       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308845,5547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308833,5557,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308823,5567,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [308789,5585,16,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0]      |\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        1.26e+01 |\n",
+       "|                                          0 |                        1.60e+01 |\n",
+       "|                                          0 |                        8.52e+00 |\n",
+       "|                                          0 |                        1.52e+01 |\n",
+       "|                                          0 |                        4.42e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            1.08e+00 |                             NA |\n",
+       "|                            1.54e+00 |                             NA |\n",
+       "|                            7.07e-01 |                             NA |\n",
+       "|                            1.41e+00 |                             NA |\n",
+       "|                            3.11e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                -1.10e-01 |                    1.09e+00 |\n",
+       "|                                -7.00e-02 |                    6.55e+00 |\n",
+       "|                                -9.00e-02 |                   -4.41e+00 |\n",
+       "|                                -4.00e-02 |                    6.01e+00 |\n",
+       "|                                -8.00e-02 |                    1.38e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7a158a3-f21a-4f87-9596-1f918156d713",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### v4.1 genomes Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30f86500-afc5-419e-ae2e-f944dc461fee",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "62ca9934-20dd-437e-898b-86a056e2606e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='genomes', version='4.1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cf4b782-f289-47b6-9123-d08ca761b074",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "09de90df-0b03-4a54-817c-c8a0606026f6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'freq_meta': array> \n",
+      "    'freq_index_dict': dict \n",
+      "    'freq_meta_sample_count': array \n",
+      "    'faf_meta': array> \n",
+      "    'faf_index_dict': dict \n",
+      "    'age_distribution': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int32, \n",
+      "        n_larger: int32\n",
+      "    } \n",
+      "    'filtering_model': struct {\n",
+      "        filter_name: str, \n",
+      "        score_name: str, \n",
+      "        snv_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }, \n",
+      "        indel_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }, \n",
+      "        snv_training_variables: array, \n",
+      "        indel_training_variables: array\n",
+      "    } \n",
+      "    'inbreeding_coeff_cutoff': float64 \n",
+      "    'tool_versions': struct {\n",
+      "        cadd_version: str, \n",
+      "        revel_version: str, \n",
+      "        spliceai_version: str, \n",
+      "        pangolin_version: array, \n",
+      "        phylop_version: str, \n",
+      "        dbsnp_version: str, \n",
+      "        sift_version: str, \n",
+      "        polyphen_version: str\n",
+      "    } \n",
+      "    'vrs_versions': struct {\n",
+      "        vrs_schema_version: str, \n",
+      "        vrs_python_version: str, \n",
+      "        seqrepo_version: str\n",
+      "    } \n",
+      "    'vep_globals': struct {\n",
+      "        vep_version: str, \n",
+      "        vep_help: str, \n",
+      "        vep_config: str, \n",
+      "        gencode_version: str, \n",
+      "        mane_select_version: str\n",
+      "    } \n",
+      "    'frequency_README': str \n",
+      "    'date': str \n",
+      "    'version': str \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'alleles': array \n",
+      "    'freq': array \n",
+      "    'grpmax': struct {\n",
+      "        AC: int32, \n",
+      "        AF: float64, \n",
+      "        AN: int32, \n",
+      "        homozygote_count: int32, \n",
+      "        gen_anc: str\n",
+      "    } \n",
+      "    'faf': array \n",
+      "    'fafmax': struct {\n",
+      "        faf95_max: float64, \n",
+      "        faf95_max_gen_anc: str, \n",
+      "        faf99_max: float64, \n",
+      "        faf99_max_gen_anc: str\n",
+      "    } \n",
+      "    'a_index': int32 \n",
+      "    'was_split': bool \n",
+      "    'rsid': set \n",
+      "    'filters': set \n",
+      "    'info': struct {\n",
+      "        FS: float64, \n",
+      "        MQ: float64, \n",
+      "        MQRankSum: float64, \n",
+      "        QUALapprox: int64, \n",
+      "        QD: float32, \n",
+      "        ReadPosRankSum: float64, \n",
+      "        SB: array, \n",
+      "        SOR: float64, \n",
+      "        VarDP: int32, \n",
+      "        AS_FS: float64, \n",
+      "        AS_MQ: float64, \n",
+      "        AS_MQRankSum: float64, \n",
+      "        AS_pab_max: float64, \n",
+      "        AS_QUALapprox: int64, \n",
+      "        AS_QD: float32, \n",
+      "        AS_ReadPosRankSum: float64, \n",
+      "        AS_SB_TABLE: array, \n",
+      "        AS_SOR: float64, \n",
+      "        AS_VarDP: int32, \n",
+      "        singleton: bool, \n",
+      "        transmitted_singleton: bool, \n",
+      "        omni: bool, \n",
+      "        mills: bool, \n",
+      "        monoallelic: bool, \n",
+      "        only_het: bool, \n",
+      "        AS_VQSLOD: float64, \n",
+      "        inbreeding_coeff: float64, \n",
+      "        vrs: struct {\n",
+      "            VRS_Allele_IDs: array, \n",
+      "            VRS_Starts: array, \n",
+      "            VRS_Ends: array, \n",
+      "            VRS_States: array\n",
+      "        }\n",
+      "    } \n",
+      "    'vep': struct {\n",
+      "        allele_string: str, \n",
+      "        end: int32, \n",
+      "        id: str, \n",
+      "        input: str, \n",
+      "        intergenic_consequences: array, \n",
+      "            impact: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        most_severe_consequence: str, \n",
+      "        motif_feature_consequences: array, \n",
+      "            high_inf_pos: str, \n",
+      "            impact: str, \n",
+      "            motif_feature_id: str, \n",
+      "            motif_name: str, \n",
+      "            motif_pos: int32, \n",
+      "            motif_score_change: float64, \n",
+      "            transcription_factors: array, \n",
+      "            strand: int32, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        regulatory_feature_consequences: array, \n",
+      "            impact: str, \n",
+      "            regulatory_feature_id: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        seq_region_name: str, \n",
+      "        start: int32, \n",
+      "        strand: int32, \n",
+      "        transcript_consequences: array, \n",
+      "            distance: int32, \n",
+      "            domains: array, \n",
+      "            exon: str, \n",
+      "            flags: str, \n",
+      "            gene_id: str, \n",
+      "            gene_pheno: int32, \n",
+      "            gene_symbol: str, \n",
+      "            gene_symbol_source: str, \n",
+      "            hgnc_id: str, \n",
+      "            hgvsc: str, \n",
+      "            hgvsp: str, \n",
+      "            hgvs_offset: int32, \n",
+      "            impact: str, \n",
+      "            intron: str, \n",
+      "            lof: str, \n",
+      "            lof_flags: str, \n",
+      "            lof_filter: str, \n",
+      "            lof_info: str, \n",
+      "            mane_select: str, \n",
+      "            mane_plus_clinical: str, \n",
+      "            mirna: array, \n",
+      "            protein_end: int32, \n",
+      "            protein_start: int32, \n",
+      "            protein_id: str, \n",
+      "            source: str, \n",
+      "            strand: int32, \n",
+      "            transcript_id: str, \n",
+      "            tsl: int32, \n",
+      "            uniprot_isoform: array, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        variant_class: str\n",
+      "    } \n",
+      "    'vqsr_results': struct {\n",
+      "        AS_VQSLOD: float64, \n",
+      "        AS_culprit: str, \n",
+      "        positive_train_site: bool, \n",
+      "        negative_train_site: bool\n",
+      "    } \n",
+      "    'region_flags': struct {\n",
+      "        non_par: bool, \n",
+      "        lcr: bool, \n",
+      "        segdup: bool\n",
+      "    } \n",
+      "    'allele_info': struct {\n",
+      "        allele_type: str, \n",
+      "        n_alt_alleles: int32, \n",
+      "        variant_type: str, \n",
+      "        was_mixed: bool\n",
+      "    } \n",
+      "    'histograms': struct {\n",
+      "        qual_hists: struct {\n",
+      "            gq_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            gq_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            ab_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        raw_qual_hists: struct {\n",
+      "            gq_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_all: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            gq_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            dp_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            ab_hist_alt: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        age_hists: struct {\n",
+      "            age_hist_het: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            age_hist_hom: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }\n",
+      "    } \n",
+      "    'in_silico_predictors': struct {\n",
+      "        cadd: struct {\n",
+      "            phred: float32, \n",
+      "            raw_score: float32\n",
+      "        }, \n",
+      "        revel_max: float64, \n",
+      "        spliceai_ds_max: float32, \n",
+      "        pangolin_largest_ds: float64, \n",
+      "        phylop: float64, \n",
+      "        sift_max: float64, \n",
+      "        polyphen_max: float64\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Key: ['locus', 'alleles']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed916197-3b0e-45dc-bacd-a13cb66d70ee",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "00b0ea2f-5685-4bae-886a-b9ea31866818",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
grpmax
fafmax
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
allele_type
n_alt_alleles
variant_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>int32float64int32int32strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strint32boolset<str>set<str>float64float64float64int64float32float64array<int32>float64int32float64float64float64float64int64float32float64array<int32>float64int32boolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolstrint32strboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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639542312"}{"AC0","AS_VQSR"}7.30e+003.48e+016.70e-02962.74e+00-1.07e+00[21,6,4,4]9.60e-02355.10e+003.51e+01-5.72e-016.87e-01772.96e+00-1.38e+00[21,6,3,3]9.64e-0226FalseNANANAFalseFalse-4.57e+00-1.65e-05["ga4gh:VA.oTAtTrgYxm81O9fu6Mrhfo1t3eHsgg4L","ga4gh:VA.Y283OnlLjyi1T1IT_JzvW255rC6YJsW6"][10030,10030][10031,10031]["T","C"]"T/C"10031".""chr1\t10031\t.\tT\tC\t.\t.\tGT"NA"upstream_gene_variant"NANA"chr1"100311[(1,NA,NA,"transcribed_unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1979,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000450305",NA,NA,"C"),(1,NA,NA,"processed_transcript",NA,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1838,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000456328",1,NA,"C"),(1,NA,NA,"unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4373,NA,NA,NA,"ENSG00000227232",NA,"WASH7P","HGNC","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000488147",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4331,NA,NA,NA,"653635",NA,"WASH7P","EntrezGene","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",-1,"NR_024540.1",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1843,NA,NA,NA,"100287102",NA,"DDX11L1","EntrezGene","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",1,"NR_046018.2",NA,NA,"C")]"SNV"-4.57e+00"AS_QD"FalseFalseFalseTrueTrue"snv"2"multi-snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]008.97e+007.57e-01NANANANANANA
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[(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]6.27e-06"nfe"2.35e-06"nfe"1True{"rs1570391741"}{"AS_VQSR"}3.30e+013.60e+017.88e-012921.79e+00-6.84e-01[97,29,17,20]3.75e-011633.79e+013.61e+017.88e-011.00e+002642.06e+00-6.84e-01[97,29,13,19]7.58e-01128FalseNANANAFalseFalse-3.31e+00-2.41e-05["ga4gh:VA.TV0UyS-jCvsVkEOMbipUnzTc4hYuVNTg","ga4gh:VA.33zGTKcHyj5wWpTfc3OfD8-UlPQ-YOfx"][10056,10056][10057,10057]["A","C"]"A/C"10057".""chr1\t10057\t.\tA\tC\t.\t.\tGT"NA"upstream_gene_variant"NANA"chr1"100571[(1,NA,NA,"transcribed_unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1953,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000450305",NA,NA,"C"),(1,NA,NA,"processed_transcript",NA,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1812,NA,NA,NA,"ENSG00000223972",NA,"DDX11L1","HGNC","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000456328",1,NA,"C"),(1,NA,NA,"unprocessed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4347,NA,NA,NA,"ENSG00000227232",NA,"WASH7P","HGNC","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000488147",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],4305,NA,NA,NA,"653635",NA,"WASH7P","EntrezGene","HGNC:38034",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",-1,"NR_024540.1",NA,NA,"C"),(1,NA,NA,"transcribed_pseudogene",1,NA,NA,NA,NA,NA,NA,["upstream_gene_variant"],1817,NA,NA,NA,"100287102",NA,"DDX11L1","EntrezGene","HGNC:37102",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"RefSeq",1,"NR_046018.2",NA,NA,"C")]"SNV"-3.31e+00"AS_FS"FalseFalseFalseTrueTrue"snv"2"multi-snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]008.53e+007.09e-01NANANANANANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+------------+\n",
+       "| locus         | alleles    |\n",
+       "+---------------+------------+\n",
+       "| locus | array |\n",
+       "+---------------+------------+\n",
+       "| chr1:10031    | [\"T\",\"C\"]  |\n",
+       "| chr1:10037    | [\"T\",\"C\"]  |\n",
+       "| chr1:10043    | [\"T\",\"C\"]  |\n",
+       "| chr1:10055    | [\"T\",\"C\"]  |\n",
+       "| chr1:10057    | [\"A\",\"C\"]  |\n",
+       "+---------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e... |\n",
+       "| [(2,2.60e-05,76882,0),(4,3.15e-05,126902,0),(0,0.00e+00,34670,0),(1,1.80e... |\n",
+       "| [(1,1.17e-05,85634,0),(1,8.24e-06,121430,0),(0,0.00e+00,39236,0),(0,0.00e... |\n",
+       "| [(1,1.06e-05,94224,0),(5,4.64e-05,107704,0),(0,0.00e+00,44382,0),(1,1.80e... |\n",
+       "| [(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------+-----------+-----------+-------------------------+----------------+\n",
+       "| grpmax.AC | grpmax.AF | grpmax.AN | grpmax.homozygote_count | grpmax.gen_anc |\n",
+       "+-----------+-----------+-----------+-------------------------+----------------+\n",
+       "|     int32 |   float64 |     int32 |                   int32 | str            |\n",
+       "+-----------+-----------+-----------+-------------------------+----------------+\n",
+       "|        NA |        NA |        NA |                      NA | NA             |\n",
+       "|         1 |  4.07e-04 |      2456 |                       0 | \"eas\"          |\n",
+       "|         1 |  4.39e-05 |     22760 |                       0 | \"afr\"          |\n",
+       "|        NA |        NA |        NA |                      NA | NA             |\n",
+       "|         2 |  3.78e-05 |     52912 |                       0 | \"nfe\"          |\n",
+       "+-----------+-----------+-----------+-------------------------+----------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+--------------------------+------------------+\n",
+       "| fafmax.faf95_max | fafmax.faf95_max_gen_anc | fafmax.faf99_max |\n",
+       "+------------------+--------------------------+------------------+\n",
+       "|          float64 | str                      |          float64 |\n",
+       "+------------------+--------------------------+------------------+\n",
+       "|               NA | NA                       |               NA |\n",
+       "|               NA | NA                       |               NA |\n",
+       "|               NA | NA                       |               NA |\n",
+       "|               NA | NA                       |               NA |\n",
+       "|         6.27e-06 | \"nfe\"                    |         2.35e-06 |\n",
+       "+------------------+--------------------------+------------------+\n",
+       "\n",
+       "+--------------------------+---------+-----------+------------------+\n",
+       "| fafmax.faf99_max_gen_anc | a_index | was_split | rsid             |\n",
+       "+--------------------------+---------+-----------+------------------+\n",
+       "| str                      |   int32 |      bool | set         |\n",
+       "+--------------------------+---------+-----------+------------------+\n",
+       "| NA                       |       2 |      True | {\"rs1639542312\"} |\n",
+       "| NA                       |       1 |     False | {\"rs1639542418\"} |\n",
+       "| NA                       |       1 |     False | NA               |\n",
+       "| NA                       |       2 |      True | {\"rs892501864\"}  |\n",
+       "| \"nfe\"                    |       1 |      True | {\"rs1570391741\"} |\n",
+       "+--------------------------+---------+-----------+------------------+\n",
+       "\n",
+       "+-------------------+----------+----------+----------------+-----------------+\n",
+       "| filters           |  info.FS |  info.MQ | info.MQRankSum | info.QUALapprox |\n",
+       "+-------------------+----------+----------+----------------+-----------------+\n",
+       "| set          |  float64 |  float64 |        float64 |           int64 |\n",
+       "+-------------------+----------+----------+----------------+-----------------+\n",
+       "| {\"AC0\",\"AS_VQSR\"} | 7.30e+00 | 3.48e+01 |       6.70e-02 |              96 |\n",
+       "| {\"AS_VQSR\"}       | 8.58e+00 | 3.83e+01 |       1.37e+00 |             180 |\n",
+       "| {\"AS_VQSR\"}       | 3.11e+01 | 3.52e+01 |       1.23e+00 |              97 |\n",
+       "| {\"AS_VQSR\"}       | 0.00e+00 | 3.55e+01 |       1.07e-01 |             220 |\n",
+       "| {\"AS_VQSR\"}       | 3.30e+01 | 3.60e+01 |       7.88e-01 |             292 |\n",
+       "+-------------------+----------+----------+----------------+-----------------+\n",
+       "\n",
+       "+----------+---------------------+---------------+----------+------------+\n",
+       "|  info.QD | info.ReadPosRankSum | info.SB       | info.SOR | info.VarDP |\n",
+       "+----------+---------------------+---------------+----------+------------+\n",
+       "|  float32 |             float64 | array  |  float64 |      int32 |\n",
+       "+----------+---------------------+---------------+----------+------------+\n",
+       "| 2.74e+00 |           -1.07e+00 | [21,6,4,4]    | 9.60e-02 |         35 |\n",
+       "| 2.20e+00 |           -4.80e-01 | [49,12,13,8]  | 1.51e-01 |         82 |\n",
+       "| 2.77e+00 |           -8.96e-01 | [25,0,5,5]    | 1.00e-03 |         35 |\n",
+       "| 2.12e+00 |           -1.16e+00 | [51,29,15,9]  | 6.16e-01 |        104 |\n",
+       "| 1.79e+00 |           -6.84e-01 | [97,29,17,20] | 3.75e-01 |        163 |\n",
+       "+----------+---------------------+---------------+----------+------------+\n",
+       "\n",
+       "+------------+------------+-------------------+-----------------+\n",
+       "| info.AS_FS | info.AS_MQ | info.AS_MQRankSum | info.AS_pab_max |\n",
+       "+------------+------------+-------------------+-----------------+\n",
+       "|    float64 |    float64 |           float64 |         float64 |\n",
+       "+------------+------------+-------------------+-----------------+\n",
+       "|   5.10e+00 |   3.51e+01 |         -5.72e-01 |        6.87e-01 |\n",
+       "|   8.58e+00 |   3.83e+01 |          1.37e+00 |        1.00e+00 |\n",
+       "|   3.11e+01 |   3.52e+01 |          1.23e+00 |        1.00e+00 |\n",
+       "|   5.94e+00 |   3.48e+01 |          7.15e-01 |        2.27e-01 |\n",
+       "|   3.79e+01 |   3.61e+01 |          7.88e-01 |        1.00e+00 |\n",
+       "+------------+------------+-------------------+-----------------+\n",
+       "\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "| info.AS_QUALapprox | info.AS_QD | info.AS_ReadPosRankSum | info.AS_SB_TABLE |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "|              int64 |    float32 |                float64 | array     |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "|                 77 |   2.96e+00 |              -1.38e+00 | [21,6,3,3]       |\n",
+       "|                180 |   2.20e+00 |              -4.80e-01 | [49,12,13,8]     |\n",
+       "|                 97 |   2.77e+00 |              -8.96e-01 | [25,0,5,5]       |\n",
+       "|                 91 |   1.21e+00 |              -1.16e+00 | [51,29,7,8]      |\n",
+       "|                264 |   2.06e+00 |              -6.84e-01 | [97,29,13,19]    |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "| info.AS_SOR | info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "|     float64 |         int32 |           bool |                       bool |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "|    9.64e-02 |            26 |          False |                         NA |\n",
+       "|    1.51e-01 |            82 |          False |                         NA |\n",
+       "|    1.48e-03 |            35 |           True |                      False |\n",
+       "|    4.69e-01 |            75 |          False |                         NA |\n",
+       "|    7.58e-01 |           128 |          False |                         NA |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|        NA |         NA |            False |         False |      -4.57e+00 |\n",
+       "|        NA |         NA |            False |         False |      -3.18e+00 |\n",
+       "|        NA |         NA |            False |         False |      -5.79e+00 |\n",
+       "|        NA |         NA |            False |         False |      -3.72e+00 |\n",
+       "|        NA |         NA |            False |         False |      -3.31e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|             -1.65e-05 |\n",
+       "|             -3.15e-05 |\n",
+       "|             -8.24e-06 |\n",
+       "|             -4.64e-05 |\n",
+       "|             -2.41e-05 |\n",
+       "+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.oTAtTrgYxm81O9fu6Mrhfo1t3eHsgg4L\",\"ga4gh:VA.Y283OnlLjyi1T1IT_J... |\n",
+       "| [\"ga4gh:VA.6pIoPAYDsphCprRcyrititenuEWlZaxV\",\"ga4gh:VA.iHyQ1mh_iQX6WcqXcx... |\n",
+       "| [\"ga4gh:VA.HdrkFJS16zwJPcGSKX9___fJcdqSATlQ\",\"ga4gh:VA.LSHuFx03sqw8WJGN2-... |\n",
+       "| [\"ga4gh:VA.xis4Nhtveh7q75mtkipQAUqZfWUfboWB\",\"ga4gh:VA.Ciou33oFTNEilAEegb... |\n",
+       "| [\"ga4gh:VA.TV0UyS-jCvsVkEOMbipUnzTc4hYuVNTg\",\"ga4gh:VA.33zGTKcHyj5wWpTfc3... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts | info.vrs.VRS_Ends | info.vrs.VRS_States |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| array        | array      | array          |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "| [10030,10030]       | [10031,10031]     | [\"T\",\"C\"]           |\n",
+       "| [10036,10036]       | [10037,10037]     | [\"T\",\"C\"]           |\n",
+       "| [10042,10042]       | [10043,10043]     | [\"T\",\"C\"]           |\n",
+       "| [10054,10054]       | [10055,10055]     | [\"T\",\"C\"]           |\n",
+       "| [10056,10056]       | [10057,10057]     | [\"A\",\"C\"]           |\n",
+       "+---------------------+-------------------+---------------------+\n",
+       "\n",
+       "+-------------------+---------+--------+---------------------------+\n",
+       "| vep.allele_string | vep.end | vep.id | vep.input                 |\n",
+       "+-------------------+---------+--------+---------------------------+\n",
+       "| str               |   int32 | str    | str                       |\n",
+       "+-------------------+---------+--------+---------------------------+\n",
+       "| \"T/C\"             |   10031 | \".\"    | \"chr1\t10031\t.\tT\tC\t.\t.\tGT\" |\n",
+       "| \"T/C\"             |   10037 | \".\"    | \"chr1\t10037\t.\tT\tC\t.\t.\tGT\" |\n",
+       "| \"T/C\"             |   10043 | \".\"    | \"chr1\t10043\t.\tT\tC\t.\t.\tGT\" |\n",
+       "| \"T/C\"             |   10055 | \".\"    | \"chr1\t10055\t.\tT\tC\t.\t.\tGT\" |\n",
+       "| \"A/C\"             |   10057 | \".\"    | \"chr1\t10057\t.\tA\tC\t.\t.\tGT\" |\n",
+       "+-------------------+---------+--------+---------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
+       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
+       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
+       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
+       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
+       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
+       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
+       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
+       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                        527 |\n",
+       "|                                        888 |\n",
+       "|                                       1232 |\n",
+       "|                                       2440 |\n",
+       "|                                       3622 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
+       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
+       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
+       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
+       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
+       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
+       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
+       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
+       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                            744 |\n",
+       "|                                           1130 |\n",
+       "|                                           1487 |\n",
+       "|                                           2622 |\n",
+       "|                                           3743 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,1,0,0,1,0,0,0,0,0]                      |\n",
+       "| [0,0,1,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,1,0,1,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        8.97e+00 |\n",
+       "|                                          0 |                        8.90e+00 |\n",
+       "|                                          0 |                        8.90e+00 |\n",
+       "|                                          0 |                        8.88e+00 |\n",
+       "|                                          0 |                        8.53e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            7.57e-01 |                             NA |\n",
+       "|                            7.49e-01 |                             NA |\n",
+       "|                            7.48e-01 |                             NA |\n",
+       "|                            7.46e-01 |                             NA |\n",
+       "|                            7.09e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22e6f759-a0ee-4e9c-8ca4-eb154cb08763",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### v4.1 Joint Frequency Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a569b77-d3d2-45a4-803a-1214c77e46f2",
+   "metadata": {},
+   "source": [
+    "The joint frequency Hail table includes frequency for the exomes, genomes, and the exomes+genomes. We have also added statistics for the combined exomes and genomes frequencies, more details on these stats can be found on our [Help](https://gnomad.broadinstitute.org/help/combined-freq-stats) page."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46d4fc43-609d-4a16-8a0a-ab1e870b5d3d",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c1c1fbb0-4ef9-4892-bd91-aae9985317a7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='joint', version='4.1')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "163df47b-70de-4e1e-91be-a65c90cf2db5",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "750c0111-4566-4b86-8c08-18c504ff1a79",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'exomes_globals': struct {\n",
+      "        freq_meta: array>, \n",
+      "        freq_index_dict: dict, \n",
+      "        freq_meta_sample_count: array, \n",
+      "        faf_meta: array>, \n",
+      "        faf_index_dict: dict, \n",
+      "        age_distribution: struct {\n",
+      "            bin_edges: array, \n",
+      "            bin_freq: array, \n",
+      "            n_smaller: int32, \n",
+      "            n_larger: int32\n",
+      "        }\n",
+      "    } \n",
+      "    'genomes_globals': struct {\n",
+      "        freq_meta: array>, \n",
+      "        freq_index_dict: dict, \n",
+      "        freq_meta_sample_count: array, \n",
+      "        faf_meta: array>, \n",
+      "        faf_index_dict: dict, \n",
+      "        age_distribution: struct {\n",
+      "            bin_edges: array, \n",
+      "            bin_freq: array, \n",
+      "            n_smaller: int32, \n",
+      "            n_larger: int32\n",
+      "        }\n",
+      "    } \n",
+      "    'joint_globals': struct {\n",
+      "        freq_meta: array>, \n",
+      "        freq_index_dict: dict, \n",
+      "        faf_meta: array>, \n",
+      "        faf_index_dict: dict, \n",
+      "        freq_meta_sample_count: array, \n",
+      "        age_distribution: struct {\n",
+      "            bin_edges: array, \n",
+      "            bin_freq: array, \n",
+      "            n_smaller: int32, \n",
+      "            n_larger: int32\n",
+      "        }\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'alleles': array \n",
+      "    'region_flags': struct {\n",
+      "        fail_interval_qc: bool, \n",
+      "        outside_broad_capture_region: bool, \n",
+      "        outside_ukb_capture_region: bool, \n",
+      "        outside_broad_calling_region: bool, \n",
+      "        outside_ukb_calling_region: bool, \n",
+      "        not_called_in_exomes: bool, \n",
+      "        not_called_in_genomes: bool\n",
+      "    } \n",
+      "    'exomes': struct {\n",
+      "        filters: set, \n",
+      "        freq: array, \n",
+      "        faf: array, \n",
+      "        grpmax: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int64, \n",
+      "            gen_anc: str\n",
+      "        }, \n",
+      "        fafmax: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }, \n",
+      "        histograms: struct {\n",
+      "            qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            raw_qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            age_hists: struct {\n",
+      "                age_hist_het: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                age_hist_hom: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }\n",
+      "        }\n",
+      "    } \n",
+      "    'genomes': struct {\n",
+      "        filters: set, \n",
+      "        freq: array, \n",
+      "        faf: array, \n",
+      "        grpmax: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int32, \n",
+      "            gen_anc: str\n",
+      "        }, \n",
+      "        fafmax: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }, \n",
+      "        histograms: struct {\n",
+      "            qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            raw_qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            age_hists: struct {\n",
+      "                age_hist_het: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                age_hist_hom: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }\n",
+      "        }\n",
+      "    } \n",
+      "    'joint': struct {\n",
+      "        freq: array, \n",
+      "        faf: array, \n",
+      "        fafmax: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }, \n",
+      "        grpmax: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int32, \n",
+      "            gen_anc: str\n",
+      "        }, \n",
+      "        histograms: struct {\n",
+      "            qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            raw_qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            age_hists: struct {\n",
+      "                age_hist_het: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                age_hist_hom: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }\n",
+      "        }\n",
+      "    } \n",
+      "    'freq_comparison_stats': struct {\n",
+      "        contingency_table_test: array, \n",
+      "        cochran_mantel_haenszel_test: struct {\n",
+      "            p_value: float64, \n",
+      "            chisq: float64\n",
+      "        }, \n",
+      "        stat_union: struct {\n",
+      "            p_value: float64, \n",
+      "            stat_test_name: str, \n",
+      "            gen_ancs: array\n",
+      "        }\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Key: ['locus', 'alleles']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6843db4-9e8f-42f4-9178-fc945f61a827",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "477ed281-4ee9-4799-b5b6-e6a0c528fa9a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
exomes
genomes
joint
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faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
AC
AF
AN
homozygote_count
gen_anc
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
contingency_table_test
p_value
chisq
p_value
stat_test_name
gen_ancs
locus<GRCh38>array<str>boolboolboolboolboolboolboolset<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>int32float64int32int64strfloat64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64set<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>int32float64int32int32strfloat64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>float64strfloat64strint32float64int32int32strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{p_value: float64, odds_ratio: float64}>float64float64float64strarray<str>
chr1:10031["T","C"]NATrueTrueTrueTrueTrueFalseNA[(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA)]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA{"AC0","AS_VQSR"}[(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e+00,4326,0),(0,0.00e+00,192,0),(0,0.00e+00,782,0),(0,0.00e+00,352,0),(0,0.00e+00,1550,0),(0,0.00e+00,14642,0),(0,0.00e+00,1712,0),(0,0.00e+00,1120,0),(0,0.00e+00,6420,0),(0,0.00e+00,29308,0),(0,0.00e+00,27334,0),(0,0.00e+00,14998,0),(0,0.00e+00,1060,0),(0,0.00e+00,102,0),(0,0.00e+00,402,0),(0,0.00e+00,154,0),(0,0.00e+00,814,0),(0,0.00e+00,7978,0),(0,0.00e+00,770,0),(0,0.00e+00,260,0),(0,0.00e+00,2770,0),(0,0.00e+00,10548,0),(0,0.00e+00,3266,0),(0,0.00e+00,90,0),(0,0.00e+00,380,0),(0,0.00e+00,198,0),(0,0.00e+00,736,0),(0,0.00e+00,6664,0),(0,0.00e+00,942,0),(0,0.00e+00,860,0),(0,0.00e+00,3650,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANANANANANA[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e+00,4326,0),(0,0.00e+00,192,0),(0,0.00e+00,782,0),(0,0.00e+00,352,0),(0,0.00e+00,1550,0),(0,0.00e+00,14642,0),(0,0.00e+00,1712,0),(0,0.00e+00,1120,0),(0,0.00e+00,6420,0),(0,0.00e+00,29308,0),(0,0.00e+00,27334,0),(0,0.00e+00,14998,0),(0,0.00e+00,1060,0),(0,0.00e+00,102,0),(0,0.00e+00,402,0),(0,0.00e+00,154,0),(0,0.00e+00,814,0),(0,0.00e+00,7978,0),(0,0.00e+00,770,0),(0,0.00e+00,260,0),(0,0.00e+00,2770,0),(0,0.00e+00,10548,0),(0,0.00e+00,3266,0),(0,0.00e+00,90,0),(0,0.00e+00,380,0),(0,0.00e+00,198,0),(0,0.00e+00,736,0),(0,0.00e+00,6664,0),(0,0.00e+00,942,0),(0,0.00e+00,860,0),(0,0.00e+00,3650,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,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chr1:10057["A","C"]NATrueTrueTrueTrueTrueFalseNA[(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA)]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA{"AS_VQSR"}[(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00e+00,6886,0),(0,0.00e+00,266,0),(0,0.00e+00,1504,0),(0,0.00e+00,742,0),(0,0.00e+00,2768,0),(1,3.25e-05,30736,0),(0,0.00e+00,3850,0),(0,0.00e+00,3424,0),(0,0.00e+00,10448,0),(2,3.37e-05,59380,0),(1,1.85e-05,54156,0),(2,6.43e-05,31106,0),(0,0.00e+00,1472,0),(0,0.00e+00,132,0),(0,0.00e+00,764,0),(0,0.00e+00,378,0),(0,0.00e+00,1532,0),(0,0.00e+00,16598,0),(0,0.00e+00,1670,0),(0,0.00e+00,784,0),(0,0.00e+00,4944,0),(0,0.00e+00,21806,0),(0,0.00e+00,5414,0),(0,0.00e+00,134,0),(0,0.00e+00,740,0),(0,0.00e+00,364,0),(0,0.00e+00,1236,0),(1,7.07e-05,14138,0),(0,0.00e+00,2180,0),(0,0.00e+00,2640,0),(0,0.00e+00,5504,0)][(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]23.78e-05529120"nfe"6.27e-06"nfe"2.35e-06"nfe"[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00e+00,6886,0),(0,0.00e+00,266,0),(0,0.00e+00,1504,0),(0,0.00e+00,742,0),(0,0.00e+00,2768,0),(1,3.25e-05,30736,0),(0,0.00e+00,3850,0),(0,0.00e+00,3424,0),(0,0.00e+00,10448,0),(2,3.37e-05,59380,0),(1,1.85e-05,54156,0),(2,6.43e-05,31106,0),(0,0.00e+00,1472,0),(0,0.00e+00,132,0),(0,0.00e+00,764,0),(0,0.00e+00,378,0),(0,0.00e+00,1532,0),(0,0.00e+00,16598,0),(0,0.00e+00,1670,0),(0,0.00e+00,784,0),(0,0.00e+00,4944,0),(0,0.00e+00,21806,0),(0,0.00e+00,5414,0),(0,0.00e+00,134,0),(0,0.00e+00,740,0),(0,0.00e+00,364,0),(0,0.00e+00,1236,0),(1,7.07e-05,14138,0),(0,0.00e+00,2180,0),(0,0.00e+00,2640,0),(0,0.00e+00,5504,0)][(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]6.27e-06"nfe"2.35e-06"nfe"23.78e-05529120"nfe"[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+------------+-------------------------------+\n",
+       "| locus         | alleles    | region_flags.fail_interval_qc |\n",
+       "+---------------+------------+-------------------------------+\n",
+       "| locus | array |                          bool |\n",
+       "+---------------+------------+-------------------------------+\n",
+       "| chr1:10031    | [\"T\",\"C\"]  |                            NA |\n",
+       "| chr1:10037    | [\"T\",\"C\"]  |                            NA |\n",
+       "| chr1:10043    | [\"T\",\"C\"]  |                            NA |\n",
+       "| chr1:10055    | [\"T\",\"C\"]  |                            NA |\n",
+       "| chr1:10057    | [\"A\",\"C\"]  |                            NA |\n",
+       "+---------------+------------+-------------------------------+\n",
+       "\n",
+       "+-------------------------------------------+\n",
+       "| region_flags.outside_broad_capture_region |\n",
+       "+-------------------------------------------+\n",
+       "|                                      bool |\n",
+       "+-------------------------------------------+\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "+-------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------+\n",
+       "| region_flags.outside_ukb_capture_region |\n",
+       "+-----------------------------------------+\n",
+       "|                                    bool |\n",
+       "+-----------------------------------------+\n",
+       "|                                    True |\n",
+       "|                                    True |\n",
+       "|                                    True |\n",
+       "|                                    True |\n",
+       "|                                    True |\n",
+       "+-----------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------+\n",
+       "| region_flags.outside_broad_calling_region |\n",
+       "+-------------------------------------------+\n",
+       "|                                      bool |\n",
+       "+-------------------------------------------+\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "|                                      True |\n",
+       "+-------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------+-----------------------------------+\n",
+       "| region_flags.outside_ukb_calling_region | region_flags.not_called_in_exomes |\n",
+       "+-----------------------------------------+-----------------------------------+\n",
+       "|                                    bool |                              bool |\n",
+       "+-----------------------------------------+-----------------------------------+\n",
+       "|                                    True |                              True |\n",
+       "|                                    True |                              True |\n",
+       "|                                    True |                              True |\n",
+       "|                                    True |                              True |\n",
+       "|                                    True |                              True |\n",
+       "+-----------------------------------------+-----------------------------------+\n",
+       "\n",
+       "+------------------------------------+----------------+\n",
+       "| region_flags.not_called_in_genomes | exomes.filters |\n",
+       "+------------------------------------+----------------+\n",
+       "|                               bool | set       |\n",
+       "+------------------------------------+----------------+\n",
+       "|                              False | NA             |\n",
+       "|                              False | NA             |\n",
+       "|                              False | NA             |\n",
+       "|                              False | NA             |\n",
+       "|                              False | NA             |\n",
+       "+------------------------------------+----------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| exomes.freq                                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
+       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
+       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
+       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
+       "| [(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(NA,NA,NA,NA),(N... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------+------------------+\n",
+       "| exomes.faf                                    | exomes.grpmax.AC |\n",
+       "+-----------------------------------------------+------------------+\n",
+       "| array |            int32 |\n",
+       "+-----------------------------------------------+------------------+\n",
+       "| NA                                            |               NA |\n",
+       "| NA                                            |               NA |\n",
+       "| NA                                            |               NA |\n",
+       "| NA                                            |               NA |\n",
+       "| NA                                            |               NA |\n",
+       "+-----------------------------------------------+------------------+\n",
+       "\n",
+       "+------------------+------------------+--------------------------------+\n",
+       "| exomes.grpmax.AF | exomes.grpmax.AN | exomes.grpmax.homozygote_count |\n",
+       "+------------------+------------------+--------------------------------+\n",
+       "|          float64 |            int32 |                          int64 |\n",
+       "+------------------+------------------+--------------------------------+\n",
+       "|               NA |               NA |                             NA |\n",
+       "|               NA |               NA |                             NA |\n",
+       "|               NA |               NA |                             NA |\n",
+       "|               NA |               NA |                             NA |\n",
+       "|               NA |               NA |                             NA |\n",
+       "+------------------+------------------+--------------------------------+\n",
+       "\n",
+       "+-----------------------+-------------------------+\n",
+       "| exomes.grpmax.gen_anc | exomes.fafmax.faf95_max |\n",
+       "+-----------------------+-------------------------+\n",
+       "| str                   |                 float64 |\n",
+       "+-----------------------+-------------------------+\n",
+       "| NA                    |                      NA |\n",
+       "| NA                    |                      NA |\n",
+       "| NA                    |                      NA |\n",
+       "| NA                    |                      NA |\n",
+       "| NA                    |                      NA |\n",
+       "+-----------------------+-------------------------+\n",
+       "\n",
+       "+---------------------------------+-------------------------+\n",
+       "| exomes.fafmax.faf95_max_gen_anc | exomes.fafmax.faf99_max |\n",
+       "+---------------------------------+-------------------------+\n",
+       "| str                             |                 float64 |\n",
+       "+---------------------------------+-------------------------+\n",
+       "| NA                              |                      NA |\n",
+       "| NA                              |                      NA |\n",
+       "| NA                              |                      NA |\n",
+       "| NA                              |                      NA |\n",
+       "| NA                              |                      NA |\n",
+       "+---------------------------------+-------------------------+\n",
+       "\n",
+       "+---------------------------------+\n",
+       "| exomes.fafmax.faf99_max_gen_anc |\n",
+       "+---------------------------------+\n",
+       "| str                             |\n",
+       "+---------------------------------+\n",
+       "| NA                              |\n",
+       "| NA                              |\n",
+       "| NA                              |\n",
+       "| NA                              |\n",
+       "| NA                              |\n",
+       "+---------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_all.bin_edges |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+----------------------------------------------------+\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_all.bin_freq |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_all.bin_edges |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+----------------------------------------------------+\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_all.bin_freq |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_alt.bin_edges |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+----------------------------------------------------+\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_alt.bin_edges |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+----------------------------------------------------+\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.ab_hist_alt.bin_edges |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+----------------------------------------------------+\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_all.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_all.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_all.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_all.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_alt.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_alt.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.ab_hist_alt.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exomes.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_het.bin_edges |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+----------------------------------------------------+\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_het.n_larger |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "|                                                NA |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_hom.bin_edges |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+----------------------------------------------------+\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "| NA                                                 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "| NA                                                |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "|                                                 NA |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+-------------------+\n",
+       "| exomes.histograms.age_hists.age_hist_hom.n_larger | genomes.filters   |\n",
+       "+---------------------------------------------------+-------------------+\n",
+       "|                                             int64 | set          |\n",
+       "+---------------------------------------------------+-------------------+\n",
+       "|                                                NA | {\"AC0\",\"AS_VQSR\"} |\n",
+       "|                                                NA | {\"AS_VQSR\"}       |\n",
+       "|                                                NA | {\"AS_VQSR\"}       |\n",
+       "|                                                NA | {\"AS_VQSR\"}       |\n",
+       "|                                                NA | {\"AS_VQSR\"}       |\n",
+       "+---------------------------------------------------+-------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.freq                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e... |\n",
+       "| [(2,2.60e-05,76882,0),(4,3.15e-05,126902,0),(0,0.00e+00,34670,0),(1,1.80e... |\n",
+       "| [(1,1.17e-05,85634,0),(1,8.24e-06,121430,0),(0,0.00e+00,39236,0),(0,0.00e... |\n",
+       "| [(1,1.06e-05,94224,0),(5,4.64e-05,107704,0),(0,0.00e+00,44382,0),(1,1.80e... |\n",
+       "| [(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.faf                                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------+-------------------+-------------------+\n",
+       "| genomes.grpmax.AC | genomes.grpmax.AF | genomes.grpmax.AN |\n",
+       "+-------------------+-------------------+-------------------+\n",
+       "|             int32 |           float64 |             int32 |\n",
+       "+-------------------+-------------------+-------------------+\n",
+       "|                NA |                NA |                NA |\n",
+       "|                 1 |          4.07e-04 |              2456 |\n",
+       "|                 1 |          4.39e-05 |             22760 |\n",
+       "|                NA |                NA |                NA |\n",
+       "|                 2 |          3.78e-05 |             52912 |\n",
+       "+-------------------+-------------------+-------------------+\n",
+       "\n",
+       "+---------------------------------+------------------------+\n",
+       "| genomes.grpmax.homozygote_count | genomes.grpmax.gen_anc |\n",
+       "+---------------------------------+------------------------+\n",
+       "|                           int32 | str                    |\n",
+       "+---------------------------------+------------------------+\n",
+       "|                              NA | NA                     |\n",
+       "|                               0 | \"eas\"                  |\n",
+       "|                               0 | \"afr\"                  |\n",
+       "|                              NA | NA                     |\n",
+       "|                               0 | \"nfe\"                  |\n",
+       "+---------------------------------+------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| genomes.fafmax.faf95_max | genomes.fafmax.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                 6.27e-06 | \"nfe\"                            |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| genomes.fafmax.faf99_max | genomes.fafmax.faf99_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                 2.35e-06 | \"nfe\"                            |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_all.bin_edges                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_all.bin_freq                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
+       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
+       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
+       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
+       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                               int64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_all.bin_edges                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_all.bin_freq                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
+       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
+       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
+       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
+       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                               int64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                527 |\n",
+       "|                                                888 |\n",
+       "|                                               1232 |\n",
+       "|                                               2440 |\n",
+       "|                                               3622 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_alt.bin_edges                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                       |\n",
+       "+----------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]          |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                               int64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_alt.bin_edges                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                       |\n",
+       "+----------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                               int64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.ab_hist_alt.bin_edges                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                       |\n",
+       "+----------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]          |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                               int64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_all.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_all.bin_freq                       |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
+       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
+       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
+       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
+       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_all.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_all.bin_freq                       |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
+       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
+       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
+       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
+       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                    744 |\n",
+       "|                                                   1130 |\n",
+       "|                                                   1487 |\n",
+       "|                                                   2622 |\n",
+       "|                                                   3743 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_alt.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]              |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_alt.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.ab_hist_alt.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genomes.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_het.bin_edges                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                       |\n",
+       "+----------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
+       "| [0,1,0,0,1,0,0,0,0,0]                              |\n",
+       "| [0,0,1,0,0,0,0,0,0,0]                              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
+       "| [0,0,1,0,1,0,0,0,0,0]                              |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                               int64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_het.n_larger |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_hom.bin_edges                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+----------------------------------------------------+\n",
+       "| array                                       |\n",
+       "+----------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                              |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                               int64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "|                                                   0 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| genomes.histograms.age_hists.age_hist_hom.n_larger |\n",
+       "+----------------------------------------------------+\n",
+       "|                                              int64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "|                                                  0 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.freq                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0,0.00e+00,56642,0),(2,1.65e-05,121094,0),(0,0.00e+00,25546,0),(0,0.00e... |\n",
+       "| [(2,2.60e-05,76882,0),(4,3.15e-05,126902,0),(0,0.00e+00,34670,0),(1,1.80e... |\n",
+       "| [(1,1.17e-05,85634,0),(1,8.24e-06,121430,0),(0,0.00e+00,39236,0),(0,0.00e... |\n",
+       "| [(1,1.06e-05,94224,0),(5,4.64e-05,107704,0),(0,0.00e+00,44382,0),(1,1.80e... |\n",
+       "| [(3,2.64e-05,113536,0),(3,2.41e-05,124252,0),(2,3.78e-05,52912,0),(0,0.00... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.faf                                                                    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------+--------------------------------+\n",
+       "| joint.fafmax.faf95_max | joint.fafmax.faf95_max_gen_anc |\n",
+       "+------------------------+--------------------------------+\n",
+       "|                float64 | str                            |\n",
+       "+------------------------+--------------------------------+\n",
+       "|                     NA | NA                             |\n",
+       "|                     NA | NA                             |\n",
+       "|                     NA | NA                             |\n",
+       "|                     NA | NA                             |\n",
+       "|               6.27e-06 | \"nfe\"                          |\n",
+       "+------------------------+--------------------------------+\n",
+       "\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "| joint.fafmax.faf99_max | joint.fafmax.faf99_max_gen_anc | joint.grpmax.AC |\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "|                float64 | str                            |           int32 |\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "|                     NA | NA                             |              NA |\n",
+       "|                     NA | NA                             |               1 |\n",
+       "|                     NA | NA                             |               1 |\n",
+       "|                     NA | NA                             |              NA |\n",
+       "|               2.35e-06 | \"nfe\"                          |               2 |\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "| joint.grpmax.AF | joint.grpmax.AN | joint.grpmax.homozygote_count |\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "|         float64 |           int32 |                         int32 |\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "|              NA |              NA |                            NA |\n",
+       "|        4.07e-04 |            2456 |                             0 |\n",
+       "|        4.39e-05 |           22760 |                             0 |\n",
+       "|              NA |              NA |                            NA |\n",
+       "|        3.78e-05 |           52912 |                             0 |\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "\n",
+       "+----------------------+\n",
+       "| joint.grpmax.gen_anc |\n",
+       "+----------------------+\n",
+       "| str                  |\n",
+       "+----------------------+\n",
+       "| NA                   |\n",
+       "| \"eas\"                |\n",
+       "| \"afr\"                |\n",
+       "| NA                   |\n",
+       "| \"nfe\"                |\n",
+       "+----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.bin_freq                             |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
+       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
+       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
+       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
+       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.bin_freq                             |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
+       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
+       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
+       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
+       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                              527 |\n",
+       "|                                              888 |\n",
+       "|                                             1232 |\n",
+       "|                                             2440 |\n",
+       "|                                             3622 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]        |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.bin_freq                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
+       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
+       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
+       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
+       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.bin_freq                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
+       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
+       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
+       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
+       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                  744 |\n",
+       "|                                                 1130 |\n",
+       "|                                                 1487 |\n",
+       "|                                                 2622 |\n",
+       "|                                                 3743 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+------------------------------------------------------+\n",
+       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]            |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+------------------------------------------------------+\n",
+       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+------------------------------------------------------+\n",
+       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,1,0,0,1,0,0,0,0,0]                            |\n",
+       "| [0,0,1,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,1,0,1,0,0,0,0,0]                            |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq_comparison_stats.contingency_table_test                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------+\n",
+       "| freq_comparison_stats.cochran_mantel_haenszel_test.p_value |\n",
+       "+------------------------------------------------------------+\n",
+       "|                                                    float64 |\n",
+       "+------------------------------------------------------------+\n",
+       "|                                                         NA |\n",
+       "|                                                         NA |\n",
+       "|                                                         NA |\n",
+       "|                                                         NA |\n",
+       "|                                                         NA |\n",
+       "+------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| freq_comparison_stats.cochran_mantel_haenszel_test.chisq |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                  float64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+\n",
+       "| freq_comparison_stats.stat_union.p_value |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                       NA |\n",
+       "|                                       NA |\n",
+       "|                                       NA |\n",
+       "|                                       NA |\n",
+       "|                                       NA |\n",
+       "+------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| freq_comparison_stats.stat_union.stat_test_name |\n",
+       "+-------------------------------------------------+\n",
+       "| str                                             |\n",
+       "+-------------------------------------------------+\n",
+       "| NA                                              |\n",
+       "| NA                                              |\n",
+       "| NA                                              |\n",
+       "| NA                                              |\n",
+       "| NA                                              |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------+\n",
+       "| freq_comparison_stats.stat_union.gen_ancs |\n",
+       "+-------------------------------------------+\n",
+       "| array                                |\n",
+       "+-------------------------------------------+\n",
+       "| NA                                        |\n",
+       "| NA                                        |\n",
+       "| NA                                        |\n",
+       "| NA                                        |\n",
+       "| NA                                        |\n",
+       "+-------------------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "354d7a5e-07a2-4f33-a830-970877cd4d63",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## All sites allele numbers\n",
+    "\n",
+    "As part of gnomAD v4.1, we [released](https://gnomad.broadinstitute.org/data#v4-all-sites-allele-number) allele number across all callable sites in the gnomAD exomes and genomes. For more information, see our [v4.1 blog post](https://gnomad.broadinstitute.org/news/2024-04-gnomad-v4-1/#allele-numbers-across-all-possible-sites)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81008401-eec4-4e95-9709-4781db066f7f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Exomes all sites allele number Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7f1c013-a013-4fde-a7e6-fcb18d8d8a5c",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3b7c75d0-1eec-4b92-883e-410337b09c92",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='exomes', version='4.1', dataset=\"all_sites_an\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6868a2d1-6e62-492a-8086-822c910e8608",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f9c6d73b-7683-47fe-bf7d-2f5bef1d23d3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'strata_meta': array> \n",
+      "    'strata_sample_count': array \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'AN': array \n",
+      "    'outside_broad_capture_region': bool \n",
+      "    'outside_ukb_capture_region': bool \n",
+      "    'outside_broad_calling_region': bool \n",
+      "    'outside_ukb_calling_region': bool \n",
+      "----------------------------------------\n",
+      "Key: ['locus']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "626f20d9-43c1-4687-9b05-01d53115c168",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ee361058-54ae-4793-951b-1e0a6df6f685",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
locus
AN
outside_broad_capture_region
outside_ukb_capture_region
outside_broad_calling_region
outside_ukb_calling_region
locus<GRCh38>array<int64>boolboolboolbool
chr1:11719[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11720[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11721[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11722[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
chr1:11723[0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]TrueTrueFalseTrue
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+\n",
+       "| locus         |\n",
+       "+---------------+\n",
+       "| locus |\n",
+       "+---------------+\n",
+       "| chr1:11719    |\n",
+       "| chr1:11720    |\n",
+       "| chr1:11721    |\n",
+       "| chr1:11722    |\n",
+       "| chr1:11723    |\n",
+       "+---------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| AN                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
+       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
+       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
+       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
+       "| [0,628784,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------+----------------------------+\n",
+       "| outside_broad_capture_region | outside_ukb_capture_region |\n",
+       "+------------------------------+----------------------------+\n",
+       "|                         bool |                       bool |\n",
+       "+------------------------------+----------------------------+\n",
+       "|                         True |                       True |\n",
+       "|                         True |                       True |\n",
+       "|                         True |                       True |\n",
+       "|                         True |                       True |\n",
+       "|                         True |                       True |\n",
+       "+------------------------------+----------------------------+\n",
+       "\n",
+       "+------------------------------+----------------------------+\n",
+       "| outside_broad_calling_region | outside_ukb_calling_region |\n",
+       "+------------------------------+----------------------------+\n",
+       "|                         bool |                       bool |\n",
+       "+------------------------------+----------------------------+\n",
+       "|                        False |                       True |\n",
+       "|                        False |                       True |\n",
+       "|                        False |                       True |\n",
+       "|                        False |                       True |\n",
+       "|                        False |                       True |\n",
+       "+------------------------------+----------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc4a4f23-d754-4e31-8e59-f62f9be65942",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Genomes all sites allele number Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65cb8d93-c5ef-409b-9c51-7c282a63bdc2",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0bb38926-f803-4be5-852c-782023b387bb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='genomes', version='4.1', dataset=\"all_sites_an\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d5c2549-151c-4b99-bac3-23fd9024f114",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "64d2c64c-b533-433a-89e6-72d473bd6464",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'strata_meta': array> \n",
+      "    'strata_sample_count': array \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'AN': array \n",
+      "----------------------------------------\n",
+      "Key: ['locus']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "140f66aa-83d4-4752-abcf-c674bf208194",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e2271a6e-16f6-48ca-805f-735e17a8f711",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
locus
AN
locus<GRCh38>array<int64>
chr1:10001[16,18232,0,0,4,0,0,6,0,6,0,0,8,8,0,0,0,0,4,0,0,0,0,0,2,4,0,0,2,4,0,0,0,0]
chr1:10002[78,32090,12,0,22,2,0,26,0,14,2,0,30,48,8,4,0,0,4,18,2,0,0,0,12,14,0,0,2,12,2,0,0,0]
chr1:10003[200,38154,48,0,48,6,4,28,6,48,6,6,102,98,38,10,0,0,8,40,6,0,0,4,14,14,2,4,26,22,6,0,2,4]
chr1:10004[948,62380,202,0,248,18,12,172,10,258,18,10,400,548,112,90,0,0,66,182,14,4,2,10,70,102,4,6,118,140,10,8,4,6]
chr1:10005[1774,70720,356,6,444,30,18,288,18,552,32,30,782,992,206,150,2,4,134,310,18,12,6,12,116,172,6,12,268,284,16,16,10,20]
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+\n",
+       "| locus         |\n",
+       "+---------------+\n",
+       "| locus |\n",
+       "+---------------+\n",
+       "| chr1:10001    |\n",
+       "| chr1:10002    |\n",
+       "| chr1:10003    |\n",
+       "| chr1:10004    |\n",
+       "| chr1:10005    |\n",
+       "+---------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| AN                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [16,18232,0,0,4,0,0,6,0,6,0,0,8,8,0,0,0,0,4,0,0,0,0,0,2,4,0,0,2,4,0,0,0,0]   |\n",
+       "| [78,32090,12,0,22,2,0,26,0,14,2,0,30,48,8,4,0,0,4,18,2,0,0,0,12,14,0,0,2,... |\n",
+       "| [200,38154,48,0,48,6,4,28,6,48,6,6,102,98,38,10,0,0,8,40,6,0,0,4,14,14,2,... |\n",
+       "| [948,62380,202,0,248,18,12,172,10,258,18,10,400,548,112,90,0,0,66,182,14,... |\n",
+       "| [1774,70720,356,6,444,30,18,288,18,552,32,30,782,992,206,150,2,4,134,310,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bf9f31f-34ff-4385-a80e-985cbb0acfe8",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Coverage\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de70c319-787b-4d6c-9058-255a1137d81f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Exomes coverage Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3278430c-4279-4d89-85e7-276184ec42b8",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7f005af0-df6d-4a0a-a2e0-9cb4ff6782c2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='exomes', version='4.0', dataset=\"coverage\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "128e58ce-c219-472a-88be-6babc2ba5a15",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "6058bf38-4ce9-4d37-89a7-d11af0e4d9f1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    None\n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'mean': float64 \n",
+      "    'median_approx': int32 \n",
+      "    'total_DP': int64 \n",
+      "    'over_1': float64 \n",
+      "    'over_5': float64 \n",
+      "    'over_10': float64 \n",
+      "    'over_15': float64 \n",
+      "    'over_20': float64 \n",
+      "    'over_25': float64 \n",
+      "    'over_30': float64 \n",
+      "    'over_50': float64 \n",
+      "    'over_100': float64 \n",
+      "----------------------------------------\n",
+      "Key: ['locus']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5969ab0c-7cee-4061-8740-8b82366ae806",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "49fcf64b-1a91-4052-bb98-b329a0e8031b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
locus
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32int64float64float64float64float64float64float64float64float64float64
chr1:118191.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118201.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118211.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118221.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118231.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| locus         |     mean | median_approx | total_DP |   over_1 |   over_5 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| locus |  float64 |         int32 |    int64 |  float64 |  float64 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| chr1:11819    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "| chr1:11820    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "| chr1:11821    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "| chr1:11822    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "| chr1:11823    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "|  over_10 |  over_15 |  over_20 |  over_25 |  over_30 |  over_50 | over_100 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "|  float64 |  float64 |  float64 |  float64 |  float64 |  float64 |  float64 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19b2e9be-48fd-4859-af3d-a0775656d24c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Genomes coverage Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5918f8e0-a723-439a-a11f-558d5ed13be8",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a8d0be07-c35d-425a-b554-c86034e367fc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='genomes', version='3.0', dataset=\"coverage\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d129b898-d642-44d1-8243-66a9cca8d1b1",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "18afb20f-7429-4fe2-a6c5-73a22dcbdb76",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    None\n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'mean': float64 \n",
+      "    'median': int32 \n",
+      "    'count_array': array \n",
+      "    'over_1': float32 \n",
+      "    'over_5': float32 \n",
+      "    'over_10': float32 \n",
+      "    'over_15': float32 \n",
+      "    'over_20': float32 \n",
+      "    'over_25': float32 \n",
+      "    'over_30': float32 \n",
+      "    'over_50': float32 \n",
+      "    'over_100': float32 \n",
+      "----------------------------------------\n",
+      "Key: ['locus']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05d8e22c-93d6-4ecf-b9e7-711945268c82",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b27cb655-3abb-4501-bcc9-3f634db64591",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
locus
mean
median
count_array
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32array<int32>float32float32float32float32float32float32float32float32float32
chr1:100011.93e+0116[0,20,65,141,226,328,386,442,415,385,292,198,362,367,337,273,289,266,241,226,191,169,168,151,227,194,179,190,177,166,143,131,150,130,125,121,136,94,93,83,67,60,68,68,59,33,39,39,39,38,20,18,21,25,10,9,16,8,7,6,2,3,2,4,1,2,2,2,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]1.25e-011.19e-019.12e-026.95e-025.15e-023.88e-022.62e-022.27e-030.00e+00
chr1:100022.10e+0118[0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,435,417,366,346,320,437,415,405,359,333,308,283,266,272,248,218,231,241,184,176,162,138,119,127,137,118,63,82,87,66,66,46,33,39,43,22,25,26,19,19,11,7,6,7,5,3,5,2,4,2,6,2,3,2,0,1,1,1,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]2.20e-012.15e-011.79e-011.39e-011.02e-017.60e-025.06e-024.83e-032.79e-05
chr1:100032.44e+0123[0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,658,649,590,548,486,679,656,640,571,533,491,439,398,412,404,349,383,360,298,263,242,207,182,186,194,159,118,123,116,96,96,67,59,61,64,34,33,34,31,30,15,12,11,13,10,7,7,3,7,5,10,3,3,5,0,2,2,1,0,0,2,1,1,1,4,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,4]2.62e-012.59e-012.41e-012.07e-011.59e-011.18e-017.77e-027.61e-035.58e-05
chr1:100042.43e+0123[0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1242,1181,1162,1083,966,845,1149,1088,1047,922,857,804,725,645,633,658,525,610,537,451,411,369,343,285,290,260,235,184,190,174,151,152,96,83,96,91,52,52,56,43,47,30,20,19,22,16,13,9,9,10,10,14,8,7,8,0,5,3,3,2,1,2,1,1,1,5,3,3,2,1,0,2,0,0,1,0,1,0,1,1,0,0,12]4.27e-014.24e-013.99e-013.43e-012.60e-011.87e-011.21e-011.20e-021.67e-04
chr1:100052.45e+0123[0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1401,1341,1300,1266,1105,966,1288,1243,1198,1068,976,905,842,725,728,740,600,678,613,515,464,414,396,338,324,300,268,213,210,198,175,165,113,100,108,102,61,58,61,50,53,35,22,22,27,22,15,11,12,10,13,14,10,8,9,1,6,4,6,5,3,3,4,2,2,5,3,7,3,1,0,2,0,1,2,2,2,0,1,1,0,0,17]4.83e-014.80e-014.56e-013.92e-012.96e-012.14e-011.38e-011.42e-022.37e-04
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+----------+--------+\n",
+       "| locus         |     mean | median |\n",
+       "+---------------+----------+--------+\n",
+       "| locus |  float64 |  int32 |\n",
+       "+---------------+----------+--------+\n",
+       "| chr1:10001    | 1.93e+01 |     16 |\n",
+       "| chr1:10002    | 2.10e+01 |     18 |\n",
+       "| chr1:10003    | 2.44e+01 |     23 |\n",
+       "| chr1:10004    | 2.43e+01 |     23 |\n",
+       "| chr1:10005    | 2.45e+01 |     23 |\n",
+       "+---------------+----------+--------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| count_array                                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,20,65,141,226,328,386,442,415,385,292,198,362,367,337,273,289,266,241,... |\n",
+       "| [0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,... |\n",
+       "| [0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,65... |\n",
+       "| [0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1... |\n",
+       "| [0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "|   over_1 |   over_5 |  over_10 |  over_15 |  over_20 |  over_25 |  over_30 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "|  float32 |  float32 |  float32 |  float32 |  float32 |  float32 |  float32 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "| 1.25e-01 | 1.19e-01 | 9.12e-02 | 6.95e-02 | 5.15e-02 | 3.88e-02 | 2.62e-02 |\n",
+       "| 2.20e-01 | 2.15e-01 | 1.79e-01 | 1.39e-01 | 1.02e-01 | 7.60e-02 | 5.06e-02 |\n",
+       "| 2.62e-01 | 2.59e-01 | 2.41e-01 | 2.07e-01 | 1.59e-01 | 1.18e-01 | 7.77e-02 |\n",
+       "| 4.27e-01 | 4.24e-01 | 3.99e-01 | 3.43e-01 | 2.60e-01 | 1.87e-01 | 1.21e-01 |\n",
+       "| 4.83e-01 | 4.80e-01 | 4.56e-01 | 3.92e-01 | 2.96e-01 | 2.14e-01 | 1.38e-01 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "\n",
+       "+----------+----------+\n",
+       "|  over_50 | over_100 |\n",
+       "+----------+----------+\n",
+       "|  float32 |  float32 |\n",
+       "+----------+----------+\n",
+       "| 2.27e-03 | 0.00e+00 |\n",
+       "| 4.83e-03 | 2.79e-05 |\n",
+       "| 7.61e-03 | 5.58e-05 |\n",
+       "| 1.20e-02 | 1.67e-04 |\n",
+       "| 1.42e-02 | 2.37e-04 |\n",
+       "+----------+----------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {
+    "height": "613.99px",
+    "width": "526.312px"
+   },
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "202.438px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "toc-autonumbering": false,
+  "toc-showcode": false,
+  "toc-showmarkdowntxt": true,
+  "toc-showtags": false,
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
new file mode 100644
index 0000000..e6dd8fd
--- /dev/null
+++ b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
@@ -0,0 +1,6288 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4a01210b",
+   "metadata": {},
+   "source": [
+    "# Introduction to filtering the gnomAD variant data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e609a46",
+   "metadata": {
+    "toc": true
+   },
+   "source": [
+    "
Table of Contents
\n",
+    "
"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e713032",
+   "metadata": {},
+   "source": [
+    "## Import modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e69953f7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T18:04:56.165634Z",
+     "start_time": "2024-12-06T18:04:55.603516Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "    \n",
+       "    
\n",
+       "        \n",
+       "        Loading BokehJS ...\n",
+       "    
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  const force = true;\n",
+       "\n",
+       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+       "    root._bokeh_onload_callbacks = [];\n",
+       "    root._bokeh_is_loading = undefined;\n",
+       "  }\n",
+       "\n",
+       "const JS_MIME_TYPE = 'application/javascript';\n",
+       "  const HTML_MIME_TYPE = 'text/html';\n",
+       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
+       "\n",
+       "  /**\n",
+       "   * Render data to the DOM node\n",
+       "   */\n",
+       "  function render(props, node) {\n",
+       "    const script = document.createElement(\"script\");\n",
+       "    node.appendChild(script);\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when an output is cleared or removed\n",
+       "   */\n",
+       "  function handleClearOutput(event, handle) {\n",
+       "    function drop(id) {\n",
+       "      const view = Bokeh.index.get_by_id(id)\n",
+       "      if (view != null) {\n",
+       "        view.model.document.clear()\n",
+       "        Bokeh.index.delete(view)\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    const cell = handle.cell;\n",
+       "\n",
+       "    const id = cell.output_area._bokeh_element_id;\n",
+       "    const server_id = cell.output_area._bokeh_server_id;\n",
+       "\n",
+       "    // Clean up Bokeh references\n",
+       "    if (id != null) {\n",
+       "      drop(id)\n",
+       "    }\n",
+       "\n",
+       "    if (server_id !== undefined) {\n",
+       "      // Clean up Bokeh references\n",
+       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+       "      cell.notebook.kernel.execute(cmd_clean, {\n",
+       "        iopub: {\n",
+       "          output: function(msg) {\n",
+       "            const id = msg.content.text.trim()\n",
+       "            drop(id)\n",
+       "          }\n",
+       "        }\n",
+       "      });\n",
+       "      // Destroy server and session\n",
+       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+       "      cell.notebook.kernel.execute(cmd_destroy);\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when a new output is added\n",
+       "   */\n",
+       "  function handleAddOutput(event, handle) {\n",
+       "    const output_area = handle.output_area;\n",
+       "    const output = handle.output;\n",
+       "\n",
+       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
+       "      return\n",
+       "    }\n",
+       "\n",
+       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+       "      // store reference to embed id on output_area\n",
+       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "    }\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "      const bk_div = document.createElement(\"div\");\n",
+       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "      const script_attrs = bk_div.children[0].attributes;\n",
+       "      for (let i = 0; i < script_attrs.length; i++) {\n",
+       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
+       "      }\n",
+       "      // store reference to server id on output_area\n",
+       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function register_renderer(events, OutputArea) {\n",
+       "\n",
+       "    function append_mime(data, metadata, element) {\n",
+       "      // create a DOM node to render to\n",
+       "      const toinsert = this.create_output_subarea(\n",
+       "        metadata,\n",
+       "        CLASS_NAME,\n",
+       "        EXEC_MIME_TYPE\n",
+       "      );\n",
+       "      this.keyboard_manager.register_events(toinsert);\n",
+       "      // Render to node\n",
+       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "      render(props, toinsert[toinsert.length - 1]);\n",
+       "      element.append(toinsert);\n",
+       "      return toinsert\n",
+       "    }\n",
+       "\n",
+       "    /* Handle when an output is cleared or removed */\n",
+       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
+       "    events.on('delete.Cell', handleClearOutput);\n",
+       "\n",
+       "    /* Handle when a new output is added */\n",
+       "    events.on('output_added.OutputArea', handleAddOutput);\n",
+       "\n",
+       "    /**\n",
+       "     * Register the mime type and append_mime function with output_area\n",
+       "     */\n",
+       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "      /* Is output safe? */\n",
+       "      safe: true,\n",
+       "      /* Index of renderer in `output_area.display_order` */\n",
+       "      index: 0\n",
+       "    });\n",
+       "  }\n",
+       "\n",
+       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+       "  if (root.Jupyter !== undefined) {\n",
+       "    const events = require('base/js/events');\n",
+       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  }\n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
+       "     \"
\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"
    \\n\"+\n",
+       "     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n",
+       "     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"from bokeh.resources import INLINE\\n\"+\n",
+       "     \"output_notebook(resources=INLINE)\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"
\"}};\n",
+       "\n",
+       "  function display_loaded() {\n",
+       "    const el = document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\");\n",
+       "    if (el != null) {\n",
+       "      el.textContent = \"BokehJS is loading...\";\n",
+       "    }\n",
+       "    if (root.Bokeh !== undefined) {\n",
+       "      if (el != null) {\n",
+       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+       "      }\n",
+       "    } else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(display_loaded, 100)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls == null || js_urls.length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    function on_error(url) {\n",
+       "      console.error(\"failed to load \" + url);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < css_urls.length; i++) {\n",
+       "      const url = css_urls[i];\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < js_urls.length; i++) {\n",
+       "      const url = js_urls[i];\n",
+       "      const element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
+       "  const css_urls = [];\n",
+       "\n",
+       "  const inline_js = [    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
+       "    },\n",
+       "function(Bokeh) {\n",
+       "    }\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    if (root.Bokeh !== undefined || force === true) {\n",
+       "          for (let i = 0; i < inline_js.length; i++) {\n",
+       "      inline_js[i].call(root, root.Bokeh);\n",
+       "    }\n",
+       "if (force === true) {\n",
+       "        display_loaded();\n",
+       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    } else if (force !== true) {\n",
+       "      const cell = $(document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\")).parents('.cell').data().cell;\n",
+       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  if (root._bokeh_is_loading === 0) {\n",
+       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+       "    run_inline_js();\n",
+       "  } else {\n",
+       "    load_libs(css_urls, js_urls, function() {\n",
+       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "      run_inline_js();\n",
+       "    });\n",
+       "  }\n",
+       "}(window));"
+      ],
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import hail as hl\n",
+    "\n",
+    "from gnomad_toolbox.load_data import get_gnomad_release\n",
+    "from gnomad_toolbox.filtering.variant import filter_by_intervals, filter_by_gene_symbol\n",
+    "from gnomad_toolbox.filtering.frequency import (\n",
+    "    get_ancestry_callstats, \n",
+    "    get_single_variant_ancestry_callstats,\n",
+    ")\n",
+    "from gnomad_toolbox.filtering.vep import filter_by_csqs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b3c44396-5ee1-4263-91f8-78bdb9417bab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Welcome to\n",
+      "     __  __     <>__\n",
+      "    / /_/ /__  __/ /\n",
+      "   / __  / _ `/ / /\n",
+      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241210-2227-0.2.132-678e1f52b999.log\n",
+      "2024-12-10 22:28:45.710 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
+     ]
+    }
+   ],
+   "source": [
+    "hl.init(backend=\"local\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "725f9a57",
+   "metadata": {},
+   "source": [
+    "## Filter to variants in a specific gene\n",
+    "\n",
+    "Here we show two ways that you can load a variant table on the gnomAD browser:\n",
+    "  - The [region view](https://gnomad.broadinstitute.org/region/11-113409605-113475691?dataset=gnomad_r4)\n",
+    "  - The [gene page](https://gnomad.broadinstitute.org/gene/ENSG00000149295?dataset=gnomad_r4)\n",
+    "    - Only includes variants located in or within 75 base pairs of a coding exon (CDS)\n",
+    "\n",
+    "We use the *DRD2* gene as an example. "
+   ]
+  },
+  {
+   "attachments": {
+    "Screenshot%202024-11-01%20at%209.31.00%E2%80%AFPM.png": {
+     "image/png": 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oGRCBFkZgyZIl9sEHH7SwWqk6IiACIiACIiACIiACIiACItA4AhIgGsdPR4tAUQlUVlbahhtuaFtvvbX97Gc/K2rZKkwEREAEREAEREAEREAEREAEmpOABIjmpK9zN4rA1Vdfbe3atUtPn3766SrlzZ49O709ue8GG2xgJ5xwgt1www02fvz4VY5rrhXTp083Juy1115r0mq8+OKLNmrUKHv++eeb9DwqXAREQAREQAREQAREQAREQAQgkKqqqqoRChFojQS22mor+/DDD9NVv/TSS+3cc89Nf2Zh1qxZ1q9fv4x12R+6dOli9957r+25557Zm5rl87XXXhuEgfPPP9+23377JqvD4MGDbfLkybbuuuvapEmTmuw8KlgEREAEREAEREAEREAEREAEICABQs9BqyTw3nvv2bbbbptRd7ouvP/++xnrkgIEjvYZZ5xh5FgYPXp0RoQB2959913r0aNHxvFt+YMEiLZ8d3VtIiACIiACIiACIiACItDyCKgLRsu7J01ao3/961/2zW9+0+iCsPfee9ujjz5qv//9723kyJFhmjJlSjj/aaedFj4fccQRRteGb33rW9azZ0/Daf3xj39sCxcuzKjnW2+9ZT/5yU9sxIgRocvDoYceatdcc03GfoT6x/M89NBD9qtf/cqIYqBc6jR16tTQHYLleK5zzjnHli5dmnEuPtx3333pdV/72tfC8ieffBJEhPSGrIVNN93ULrjgArviiivshRdesFdeeSW9B90e7rrrrvTnuDBv3rx0nY8++ui4Oswfe+yx9LZbb701rCOi4Hvf+16aw+abb24nnXSSffbZZ+lj6fYRObz66qt2+umnh/vBcYsWLUpvu/jii9PHLF682C677LKwja4k3D+umzpEy+bLfd1xxx3D/dh9992D6MK+iC+cn7piXDufk9f3zjvv2HHHHWcbb7xxOH6XXXYJOSkWLFgQjtE/IiACIiACIiACIiACIiACIlBfAoqAqC+xVrz/TTfdFJzj7EsgcgDnHaNLw9ChQ4MwELs3IDqMHTs247CTTz7ZbrzxxrCOXAU4qLlsr732CmJBp06d7D//+U9wmtlv+PDhq4gFu+22m33xxRfpusTyjj32WLv55pvjR6upqbFNNtkk1Im6IarssMMOYfvPf/5zu/DCC9P7JiMg6GLxxBNPpLexQNeLY445JqwjJ8Rf/vKXjO18wDlHsMDgNHDgwLB85pln2l//+tew/NxzzwXRBJEjl9HNA4GAeiPUIA5gSfaHHXaY/fnPfw5dItiGwPCPf/zDli9fHgSNeD/YlrS///3vQTxI8qUeufZnHWIRIk+2xa4Yzz77rO2zzz7Zm8NneHN8x44dc27XShEQAREQAREQAREQAREQARHIR6Ak3watb1sE6ONPC3u073znO3b99dcH4SCKD3Fb9rxDhw5h3+OPPz69CUHA84eE7gwHHHBAej3OP9EJ++67b1j39NNPG6JAttHd4aKLLgpREHEbTvy0adOCc54UEe64447ghMf93njjjbQg8o1vfCOIJTjPGPVCoCjUknkfSMqYy4j+iMb1YJwDhx9bf/31Q66GKMiwjuiO//3vfwZnjMiBZNRGWOn/wJ4oE6IbmOcyolSimIDwQ7nkiYj2t7/9LS6m5+x/9tlnG4k6EQ2iIdYgFlEXRBGMOZ9jFMftt98ed7cnn3zSJkyYkM6tgRD1+OOPp7drQQREQAREQAREQAREQAREQAQKJVBW6I7ar3UToNU6GiH/f/jDH8JHwu4322yz9MgLcZ/k/J577rFhw4bZt7/97ZA3ITrDhPDPmDEjONfsf+qpp6bFBpxcjsHxTnYTiOWeddZZRpJFjHJ++9vfhuWrrrrK6P6BEVnxyCOPhGX2iZEH1CfawQcfbKlUKrToE1XAfq+//rptt912cZc653T1wAGnnogBiCp0cUgakQlRvOFa6FIBgzhaBaIAdUBoIbqhtLTU1llnnVAEbP/0pz+FZYSTbDvvvPPskksuSa+eO3duejkuEI3AdWGUS/k777yzITy8+eabFkWRuD9zokZgiQ0ZMsS+/vWvh+Vx48aFpJxw69q1a7huohn4HI0olGjsTzcZ6hiHBc3mE/fVXAREQAREQAREQAREQAREQATqIqAIiLrotKFtSQEiGbGAExqjFfJdLkJCtGTEwLJly4KzH7fR3SIayRyjCIBjP2fOnLgpzMmNEK1v375xMXT/iB/IcxCturo6LK5YscJuu+22uNpKSkpCHaLDz4akQJHeMc8CuSxiXgMiBXI513369LEDDzwwlEDrP9dNHoVoRx55ZFiEJZEdv/jFL0KUAfkTevfuHXezeA3pFb5ArozVGQIB/BCNqAfsYIP4kM9ilxS2J0fSQGBZnR111FHpXRCDuIatt97afvnLX4bRMsrKpFumAWlBBERABERABERABERABESgYALyJApG1bp37Ny5c/oCSGiYNBIf5rMYph+3ZzvoyWOztyXzBGQnkiRioCH20ksvpSMPOJ7kitlGTgS6NBApsDp7++2307uQsDGfkSeCrhCIFURmsIyRa2GLLbYIy1deeWXoVhI++D90C0nmeIjrk/PkfUmuTy6PGjXKvvKVr6RXcU8QbWIERnpDYqG8vDz9qRAO6Z19gS4ndLu57rrrjG4xGBEfTIgQrEsKHGEH/SMCIiACIiACIiACIiACIiACqyGgCIjVAGorm2nBjkZffxIbYoxw8cADD8RN9Z4z6kU0Rk6IRpQAjjqGI96vX7+4qVHzf//73xnH44zHKW7AMc+XzyHuw3zmzJl2yimnpFclryW9cuVCsosCuR+iABHzYhCZQd4HjJwQjHpB3g3yNTTWYqJLynnqqadCvRluNF/Cy/qej2FJs42oDs5FFxuu9ZBDDknvcuedd6aXtSACIiACIiACIiACIiACIiAChRKQAFEoqVa+37bbbpseXYHEgrT209JdV6t/IZecFDaIOsBZJV/Bueeem26hZxSJYhiiSbL7Bd0SGOUiTowYES1bqGA9Q4wy1CYCDDkd6JoQR/eg+0WukSFied26dUvnUYhCA9viEKCVlZXprhxEgjARLUF3jMYaiTmjUW7kEHNxxG31ncfEndSTRJ/cN7qJkL+D7iNEl3BdDNdKzo5o2VExcb3mIiACIiACIiACIiACIiACIlAXAXXBqItOG9qGA/3QQw+lQ+fJVcCEM0legdiiX99LJtcDyRBjJEF2TgOiAQjbL4YRTRDzNdAin919ITl0JN0wrrnmmozTkoviuOOOy1jHBxxxRJnokK+yw8oVCDZJYQPxJo4wwTCj5NKgHESN/v375yum3uu5P7ErBEOVFssYdjTmkSCRJtdP1AaCA6NncB3kmuAao1DDuaPoUqx6qBwREAEREAEREAEREAEREIG1g4AiINaO+xyukmiFqVOnGkMxEqFAH3+6YCASRCOpI5Yv0WCu3A049bfccktGNAXCBqNHkL8gJpNMHhvPw7nyrU/uw3JyGEvKzrbu3bvb/vvvH1YjVDz//PMZZSf3x9lGUIDByy+/XJBggMCQbP3n+KQRWRHPz3rOkSshZr7r5ZjkNj5j55xzTnoUjto1Zpdeeqklk36yPnlskl1yfXKZiI/YhYTjY96Iiy++OAgQ8Vqj+EBSUbq2JKNeOE4mAiIgAiIgAiIgAiIgAiIgAoUQSHlW/JpCdtQ+rZvAxIkT0xEB5A4488wzwwXNmzcvDNOIw47DPGHChDCyREOvlqSU5GBgyMykE9zQ8lrjcST5jDyTDn9jr4WuF+RkYFSKfAJRQ89Bzg7KzK4v95KhQbmf7du3b2jxOk4EREAEREAEREAEREAEREAETALEWvIQ0Ld/+PDhRjcEjGUiH5JdL37yk5/Y5ZdfvpYQ0WWKgAiIgAiIgAiIgAiIgAiIgAisSQKtS4Bg4IZKDzVfWjs3Plf7tMLX+RSWfWbei6CGERiZ6FFApgtvvK3pUDsPn31xbbOPP/7YTj/9dHvhhRdWufQrrrgihPqvrVELqwDRChEQAREQAREQAREQAREQAREQgaISaHkCRJWLCYv8Guf73KcwX+hzRgr0KYgPCBBLUxYEiKT4gAiBrRQe0iIEAkQH72niAkQQITr6Z59qKnze1ec+hXlnn7fzqQ0bkRAkGiT3A90Ehg4dahtuuKHC69vwPdeliYAIiIAIiIAIiIAIiIAIiEBLINC8AoRHLwSxYZbPmWY6ktk+n+fz+anaOcsIEIt9Wu6iQ2MzVngRVuaFdPI5AkQ3L9In61pTO1/HP/fyqaev86kGUUKpOh2CTAREQAREQAREQAREQAREQAREQAQaTmDNCxB0oXChITXFp2k+TffKz3SxYYbPmRa6QkC3iuYwhIYKFyd6u/Dgk/VyUWJdX+7r03o+uTBBVw6ZCIiACIiACIiACIiACIiACIiACIhA/QisGQGCrhOIDZN8muzTFBccfNm+8GkxIQkt2Dq5INHHxYcNECFckPARK8OyixJ045CJgAiIgAiIgAiIgAiIgAiIgAiIgAisnkDTCRCeIDJ0qxjnc6axLjpM8ArRzWJZA0UHcju0d0GAZJJM5GuIySZjwklfFRNTkpwyTJ5XwkLeCJ9X+rljrgj/WC8r93PTPWOAT4NdjBjkcya6a5BnQiYCIiACIiACIiACIiACIiACIiACIpCTQPEFCHf0iXIo+djnn7ro8Lmfd6pPOP6FGN0gOruj393n6QSR/tnzNYQEkggPJJCkK0RSgOA4Jqx65ZQQIFLe9SMksqR+iBHklSDPxMpElzbXPy/yOnJsIYYQ0s/rMcSnYTVWvZHPPToiCCOFHK99REAEREAEREAEREAEREAEREAERGAtIlA8AcKTRJaMd+f+I59/4I78p05xns/dT6/T2vkOPXwP8i6QY4G8CySCRIDo4nOfwjyOUOFFNsqoTxxpY4HX1ydj7gJEyhNghnwURGmQj2KOT1WrOSGbu3mhw1y72MzrvonPB/o6klzKREAEREAEREAEREAEREAEREAEREAEAoHGCxAeTRCEh3d8/p5740Q8kEgyn7GJRI9ED5DYsZ877X19nXdjoCtDEB6IcmgO82sJQoSPyGGMyjHNp6keJTHFPxPFwXUhYOQzrssjIqq38GvacqUQ0VzXkq+OWi8CIiACIiACIiACIiACIiACIiACzUCg4QIEOR7cMS9BeHjdHfNPvPZ0YchndKsgkeMgd877+zyIDz73aIfQlSLfcc25nkgJhgV18SGM2jHR5+P8Gkmgubpr3dAFiBHeNcOFCK5VOSKa80bq3CIgAiIgAiIgAiIgAiIgAiIgAs1NoEECRMq7JqTec+HhVY8O8LnNySM8lLnowDCW3j0hnbRxgC/TvSLma2huAoWe33NDhOiICYgQPpFUk24mDCO6PM/193CxZQsXIravndfQ1UQmAiIgAiIgAiIgAiIgAiIgAiIgAmshgfoJEEQ9uANe8hKTO92TfcqVtJEEjUQ7bOIRABv7fKhPnuPBytsI4WXOwXNEpD5zDmN8/pFzICoiV6JNhJb1ncNOTM7BBRhFQ7SR50CXIQIiIAIiIAIiIAIiIAIiIAIiUDCBggUIkjWm3neH+5mVUQ8LcrT6k1ByoDvZw93Z3nSl8EC0Q45dC65hS97RLzdERSBEfOjL7/qFjvcK50pc2WVlNMQePt/c2ZBcUyYCIiACIiACIiACIiACIiACIiACawmBgjpCpHxUiJJnzEr/7eLDK+5kZ4sPdLUY4I71ATW24hifDvPAiO3cyabLQVsVH3hA/Nq4Rq6Va15xdC0DWBhMkubMYAdDWMK0Jdr999+fUa1nnvHK5rB869n1vPPOy3FE4avqKrvwUurek3OsifPUXQttbYkE9Fy0xLuiOomACDQXgcb+TW+ueuu8IiACIiAC9SPw3e9+N6cfd/PNNxfVb6pbgKCFf7KLD4/79JB725/5tCLrQshzsKuvPsqFh8PdGd/GnfKuWfusBR+55uptnYMzgAVMzNlkGOycISxhCts6R9XIOLjpP/CS8dprr6UfMByxv/3tb8bDmLTf/OY3du2116b3S26jjLKyspzbkvvlW7700kvt0Iu+Zlvut1W+XYqynuv617/+lbcsrn2nnXZq8HXkLVgbWjQBnt/TTz/deA5lIiACIrC2E6BRYvPNNw9/D9d2Frp+ERABEWjLBHgHxvcZPXp0xmVG364uvynjgAI+5BcgSLo41qMeHnLx4UkXHqb5lLRyd6439K4Wh7jw8LWV+Q0Y0SJrt+QhbX7Zr51RPcj1EJg4GxgZrJLmLGFa+rDjGucbcuXRSO6/hpanTZtmG2+8cdoxf+SRR2yfffaxHj16pB1xHPMXX3zRDj30UJs924cIybLsMrI2r/bj7Y/dYeWH9bPxPcnuaenzrvbAeuzAFyleV3bERyyGa0d4KeaXLZatecsl8Nxzz9mJJ55ojz32WMutpGomAiIgAmuIQPwbOWLEiDV0Rp1GBERABESgGATw2fB5CjV8OGzffffN8L/wDbGkPxhWNOKf3AJEFB8e9m4Dz7pXnT3KBfkMdlwZ9bCfO939vQaljahFWzvUWcBkhbMJ0RDOypxZhjlT2JZ6NARCT3OIEPHFgnrR4rvBBp451O3ll18O83XX9SFM3HjwcMgx5ogP2BNPPBHm8Z9kGa+//npcHeacq5Cogso+tSE2K2pWhDqdc845q0RgZBSc9QHRgDChugx1D+O6sq8hHhe/hMX8ssWyNW+ZBHh+u3SpTc7Sp0+fjB/flllj1UoEREAEmo5A8m+6/hY2HWeVLAIiIAJNQYBo70Kj0vHRoiX9Ptb973//C5tYn6vxOR5Xn3lOASI10fWER711Hj90UVZIQ28XH/b2qIcjPerBu1tYRX1Ot5bt62xgBCuYmbPLsIUuPjjjwNqZr0lDEVuwYEHauV+0aFFwyKnDgQceGJyvd955J10l9sXinGWOSRqfO3XqZBMnTsxQz+K5br/99jqjCnjZmbN0XiiybHgPo6vHD3/4Q8sWM5LnTC7z5Zk+fbp99tlnlhRXkvuwXNc2tlNOFGP4XGxLfsmLXbbKazgBnt/+/VFTzfr169fwgnSkCIiACLQBAsn3gjZwOboEERABEVhrCOB74csgGqwumpt9v/e972X4PtHfw2dJ+nv5Gm7rC3YVASLl0RelTyE+uPCQLT70d9HhKy4+HOQt/IP9VIp6WD1voiGcFcxgZ84ww5wxrAPz2siXjM1N9SG28MdoBx7QpBHpkHzg4rbkOh7spDNN1wy2MyEERIvnevrpp8OqoUOHxk0Z8+mLplvZ0FpFq7yivQ3bfFjYnh0KlHFQ4kOMzuBaCv2CJK8nFkU5kQdzPhfL+JI//vjjq43SKNb5VE7hBJ599lkbPJgfNrNu3brZT3/608IP1p4iIAIi0MYILFmyJH1Fxf5bmC5YCyIgAiIgAk1CIPoyq2vIpcGXxuPKyspV6pH0ie69915btmzZKvs0ZEWGAJHyxueSF1x8eN7Fh+yRLnxkh+qDfNrbHerayPyGnG+tPQZmsINhGCUjSYIRMpx5YF8bAJDcWvTlZAt/jHaYM2dO+jw8sDfeeGPaCWdDLkc9fcDKhSlTplhFRUWYunfvHhz3GMLJQ3vUUUfZrFmzQvhOUriI5Xwy+zMrGVJhQ16Yb11Gz7JuHbqFTYW++EShg4Py1Td57eyXLaKwLtuiCpi9viGf6dbC9RClcdT3j7bNjtkyQ8RpSJk6pjgEPvjgg3RBPMft27dPf9aCCIiACKxNBOjKuHz58vBSGq+7mH8LY5mai4AIiIAIFJ9A0ieqqyEXv4iu6dtvv32oBH4alvSjopCxdOnSnCJFOKCe/3wpQLigkfKI+5JnXHyY5VPSoviws4sPtT5hcquWCyQAu2pnmFOEcOaw5x5YccSlvLVKqlnRuU92t+DAzTbbLEOAwFGPYkIsOB4bP5MpO9rcuXNDdw0e4Pjg4tR98cUX4XOuqILZ1fOC8HD4zv2tdHyN9dtwvVhcRteP9Mo6FvIJC8lrz3d4U75kJTm/NfU9G3TENnbd3dfnq4rWr0ECG220UcbZ3n777YzP+iACIiACawsB+vx+/vnnNm7cuLXlknWdIiACItDmCIwZM8beeuutvN0w8Is6d+5sNTWZEfrRj4p582hI3mOPPTK6aTQGVlqASI33HhXPufAwMUt86OdRD/v5JPGhMZzTx6ZFCGdqzjbDnD33gHvRlBYfpngOHO6xY8fGj3nnMQlJrh1Q0KqqqtKbevfubR999FFaOIghOytW1CaZzOXkT3h/rG3cuZNtseU6NmLzXlZesiJETKQLrWOB8yN2ROWujl0zNmWLKGxMqn7xM8w4R2MsKeC88v6rtnSTslDc6y9mJuxszDl0bMMIcG/XWceHsEnY8OHDE58yF1eX6DRzb30SAREQgdZDgK6CjBSF8bc8/l1dXRhv67lC1VQEREAE2i6B6BNxhe+//77tuuuuoWs865n4jY+GPxbzn5HHD98tabFLe/TjktsasxwEiNR8j3x4zYshAjk5JGQ3Fx52Wyk+dG3MaXRskkCNswyREM7WnHHaYO/3gHvBPWkqy07CyGeUrnfffTd9SkIvk4ajzstHjGaI26KQgDhBGdkWnfkoPGRv5/Oee+4Zvgwd1uuc3lxVVWJlNbUOOisphy/MdtttF16MWGbEi+SXiP1mzJgRriOXsMD2aCRkeeONN+LHvHP2e+WVV+pMnpn34KwNXEPkt2Tgl/2s+h4ypNHiRtap9LEIBOguww91tiE+kFWY508mAiIgAm2NAII7rWa8lMbIRa5xk002aWuXqusRAREQgTZJIPobdJvAGMmI3GaMjEHEenyHvfPOO0OkG37fzJkzw2ACEcgll1wSIuEQoevy4+L+9ZmXmPu/qU/c6X3dIx98VIa0lfvIDVu7o7ybd7vomV6rhSIRgGlg64zNWafN7wH3gnvCvSmW5XKkKPvBBx8MpyD8hm4T0UpK0sExcVXeOS37f/zjH23hwoX28ccfh4f71VdftQkTJqxyTHV1rcIVhQm+EDz05ESY8p/xtvu2vfxzlS2rKrfKquW2eNniUAbiBv2ZeCHiC8LL0YYbbpgeXoYQojiEYnl5eWixGT16dPr8iCyIFQgmXDPRGjiYV1xxRTpKI73zygXCjQg/3XTTTe3RRx9t9Pi3Uayh+NmfzvYvc63I022Tfnb3I3dnn76gz9zXXEJMQQdrpzSBZJcgooGeeuope+mll9Lbkwvsi1j3/PPP5xQokvtqWQREQARaGwG6XiSt2C+eybLrs5zdclefY7WvCIiACKwtBHhPRYCIQjLXTW6+0tLSdGNxHISA7sfxN75Dhw6hITdyIuqBZMQkaY/7IEoXw0pC4kkavrN9xYE4yC5CrNqoXYzzrhVlLF68ODjl+S4WtjA2Z51hfi9K/J5wb4plDK9y2mmnrRIxgPOPEjbYs/8nQy3pCxTDLmMdDj744LiYMUdM2GGHHYIggMN+9NFHhwR+8+Z9eQHz58+3Tz75JIgNfCGIPkAQGDlypH31q181hIle63Sw4Vv3tI/enxumubNm26MPPWoocA899JBtvfXWYT/2R9Ej6SVfsDhaAV8c6s01sW3EiBGhnpznjjvuCGIFoUVs32uvvTKuIX7gBSdGcnCOyZMnh1Yf2PTq1asoI2Jw/XNXzLdOHdvZnNkzw6nr6t4S65ZrjnBDa1Uy2Uyu/bRu9QRiCNqAAQNsl112CS1/F956sW2585eJQnmWCGVDgOA5/7//+7/VF6w9REAERKAVESBXEWG7vBckjb+N+Rozkvs1xTLnzW65a4rzqEwREAERaCsEsn/HabiNkRFxEAIanGP+h44dOxq+FL+1119/vSFOMKohDcrFHp6+JDXWHd0PPPKhKhH9UOHdLrZz8WFjvwUlzX8bGBakXbt29s1vfnOVyrA+TvyxxMkuJLR+lYJWrkDtwfmgzHx5EWjl33HHHdM3MbssbuRZZ50V1Cb6lZ988snB6c3eD7YwhrU587T5veCecG+KZQyvQhISnF+uLVoMzeG6UbVw3OtjiA84vxyPQsa1M/HQElHBC0tU4HDwiE6gK0ffvn2NKAn2IXJi2oJpNnynHnbPHZ/aRde/YakBJTZl0kwrLSm1Qw45xBBz7rvvvlC/0R7ZANcY6YDQQNcLoil4VhANEEKi8WVj4vr5Es2aU5vhle277757qE/cN86p86hRo4whQ1OpVBBUYJOMYoj7FjqPUR+U3XloZp+mTbbbtNBiMvaLLfdwzu5ak7GjPhREIEY/sHP3/j3shXdftM8rJtq+394/vHg/99xz9u9//9u22WabUB7PWnO9kBd0QdpJBERABOpBgN+z+LcqHhYjF+Pn5pjzty7mpWiO8+ucIiACItCaCfA7TiNsbFzGL2J0DNYNHDgwfWk00h5wwAGhgZhtffr0Cd0yaIgtppWkPvXiJmUVOdQd46186pK1vpk+4ghi99xzT2gBz64G4dI4wXfddVdwovfbbz9LjjaQvX9dnxkLNTroURFK7v/www/bFltsERzrZNLF5D4oR4T5jx8/3iZNmhRyCFx33XXJXdLLMIa1OfMM83sS7s2XqQIyNjfkA2oXCUbiw0cZn376aejSkO9a2AeH+fxrL7AvlkwPxxJ6nhyyEOcXxx4HndZhuKGica54fGSJODRo0KAw3AutyBxLf/plVcus/+AK61JWbhXtym1Qp4FWUlpiO++8cyiH8riniEwIAuuvv34om38QHkiSwlCiOIQIHIgasY633357eKGaUzXHFvl/qdKUzVtQG51BeZw/21ANic6I3TrYjlhSjCRc85bPt86Du9mMBXPtrY/fClEQH732YXYVVvs5tsazIz8kMVHMag/UDnkJIFzFyJlPzMOQD+5hVSP9S7prJ/veud8LzwPiGj/kRJ8cc8wxdsEFF+QtTxtEQAREoLUR4O9J0hiSOPnekNy2ppZpsKCRg7/r/M2X8LumyOs8IiACrY1AbCylcbgu23bbbdObYxQwDbw09OLz4COynigJum/QyFssK0mN98iHJYnoh44e/bCpTwNqT0Efkb333js45WeccYadeuqpwdFnKwrJ1VdfbVtttVVwgK699tp0vbh4ohF69uwZXuhvu+229LaLL744hC7j4LH9sssuC2XijDIl9+UghIWrrroqOJ25+mV369YttIgzhunvfvc7o6vAr371q/T54sKvf/1rO/vss+PH4EDwh5buARgOBc7EDTfckN4ne4FhJHHAv/Od72RvSn+mvj//+c9DuAp5Bs4//3z7xz/+kd6evQBrmJuzT5vfE+5Nanp6TaMW6L6AIRIQRsPLBCIJ9w4nmwgDWn+ffvrpEEHCg8a+v/vz7+2Pr//Vvug/35YsWBKiFog84CFEQODhRqDAiY/CDQIBggNs6baAuEHyKlqPcdw499133x2OHeRiBNajyzJ7ZtRUu/HfY2z5wFIb++nn3kWhUxAbKCvmk6A8zo9x7igy8NzwbPDF4ZkiwgK1DnWP+/vmm2/aws6LbcnMJbbdTtsHESIUsrKcuBz7TfE5RlFQX55nBBW6O5Bz4aSTTlqlO0ssI9eclyXqCLd58+fZxC+m2PJ1Sj2/SjsbN2WcrbNZYcoi5XB+EiHSL4vrhytROdmtVrnqoXW5CfBiixjF9xuODzz5gC3qU232rOdFcSHMepRZh54dw8Eowi+88EJYRvDiedTLcG6uWisCItC6CMSouny1nj17dr5NTbae31dEEH6jeREuVnfIJquwChYBERCBFkAg5m2gKkSKb7nlluE9N1YNnwwfP2k0FPMejK8bG5LZjl+I31EsK0llRz+s5y3ytMZ74zXqx0EHHRQEhNdeey386P/9739Ph6HjoN1666325z//OTjuP/rRj4LTSuXOPPPMkDnzscces3PPPTeIEbGfOw7djTfeGI7BkaKPP0kM6ef///7f/wv7EgKC4fiRofPwww+3Y489Ni1+hI15/jnssMMsJtdI7kK4PeJGbO2PzmbXrrXh8D/+8Y9DXbPV/2QZp5xySmi9T67LXqb1PFkGyzjAMQoge39YB+bOPmncm1T9ekQkD89YjsoWK4kmwNGCLX/QEQx4qBAUiDjgvvPQISrM7zDfOry5yC7YeUP7Yuy08BKA4MP2qVOnBuGC+8mzgOMfjWvlYcViVxaUtBgpgQjCuXn4yc/w1ruf2sLpy22DoZ7cZFB7W750RVDdqNfbb78dnHfCgKIhPiAsUBcEEL5YXM+HH34YvmCIIUSfELFAfyZswYJaoYm6raheYUurajPDxjKT8/XWWy/cL5JzItDQPYXzURcc/vfeey8s18fxRNChnjWuNSytrlUlaz5fZOXPLLY5b88sSNCgDOqE4ECXEq4TsYXEnNzX+tQneb1arn1OEbCYqv2RSU33ezSym6uDnp/k9fY2Y0xtBBDdcjAELtgPGTIk7/jK4ioCIiACrY0A7yy5Ws5Yn6sRqKmvjwYL3t8w/hYjvPP3XiYCIiACIlAYAXw2GkLx2aKREwKfL5cl/Vi24yvm+ruQ69hC1pXYjMzdarw1vqZ/7TqcZpwaog9QTYhUwGlMGq37JCA87rjjbPjw4TbawzYQD4gC+P3vfx+GTTzqqKNCTgS6L0Q7/vjjQ3++Qw89NCQvJGcCrfRETWA4ath///vf0N+a8yJCIIBEASHskOMfnG2c3qTyw25ESCA2oOpg5BQgkRxGNw9C/GJCw7CyAf/gUJOwI+mM49BgdAvIZzCHfYY5ghAB4Q2xjTUS60WjBeHJJ58MIk2y+wFOLKIBjjZO7hMvPGmzqufZ5nv1tE2H9wiH44xzjUxcFw4/TjHPSbbh+BNZQf4JnHY+03LBsJvUgRYN7sXihdNtn10G2jZb9bQFqeXW4fNU+EJQB3JU8GVBTKClmSyuLLMexggU1JuEl8kvFtuo22abbRbq+sI7L1jZ7hXWa/3eVpPySBNv1I6hRNmJVTgf9aVcniXOy0R9eLa5Jrrh8J0oNAkh4ls836QxkzzlylJbNmq6dbpvma23vI/NmT4njLRRl4DAaCMIaPxY0B0pihGwoG4k+WyO1qns+95aP8fM73SdGrd0QnhubLF/+f47y6ORqq1qcVX4XeJ5Rh3mN4pnhN8jRDSZCIiACLRFAjj9xXzxrC8julHKREAEREAECiMQI6Kz/WCOxo+Khj+TbTTS8n6Lv5P0HdkPX6hYVpLR/YKhN/v6VOsvBweHk+MsYqgfsfUvViDpvLEvDmEMl0/ui5JC6HK0pIOOY8ofOCy2Vsf9iKRACCGRI3kUUL3J+VCX0ZWCoRNjC3zcl/qf5KHzRD4gEuBk0l0DyOecc04YkjHZ5z9vxEIsMMccVuQnIMIgGuei3GTZcVucwxz2GUNy0g2DaMf8DfXx8HrN4UBLPoYDRV1pUSB3BkICDjjsiBjoPKfSzjhxY/vgnTlhfyIfCD9nYpn7HUWMZJQF54BfHAkDwYLnIylUoLxRj0037GfPfTjFqhYtt6r+ZVZe3S6cizLo68kzQW4JvjQvrzwv5fBCxDbKxilHgIjCAxE1CFqcn2tjeeYYH3GCR9l7HC1auCjkiwgn8n+Sjj9fSL68dJ9J1veZ0Y+GyAcSYfHcc904/sljY3nZc54BRAtEqHk+AoZ1KrWe07uG87Rv19522XWXILTVJYBRBs4u0T2LOiwJ3Vp43qgHyiYRIs3ROpV9ra31M1E8PKOIl0uXE2bm4sP0Kuvap7v1GdA38OWZiM82zxq/HXyv+e4U8hy0VjaqtwiIwNpNoDnFbfLuJI3os+T7ZHKblkVABERABGrz9w1a2c09H4/om+G38/6L8Y6LxciIpG/H+mKJ0T72BsWtNJJOEkW/UuBANKDrRQzXoDK5ujbEw+M8ihI4RdFotSZUuT6GAoPg8IMf/CB0DWB0CoxuH/kMh4B8C4xSkcuOPPLIsP3RRx+1I444Iji11JPWcqIvCGmPIggiRkNGFmBYy+jgUweWaYmv02AOe+5BNO7NPBchihxpSMs59xahBF6IDTjdOPu0olNfWuvHz5xoKyZX29/+/rH98TcTQsTCiy++GB5SBAGWiZbAKctlvCTQTYDsqjjOtDDHBxxx4p///Kfttttu1q9PbReY/73yha2Yt9zKp5QFcYP7gNDBiwbdLBA7xvtzxMsI0RPUnS8P14MzSJlxGjZsWPiScB18eSbOmmQLF8+3Xr7f+DHj7J3X3rFJEyaF+mXXn8gYnHrOHwWI8R8/ZVO/mBecUEQEEj5SN17KVtdnFja04PCc3XH/nbasssrsOc+pMXGR0V2oammVayKpkIOkrhFceGZxcj+c+KFVTpoX6sePBkIaXMlHQASSrP4EEKn4nXvllVdqswG38x/gSSvMupVZj3Gdbfb4WUH55dlDoCB3CjlUuCfcW34nZSIgAiLQ2gm0tK4N/M3L1YrHb7FE39b+tKn+IiACa4oAPlS2xS7y2euTjejZ24r1uTa0IZbmfmBy5AtajXEQ999//9D9gqSThRhO21577WUXXXRRGJkAp+pPf/qTjRw5spDD0/s88MADoVWYhJKnn356mHC2CD9PdmdgBAwEDrpRkKOBsH5GCMhldBMhb8Dll18eMtizD4IJAkSc6N+P4VzQskluBJJK4tzmM7qnRHHmhBNOsCuvvDI43IRrs0zkxeossK/1xb/cleiYVSNkvtxezyWuIUaooGLF5IUwQHjA0cfIY9GjstQOOnaAverOFw4/+6KMRTUsZk+l5RhnPa6PVaLFHkNIYhlnnX14maAcogdg1q6k0qbNXWQ91mtvVRPdOV9cmz+CyBeiGhBL6LrBKBjUnXojmPBl4mUJcQMxhdB5rgNHnPMxR6AgUmPWUg8l6Vpui2cvts8/+TxEIyAwUQeczmg4oXBAlImRHZ988D+7a9Tr1rtP7yBAsC/JLenSg+O/Oqc/viTRxWfKnClmHuDR+Y12QUihKwx9Wwe5SolohkgX9491inOiVfhOWq8Sq+i6TnCUaX2Pfbg4nuuRNYwAzxC/DR/NdDFhukc/YHNd5JqzPDwLPEcIahg/2giy/DYyPDDfj0KEqHCw/hEBERABESiIAL+rNGYkjYhZ/h7KREAEREAE6kcAvyhpyc/RP8RXzP7d5Rh8o9idPFlGQ5YzBQhy9dXm6wtl4YCR/AenHif/wgsvTCcCyneyGLpxyy23hJZpwsJx4kkuSQ6H+hjJJ7/xjW9kHLLddtuFFneSW0ajGwWOGxEM9E9BiKgr2uLEE08MDkQUVLhOuoskJ8rGKcShQ0BhZIxc6hH74VAjsMTwd3Je4JQw8gNlIrzQhWS1lsWf/VPuj6fqHkVltcXm2gHnifB9Wn55yBBfcPi5FiIhyCtQ1bXGbrv5Y5vf1xuC3TEjygCnHSEhhmPGxJDsn8vYj+N4WcDBQzwgwgBRIkaavP7uNBu0bheb40KCDW8XzgU3nD2+BCSv5PmjHMQIBAe+BDFig3tOVwv2Q4hCnOI5JPICAQhBrEMP9/o9JyZfLo6jiwX7IVggoESjbl/5ylfC9XGeJ158ws/niR9rvBV85uwgwnCO2PWBSAuiQFZnPEucp2Izz5XhUSVde3YNAgndMqg/YgnRN7SsR7bJMhHU4PXWp29Zr/ntwr6IN0Sh8DJGNAjHcd35BIxkeVr+kgC86N5DThOelfFjXSTqngrP4rZT+vr6nuGZI9KGe819Ys73m2eM54lkvY8//viXhWpJBERABNoQgVxRCG3o8nQpIiACItCmCNCQvDrDl6KhtzmsLOOk7qCFPvIrV+Kg0fLPsH9MtPqhRjNaBBZby1fuHhzLuIxjR64GWqpxnHDYov3hD3+Ii2HOSAXR2BfHD8uV64FycACjxX3j50Lm9LOvq689NyRZLoIFSTZZHw1RJYausJ7W9+R28lUQZYEDnUz4EY/POQfRl6eo3QWhKlOsynlofVbSyg9nHKnBgwcHJ53jcabpJoBDy8vGrA7LrGLzDtZzcUVwzBBqWB+3J7vY1HV+hBvYoJoRBYAIATtGmujer51t6s/+y5/Xtmb0+qQkiD50u6C1Pz43CBycF2edqAMEDJ4DolVikk+2EzaE0MH9o4sEAtL8OZNtaftltuHE9W1Rt9oQJLqaUA/2wYGMxr2iqwj1DaLKzGk2rjxlJVt4borx5SECgu4n0diPqBqc2D322COuzpjznUFhhHvFJBdC3GqqagJTroOuKJRJXdiPiJ9ssY5uR0Q7zPKkIEs8V0a3HqXh+onEINoD55gRGahPLgEjo0L6kEGA+8OzwDOxYr1qm1/iit+0Gtss1dEWz6q0GQvnhO8LzxTfAZ4LIm8QqqIhXPCbV9dzEPfVXAREQAREoHEEkq12jStJR4uACIhA2ySAj9cQK9hvbUjhK4/JFCC8y3PS2cWhw0kk1B6njlba73znO7bTTjsVfErCltuC4djVZbm2EzVQL0No4B4kDVHiS+0muaVeywghySgF7gsiBPVmPa25CE44sThjnTtU21QfgmPhzCobsLSrlbUvCw4wggB/+HH+Y7QLggCJP6MRdZA0WouJssABx4HjOJ4rBJDpPrTn0ull1ndIR+vcq9xKJ9dGMyAqcJ4YkYGIQZg7IgYONnXfZpttQpQEy9Ex5DyxKwaqHte9oP1s6/yxJzrdqKulKkpDmXR7oJ50CXn22WfTTjs8KA8+XOsG63sEzMTZtv5yH5azW+1oGFEo4HiGLSWKpC4j2gIHFUFl2uKPbVn58vAZcYRnhNwmlBXnuZJrwSOIMZ4PpP+APlZdUxq6pdAthvVcB2Vw/UTiZAsYddVvbd/G/eFZ5B59sugz69O7k/Vp196mvL/Iers4hbCAkIoRacN9iz/OPKPhvvi2en/fQ4n6RwREQARaFgH+VmO8E2AI5UTZ0ei0JnNEIOjyvkm0H+8l/P2nLg19qQ4Xo39EQAREYC0hkPzdJN9djNjmt5yI8qThZ+EPZkf740dRTlxfrPwQmQIEUfRZkfQXXHBBSAKJ+ICTU9dIDskL0XIDCOTgX+MN5jVFGPVkzpw5wVGOLxY42jhTCA4kyMSJwrHC0WKo0mVlldZ1frkdv9sge31M9/Dg8QASZs5+dIegWwSGGBFfVHDOeVgxzkXuBspDdBjk3RA4lueICAjEhQN3bW/j5yyyJe2qrdSd6j6du9tYjzQg0gGHnC4GOHsDO7lY4dupNy8gbOOaEAuoO+t5NvnyIIhQV0QzbJ3+U2xYux4230ES7cC+dNOhXiQPZD+ec5x8zoWYwfVR7rp9XTRZVGbdrbeXuTy8ANENBcGF8Hu6dDAniiFfBAQvbNSnQ48VXrcuNqR/X7MlfUO3FEQS6sJ1wIT7gdCSNIbfZB3XN7BfV+tVtYFVLquNqECMoT5cN4IhXV34YfnlL3+ZLELLdRDgpZaX6/BseN6TzuuUW9/OHW2jnhU2+Ys+4TvB88B3g2cEwYI5xrOIeMH9494RTZHvOaijCtokAiIgAi2CAAI4f8MR8vlbSvfM+Dedv1Gxj3BTV5a/e/xt5D2C94aYLJx3Cf7GkfNIv7dNfRdUvgiIQGskQC5E/Av8tPgbjh9Pt3m6ZuDf4ENFozENgYF32myjyz6iA37Gx97gusJ/g4thmW3r3pU914gLtOzhpEp8KAby/GUE9tnpBAggKUIQCQ4SUQcYzjB/xHHyCStnouUcB5ZtOMyzZ8+xqvkr7FVv/cc5w3Dco3OPiBCjHigrGg88okNsDeah5jw4+7QwcxxGmQgglUuq7ePP51uJaxbzfMjPCVMqwxeDFx7KZX/qxzIvIXxBWMecFyHKRsjAqB+fOTfl82Xhy7R+h442c9G84JzHL1dUAaM4Qr0xzsGXjeN54elsZdZ+aSoID1wL3TOIjqBOGPWKDmxYkeMfroU6dixZYjXLamzajKp0NAi7w4XoBcriPjFP2p577hmcXBzgJUtX2KIltfeMfbgfSYNJTA6aXK/l/AQQd+DIfayurLaKdmWerHS5TZi8ODwL3D++FzzXPBc8Z7wUx2ebknnW+CwTAREQgdZOABEeQ1xFoOe3L4quUdhv6mvk7x7CL0Z9eGHmbyWGOCITAREQARHITQChgXfXLu1Kg++Hj4cPz/suPgZ+VBwogBLYF/+pv/tC2Y2g0Ydj/br9vAG1SFbio/99aQz3SORdkZMeMopEHFniy5OtuSWcB5T0pOEs4GjWx3A+6Gufz9EgCiBGGMRyUZqSOSvi+lXmMId9cshN7k03H1qyyyp713sFf7gJJecPOg8T10JLLteCM08oP33gMYSI7uVdbWn5Cnvlwxm2ZPnC8AKAg0Y3AkY44RhGAODFAOcaxxnDkcNBRwTgXOwHEx5ukkT+5z//Cc42LfW8UDz9zjTbYcOeNmt6pZV2924uPkRlcLT9OEY84YvC8W9PmR7qyDacbBJmkrCRa6EO7Ie4wHlw5jkvih3zJ5+fYuOWzA/15JzUlyFYEWT22WefILiQ/4KWaxRC6o04wnyWD5G5aOkyK2+XCt00SI7Js8w5eQnivNSHHCH5DJEDmzWjk42Zt9Bmer04DvY8G0RzkAOCbiGsj+JGLI96IQrxvE6fttgRzQ37cD94GeS5gz9zPtMSLyucAC/Z3AdEhBoPPhk31b/HSypt+hTPF1K5NKjEPGe0DPI94RnDeJZ4KYc90RM8OySjlImACIhAayVAd0xGeCKyD0GeOb9zRAfyd4q/V2vC+LvHiGfMeXHmhRpxnVGfzjjjjNAAUp/uwGuizjqHCIiACLQEAvg0vKN2Xqe3DRo0KIy6FyPAGcIYX+3QQw9NV5WGNXy/pe5XxHfcuJH1+DG86/bp2y/8TYjbGjN3STkxtOSylKWm+eQjFhbTfvOb39j111/fqCIZnYBw+YYYCSEZUhPD8T7//PMDSPrD0PIdt7Ed55QkgDgld911F6uC/fa3vw2ONQkHUZKSggbOIX3u+SONkz9y5MjQes6BOI2MiEG5dRnMYW9+D9Lm96aGSO9a3z69uiELOLax9YDjo/OEE0sYDg8f6hZiDc73uAmeFNKrUjHAu0gsqk36ifOPk8vDiJMbIw2y64OyxoTxIPPg4qQjTCBg4HhzDkSIeUtW2McTFtg8d/I/edWjLrosDqIC9aBOiAksw5h7h+PPCxLlEmKEUf8oBuAEUjbXh9PI8vq9e9mCnotc25mfvlaORcSgjykCRxxtBScUkQkuCDIvvTXbengXjPfGjgnn5Ly8gCEWcF8ZppbnqC7jJY5rpm7zF1Ta/IXLwjmJwiDKgm4rGNcKpy233HKV4uhqQTnm3XGm+zEIKzyjvBTybHH93BMilWT1I3DYYYeFe8vzaLM9CaWLYR9WLbBeQzrYUv955PkgjI3vD6IUz1uyD1z8XnHveFmWiYAIiEBbIMDfeqbBnsiMKIjsPsNr4hpj5C1/z2NdOC+/xTEqY03UQ+cQAREQgdZEAB8t/m7y20njJnN8sly/5fiJiAz4XhyXbfhVvO8WKwquxLu2Z1hqgvudmTkEM7Y35MPf/vY3++Mf/9iQQ9PH3H777SEfQHpFgQs4aueee25QzDmEHAYMmUmfGJzMK664IqhAOJ44uAceeKDde++9AXBUgWjxpgwiOXBGGBmEkTHi9h/96Eeh5RrHFYeXP4zkFMBoMcBxT4ocYUPWPzCHfYb5vanBt83sKJOxS0M+UB8cWhzgmMeBBw5HFl44WDx8PZa2sy7Lyqxqg+VBAIAXDnMUA7Jb6nPVhRcEnG/EBx5sWlbgRvmcq2/XgTZoQEU4tMrzGnywtDZSge4dDMMZjWMQSPgC4OiRgBIhAKGDLwRfKLbRWkO9cOYRS7gX/YfsZCvm+TCVuy4L14gjP9JFIoZ0JQICUSMaLzs4opSBQjik73Crdv4p/4f6sp7zUx8mPvNiVpfjSas40RkIFe3pcuWjamDUj2O5DsriWmCV66XqyCOPrBW1plRbzz0rwv4M4UlfWFqEaBlCkEBQUf6HgLde/3Af+bHeaf0drWsHT7zio5S81nGBlbkIyHPHfWHObwTPGc9QFI44EfePZ08mAiIgAm2RAO8ILcl4T5GJgAiIgAjkJoBvQN6epPH+WojF7uqF7NvQfUpqaru+f3n8FHe2PvOPnvT917/+tZ199tnpbTiFqCY4rjiHtALj9JAMiOEVMVoGB7tafuWVVwbnjyRBOPlEQWBcFC3HOIaUdfPNN4f18TiG6CQqAQfxscceC9vOOuusELr/i1/8Ih3i/Morr4Tzcn6GxCS8P5cRxk99CEfB+KNFKzGh9Kg9dCfAonOJY/v000+HSIawwf/B6bjpppvSitEBBxwQ+s7gyOOU33LLLSFqAscRx5qEhL/73e/i4UYL68MPP5z+vMqCsw7MnX3SuDc1tWkbkqsbvRxDKHGoYpglDhUPK4aIwj2eM7fSerTzPBFveubpRbXJ9hBuuE4mwniigIG4kM8QdxAtyJ8AH6IGOBf8KW+rYb1t3HRvca72+PcNysO+KGxES1BXBALqSTncJxxFnHfuZXweWce9o1sFdcEhJJKA844ePdq6prqG6vXr3y/9heSFaty4ccGxjHWnfpSFw8l5OX95u8623969gnLIesQNngmWGXEi2Y8qlpM9J3yUZ66SiKM+qdD9BbEDcQvnl2eeMlnOFcZPBBHX27NDT/tkinfjcJGEa+UZ5DvAjwoRELnEi+y66HMmAcQj+PHMBCGyswsQG7goNd6jckbMDFxhzL3hmWAi8Sf3ABGC6BO+30OGDMksWJ9EQAREQAQaRYBuFtkv0Y0qUAeLgAiIwFpAgPfW1RkNz9lpAmh8xXIdz3tysaykZqAXhlMUbUnKSj70aYIFgeG2224LL95sRkzA4aO1GVGAF/fXX389DEV40kknhRJwygg1p7vEQw89FLbRDSHmW8AxJ/cATn6MTEB8iMchLHAcLeU/+MEPQplEE5CQ6Mwzz7QbbrghrDv++OONVmGEB+p04YUXhvXZ/7zxxhs2YsSI9GqcO5xryr7FhYNvfetbdtppp4U+3LTSX3311RkOKQfiCMfr4zP9EhFdcNpxPnAkH3jggeD4Usb3vve9jBtHt43YXYDjsw3WMDdnnza/J9ybEAGRXlm8BZx0hAac9igekFNhhx12CNdF5EanHp1t+vgltu9+61tZVVnog4mDHlt+meMEDxo0KDjCOOZJo+sE4T50WUFwwEFmH4QHDGcZkeC9z6qsb/fOVtGxzNqRCLVrbR4E6sY5YIpYgDDAsYgKLLOeiQgInrnoHLKOer399tvhuWPf8pQ7lJU1PnpEpW282cahXMQwolrYNxriEsY1xS/l4YefYO0qe1pZaW23B0QJntMnn3wyRDAgxNVlfE94TvhSd57iSbQ8rn/xNstC9wmOoyx+BBDLeF7zRVNwHiJwql/2XAX+I4ADTD0RcbiHRD8g7sjqTwAhh+5T/DZ8deiB3hVjRW0h3T1Sp9yThzpvfvcQirhXPFNEr/CMPPHEE+H7nks4qn9NdITCEeYbAABAAElEQVQIiIAIiEAkwHsC7xFJowGCfDz5/lYm99WyCIiACIhAYQTwU4jyxtfCr8o2fPViWUkNgQHZURAeAZF622yHTbYPL93ReSYPw9FHHx3OjWN4+eWXh8iCr3/966G/PC240Yhk2HnnnYNjH9cx55gHH3wwtCDGBBj8IYnGdhx+xAlUbxw3HANe/mlFjmHOOF+0hrOeHA933nlnLCJjjsOG0xCNVn8cbbphEKXB+NI43YXaU089ZQwPFSMccKhxiGGDGPPss8+Gbh6XXHJJukgcb+qRyxj5AtZG1EnSvMrh3nhQQDGNh4roAlpzcbb4jFBARAo8EQqYyC1QurTEZpVV2c5b97bf/214aB1G9MHIn0A5vAjg+OcK60E9I/KEexj7zEf1jC4gdDngxWLqF7VDfyxc4iMMzK22qhVV4RzUB2cfR49IE843wkUJjPMhSjCxjFNI2R/4EDEIF4gelI84xbn799zASqtqQ0gHbjzItt55G+u2TrcQjYNgkXyRoWsGzx4CE3XABm60tw0ZOMC6dukUvpzUC7GCqA72W51RD64B4a5DZx/WZPcuNnb+ONtimy2s/8D+NnHSxFAeybXyGa3zCGbhe9VvndCNhu8DdSGSQ/kf8pFb/XrY8ZuG8Bla277wH9kSP27BMlvYcYkN2G5gEBr53vC88H3mOeG7zfOGwJp8hlZ/Ru0hAiIgAi2PAO8xLcn4XeXvfNL4W8rvrkwEREAERKAwAtEPS/prNConP1MSAgT+Ntvwo7INP7oYVlIz2B3dzTwCol0iCmKhR0C85tPHKTvlxFNC5ANh5rTQHnzwweG8RCLg2NMSuPfee4d10bnkAw5tLsNBRwDAacMpw5LHcdEYTicWHcDwIfEPgsPdd98d+tXT8pjMF5DYLTjISQcR0QEHAwGD/vMvvviinX766UE8SB6Xa5ks+CQrvPXWW9NRFfE6SVxJpAOREXQ5QZCIRgt9zjB993FSH7mf46zNmafN7wX3hHvTFAZvcgYgPnD/EBAQjxAK4I3zjkBw0B5fsTJ/AP9060cWa4cTjAPMH38EAZwxHlYe1KRRNlO02F0HRxmDGy8R7DNjtjt11WW2zVY9rR2h8DuVhqgGuNHtgGeBOuP0DXVxhDwUOIK8lCBycA0Y5SGmsD/l0mUIgYPnbNb4Wda7Ww9bgXrn/5emSm3Y0NpuOeHgxD/UFRGMyBZEmvh8lrbvZRMmTgnPGg4n277yla+k830kilhlkcgajPpUpFywYGjPke1s+uzpQbwbN3ZcCOO/9tprVzk2ruDZ5YeCL/+G/YcF8QM1kmvGEY7fnbi/5vUjgJjDc0O0VefuFZbq4+FrFWU2c+NFNmPS9PD80eWIZ585zyPRVTzLMWStfmfU3iIgAiLQsgiMHDmyZVXIa5P9ftHiKqgKiYAIiEALJzBo0KBVahgjHuKcHRAd8LF4H6aRPRndTmNvsX6PS2rcd6se7mcckFWv8e4YP5eyb+52jP3jH/8ISRQZupCWcRzAH/7wh3bjjTeG0HtC3Qs1ujt87WtfCw4uIgBOXqEWHUH2p/WZIS5p8SZE79hjj81ZDNti9w92INydyAsiJzAcCISD1V0DyhHOJpEZ5LCIFh2PpCLEcjIaBEcVZz3bUpOc8fPupDvrDPN7wT3h3hTLEAmi8QCRv2Gw58ZgfXSgcKoQIHjoeMBYv+xzz/8wb6nd84+x4XAEgaiW0YUBh5ooB64xRsqwY7xXRK/wMMMJQQBnmZZm6kD0BCLW4spS69Gtg7359izrUlNmC8uXBoGI8hk2BiPBJ6IIIgP3M84JgY/dJngeqDMiCdfGaBlcE2FE1GGxr585eYZXzscVLy/1Lxhjn9bmLQkLK/+hDIQNWCCYwIN64+DTQo7AgYhF2fUJu49RMF27dLX2y8utdFgXmzBirs1eMsc6965I5yNJ1iW5TEsQItEg/xFBIIEP0TcIbDxvCDL1qU+ybC1b4M+zyfNdPtvDH/p7XhNPXmpDKmxWu7mhqwviHM82vytR8EGE47mQiYAIiEBbJMDf6vh71xzXR8LlEJm28uS8nyQblpqjTjqnCIiACLRUArGRPykexBH7khFlvL9mWzwGX6oprYSm7RrPPVg9wr2yikQUBENy+iAEG0/f0LbouXnoOnHMMceEusRK4YixTLLKQo38D4gO/AH561//GhyoQo4FHAn/+ENI7gKEA7pD4PzSIk8rfi7jj2byDxd95G/x3A9EP+BUks+BPAB1ORD0D99///1DtAUh8Di0TChEOCJ0QaFbBs4gQsVVV12V4Qji5Ce7gVDP1CwEHp8z0ENy6E2/B9wL7kk67IADGmnxgaIeOKtwwVnFmaJ+PIQ4ttwXrhcnjLwCQ3YaZu16dbB7Pp9iK7xbCMIB/OMLCS8GOMMk5ct+IWAfLF477Ilq4DyUE8+JeDRrfo1VrFtmPcq91Xmc52nwqIVYZ8QSIja4X9QNMQMBCfZR2BnsggP15ZlEoOALxjNCqzb7MW/3YY317t/bprqYUV1ZbV0rasUvIltyGcID+SF4xmGGIIGIQFIshBeSStYn7B5GCDXdyrt+GVI6pLPNOshs7JSxBY1ecdFFF4Xnne8R10d9EEJgjxhTn/rkuua1dR2/HzxDcEToGrntSCuZWNsVyL8MNn3TRdZ3cN/wO0HUCROiJd95RM3sZ39t5ajrFgERaN0EeC/g/Yh3gWxjfXMY7138zcP4m0eXufo0XjVHnXVOERABEWhJBPCLYm67ZL1i42hcRw5AfCZ+Z+luHw1/MdnYHtc3dO7NfC5AeDBA9Xa+sJlPYQ1r3eZ5NwyPgjh/5P/ZwvEL0i3NvKjTl51QdZy7XApKbQGr/nvNNdeEKAL+eIwaNSo49avu9eUanEAMJ5W+iXRxIOT+hBNOCENm0lJN9wfyQOQynF66fcTEGQyZiWBB1ANOA90v/vKXv4TPuY5n3X//+9/Q+smNINM918/0zjvvhEM4P10saDmnpR3n/rLLLksXR70Z7jFaylMelLyIAOHX5ozTBnu/B9wL7kkxLT5gOPA43NQVJ58knkQp4LwjJiAQkOcDp5aw/u8cebrtMmgn22b94Vb58eIw4gj3mymZc4CIhBj1QL25b0RLEOWCeIDIEZMoIhKMHDkyCEPxy1DRdV3r1M2jAqpStvTlJVZdsSK8aLgUE8oiEgEBgutAmEieizIoE0GALxj3if1IhDp69OiQN4HPX93+IJs7f665tGalJbkFq/jyxbXxBeRcMEGQQQTj/jLM5fe///2CBIN4DxEGqCflduvazQb33cA6VZZZ9YR5tnH/Da3/VgPjrnXOKefkk082RsVANKNLFMJG8l7UWYA25iTA84tQyT0mygGmnmHERQhPRlm5zCNmfOzjvuuHnDF818m7wXeH/YkKi8Pu5ixcK0VABESgFRPA+eedAWuOKDv+7jHSEL/RCCMIIc1Rj1Z8C1V1ERCBtZBAMtqBd9tsIzI9+tlxWwwywG+Kv/v4cEm/K+7bmHnaC6tx/2fFbjVW+oUXNz7hFE9N2d4DRtq0B7zV2CPWY4wESRjpk47hoJF0EsOZJTIgaYgO0ei+QB4FHOHYDSJuyz4u+RkhgT86sWsDXUBwAmmBjDkAYjnJOfkmcE6JnsBpwHFGcCAJJQ5yrhvC8YRiR6PLSLIucX2c00qOyEE3AVjEbgNsp77kqiDxJpaa58LDSz494YydbYb1rwn3gHtRbONBoosErbuIONFwoLg2hBws+bDymYfv+COPY9EeK60dFhWhImmPPvpouE5Y8pASocB5iBiIxmfW49gxZCuRCyzzQoFTv2n/TeyDCZPssGMG2FvXz7buG/SwMZ+MsUkTJ4VoA54rjmd/kv/hBHJfub90CeL6iMTgnjLnmtgP4wuGqMIIE+XV5TZ5/GTbcpMtwzbKi7lIwoqV/8CDLyXlRkNsi9fUmEiDHTbf3u4a+6B1W7ejDd3GE7Wu40N8dqp/qBMvYI8//ni6/ghGsoYRQBDjGUVYjM9w1+kdbOYG8630+UW2fPxSe3HMRDvuuONCNxgEKYSge++9Nz0EccPOrKNEQAREoGUTyBdhuiZrTSMU0YcYjVeN+Ru8Juutc4mACIhASyGAH8R7bDR8HRrgkoZvhY9FtAP+HhHr+Gk0AkdBIrl/Q5e/jHfwaLsa98mq93CJoWeUGVYWO8EjIR7xyVvtcaCj4Wwz1df4Y5YtPhRaRvIPIU5AXeIDZbIPEQoxCWA8D+XkEx/iPvWdI0QkxQeO/+c//xmSdIakj4gPRD44S3OmGebMYc89sFUjHzN2bcgHxBfUq4svvjjkRkiWASNaFegyEfkmla+4b+wSET/HOYIGjhsRDoSl47gTlp58NuiyQJm0Fp966qlBTKLbCudkP877xSdLbAtPRHn3TfvY7DGTbP8D9w5hlyQchS0iFJEsRDoQERFDMHG8R7oDiWpH+dSFbhvREB8Qr4h4GTZ4mHe9+DK8hGuiK0O2IUpEEQJufBERu+I5s/cv5HNdYfp9Oq9bSBEZ+/AChuCC8UJGZIascQR4dmJUzua9N7Nd+uxgPz3uR1Y1plYgojsRP8j8iGOIXTIREAERaCsE+LvCbyCjYSHQY435u1csLvwdpyGHv+US24tFVeWIgAi0RQLxNzu7Kx2NzEkRAd8m/s5HH49u9jQQ02WcYAH8LRrviWgvpn0pQHipISHlLj7f1QWILnlEiKdchJhezCo0fVmHHHJISJjZ9Gda9Qyc+6abbgrMSpxdTvHBWcO8GvZFTDyZrA3RIqO9O8Lhhx+e7jrCdhwowit5AFHBouNOa3+2xS4nyfU41bwYkByRkHSUMyIi4vG8yODM07pM1EP8UuDE8aJDme+9914YWWJh95S9/4471C5gDBzQ3jpUpGzoxkMtjgzx5JNPhkSgJB5F0EBw4IUkhmLyRYk5KhA04rk4D5EoOOucl2uOXzi6niSNfWPd6daAmMK+5JXg+uK5ksfUdxke5ePMFi+pCtEP9T0+uT9dMUi+ecoppyRXa7meBGhZiz++e/uoPuR44fm65MSL7OITLkiXxv3nx5vnhvwxxx9/fHqbFkRABESgLRDg73qMiuR64kssf8/5G9kcxnlpfODvuMT25rgDOqcIiEBrIRCjxTbffPO0v0NjM8ICIi5Gw+UTTzwRfLTY8BajvGm0prcCfiMBAwgVyb8JxeCQIUBQYI1Hca/Y2+c7ugDROUuEmOhREP/1/vOPuI841nf27tGtxZorg3O3im7WcWqHwAx25gwzzBnDOjBvwgj65EtDsiUeZxjnGhEivmRk1K+AD5RNFxwezv322y+MchKTesZzoaDlMrpKENpDdMPSL3wEDs/NUJKq7Rm0dMVyW2/d9cJhlEOUA+IGYgb5GYhgiYkgozCAiMK+UUiJ5+SFBWcd45rpLkOXlCOPPDItVMR9o9BCnhBaxB955JEQ+km5SY5x/0LnURBh/y4lPhTnSpv30VT7xkHfiB/rPefaGlOvep+wDR4QRad4aTxXWDZXPvMCzHMTRbR4jOYiIAIi0BYJEP3XEiz5d7wl1Ed1EAEREIGWSIB3WoQG/B1SBGBEPxA9RmMuFiPJaLDF1yFKnUZXurxHoys7PhGCRGykiwMMxH0aOk/ngEgWUNPftYUDvbLVLjS87CLEooTTPCNlKW/JL/Wu8dV7eqv9Jn5kRfJoLacJLPSIh498+p8ze9vXLkhwZKcgPtSyhnlzGY41UzKRIeGXSeNBPuecc9IPdNyWdKqTAgaCBjkbokWHLjp6nC9abM2454377P4XJ9oWW9ZGXyxbmKlwxf3icdlzWqaJwGBOfZPniPvG+hK1kc+Sx+Fo8qVDvMjuxpPv+HzrEUlizoYpn02xfvvWdhMpn7BiFUc3Xxla33QEGNa0EONZuPnmmxV1Uggs7SMCItDqCNCQQBJn/qYnu2a2ugtRhUVABERgLScQ/S8aU4koxxchqiFG8BI5jiE+4P/EJJSsu/DCC+1f//pXECuycwSyvTG2SgREKMzX1gx2x/hgb53f3QWIHlmREO5Ip152EeIuj4Z4wis90Y/K9BUbU6fWf6yzgAlsYASrVcQHZwrbwNhZZ4w+0sQEohMeT0NkQXaSzdhFIe6zujktwx9++GF6NxQ1un1knyu2pGSv58DBGw21T/uW2HvvzLaqch/qsF1Zuq9SUhRInySxwPkJIYp9mxAg9txzz8QetYt88dhWH6OlmySm2a3h9SmDfTk+CjAnHHG8zfuoti/TcM81IGteAtyb7B9XRoLJZySgbOzzkK9srRcBERCB5iQQ/059/vnnGV0zV/d3uDnrrHOLgAiIgAjUEuD9NHaroMGVKAYizVnPlMxfhg+I2MygBEnDV0q+58ZG5uyklclj6rOcW4CghChCfNUjHfZ1AaJvlgixzFvzP/EuGf9xEeIen7/kTvdsPy5rt/pUptXv69cOA1gEJs4GRgarpDlLmK5wtgg9a1J8oBpJJzxGCmS/WOCMJR11HuTsoQbjsfHSkmE5dHnBaY/n4mEn1wPCRPZx8fjZn82wkiEV9uf/fW7Pzp1mU8dNiZtWETLSGxILyWtgOfnFSeyWsUhdqGPSGDY1aclyk+sbssyIHBg8tyndxMbd92ajul80pA46JjeB5PPOHiQ+lYmACIjA2kaAv510NeM9IL50wiBXw8HaxkbXKwIiIAKtgUDSdyEPXuyGnl13fKCXX345REDEXHnsE4+Pv/vJfBLZZTTkc34BgtLcb65Z3wWI/X36qnvXQ30qzTrNHG/hf762pb/0fvel3/TD5mftsxZ85JpLPDE+DELUgzMxZ5NhsHOGsIQpbGG8po2XC3IpYLmEhbrqE49jHxy2pChBq0kUIRgFA0ueiySR5GbId879d9vPqj9faAtG9rR2+/W1CZMmhDLyCRZhY+KfpHCQK/qBXakP54/GMuuSdt555yU/FvWlK2bypu/VXdf+y/501nWrnD/j5PqwxggwpGs0nuOdd945ftRcBERABNYqArys0hc42TVzrQKgixUBERCBNkIgigm5LgcfiAgJBhNI+kdbbrll2D02JJNPgq7KSV8rV3mFrqtbgFhZSk0vFyDcR1vxde82sIOLENkjZCx3L9qHlUw95tEQ//TpAXfGX3Pf2gc0aNMREUQ8+DVyrVxz6b9qGYQhNmGSNEa6cHYwhCVMm9MYOhOLSUiiwsU6HP7LLrss40GMD288jv3iOpYx8j4wTizDVmIxsoBjeHBJHonlEweSiRiXLay0Puv0CfvnEyzCxsQ/5GiIrdhxnticXvzoI0/MUYchpMTj47yO3eu1iS8645nHfBbZ4ke9CtPORSVAV5vY9ahYIWZFraAKEwEREIE1SCB2aeSU/C2Mf9PXYBV0KhEQAREQgQYQoMEz+jDR18tXzLe//e3w/ksevWg9evSIi+k5efF++MMfpj83ZqEgAYIT1HRxEWK7WhGi+kB3MPv7lH10lTvd3uUg9fBKIeIe3+U5d9KJpF/WmGq2sGP9Wrgmrq3UrxHRhWsO3S1gkDQYOSuYBfHBGcKyuY3WDUSH6AhHhYt6xaiApMAQBYqk8pXcznEIAIyEwagWbIvOdTzXpZdeGrplxIQoHJM09m83emlYVbK4xpb7fwwTs7ovTiyD4wkfItQoXlfclpyPGDEi/TFX2ZQTIz1gUVdZ6YLqscBQqLKWR4Af62eeeSZUjCFXi33fW94Vq0YiIAIikJ9ArveC/HtriwiIgAiIQEshgC/DiBcPPvjgKg3G2XXET2PfZD7AKF4kfSKOo3t9MSznKBh5C/a9a4a4EOGiSI071SWvuiP+nu+d3dWg0p1w7+qeGu/O+Zu+7zCfBnsEwCCfD/Cpux+TLV7kPWkL2VDt1zPXpwk+jfNprIsOn3rdyCOYHe0Qq0yiyS2c1/a185pVxaS4Z7PMk44wDxijXBCpkFxPxXh4SSiJITLEkRyiKBE2+D+UwTYs27GPZbJPXTZoG09EafNs4T0T7MUxH9e1a85tfIlWZzHDN9eaz5KRHvn20fq2RYBnM6n+tq2r09WIgAiIQP0I8JvIaBhYDMetXwnaWwREQAREoLkI0KX8iSeeyOgun68u/N4z4gWW9Pv4HH2i7PVsa6jVT4BYeRYc6RrvHl0z0EWIjVxLeN03fOJTcrhO9sUx90gBogVS7LOBH4MQwdzzu9X4UKM1jLjYjp1boFV5vb3XQGrqymuY5HMXHsznq1xrsvo+vKb5KJbVIzzywbvQcK3WINLJQpt++bvf/W4I1yGCAMGAFmGGbKEvfBQOmP/0pz8NURK33HLLKpUiAgHVLDs6YpUd86y46tTL7ZA/Hm1nHPHtPHs0fjVdLIhsYMp1DZyBa+c6s/NBNP7sKqElEyAPxO23326HHHJIS66m6iYCIiACa4QAiZN79+5t06fT2iITAREQARFoLQTw5WIDcCF13m47D9N3S0ax8xmfiAbm7PVsa6ilPNyitsN+Q0vwiPkSj3RIve3z9905/9wLWujzfMamCj8l4kMQIVyQ6OvrevpnJqIE2uc7uInXV/p1eE6H1Cw/j08pH4UvNdUjHVxAMRchwnXVRYvrIkJkC78mFx6qB/oxHXxqRUYIehQbqPb999+f8+HN3i95iXVtS+6Xb/n7533frv3ltfk2F2U914UQkbzWohSsQlo9gcY+v60egC5ABERABBIEbr75ZmPoYZkIiIAIiEDbJsDvPQ3J2d2QaZClW16x/KbGCxDxPixyAYLuCZ7fr+QDVxnonjDP53U57BzbzndAdOjtu/Zi8s8eFRG6aXTxuU/W1eed2NcnBIzGGPUhssHrawt87lOY073Cox1spgsOM30+wycXIyw7p4OvyjDq080LHeaCw2YuPGyyUnigvjIREAEREAEREAEREAEREAEREAEREIFAoHgCRATqERGpyS5CfOzzT92ZJyKC6AHyQhRi5IagCwN5IhAefLKu/rnClzv6MhEFPtUQJYEgwdCWTBzHhHm+hjCt8DkTgoNHN5jXjSm1xOcLfZrv9WPIUCYXIEK3Co4txNp7nYjiGOLTMO9qsZHPGVazlUU8FHKp2kcEREAEREAEREAEREAEREAEREAEGkug+AJErNFyd/TpxjBu5UTSxgm+keiCZQWKEbGsOEdowPFfKUJkCBBRhGBfRIQoPqwUIKL4EIQQ1jXEyv3cRGkM8Ckm1Rzky951pDXkeGjIJesYERABERABERABERABERABERABESgGgaYTIJK184iDkE+BJI4eHZGa4mIEiRy/8GlxA8WIZPlNudzJRYc+LjKExJnexcKjHMJyXz8pERkyERABERABERABERABERABERABERCB1RJYMwJEshokevQoiDAyxjSfk1iZvAvkXGAigWWh3SCS5RZjmS4cJJIkH4VP5vkoatb1ZRcbQsJMj35otgSZxbg+lSECIiACIiACIiACIiACIiACIiACzURgzQsQyQt1oSEkg6SrBqKEz80TQabm+dwTWIb8DCyTr2GxTwzr6fpAo4yAizIvhCSRnlfCunmR5JnwRJI1vhwSYHqXChJihpE5Ovs85pbwRZkIiIAIiIAIiIAIiIAIiIAIiIAIiED9CTSvAJGrviSMRHBghIqVCSLDZxJH0pVjZSJJW+pKgueZINdDKuZ7iJETCAaeE6KGvBBMZT51cNGB5JXkj6DrhE81CBAx0aWPthE+k9hSJgIiIAIiIAIiIAIiIAIiIAIiIAIiUFQCLU+AqOvyEBzowoEIwagWKwWIIEIgPiQEiJqVIkRagPBRM4L4wOgZCBIyERABERABERABERABERABERABERCBNUagdQkQawyLTiQCIiACIiACIiACIiACIiACIiACIlBMAspuUEyaKksEREAEREAEREAEREAEREAEREAERCAnAQkQObFopQiIgAiIgAiIgAiIgAiIgAiIgAiIQDEJSIAoJk2VJQIiIAIiIAIiIAIiIAIiIAIiIAIikJOABIicWLRSBERABERABERABERABERABERABESgmAQkQBSTpsoSAREQAREQAREQAREQAREQAREQARHISUACRE4sWikCIiACIiACIiACIiACIiACIiACIlBMAhIgiklTZYmACIiACIiACIiACIiACIiACIiACOQkIAEiJxatFAEREAEREAEREAEREAEREAEREAERKCYBCRDFpKmyREAEREAEREAEREAEREAEREAEREAEchKQAJETi1aKgAiIgAiIgAiIgAiIgAiIgAiIgAgUk4AEiGLSVFkiIAIiIAIiIAIiIAIiIAIiIAIiIAI5CUiAyIlFK0VABERABERABERABERABERABERABIpJQAJEMWmqLBEQAREQAREQAREQAREQAREQAREQgZwEJEDkxNL2Vj7zzDNt76J0RSIgAiIgAiIgAiIgAiIgAiIgAq2GgASIVnOrGlfRa6+91r773e82rhAdLQIiIAIiIAIiIAIiIAIiIAIiIAINJCABooHgWtthhx56qE2fPr21VVv1FQEREAEREAEREAEREAEREAERaCMEJEC0kRtZyGUgQtx///2F7Kp9REAEREAEREAEREAEREAEREAERKCoBCRAFBVnyy/siSeeaPmVVA1FQAREQAREQAREQAREQAREQATaHAEJEG3ultZ9Qa+//nrdO2irCIiACIiACIiACIiACIiACIiACDQBAQkQTQC1JRe57777mkbEaMl3SHUTAREQAREQAREQAREQAREQgbZJQAJE27yvuioREAEREAEREAEREAEREAEREAERaFEEGiVAVFZW2oMPPmjLli0rykWRn6CYIzVMmzbNRo0aVZS65SvkxRdfNM6Tyz7++GP76KOPcm1q1nWzZ89u1vPr5CIgAiIgAiIgAiIgAiIgAiIgAmsfgToFiMsvv9wYOSHbTjvtNDv77LNt7Nix9vWvf90mTJiQvUu9P1dXV9vJJ59sTz75ZL2PzXfA448/btS1vvbaa6/ZfffdV9BhP/rRj+yVV17Jue/f//53++tf/5pzW3Ot3Hjjje2ll15qrtPrvCIgAiIgAiIgAiIgAiIgAiIgAmspgToFiCOPPNIeffRRmzlzZhrPkiVL7LbbbjO2bbLJJrZ06VIbOnRoentDF0pKSoKQ8a1vfauhRaxy3AknnGCfffbZKutXt4Kohttvv311u2m7CIiACIiACIiACIiACIiACIiACIhAgQTqFCA23XRTGz58uD388MPp4p566ilbd911bZdddjFC+REfFi1aFLbT3eDwww+3nj172hFHHGHjx48P66+88kq74oorwjKCBa3wo0ePDp9x9vfbb7+wTLRFTJB4wAEH2NVXX21bbbVV2P/aa68N+/AP3TS+8Y1vhPNw7KWXXmrnnHNOentcoEsH+2GPPPJIWD7rrLPCcQgddJHINsSVX/ziF/af//zHBg8eHMSXGTNm2De/+c1wHHW/+eabMw57++23bccddwzbzzjjjCDKZOyw8sMvf/nLcC2bb765XX/99bl2WSPrFixYsEbOo5OIgAiIgAiIgAiIgAiIgAiIgAiIQCRQpwDBTscff7zdfffdcX/797//bSeeeKKVlpbaihUrbPLkyUb3icWLF9thhx1mG264Yci70Lt3bzvqqKPCcTjcDz30UFhmGEi6bsSuFs8++6wNGzYsbJs4caIRYYGNGzfObr31Vvvzn/9sF1xwgdHVYcqUKWEb3Srmz59vdLE46aSTggCRjNIIO/k/lDV16tTwkeUHHnggiApEdcydO9eSokY85pBDDrEzzzzT9txzT3vsscesR48e9rvf/S6ILE8//bSde+65hsjwxRdfxEPC+X/+858H0eK5556ziy66KL0tLnAt1113nd10003261//2i688MIgisTtTT0fM2ZMU59C5YuACIiACIiACIiACIiACIiACIhAXgKrFSC+9rWvBUefKACc+DvuuCMdVZAs9d133w3CAmIBgsIll1xib775ZhAodt11VyOvAiIFkQ/HHnusEZ2AvfDCC7bXXnsli0ovn3/++bbDDjvYcccdFyIxOJYyEBAQD0aMGBHKOv3009PH1LWw/vrr209+8hPbfvvtjUiIKIokj0Fw6NOnj3Xt2tU22mijILSQC4Nkm0SExJwYyegJrhnhgqgQojbuv//+ZJFh+c477wwiCnXebbfdQr4LoizWlD3//PP2z3/+c02dTucRAREQAREQAREQAREQAREQAREQgQwCqxUg+vfvHxxmumHQ/YIIh6233jqjED58+umnYV2vXr1snXXWsQEDBoTPRDvQJWObbbYJggTCAwksiZwgioAIiN133z3sm/1Pv3790qsoDwEkRh6st956GdvSH+pYGDhwYHorZRNFUYhRR7qCdO7cOXSh4Jiampr0oXTLiIb4wjVXVVXFVWEeoydgw/SHP/whb/LKjAOL9IH6plKpIpWmYkRABERABERABERABERABERABESgfgRWK0BQHBEId911V+h+QZeMXEZ0AROOd3KitR/bf//9Q9QDQsUWW2xhBx10UOiO0LdvX0sKDbnKTq5DEMFefvnlMMexjnkjwooi/UO3kmh0+SAShJwXdPXo0qVL3BTmdB2JhrBCjox27drFVWEOB0bFSLKhO8qaMs5/9NFHr6nT6TwiIAIiIAIiIAIiIAIiIAIiIAIikEGgIAGCbgfkbMjX/YISiRAgouCGG24IkQqjRo0ycj/MmjUrnHDkyJEhESXJJRnxYt999w25Ew4++OCMCq3uQ1lZmZ133nkhD8XFF18c8kyQyLKYRhTHW2+9ZdOmTQvFkmQT0aG8vDwMq5mdxPG3v/2tffjhh2F/up4ceOCBq1SHZJkk4iQxJ/knEDWuuuqqVfZrqhXJKI1sAaWpzqlyRUAEREAEREAEREAEREAEREAERCASKEiAwCFHKNhuu+3yDrlJ7oR77703JFrs1q1bEAZI2Ej3C2ynnXYK83322SfM99hjjzBHmCjUYhcCEjiSa4G8FHvvvXdIDFloGXG/WFb8nJwjFnTq1Cl0I6HLxzXXXBPOgeOOsEKEQ9IYkpQIA7qJIFKwf7bF3BN0YeF4oiZOOeWU7N30WQREQAREQAREQAREQAREQAREQATaJIGUdwn4MplBkS5x3rx5IWKASIemMIbdpBsHQ30uXLgwdO8gCeTPfvazop6O7hKxK8Xy5ctDAkySU+YyRgQhR0VFRUWuzel1lElZCBxr0uj+Ee399983hgSViYAIiIAIiIAIiIAIiIAIiIAIiMCaItAkCgEREE0lPgBm2223DV0YNthgg5DQEZHg1FNPLTqzKD5QMF0/8okPbGdY0tWJD+xHmWtafOC8MhEQAREQAREQAREQAREQAREQARFoTgJlzXnyhp6b7iAkeySfAmIHiSxlhREYM2ZMSABa2N7aSwREQAREQAREQAREQAREQAREQASKQ6BVChBceocOHdJDYhYHxdpRyqRJkyzm31g7rlhXKQIiIAIiIAIiIAIiIAIiIAIi0BIINEkXjJZwYaqDCIiACIiACIiACIiACIiACIiACIhAyyEgAaLl3Is1UpM999xzjZxHJxEBERABERABERABERABERABERCBJAEJEEkaa8EyOSBkIiACIiACIiACIiACIiACIiACIrCmCbTaHBBrGlRbON+oUaPslltuaQuXomsQAREQAREQAREQAREQAREQARFoZQQUAdHKblhDq/vggw9qtJCGwtNxIiACIiACIiACIiACIiACIiACjSaQqqqqqml0KSqgxRN45plnNPpFi79LqqAIiIAIiIAIiIAIiIAIiIAItF0CEiDa7r3VlYmACIiACIiACIiACIiACIiACIhAiyGgLhgt5laoIiIgAiIgAiIgAiIgAiIgAiIgAiLQdglIgGi791ZXJgIiIAIiIAIiIAIiIAIiIAIiIAIthoAEiBZzK1QRERABERABERABERABERABERABEWi7BCRAtN17qysTAREQAREQAREQAREQAREQAREQgRZDQAJEi7kVqogIiIAIiIAIiIAIiIAIiIAIiIAItF0CEiDa7r3VlYmACIiACIiACIiACIiACIiACIhAiyEgAaLF3ApVRAREQAREQAREQAREQAREQAREQATaLgEJEG333urKREAEREAEREAEREAEREAEREAERKDFEJAA0WJuhSoiAiIgAiIgAiIgAiIgAiIgAiIgAm2XgASItntvdWUiIAIiIAIiIAIiIAIiIAIiIAIi0GIISIBoMbdCFREBERABERABERABERABERABERCBtktAAkTbvbe6MhEQAREQAREQAREQAREQAREQARFoMQQkQLSYW6GKiIAIiIAIiIAIiIAIiIAIiIAIiEDbJSABou3eW12ZCIiACIiACIiACIiACIiACIiACLQYAhIgWsytUEVEQAREQAREQAREQAREQAREQAREoO0SKL3gggt+0XYvT1eWTWDq1Km2YsUK69ChQ3rT2LFjrXPnzlZaWhrWLVq0yN555x2rqamxrl27pvfLXmD7559/Ho5Lljdx4sSwvm/fvpZKpdKHzZkzx1555ZWwf7du3dLrkwuLFy+2l156KRyXvQ91WrJkiXXv3j19COeaOXOmUTYT11FWVpbenr2Qfa2VlZVGGT169EjvOn36dHv//fetU6dO1rFjx/T67AXqOmHCBOvSpUuaXa7y6rqmWOaMGTPs1Vdftfbt24fy4vpc5RVav1hGc8znzp0b7gdsomU/e8uXLw+c2Rf+JSW59dBczxnH8jxw79Zdd900f87FM/nuu++GMuGZy3gOxo0bF/ZJPi/57kO+9bnKbo51xeRN/SdPnmw8txUVFenLyce1kO81heT6vtT1DOS6pnRlmnEhV72K+WxzabDO9Xzy2/3GG2/YrFmzwnOf/H2NSObNmxf26dOnT8b3Ih/r6urq8D387LPPLPuYWGZzzov5mw03fj+Tf0O4tly/MazP98yzLdqnn35qY8aMsd69e6/ytyfXM89x+X5/YpnNOc9+lmN9G/KOkO/6KTPXbwy/52+++abxtz/f395c94R3Fo6N7wH83eRvD+uS7wd8N5J/65uTM+cu5m8J5WUz5dq5n5ELvxdwzceLMpLG371870K5nvt850uW2ZzLud6nct2DfL+/2XXP5s12nvlc77Cs5x0P9r169couKv05+7co+15xL3lXiu/cdX3H0oU2w0KxWef67c739/D/t3cWcHYV1x+fKPqHIsUpCUFKobhrkAKB4g7FnRIo7u5QoLhTtAQrxRoktLgUaSkUKFKgWNDikkD2P9/ZPS+zN/fed5/lvbf7O599e21m7sxv5s7MOXPOmaJjWhpPAVRZ7T5rDM2Ct559abJdJL812oXN1xhf7Junfaf1eVnt9Ntvv3VPP/10iM98IKa0byV+Xul5+oy/0lQUvuUR4EPYaaed3AILLOCuv/76kF8a2RprrOHmnXde9/LLL4d7f/3rX93666/v/vznP7t9993XHXbYYZllO/nkk92SSy7pbrnllhCGj/43v/mN23///d11113nVl555fAh8/CGG25wG2ywgXvwwQdDmNNPP32CdG+++Wa31lprub/85S9u7733dr/97W9DGD6kVVZZxV111VXu8MMPd7vssoujI4BWW201d9FFF5V+fJRplFZW8rTiiiu6xRdfvBTljDPOcLvvvru7++67Q35vu+220rP4ZMyYMW7rrbcO5edjh9LSyypTnNYf//hHt+2227pHHnnEbbXVVgG7rPSK5i9Of2Ke03mdeOKJbpFFFgnth3entT0G7mHDhrkRI0a4Cy+8MNQ7A1YaJdsZHee6664b2vFNN93kfvGLX7gvv/wyRD3ggAPccccd5x544IHQ/p5//vluSdJuNtlkE3faaae5W2+9NcRlEIKy6iHrfreEm3TRCLzvv/9+t9RSS7lDDjmkVKosXIt81ySS9r1ktYG0MpUy0sSTtHzVu23TPrfYYgvHd06/Sr/HRB964YUX3AorrBDa6cUXX+y23HLLgGsMyR/+8Ae33nrruZEjR4awTz31VHichfXnn38evsNLL73U3XHHHW6dddYJQtU4zWad17vPpq5WX311t/zyy09QpGQfQ4CsNm+RmYzRD/3ud79zd955p1tzzTUDw2vP09p8Xv9j8Zp1TGvLaXVQdI6QVn4rW1ofs88++4S5w0MPPeQ23XRTd99991nw0jGrTmi7O++8c2kecNddd4U4N954Y+ne8ccfH76JUmJNPGlEX5KG6ZFHHumOOeaYEgYI5qEsvGJImKOBKXOhzTff3NG3QHntPut9cbrNOk/Oz9LqIK//TeY7De+s+d5LL70U5ji0aeZHe+65ZzK50nWyL2J+Gc9xmSPbvDTvGysl2ISTemOd1ndnjYdFxrQ8niKr3WeNoWnw1rsv5R3JdvHYY491axfzzTdfGH/efffdbnzRZZddNkEWs9op82XmHPQPF1xwgdtuu+0c30natzJBolXcyF4qriIxRWldBF588cXA3M4+++ylTDIYnX/++aGR2U0kszCF0003nWNFjM7uhBNOsMelI5NjtBn22muv0j2EGEwekPIioT322GPDoIUwgUGQTnPIkCEOQQXSWwa1WWedtRSfcKx+fPHFF+7ggw8uCRlgABdbbLHANBJ46NCh7tlnn3WLLrpoiHvUUUc5Op2ZZ565lFbyJK2sdOx/+tOfglDGwvMe8oE2yBxzzOHuueeeMMm053Y86KCD3EorrRRW0+xeWnpZZbI4HMHqiiuuCCuaSy+9tDvrrLMCE5KWXtH8xelPzHOk1KwE0nldeeWV4dVpbY82gODB2sNPfvKTsFIVtwcip7UzBg8GcNomBFPGYER7+OlPf+p23HHHcJ93UH8I3YxoW3TcrC6j+cCE49FHH3ULLrhgZj1k1Y+l2cxjvfF+88033a677urOPvtsd/vtt4eigVEWrkW+axJJ+16y2gDhk22Ie82memOd1rbR8Npvv/1KQlEmPWg8zDXXXA4GCrx/9rOfhZWOAQMGTLDiPnz48LAaTz9OP0nfzXuysGaljjEBJhqink466aTw/TYb73r22bThX/7yl0Gwg5A3prR6yGvzFhfmduGFF3YwzkzQvDZp6EPseVqbz+t/LF6zjmn9dFodFJ0jpJWfsqX1MUzYwfPtt98OxX/iiSdCvw6DZZRXJzbhZyxg1W7gwIEhGosoRgim4/mK3W/Gsd59SRqmlAtmBIaKvoLv3DSmsvCKsYBJgfH9+c9/7hDoMJ4j9Mxr91nvi9Nt1nlyPpVWB3n9b5zvLLyz5nvMT+lj6S9YQEEDizGWuUVMaX0R85dTTz01BGPuA/4sEkJZ31h42MR/9cQ6q+/OGg/vvffesmNaFk/B/Dqr3WeNock5K7DXuy9NaxcsFvCDEAojPGA+zaIDgvYDDzwwzA+S2n6Ez2qnLETstttubptttiGYW2ihhQLfttxyyzVkTiYBRIC55/9DqgWhgWCEdCtJDDYIHhBMIK0988wzk0GCGQQCArQkrrnmmtJz1NaIb50qEjl732yzzeaQAvOBoBYJYb5gHy+DAYPkhhtu6D755BM3evTosELNahX5YdJtxDt415xzzhmYVhhP1NQ40jmnqd2nlZXOm8EgJoQKfMx0Yki4r7766vhxOIfZhbH9xz/+Ed5nAZLp5ZXJ4nC0wQQVK5hy61SS6RG2SP4I1yyiPlk1iVev0toekyEml6wGIABA68TaguWdyUBaOyOchUVgxaQVAQICBRM+oGp7hRfqIMmNCVU0JqVovqD5g+q0rSZk1UPW/TjdZp3XE2/KABN19NFHBxVoK1MeruW+a9LI+l7y2kCyDVlemnmsJ9ZZbRu1cTSymOzzXbDaaP0BbfXyyy8P6r1MaNEGg/k1YnIKmXov/S/YQ1lYw6zBZDLJo55ZuULw2gpUzz4bPNHWW2KJJboVLase8tq8JfDKK6+EsYnxCIL5tfEiq83n9T+WbrOOaf10Wh0UmSNklZ+ypfUx4IJpDOM+ppu0dcjaJed5dfL++++7Sy65hGAhjWuvvTZopIQb/h9zCphpzAlagerZl1CeNEy5z1wLbRI0BBlvmdPwfZfDi7gIIlkkYg728MMPh/GU+3ntPut9xGs2JedTaXWQ1//G+U/DO2++R99jxLxk4403Ls2T7X5WX2TPOZ577rlBAxjT6LxvLI7TjPN6Yp3Vd2eNh0XGtCyeAgFEVrvPGkPT8K1nX1qkXSBcZcEX4tvmO0dogEY4Wum0V6O8dgp2pjWMRjKCFIRt9CGNmJN1F79ZDnXs1QiweoAGBD4QMAtIEkzyRhttFIQJ8bOxY8eGSYLdY8JgavV0SEjVkKCzco3dPgIHIybUrPYxoWaSwATi17/+dXjMwBmHJV3useqEWQdqoggz0Fow8xJLt5oj5Tb/EwgZknTooYcGbYw4T8kwXOeVKS08EnEEKdtvv33a49K9cvkrBWzxE1ZjWOlmwomgivqMKaudWRg6VyZIMGVmD8kz7AJ32GGHYHI0//zzW/BwZCJG24LJY2WBzhghRkxZ9ZB1P47byufl8EZzCXX/zTbbLLUYabiW+65JKO97KZen1Iy0wc1y5SrXtlm1oA9mpRJtHQhBAcTqEuesAGGrakT/G9vNIwimPzFKyxOmNnyDCOTQEGBim7ZiYmm06jGvT0TrAKxQD09SuXpIa/OWBsIhtIRgyKgT6gqhPJTV5ov0P5Z+Kx/LzRGyyp/Vx9B/o8m4zDLLBO0/6gWyxYwYi7Q6YeIM48DcAfMjzE1jOu+884KQ28b1+Fmrn6d9t3GeszAlDCvuzIlg1tCOglGByuFFGEwuBg8e7BhDp5122mCKwf28dp/1PuK1E6X1v5b/LLyLzPfQ7MH8M80EuVxfZEI0VqihrG/M8tkuxzys8/rurPGwyJiWxVOAWVa751m5b5EwlVK5vrRcu2DBFN7HTAvpSzHpRniAVjqCVxZsjfLaKfEoP3zVsssu6+aee+5uvJelUa+jBBD1QrKHpIMK3SyzzOL22GMPh309jT/pV4HJHIwbUrVRo0aFcHTKSNbjho7kDI0HCIkgkrVf/epXIQ4TgUGDBoVn/DPzCVSHoFVXXTV8QKyAoOlgk3CeMdljYKTjwi4KxytMVNZee+3SygnhKiUmNpSfDgzGCu0PVuhjQurMJJPVScpPXhhMYkbAwueVycLYERVH7LrRKEHAkkZF8pcWrxXv0X4YBJCqovaF1kqsNUGes9oZzxBsIZVFYIWvkZiO9iv4pBf7MLDntFfaE8IEVN2p43POOcceB1XTtHooUj+lRFrwpAjeYMFEE0YAu0FMrFgRN0rDtdx3nfe9FMmTvbudjkXKldW20cgiPn4c6HsQ9OL7BqLPw88ARB+NxhfMrxGCBFZLTOhrmkE8z8oTQlTUO5kUo20Ek86ko12oSJ+ISSECCrT6MGFhpd369ax6sPKntXl7htkYPogQ+oAjYxeTvrw2X67/sbRb+VhujpBX/rw+Bn9TaKwxCabNo8GTJoBIqxOEpmaSx2oz3wFacJAxbsxp2o2yvtu4HFmYolHJqijmWxA+l4xpy8OLsKyiMv/DRBWzFQRtpMV8LKvd572PNNuB8vpfy38W3uXmeyySsWCClq6FtTQ5luuLEKJhHom2UN43FqfZyudFsM7ru7PGwyJjWhZPkdfui3yLleJdri8lvXLtAqEiwigj5gV8s/SdLGIMHTq05OOPMNb20ngt5hW0LYRcaPPhX814OEu/nkcJIOqJZg9ICym5MR2m0svkjUkbHyCEc0gGeQQICBJQ+UV1bZ555gkNnUkYzCUqkSaVw2Yfpp1JAjbNCA2IZ+kitcO8gNULCBMHVD1hxlkVgSFiUoGAAz8TmGTw8aJmxKoSgx/OkpIr3iGxgv+w8aJcqGdBqIPaignCFDpM8ox/Azo/yk9nh2oWxyTllcnKTRw+eFZsmHzBAGZRXv6y4rTqfQYWHIpRb2g+IOSiTVCXZgec1c4oEx0kWgy2GmDlRHpL26G9xJNXqz+YNKTCSNYhNFzQwICy6iHrfojUJv+y8IaBMwe0aCgh+aZds0JG2wcvKAvXct913veSlac2gTQzm1nlKtK2qQsmBqa5wKo6uw9AMGbmVJJvhH4U3xzWl7A6w2TDzOLQDEKzAcrKE301fTKTFr4n7JQR/rYL5fWJ9s0jUMbsinYN8wRxDuX1MVlt3tJlvGLCyrjEeMfYhVZVXpvP639ChtrgX7k5Ql75s/oYGFvqiTETx9j032jkQNa+Oc+qE8yRUNWG0GhDk8fGbhg3HFfDuLUbZX23RfptxlYw5RuHEH6hoQBl4WVYM59jjLQFJQQXaK0yH8tq93nvCy9tg39Z/W8RvPPme2AJM4cJBf2sUZExgbBJIVreN2Zpt/oxC2vybX1sXt+dNR5mjWlxHWbxFHntPutbrAXncn0paeeNUWg/MAbGi3AICxG28j3Cw6HFzjzB2lpeO2XugMCDPpg+FCFk3F5rKWtqXD9wdujXezDwA0+HX/HtVuf+I+/wg3a453cE6PArDx1+FazDDzgdfhIb7vuJQ4eXiHWLR7vxkrcOv2Jcuu8bbIhHXO/opHTfm0l0eJu7kK5n3ju4Jn6crp9Ql8IQ3wssSvH96lwpXf+RlO57TYVwn7T9qmGH7+hLz9LadVxWnnt1wg7f6ZTieOl0SI/yk0+vMheekR8vmCiFs7RJz2t2lO4n08sqU1xuv6rZ4T/O0o93W/rJ9LLyZ+Fb5egn4x3ezrFUDvIVtz2vMdLhNVZCfYOtN7fp8AKeDq8N0eGFMd3iETduZ14AVcLKcKNtEt+u7ehXDEJaVn+eWejwWhOhzmkzvnPt8ANhCJNVD1n3WwVr8lEt3n6wDnWQLIv36t/hnfUFXPJwLfJdx2nH30tWG7DwaWWyZ808puWrXm3bay90WJ9G/+MnFh1e0BrqAbz85D/0S/RZfkU/3I/7EuqTeLRtr0IZ+jewysPaT1ZKfSvfYTOxTXt3PftsPyELZU17T9zH5LV560tIgzGOa37edK6D/iWZdtzm8/qfZLxmXcdt2fIQ10Glc4S4/JYex7iP4Zo+nHZN2/ULFx3UFfetfefVCWMw7Z65C3XhNdZCXC8gCtf2DcXvb4XzavuSIv025fPO9gKmNqfzAoWASxZehjVxqQ+wBFeO5NUwy2r3We+zeM0+JudT5Ceug6z+tyjeWfM9bx40wdyEeUeR+Q559JppHcyBs/DL+saywk+M+9ViTd5ob8n5drLvzhoPiZ82piXrMIunyGr3eWNoFp717kvjMYp30k/Sj8bv98KqDr+IUfpuvUAhPI/bWlY79QLIMG8w/u+KK67olnb8rcTvrPa8DxH9ZF0kBLohgPoi0kBbRcYeCO+wpr7TLXDiwrepsCpkKxD2GAkkEvY4jWS6FoYVWHu3xcd2CdX6pJNJJHvYP9drhQPJIeU3W2jS9x181Q6s0sqULLeVscgxmb8icVo1jJ9UBvs1q1NUwPG8i+p5vSit/lhxY+WSNt6bKIk3zlbZQtB2QagGC2vfed91XrrJPOWFbadnyXJV0rZpn2gG2b7ecbnRzKKvs28m2ZfQP6BRZNorcdxknuyZnyyG1c2091mYVj4m+8S0b74e+U9Ll1UmxqrkeJf3vp7Q/9QyR8jChjYPxqzwGiXbt91PHmkDfBueeQljSvJ5O18nv9tK+m3ammeeus27wCINryTWhKFvSJtbZbX7rPe1E/7J/rcSvG08TJvDJjGoZExIxu0p10ms0/rYvLImx0MLmxzT0uowi6fIa/fJb9HeV8uxEX0pcwC0J80vVLKt5bVTNPsw38gyB6+lrHHcdGPzOITOeyUCyckUTGHMYOSBglMVfknCUUoyjWS6aWEsHVSCnq+0+QAAQABJREFU0qjek2ZUmU34wPv4kG13hbT3l7uXVqZkuculET9P5i9+1m7nmPfEhCmLqY3H92s5T6s/OtbeJnwAwyTeCGFsl4VqMa61fSfzVG0+Wi1eslyVtG3aZ1a/ZiZDVt5kX2KOsux5fEzmyZ7F/Z3da6djsk9M++brUZ60dNOYs3Lv6gn9Ty1zhCx8UA/mF1OyfcfP4nPaQHJ+ET9v5/Pkd1tJv01bS8MlDa8k1oTJat9Z97Pe1074J/vfSvBOGw+zyl7JmJCVRrvfT2Kd1sfmlTE5HlrY5JiWVodZPEVeu09+i/a+Wo6N6EtjIS55S7a1vHaK8GxikDQgJgbKLfAOBAJoJogaj8DSSy8dHJ5hVy1qLALYyIM3NoOixiOAw1Cvvhfad+PfpjfgkwP/MDjYFTUeAY2TjcfY3oDmFf0JTlBFjUcAu3DmJMkdQhr/5t75BpyyP/7446nCn96JSONKzZyE9g3eosYjUK9xUk4oG19XeoMQEAJCQAgIASEgBISAEBACQkAICIFej4AEEL2+CQgAISAEhIAQEAJCQAgIASEgBISAEBACjUdAAojGY6w3CAEhIASEgBAQAkJACAgBISAEhIAQ6PUIyAdEL2kCaU4he0nRVUwhIASEgBAQAkJACAgBISAEhIAQqBGBevgU1C4YNVZCu0SvR2Npl7Iqn0JACAgBISAEhIAQEAJCQAgIASHQegjIBKP16kQ5EgJCQAgIASEgBISAEBACQkAICAEh0OMQkACix1WpCiQEhIAQEAJCQAgIASEgBISAEBACQqD1EJAAovXqRDkSAkJACAgBISAEhIAQEAJCQAgIASHQ4xCQAKLHVakKJASEgBAQAkJACAgBISAEhIAQEAJCoPUQkACi9epEORICQkAICAEhIASEgBAQAkJACAgBIdDjEJAAosdVqQokBISAEBACQkAICAEhIASEgBAQAkKg9RCQAKL16kQ5EgJCQAgIASEgBISAEBACQkAICAEh0OMQkACix1WpCiQEhIAQEAJCQAgIASEgBISAEBACQqD1EJAAovXqRDkSAkJACAgBISAEhIAQEAJCQAgIASHQ4xCQAKLHVakKJASEgBAQAkJACAgBISAEhIAQEAJCoPUQkACi9epEORICQkAICAEhIASEgBAQAkJACAgBIdDjEJAAosdVqQokBISAEBACQkAICAEhIASEgBAQAkKg9RCQAKL16kQ5EgJCQAgIASEgBISAEBACQkAICAEh0OMQ6N/jSqQClUfgkw9d3/vvdH2ef8q5999x7ofvy8dRiNZHoJ//nGec1XUssLgbN3Rt56b9ccV57hjzsRv78X3u+8+fdeO+G+1ch9pGxSAqghBoBQT69HN9J5nZ9Z9qITdgulVdn4HTVZyrtz5/x13w1BVu5Kuj3Msfv+bGjlN/UDGIitBQBAb07e/mmW6IGzbXam73xbdzs081a8Xv+/TTT90TTzzh/v3vf7uPPvrI/fDDDxWnoQjtg0C/fv3c9NNP7+add1631FJLuR/96Eftk3nlVAj0EAT6jBkzpqOHlEXFKIBAn4fvcX2vu8B1LLacZ1BndB39+7k+Pl6HbwV9/Ek9jx3rbFkgRwpSNwS8IKnP22+4Pk8/5Prcd5sbt8XurmP51QsnP/bjB9x371zl+k+9hOs7cHrfIPoVjltpwIEzbVBpFIUXAkKgEgS88HDct2+77z99wo358G43yazbekHESoVTuPwf17rhIw9xG8+3jptj6tldfy/gZKxoBB2x4v6NSFZp9gIExvww1j3/wYvuxhdudWf/7RJ3zrCT3A4Lb1W45E899ZS77bbb3AILLBAYUZjTRtGqq67aqKSVbgUIIGAaPXq0e+6559yjjz7q1l13Xbf44otXkEJ60HHjxrl3R7/vPvnfp+6bb7718+nGs1d9/MR9sskmddNO8yM3y0wzur59pdieXju622oISAOi1WqkgfkJwoe7bnAdq2/oXBhk+/gJJR2kP/ap/7HxXW8DwWrHpD2D0DHHXOHXZ7EVXN9LT3XjfDmKCCEQPoz94HY3cIa1ugseOpvFeDTqfT0+ZZ0JASFQTwT69Hd9JxvkBvpf/x8t5b5947yQehEhBMKHkx852+23zB5uQL8BvhPxUYOkuuto+Uzet+tKn1t4HYVAhQgM9O1z0ZkXDL9Nfrae2/KWXUMKRYQQCB/uv/9+t8IKK/gpUeMEDxUWScEbjAB1Peuss4bfz3/+czdixIjwxlqEEG+/+5575bU33NixYxuc+wmT/+zzL9zo9z8M7597yCA32ywzTxhId4RAiyEgUVmLVUjDsoPZBZoPi6/oOvr2C5oO/ENAi5Q2CGrrfd2wwijhcgggiBi304Ghzp2v+zzC7CJoPkyztOczfJfQ2SjC0beMzmPXPbu2MHYdJP20n67wZZ/nZUjPhIAQqCsCCCImHfRr/51f6fje8wizCzQfNp9/A4d6e9fg0P1ofYR978ljxc/zcqRnQqAYAggi/rDBRaH90o7zCLMLNB8WXHDBsGrcOQ/yIxjjWIN+efnRs+YggCBi8803D22BNlENvfLa6+6Fl15pivAhzi/CD/JBfkRCoNURkAZEq9dQnfKHz4dgdtEfNdrxS1mmAeFnl/5N4zUi6nFNij2Vvv76a/fll186b8IUJiuNLidqdgMHDnRTTjmlm3zyyQu9LmhDrLpu8PcxbsPtMuPg8wGzC+dXTPvAOES2OHadPAamxIdL3rfrss8zc6MHQqD9EGiH/iBoQ/x4jeDjZeDMm2aCjM8HzC46NR+69wf2Xdf9mJkbPWglBNqhnSOE2GvJnYPvkhNXOSwTPnw+YHbR38+JEDgwxjb6mJmZHvqgHdoL0COEWHbZZYMfkDXWWKOi2kDz4fU33yrFeW/0B+699z/wZhj/875E0EFtLPXr19ebX0zjZp5xBjfzTDOEl5EfzDKkCdFY7JV6bQhIAFEbfm0TG4eTHQsu1TVvRMIPn9nYY9uAU2FGkZJ/9913bqqppnKTTjppmLhUmETFwZkYffvtt+7zzz8PQo+iTpM6MMW48nfO5QggcDjZf6pFulY4fdZoHFCjj51v0X8h0NYItFN/EEwx/nuJyxNA4HBynbn9JLzR338y/bZuBT0/8+3UzjHF2PH2vV2eAAKHkz/96U99M+8c7ybWsee3lM4StlN7IceYYtx8882uEgEEPh8wuzB6+dXX3Td+nrb2Gqu6eeYa4gYMaDyLNXbs9+7lV19zd426333x5Vf+vYNDdsiXfEJYzejYigjIBKMVa6UReWK3C7QfghcxfD7wkkYfG1GQ5qaJRB/hwwwzzOAlzJNNFOFDqClfYbyP9/J+8lGEOmYb1LnTSU7gsNuFN8vpbBS+YdA47Ie2jJ1zrNd1Tn70SAi0CwLt1h/0nXQ2v7vNe7nwsttFf/w+xN+7ffcNO+ZmSQ+bjEC7tfMFZpgv7NqSBxu7XeALAM2H0s+379J5aP98BtG9Gp/n5acnPWu39gL2M800U9gBpZJ6wOGk+XxA8wHhw1677ejmn2/eiSJ8IK8IOXgf7+X95AMiX+RPJARaFYHGi+dateS9LV9stekl/cj6MbsIxy4NCP8goLHXJVe6qx941H3pvffm0ZRetetXKy3rzt1lm65Fss74pXS70s9Lo12fYXaB5gOTkmYQ7+X9X3zxRTFTDLbmLLfNKltthirsahed1RmKxyn+Sfc5/DJ33c0Pui+/KtM2ppjUbbHRiu7M43fsFt+wsvTsWkch0M4ItF1/4M2sXEf+FoNstRlM86wfSDmOPPs29897/+7GfPNdbvUNnGwSt+AvFnHD9lrXhpnsY25KethMBNqtneOYstyWsbbVpmk+lPC19u5vnHfeee6+++7zOxp8U3qcdsLiADtc/PrXvx7fvrsCTpB+WgI97F67tRfgRxhlbaJodbDbhRFmF2g+TAytB3tnfOS9a6421N15930lUwzyJzOMGCWdtxICEkC0Um00Oi9I8jtFD+OPXbtfDL/4Svf1mLHu1QtOc9N7BrdzFIXJDixjt+NHn3/pDrrqejf84qvdObts7Z+NDxenH43jPkzPIHw+YHZRL0JK/dJLL7khQ4YUEyj4F/P+jz/OdyRXef58HfKHOirCla5q53qfwy/3kvUx7vlHz3XTT/N/3Z4HCVQU/qOPP3OHn3htiHPmCTumphfSrzyDhWOAze9//3u34447umm8bWRPIdQ9KRcqorPNNltNxWJC/ec//9m9+eabbrfddivc9mp6aQWRmbSfe+65YVL/s5/9rIKY44M+9thj7n/eDnettfzOLmXo5Zdfdo8//rjbZpttyoTs/rje/QH5ffvtt4M6cPc3ZV81pj/w7wvftfUH448jz7nNjf1urBt+7QFu8qm8P5qMcNz/+tMv3ahL7nIILIbtvZ7vV8an0y1edvEyn9BG3njjjfB80KBBPjnfb4kagkC92zmZZNybdtppg1ZfkUw3qp3TbmhLySPCB7QNr7jiitKiQ1o44mFucPnllweBxZ577pmaXq3tE42Nzz77LMwViuBVbZ9WJO1yYXpye4nLzlabRvh8wOyiVornhAMGDKhofsj7rx5xUykLcf5KN3UiBFoEAZlgtEhFTJxsdPp8gLtkHhgf0Xw4ZZtN3XT/5xnMlOdx+OmnmtKdvPWmXlvikQnSicPVs0wM8DhhvOuuu6pKFmaL+MnfUkstVVF6NgGpKFJO4HfeeccttthiYT/qnGDdHtlEqdvNmi98g+isvK7j+Ovrbn7AHX/oVp3CB94Two1/Hl9PP+1UISxxstIr3S+YZ6v7DTbYIDUG6p5WrwTAudihhx7qnnnmmdTw7XrzxRdfdLvvvntJ5TMuB+34sMOyHa7FYTnfY4893C677OKefPLJ4FMk+bzZ15988onbb7/93E03jZ9MVZqnP/7xj0GIUSTe3/72N7fPPvsUCdotTL37g5EjR4b+oNtLylw0pj/wL+3WH4y//uc9f3er7bJmp/CBvGWE4/7kU0/hVtt5zaAtkReOZCqhq666yk0//fRu3nnnDT/Or7vuukqSUNgKEKh3O+fVw4YNc9RjUWpUO6dsUPKI5gNCbDQO057H4aeeemq3ww47BG2J+H5avJBYBf+uvPJKR/8+yyyzuPnmmy8In/fff//gEypOBiz/85//lG5V26eVEqjhBAxqFbgkX98q7SXOl9U193A4WQ/tB+aESyy+mHv+uedcfB6/N+uc98eOL+P8ZcXJuk9buvDCC92aa67pTjjhhHAet6+seLovBIoiIA2Iokj1iHDYMjLYTnjE7CLWfMgK9+C/XgzxV1pg3mCq0bnoNGF6wcFlC2LGKieTBaNJJpnETnv30VYPO5vHeCz8NWYX00/XhVnK86AA42M89Ni//CzOuRWWnb/TVIM0c8KPf0mxszvvvDOseA7yq50xGZM699xzh9useL///vs9SvuBgj366KNhtXDw4MFx8as6v/baa90555zjdt1116riNzrSdNNNF+xx2fVF1AQEkv2Bfcf+iNnF5D/qqpfofugH7Npn+c1nPTPkr+dYeM5OU4069QcPPPCA22mnndxFF13k1l9//QDO9ddf77bddtvAnK2wwgpNAEyvbEcE8phktMSKOHv+5z//GYrOdp7EyUuzUoxOO+20IFg++eSTHX32//kFoqeeeipoar3wwgtBQMuuWGjH8U1cffXVbs4556z0NQovBLohgNDhL3/5S+lefH7UUUdVtNhRSkQnQiCBgDQgEoD06EsvlUbYj1SUI//sOpQ7uk4+5/rYEbe4VY882R17/S2d8TuT8OcTphfiVwAm0nsYR1axWNVCxXz48OHB10HRZEhjlVVWcTAvG220kXvooYcmiIqpQ/yrVZXdXoD6JZoMvPsXv/hFWIW3Z6zcUy4mCDCPQ4cOdUyik4TKJxh8/733ydBFN9xwQygTPh8aSUFSTj3CMVB5Xb9w7V+c+9yHPeGMG9ywTY9xJ/7uxs64ZDZKb4L4VRYGddgkgRtkgqVXX33VLb300kGVnftoUIA9dW3tChvVcs94jhr/JptsEup13XXXdffee2+Ixz/a5xFHHBGOpEvdXnbZZaXnee8lUF7apUSiE9pzEQ/d9i3BoPEt0SZ/85vfhHaFWi73oOOOOy6cs7uKxWHCS1mOP/74EObuu+8O25ORBu2bcEaU//DDDw+aFDxnGzPUqn/729+WsOYdMV1zzTWlbxRzh9deey1+3O2c7+j2228P98phTaDf/e53pW9w3333DerTcYLvvfdeELhQPvJq7SYOU49z8DzooINCe+Bdv/rVr9y7774bkv7vf/8byg+uv/zlLwNOMNH23N6PKQZ9GYy1Ef0CcViVajhZH2D9QXzk5XnP/bMHrhzlrtr3EvfAVfd16w/8xfi4pGHXFRQIrR1o6623DkJGzKwwIxo1alTYTg/NJ9o4uxwYgef8888fNOhg3Hh+4403lto2dUD7MOIcDSHqb6GFFgoTbtTKobz4qOwT/g9/+IMlFY6MAfZN8Zxvk2+Z72azzTYL76aP4prnt912Wyk+Ktn0M9wnP4TDTMeI9kXbj4lv9Y477gi3yvVDcbxKz3vCuNc5f2EO0/0HFsl7yWsY/gMPPNDRr/GsXJwQoOA/hBlotR199NGO/myuueZyM844o1t77bVDW6e9//Wvfw1CeTQjIDTbaOcx0Ra5R9uhH6U9GeWNQYTl/dttt11olzETavErPfI+m6PZeGK48X2cdNJJYbWd/NLeL7jggglewThIGnyHRow73CP9diDyO3z4nm7InIPdiiuu4I703zd9RysQ2qRW1yuttJLjR//DETrmmGOCxmkr5FV5aG8EJIBo7/qrLPd+BYpFKCT0HPln1yGh6Dr5/Lgb/uSO9T/oyM026Izvzy1+Mny4DqGL/WOSxIDKYMuAfsghh4RJXNEVWiaTTMwY1GyCjlOoeJAqlpPKQ2GXzwQYFUEGTLQqWIUz5urBBx8Mq3PYbzJBeM6r1m244YalCYu9cZ555gkYsNJtZAwtKx+NJJpDaA9dx/AuX4nhPo+67tsxfn7imTe5k/wPOvQ3m3Q2Ci4sfsqRx5XSVlttFVbtYfCMWA36+9//7nhmxPPXX3+9ZFpAe2LScumll7ozzjgjtCtWlKC8Z6gbwvBBtCmYAyZ/1B8EE3DKKaeEVa/TTz89TPIwkTDhRi1phxdE/5ikoQGy4oorRnfTT+1bQuiFAABG7fzzz3cPP/xw8PQNBhCYcY6dqcUZMWKEY4WDcr/11ltunXXWCcwa2C2++OLhGzPTFsp/6qmnBv8RF198cbBPXm655QJjd9ZZZwXBDe9/5JFHwvtgqlBTXmaZZUJ+EBQh1MkSrvHcnpXDGsEIeDMJ5Rtkgmf9AC+nTSCUpL2QZ+oRk4s0gVbIbA3/6AvOPPPMIIQ48cQTg5kLwhQYWJgK6gFc2fZtiy22CL44UKmOCaYaRgHcjTAtuueee8LWgXavoUfrxJNHe2nyftc1QocHETx4WmmbVbr1B+FmRrzwrMA/hEcQ2CGMMMdxfBus/rISjb18zMQjOHzllVdC27P+gfpnfLHv1PpaTLoQSNBuqT/GFdoSDBmUF5++H+YpFkDQdvl2afcQplSkRdun/7jlllvcAgssENoHeZh11lndxhtvXFKxh/kkHN8O/RZlXm+99Ur9DOknfQLRR7FlM2TlS+v/QoAq//WEcY+im8ZC8miwJO/bNRoJ/CD6Urtf7mjpljvSTiD6iiQtssgiQRvuH//4h/vxj38chL6EoY3QvxnRf9IXH3nkkWEcQPCFbx2oyPiGQPqrr74KaZiQw9Ku9MhcDCaWcZTxgjkS35aZTiEcZ+xh0QU/Gghc9957b/evf3nNyojoN+lD+W6M/vSnP4V7fEetTpRrk403cg/7Ocmhhx4WxqVTTz3FXeuFWM0mTC2MEDrQb/Kzc45GaEmIhEAtCPSvJbLithsCSPgZcCc8dpakwx0z4k8OYcOoYw92Qxdgj2y/UnrDLV7roVP4wH3ML7qE/Znp2fNKEWJFYckllwzRcFCFWi2T+XKmEoSB0WLAhJjcLbroomFlgkmkERO3OK299torMCP2vJojnTLMja1wMWFgtQFGiNVgaPXVVw+MGefsPU74eMWN+2hGIGhg5YrJNJNoVjhgHicKWaUlj10vP9FrOSBsGHn9UW6FZbxjQB/uxDNvDPcIEt8PUZLpJK+70i16YKWPCd+tt94aVg2Jd8kllwRscVQYr3jGacK4MdmxARN7WpgIKO8ZbZGtuWAmYNIRGjGJ4pqVGohJGXlg0kmdzTHHHGH1gPZXa9rhBV3/YJ6YTBoTEz/LOmf1nBWzTTfdNEwiYIJoYzDfEN+ZnVsa+FhhQguxagwTxmS3f//+QSiBuQu2xXxbEEIJW32FEaSOYOrBBbzQMnj++ecdggm+TVaVbZKDsABmCwFdMh8h8cS/PKx5D0yAff9oEvGdGVEOhA8IBWefffZwGwYNMxRW+OpFpE/7OPvss4NQknRpmzDN+HewPNFXwBgaxcyy3dtyyy3DBPzDDz8MdUK/wDa8yy+/vAVp7DH5vdp111vRckDQsM3pO7k5Fpoz9Aex8CG+H6JY/KxjwdKAJUwUQmr6AvpMNAzYgcDaKrb7tNUDDjggpAqzAlNvWlLcpM0wHkD0xQix0eihrSDU5Ftn1RmCaaKdmOCSe1nxqTfaPn5MGMP47qg3W0EkLnUfdkzw5zCRMGEw9HjiHzRoUBBEI4CjP4FpRHhgDlKpf4TVfDe083KU1w+Vi5v3vJZxD38GRs0e92wFPnm0/DEOMO4gBEK4RTg0Hkz4EN8nTjKd5LWlW+5IvwmZaWEyPH0vbWeKKaYo9Z/cS/al5J/2gqYNGgIscCB8KzK+wewzjvTt2zf5+oqvEXbzrdLWGU+YA+EEGfz4ZiCe0675TtE8pJ3wHcZaHZjF8L3j48f8HvHtMs4Rv9WJcZxx4Mijji45G0YYQR+1g++3mkUIpNBuMDJhA9q61AnX/DjnHloSxJHJjyGmY6UI1N6rVPpGhW8iAl2aD349u3MRavyxM1N27dxq3tTigef/HYQRJny479hDglDCizC64tvill0nj5UXdYkllihFYmIC2UpAuEj5x0ohzAUaD0YIGWBwTF3X7sN8MoG1HxPDWohJ5gcffNBtcsmWXDCKrMIaxSvXxgAlV61gchGiMJgyaWG1E0pbAbF063akQYRG0XWMr3mJPfOnwzY7xj30+AtB8IBAAhp5gxdKeN8PtI3ujSMlvZBWiFbRP5yBITCyFRzb7YIV5zxicsMKOXUO88sKp5kh5D2DkYbxZzWGlVK0V2BMcGhqRHuyFa+ZZ545MBqsSEK1pm3v4MjEkbaaNRmNw3JOWIQPRrTHN954wy5TjwgDTPhAAFbhmdAh3GPFdvvttw9CEPAzWnjhhe205HMDpgkCl8GDBwfVUlZ9YIRY+QJLfjB70LPPPhuO5f5lYc33T72gZWCE7wi+fyMm6hCMv72fcrFSXE/VV/vmEbgYoUoMscJnFPspGDRoUOr+8+bfAHMN+gO0U1j1Z/LeeLLvOOUYXu7vd9FV+13q/T287roLH3b2vh+G+BAWn1Pi2HXy2JVYwQOCLL4zJu30mXzftA/aGATzQpvA5IX2evPNN5eYHHsFzKQRjJuNM7RHvgUTPhDGxiJjCrmXFX+11VYL3w31BsEo0RfE9QZjZ4SQgmuED5B9g2jMwHxB8fhBe+G7sjYdAuT8y+uHcqLlPuox454vJf1U6efbp50bAFxDmFU998/nguAhFj7wfcdx7NziJa8t3XJHhF4Q2mlpxLzD2krac7uH8MEIobPNO4qOb/UQPvB+vkcWYuLvgDGZ747xAeIbNiEhjmUhy2+46PqHMIW+GwYYbUfOEUC0AzGuXnzJpe4xLwja1wuC1vZCRDM1bGb+bb5pecAUgzGVX9KU0sIk49h9HYVAEQQkgCiCUk8J4yex/i+sVIWjt7/laBJ6Lo7YdH13pP9Bqx55UnfNh/m7NB98uM74IakQP1zbfTuGVIr/g9mxQZtYMOSQrVaHi5R/NnjFmg0E4zoZl90RUPOzXyzwSEm67C3MKiCYz5hiO0vuF3Wmx8QZz8dMLpk0M2DHjGT8jrqeU4HUm9lkx9e8yF8fus/G7hD/g4K/B6/9AAXNh6U7NSJK8XmQlR5pV0msbMJkgA+TQBht02zISpKJI9J6JjesJDJhNK2SvGeW3k9+8hNnP1ZBEUgYMUjHZG2We7WmHafLqgMMdfx9xM+T56yKxWTMTXwveZ4sCyvBMFmsJPNtxsIGi1t0cmrfAwIUw5IjeBb9BpP5M6zt+7dry1scHtMHKH43TCwraKbCb/FqORqzgOaMEQ7ioLjuEFIaxfftHkf6lJVXXjkw2TDF9Aus4k8c6uwP/EccvuNux5CBDm9esapb0f+gq/a7pGR20an5MDgRzwcK331KelX2B2DIai8aOKykQqbOjUABJh3tA7TIaL8IBmKK20fcjhFIxc+IY0yT+YHgXhwmjs92kQiKUAuHeeL99OsxZdV5HIZzE44lxzbeYd8U4UpjOBcJKtIPJaKUvewx454vKdiVfmFO1HltIKBZxQ868KBOfw+cx5oPFp/7ds4x7TrcLPDPVv1NqBlHoc9j0cWEm/Gz+Jx2H1Oyj+RZ3Ccmx7e4XcfpVHOOY2gEezFZ3233kuOW3U8ezRcS37ctCMQC6GT4VrpG826euefyY8+h7quvv3JrrLmGF3Cu3PQspgl60HRAc8t8X3HNz4g+TiQEqkVgYiylVJs3xas3Akj6mVAi5bdj164Y4VVdz4/c3G932Mc7newyu0DzAbOLbvFCfH/HhwuBLb3o2Dn8hpQL/UM1DbVTW3l6+umnQzwbiLMSYSLKChI7XKDWBzH4o2oI09lIsgGVd5s6L4Mqk05WzCslnCfCVKPGjVScVYqJQ74i+WPSRKV2NpPO65CBzueH7bNJaD2YXkBB+IDmQxy+s1GEdFLTs+chhcr+oYbPgIhaMkIFNCKMOchKCfVqmFxUl1HRR5UZ9WnqJ+8ZzB71amqepM8gXZRhrWfarKYiNJuYxOSC9g0GMEwI8xDgVUN8o9QdqrMxnjhfLCqcy3ovaSPYwDTEGD2+QbRGTBvDJuoIj2yFDdtmVnLxIl8vMu0m8mKq0GbDXI2qKowsghLiUhdmnlav/OamE/oB6w+io0XyzxFCQObzIQgf0Hwo9SNd8QiUlR73KyD8ZVB3sYM6hDW0LyPaK9852gdgB44w7UUIQRuqyGb6Qhzze8JYlHQYmpYmq7EIPBAaDh48OJgqpYUrd8/GPphN04SjzbJqbBoYfD/WxkiPlfGY8vqhOFwl5z1n3KNZ+tmQb6/Jo+HBfUzLOGJ6ASF8oE9JxuNZMp34mudFib4LAQJae4xd8ThnfnzweRWTCazie1nntY5vWelm3ef7ZF4UE/0kfXdctvh51jmCCoRC5gcC7TzGgXagm7yGK3Pdp5/5e8m05M93jteqbFYZ0py2k5dY+JAU8sSafM3Kt97bvghIA6J9666KnCPZJ9qEx87Ext8PmhCbrR98QYz3+TD+eWc6nfPMtPTseaWZZNLIai+dHqr1qOjhT8GIiRjP4h/MBvFwtoQTL1bHWfUhbKNXDFkFY7KJPSUqhkyMze8DqsGVEivV+L2gLFARG99K35Ee3jcMq7RwjK5DhPHXaELw6+7zYfzz7un4yMn07D3pGSl7F8EB9YzqJfWeR7QNhA8wpviIwEYfUwTs8vOekSZ23Ghb0JZQ8UQghDprzPhkvbueaVNOmAq+hYlJrOKz6o7vAsoPI1wLsbqGPxaEajBQ+EkYNGjQBGZS1byDb82+f5hFBFOYzxhhEoFgj0krdYpwgskU6un1JAQEMGc4JYXxw48LDASMRDlNnbR8GNNJ2cAPZmaikX2nyaNloOu+aUJU7PMhK11LP+MIxnz/xx57bGC8aUsItTjGE2S+eVbr8K1ggqmMJLvdxlyJOoTppJ0g/MO5LNposWZLt0iJC2tvmC7RNqutN3N8y/vxz0J+6PNoyzb5hxFmJRihNSYi5luCLJXrhxLZLnzZc8Y9hic/dnlKHg0Mu2+aELHmQ1o8C591tHTLHWkzaEHShmlDzHmY02BGhvka/k3Q9IHQVICRx4GjLdyUS7+W8a1c2mnPzWyCMmEWRXum3VY7rvBN44ySX7uYX4DLTDN3ase9+eYb7hO/oHHOOWe7++//q9+6/MuSKUoafo2+ZyZ/yfdgfsEv7lstjJkJ2bWOQqASBKQBUQlabR/Waz50aTwkj1NONqn76PMv3fRTTelL2RmO3S78sFy6tvscP/7ic0eczvlwerqdw3px0Ab7lSJ2kjBVWVa6se2NJ29MOpOEah/eklmdZpUYZo3B+MorrwzO7+LwcVrx/VrOWVVnAIy1LWAYkqsT9g5TayQvaflhYkCaTHiK7ENu6dZ0NMams7rHJ+Wvp5zCt43/feGmn3Yqaw7u0H277C1Twn/0P982fJzQOFKe++ZTE7FDAxNwVheNIYhxjM9ZWcF2HtMNs7tGrZ0VpLxnZBBGhBUvVkPxFwDhtRuBBGT1GC6if7y/1rSj5ALTAQMbr/DGz5PncfntWVZe7XlaHDyq33fffWFHAMLB1PNdWdisNO15Mm22AoUh32+//cIKEGXiO4l9t1ic5LHcu5iM4xMARg2C2Ydht9Vq7OwRpCC8MsaN/oVtBOtJrLLDCDI5Nu0LyomzxCxNiyRecX6MAWWizir+RKNkf2DfsT8OnGwS9/XnX7vJp/ZmPl33V9rGmzfwXVs4y6i//vrzr0KcevUHYEufTzuCGYPAGIeipnXCPYSFCBIQosWOO9Pwju/RVhA20pZoIxCCIIQeUBw23Ei5Z4Jk+vFKhB+WHkd7D+PYzjvvHBwrc59xBoekZiMPDphL0d4hc25K/HL9UIhQ5b9axj17pZWR62aMe/H7LU92ZEUddXnTmOJ+3qICZinEyUvT0i56xPcIYxACTWvbtGnqPDkXwikr8yDGrdhUKPkuM8erdnxLplf0moUVhCNxP4YwDZ88eRTjGZ8zbvDdg7l9p3nptMqz1VdfwzPzq7sNvTYghBDpGD+vPcrvVHLM0Ue7nfy3bhSX1+416oiANY3wP8OiYBplxUkLq3tCIIlAH99RMWUQ9XAE+u22jutYe4swP7Si2nyR6+GXXOW+GTPWnbz1pl4I0Wk3GD8njF1/9PkX7uCrb3CTDxzgzt55/Aq0PScs1LFO8dVFVkNRi2eVGlVvBlCcDlZKrDpgh42jn0YQTA4TgOTAgJ03dshMQpj4ZjEbRfKEszq2k4L5M2YpjkcZmVTHmiHx8+Q5df/Dhbcnb5euv3x2WzdgxvVL9Us9GtE57Hv4Ze6bb8e44w/dyk3nhRDJ51bvHD/85HN3xInXuskmHejOPH7HEj9COnG4gTN1Dr72nolxpG5YuUtT1cx7Rt7YEQJV5zQb2nJ5rzVtGCHUrvFG3gxCXZRJay1tOs437RdhIYxelmAhDl/JOf0G/Ude34GWElTU3jjv/Vn9AfngO6a9DBkypGIV4/idMJZ876yAplGl/QHf+5QLXZmWVLg38IRZ3BEr7Jv5fOTZt7mx3411q+2yZqcQIjOkFz589pUbdfFdbsAkA9ywvdbNDHnEivtnPst6YH0ufTF27Mm2hDo6ZgowPEf7iX01RFuhDmN/D0XTQS0ck8Is1eai6Vg4fD7QrrLaLcwyAoes77RcP2TvSTtmtXOrA9JutXGPdjzmsHfTihPuoTWDEDuLmI/QhhBex0KItPCUHwEVvjpiDZRk2CLC1mQcu6bPpC8eNGiQ3ZrgiHkgv0rbay3j2wSZ8Dey2gthER6yxfPgwYPD7jJp8YvcQ7sHLBAqp5kGVtov8k7ahO3SVCQPTzz1d/eZnw9Do+5/yB13+EG+v+hfJGrAgO/V/IehDTHQt59aTBLHjv3eHXH8KW61oSuEPEzt5/JLLb5IofwQCME9pq1GCHZs3EEDIvb/wGJfbE5pcXQUAkURKPalFE1N4VobgT5+xT3BEtr1ubts44ZffLWbe48D3JffdG5RmFUYNB+2XmlZd84u2/og3VlLS4/7PKmGWEUsaq+bTJ/JaKOED7yLgR0GJ8nEMvllQK2FmMzR2eMokdX6eNUuTpf3VzrBiOOnnffhJu0DdVR/DGYTXddnnrCT2+ewS90Cyw33aoJl2obXfNhio5W88GGHbulZunZMy0Oj7+VNIvOeka9a2lStaaOqaquajcYoLX1WmepJfKO2elvPdEmL76Lct5HFwFWTl6z+gPu17kmPMBaVejQq8GWQRY3oD8K7on7A+gOOw/Zez40861Z3zq9+68Z8811WtsJ9tCUWXH0RN2y4Fz5kpBfu56aS/jCvz8U2HO0aNG5MKyY9lfy71bQVTAAxw0MdHs2VehGCkDwBaJ7QjTyU64fy8pnVzvPqIC+9+Fkzxz3yQX8Es5o84i+GHZcQJJkT2zjf8TnzAYQLCB+S6cTXcZxKz9GGKqfyjqDYNBwqSb+W8S3tPVnthbA41a7VsTZmdMyV0HalftKoYf1i9LLJ/FzYBBDT+kWvl199zc0/H/7SypP5C7KQ03btemLX1Rx5P/kwIn+VEHMN6s4IgQPmF2hBxMIHnkv4YCjpWC0CEkBUi1xbxuv04YD5RSef2f14zi5be40G7FVL/Gc875zgfhAyBH61ezqWfiUQ0cGVG1wrSa9RYZFOs9KEgISJRT2JiRiqvqi1sZqSlj4TJd5fb6YwlINGAaUc0WbgVxGlpBPi2/2KEuudgVnlQfhgvgB6JwqtW+pG9ge33XZbYKKP9Kq5pn6dRKJZ/QHaDHkaDcl8hmv77rOOqZGquwljjP8chLm1MjuV5oB3s83vqFGjum2fWWk6rRS+ke282eMe3xCUdkSgkKfRkFZHaenE6afF6Wn3GtlewAqmGC0Kdr0xDYIYw4b2i9GLpp3mR270+x+GOzPPOIO7a9T9bp65hhTWgoiSqvkU7QfeTz6MyF+lhJYVWiCYnkJgHRNaQwgqREKgVgQkgKgVwXaJ34+q9ivcGT4gGnG/i50thBCex9O2+SsUeSIGQr2VDhrJOytO9RREoI5H2mnEgIpEH+EDap5ZarYTxP3B768d6n6CJ+Nv9PFtI8hSkDz527FcpdHX43OhsxQEaBO1rOCmJKlbdUSgkf0Bfi34pVHV/UGH7w/69EtLsnRvQN/+vhtgrPC37Ptv9LH09vqc2I5E9UmtslQw+7AdKiqL2bqhG9nOGzHujflhrKMd55FpCnQT9Fs774rId1bX53kZ6kHPGtlegAkfKGlUdb/oE8N0xdpEWtpp92aZaUb3ymtvhC1xZ55pBvfFl1+5sy+8zK252tCJJohA8IDmw11e+DCZXxgjHxDaUuSvGkK7AdO1e+65J5hLYkaGk0oWx6rZzamaPChOz0cgv4fu+eXvPSWc0e+/7FdTO7yKHmYSYZzt0oTonGXCd3bdt2ONz3squDiGxIsz9pjYZTLoNZqYBKEah+ZDYeGDz1Sft9/wOo/d995O5rXvJN4r87gfAmMS2kVUnEZfJ/OiayHQbgi0U38w7tu3Xd9JZs6FeJ7phrjvYeBMcGn9QaOPubnSw2Yj0E7t/PkPXnS04zzCDCyV6bR23hV5gvG91ud5mepBz9qpvQD76NGjKzYNRNtp7iGD3Asvde64NM9cg917oz9wd959n7t6xE2+fY1reI3269c3mF2g+WDCB15KvshftYSggZ3oREKgUQhIANEoZFss3Y4F/HZNn7zv+swwi8+ZX93qFEGUNCJsqat0vw7PE+N0iyFSW3YQAlQiCKjtbdXH7vP0Qy7UfU4S/adayI0b85HrO+lMkQ8IH8GvgJZ8NlCZ9bzOyY8eCYF2Q6Bd+oPvP33C8b3n0bC5VnNvfvaWm2u6Of2w4D/8ojZ5tYTLy5CetQwC7dLOb3zhVkc7ziPMZHBYjSDCNB0afczLT0981i7tBezZapo2USnNNsvM3kfIt+71N98KURECxIKAStOrR/jBc8zuyJdICLQyAtWLx1q5VMrbBAiMG7q26/P0I0ELAmFD56J9o48TZEM3JiICfd581fW57zZH3efRgOlWdd9/9qRvFl49GwqNwzMeptnRkOvOV+m/EBACEweBcd+84cZ8eLfje8+j3Rffzt304u1urNeCCNStH/B3GnWdlyk9EwIFEXjmvX+6s/92iaMd5xHbmeLkFS0IyDQdGn3My5OeNQcBdhl69NFHu22lXklO5h4y2P3sp3PnOomtJL1qw2J2QT7Ij0gItDoC0oBo9RqqV/6m/bEbt8Xuru9dN7iOJVbyZsD9fMqN9QnRkzUg6lUtjUoH4UPfS08Nde583edRn4HTuUlm3caN/eB213+aZXyzoG10UZfmg112KcaULmu+Hp+SzoSAEGgQAggfvn3jPP+db+v43vNo9qlmdecMO8md/MjZbvP5N/CmGAM8d+ZjxD4hLIHkfbuu9LmF11EI1IAAwoctb9k1tF/acR5hIrDuuuu6+++/3y200EIV2//npa1n7YMAwocRI0aEtkCbqJbQOMDnwruj33ef/O/ToBVhwqxq0ywSD/NcdrvA4STvr8Xsosj7FEYI1AsBCSDqhWQbpNOx/OoOi7S+113gOhZbzrlpZww+ITpnl8wvO31A1Ou6DSDpWVn0Difx+YDZRdB88AIn6rwIDZhupRDsu3eucv2nXsL1HTh9EEQEfsL/M77Cri1Nu672uaWjoxAQAnVGwGs04fMBsws0HxA+2Hde7k07LLxVCDJ85CFu4/nWcXNMPbvr731CIIMInUGcAB8/lDx23p3wvoVLPrdrHYVABQjgcBKfD5hdoPmA8Mzab7lkFl/cm6Z6YscZts2FAa3UEWG5d+h56yGA1gs+HzC7QPMBQZS1hVpyC/OPIELmD7WgqLi9BYE+3ut+cjrQW8ree8v5yYeu7/13uj7PP+Xc++94979dqve9F5GeUXKcxnmHk/h8CGYXZTQf0grdMeZjN/bj+9z3nz/rxn032jMPahtpOOmeEGh5BLwmEw4n8fmA2UU5zYe08rz1+TvugqeucCNfHeVe/vg1N3ac+oM0nHSveQiw2wUOJ/H5gNlFOc2HtJziC+KJJ55w//73v91HH31UMstIC6t77Y8AQiZ8f+DzAVOcWjQf2h8NlUAINAcBCSCag7veKgSEgBAQAkJACAgBISAEhIAQEAJCoFchICeUvaq6VVghIASEgBAQAkJACAgBISAEhIAQEALNQUACiObgrrcKASEgBISAEBACQkAICAEhIASEgBDoVQhIANGrqluFFQJCQAgIASEgBISAEBACQkAICAEh0BwEJIBoDu56qxAQAkJACAgBISAEhIAQEAJCQAgIgV6FgAQQvaq6VVghIASEgBAQAkJACAgBISAEhIAQEALNQUACiObgrrcKASEgBISAEBACQkAICAEhIASEgBDoVQhIANGrqluFFQJCQAgIASEgBISAEBACQkAICAEh0BwEJIBoDu56qxAQAkJACAgBISAEhIAQEAJCQAgIgV6FgAQQvaq6VVghIASEgBAQAkJACAgBISAEhIAQEALNQUACiObgrrcKASEgBISAEBACQkAICAEhIASEgBDoVQhIANGrqluFFQJCQAgIASEgBISAEBACQkAICAEh0BwEJIBoDu56qxAQAkJACAgBISAEhIAQEAJCQAgIgV6FgAQQvaq6VVghIASEgBAQAkJACAgBISAEhIAQEALNQUACiObgrrcKASEgBISAEBACQkAICAEhIASEgBDoVQhIANGrqluFFQJCQAgIASEgBISAEBACQkAICAEh0BwEJIBoDu56qxAQAkJACAgBISAEhIAQEAJCQAgIgV6FgAQQvaq6VVghIASEgBAQAkJACAgBISAEhIAQEALNQUACiObgrrcKASEgBISAEBACQkAICAEhIASEgBDoVQhIANGrqluFFQJCQAgIASEgBISAEBACQkAICAEh0BwEJIBoDu56qxAQAkJACAgBISAEhIAQEAJCQAgIgV6FQP9eVdpeXNj//e9/7v3333c//PBDWRT69evnZpxxRjfNNNOUDasAjUPg9qf7uLNGOve/L8u/Y5opndt7mHPrLNZRPrBCCAEhIASEgBAQAkJACAgBISAEmoBAnzFjxohjaQLwE/uVL730khs3blzh1/bt29f99Kc/LRxeAeuPwGrH9ykkfLA3I4QYdbg+Z8Mj7yjhTh46eiYEhIAQEAJCQAgIASEgBBqDgEwwGoNry6VaRPPhxz/+sZtkkklC3ouEb7lC9rAMFdF8uGyPfm6JIX1CyYuEN4jGjh3rXnzxRffBBx/YrV51LKpZAijgSnhRz0PgP//5j7vwwgvdmmuu6U444YRwzj2REBACQkAICAEhIASEQGMQkACiMbi2XapTTTVVED4ghChKSy21lBs4cOAEv4suuqhoEgpXAwJ7rNE3CB8QQhSl7777zh1wwAFuiimmcAsttJCbbbbZwvGZZ54pJUG98uybb74p3ePktddeC3X9j3/8wxGeun/ooYe6heHijjvuCM8I36pURFhTrXAnrcwff/yx++1vf+swhYKuu+4698ILL6QFnWj3qN9LL73UjR49uuHv7OjocOecc07TyxwXFKEDWl577bWX+8tf/uKOOeaYcM49hBEiISAEhIAQEAJCQAgIgfojIAFE/TFtuxQRPvCDPvzww4ryv/3224eVdFbT7bfZZptVlIYCV44AwofdVu/8fHc8v7xfD3vDaaed5i6//HJ37733Bmb49ddfd0svvbT7xS9+4T799FMLFurykEMOKV1zAhNptOiii7q5557bXX/99XardPzDH/7gEGIMGTKkdK/dTioV7oBdmjCOewgdnnjiCXfooYcGwQ1YIAQy4Q2+Wa666ir31VdfTVSYvvzyS7fHHns42kCj6ZNPPnH77befu+mmm+ryqueee8798Y9/rDot6gWhA7TSSiuF3xFHHBGO3EMYQRiREBACQkAICAEhIASEQH0RkACivni2RWpmZkFmOTfhw+eff+5YIa+Epp9++sBowmza70c/+pG77bbb3Lzzzuv++c9/huQw6Vh//fXdb37zm3CNCQATfluF32mnndzbb79dejX30aRYY4013HTTTecQarz33nuOcFzznHcYff/99+7oo492888/f3jv/vvv777++mt77PCBsdZaa4W4hDn77LO7MdSlgC16YmYWZI9zEz5ceM849+Rr4wUD5bL/+OOPuxVWWCEwWmhBzDrrrO7MM890I0aM6IbHIoss4s4///wgqMhKc4cddggr+XGbgRGHyUQwBbUj7tUKdyjvBRdcUBLEmUBu6qmnDm0PQcOqq65KsG6EpgjtGia9pxLf7EcffeQOP/zwuhTxz3/+cxBoVJNYrN1AH4Qwjp+dczRCS0IkBISAEBACQkAICAEhUD8EJICoH5ZtkRLCBvP1gPDBTC4QPvCrhlgZj3+ksfbaa7uZZ57Z/frXvw7OL1nhhWkwAcS+++7rTjnlFAcTe/LJJ7snn3zSrbfeeo5VWQjmbfjw4W7dddcN4W655Ra3wAILOO801V122WWBcd54443dt99+G8KzWo+K95577ukOPPBA9/vf/z68m4fkbdiwYWFF8+abb3bbbbedQ0Bx9913h7it/g+G2MwBED6YyQXCh/PvLu5YlHIifKAeENaYicTkk08eNCDiXU8Q/LBKjyABxjGNEAp98cUXbtSoUaXHd955ZzjfYIMN2gb3egl3KDgCHRPE2RGHrq+++mrQNImFbIRnFX/TTTfl1K244opu1113Def8u+aaa9wqq6wShGbbbLNNqb54hgDuvPPOC89ZqUfYVk4I9/LLL7stt9wypIeGymOPPUZSqXTxxRe7ZZddtpvjWph0hHeYk3z22Wfh+xw8eHAw46EtmD8RTEsQPl5xxRUhn5QBQsvm9ttvD+dF4t94440hDwgvEF4igIT41k899VT3zjvvhPeg0QORNuUiPHm/7777wv34H/4d0G4wMmHDAw884I477rhwm3toRUBoScgnRIBC/4SAEBACQkAICAEhUBcEJICoC4ztk4hpPyB4MM0HVrCrFT6g0k+a8e9f//qXYytP7MtRPT/99NMDw3/WWWe5QYMGBaYV7Qae77333u5Xv/qVu/XWWx1q1Q8++GAJTAQJCDBYHd5ll13CfQQLMCMnnnhiuIaxQ2hB2qw+77777m7HHXcMKu3XXnttUH+HYYJZQWAxdOjQwFg//fTTgTkqvayFTxbrcjKJ4GE3L4yA0HqoVPhAPNTgwRL85ptvPgcDeeyxx5aYO8IYwYjBzFEHaYT/iNVWW82BsxHn4Iwwox1wr6dwxzBIOyIow9QBAVpMiy++eGDkuQfeCOQgtHs4X2aZZYImCu0cYRwCHwgB3T777OOWWGKJwOgjhMgTwvGNIBR69tlng8YLQg/TUgkJJv4tt9xy7qmnnnJ8J0YIBNielzaBJgOmNkcddVRoP3/7299KwhMrK20NwclBBx0UkqAMlv8i8SkfAhn6AcxVEDxC5B0B5//93/+5M844IwgLEBJstNFGoT2igUPbRuhIG4zpnnvuiS+DUBLBCD8TQHQL4C+ScZLPdS0EhIAQEAJCQAgIASFQHIH+xYMqZE9AAB8PsQYEZarU70OMA1oLMPwx/eQnPwmXc801l0NAwUo6K5ImRDDne6z4GiGYgBnGwSGmEtDPf/5ze+ymnXbacI1gAzLNDRg6BB4QDIqteqLuDj3//PNh1R9GGYYODQhW92HGYKbagfDxEGtAkOdK/D7EZezfv78799xzA+PIijaaJccff3zQGIHhhLk0mnTSSQNzy6oyGizUYZK23XZbt/XWWwfhD6vwaEOYaQzmOa2OeyzcMVOWaoU7YAOzjUmLEW38sMMOs8sJjnwraKVAMMGzzz57OOe74XsxcwG0CNCuQEAH8w0hvEOLCDIhHFoTplEx00wzhXYOk462wyuvvBK+h3nmmSfEQfOFNNIITQe+PzRaEHJgMsW3gyNNiLwhGLD84tDyyCOP7JYUGhDrrLNOt3t2USQ+Gh70LxDaDwhAEFxgHkT+EEoYFqZZgbCSfgQBym677TaBH4ekQIK00X5A48HMQ7jmZ/SnP/0ppGXXOgoBISAEhIAQEAJCQAhUj4AEENVj17YxYyFELcIHAICZybOTxgwDgtlBFR0ynwGmjRFu+n8wvDA6Rn369LHT3KOZYbDqaYIJGCMEDCZkgNFmdwZ+eL3nBwMDc90OFAshqhU+xOUEJ9Tx+WGSgVbJ/fffH1aR43Awe6xyw9jBiCXJGEAYVfw/sCod+zloddxjXM0UoxZ8f/azn4WdFQwnY/btusgRUwo0h/jF3wNx0WAwzNGeMConhEMQhwAjzs/KK69s0VOPJljCXOeRRx4J2gtoYUDsFEHdIsyCqUdAlaSFF144eat0XST+ggsuWApPWfFJkkV8xzPMMIMjHMJFhIzgRL8Tkzn+jO9xjjAOQvCAICgmExDF93QuBISAEBACQkAICAEhUB0CEkBUh1vbx0LwgAmGCQMaUSDegUkEq6yYSFx99dVBJZvVS4gVd1shxQEfauUx01E0T5YeDIcxDzBx7777rmMVGC0JdiLYcMMNA4P9u9/9zsEcYS7QLgIIsIAxxmTAVuqL4mPhxo0bF1Z6Ub03VX+eLbnkkhYk9chKN1oNCCmSNOWUUwZVftTxqUNWtgcMGBCCtQvusRCiFuEDhUYjJE8gl8Qv7dqEDuwyYtpEhMN3CdoIRrGArpwQjrpI7upg9WTpJY+YNFD3aCwhuONbNQ2ZTTbZpKSBgFADQdXf/1DWrxgAAB1vSURBVP73bkmYwLHbza6LIvHj/OalRZIIHxDO3HDDDW7kyJFuq622CpoQjz76aCnPhKMN2+4XXBuZ+YUd7T5HK3N8T+dCQAgIASHQexBgPokPJ7Rw8XHE3KcZxFiOSTELCvEYObHyAg4sNPFrFcLP2xtvvBEWHJOLDuQRJ/hoUTJPaAZmrYJTq+VDAohWq5EG5YdOEwY0pjy/D2bqEIdPO8f22lYP7TmaCPgHwAYcM4yTTjrJsRMANt0ICNCKQFiAcAImiGeomtM5VLPaiKo/ZhswvxdeeGHomBEysCo7evTo4EQRB5aox2NTTp4pO/lsZZrGj2//6/TJWcpmnt8HwucRTBzOCzGJwRQDNXUEM/iAgLKwp47wvZG1oo0WhQl+4lVqBoR2wb1W4U4e7kWfMbGAJptsstA2WcmPzTcY+LMmPeWEcNQDPihwFMl3BiEAzCO0iDBNQPjELinsHAORBlozV3gTC+oeYlBPCiDCg5R/tca3JE1Qw7U5r6VP4cfuO2hDJLV6Vl99dYve7YipTOx/Jn6YFScOo3MhIASEgBDoeQjA3LLAYqbDzJ1Y4EIwjY8j5p8Tk9A4xH8ac9l4gWJi5QFfa4yXNuer5b34V4JHmHPOOSdIBn9RaCQuv/zyjp318gjeBkwwP2WOmyTmueQbc/G0dyXD63riINCpEz9x3qW3NBEBTBFgQOPdKrLOCWemC+WyjF04woT4hzo+DApMCztT0GGz6wUaFxyhK6+8MjDAqHTD5OAVn1XWSjtzWwUmPRgOVmkXXXTRsDpL54bvCFZoyQfMMZ0Pfgm22GKLsFpdrnzNfL73MOfKCRUsf4QjfDli9wBWtjGpQACDXwecf9LRG2NKGskVZ0wLzN9A8h0MEEjjSS8WUrQy7mm41iLcSWJSyTU+CyB8mCAkgNB2wFEoTD6aQTD/+Elht5g0ioVw7O6CyQU+EBjcYc4RNlG/mFXgD4JvdI899khLqts9NAnwR4HQwDQ7bDLA98qkjJ08stpGt8S6LmqNTzKsQJEnNB7QtMLxJIJN/ESw0oFTTAgtkpj4/m1XDrtP/8Mv3v3CnmF+pAmLoaGjEBACQqB3IYCgnnEO7TkWa9hFCa1e5qzMcXsbsdBXTmu2KCaYRGbtMsW8hbkKOIt6JgLSgOiZ9TpBqdiVIN5mcYIAVdzARj2PbEWXMKhFGXPFNcKI66+/Pti4E26KKabgdoniuNy0VXoLAMMbh0GLAuaDe1999dUEZUVazA/pMWERirQ6rbNYh1tnsfrmknpgi0UEMtQHbSIpbMqqVzRY+CUJbZm4buPnrYo7wpqzRk6oYRLn3c6LCncsfNrRBGXxM7sH44wwAAePrNzD2LNdLdufokXESgDqjpgxxf414rQ4RwiHYMnMmhBsmBCO5zDraAfAbJMeAg62us0jBIRMOFjpMdVGtB0wpyLPMP58iwg7EFRAVq6sdKuJn0wTISLCM3bQoRzs1MKOHQhMIMMrzaTrrrvu6qaGifAN0wtWdTiPKdZAie/rXAgIASEgBHo+AmjRQmyrbZrBCPMZZ//73/+WAEAQjl8kBOAI2VmYWWyx8RO4v/71r2F8x1R1jjnmCOMXvpAgxiQ0+vCLBtONliqOnZnTYm6BtgUr+0OHDg3h+ceuUjhfRhCPqTHaiMxtY2Lln0UMxjbLCwuPaAOwYIQfKBYreCfmJZQL59OEh9LyxQIHJpekRx65fumll4KZA3MONCPwMWa+obhGExF8mGuy+Me8E21lNBfwL4WJBH6bjMCIOQZ03XXXBazR8szDkLBvvfVWCMO7WZRhsQ1t7CSBAWkhXMIMnUUGBExJPiQZT9f1RaCPZ9g66pukUhMCQkAICIFKEWACAqMdC8cYKBlM0eRJaqVkpZ8lhLPwTEpg0OP32LNKjkwemCgwOUoKCIqkU2t83oHvCwQahg2qsayYFPHbgNkXq1lphJYEky+REBACQkAI9F4E0BBF0wHmnm2x0eyEUY/HPBYJ0C5lLEIwjtDCtCbw24TmIk68Ye5h0rlmXEfAzTgMsw0TDyHo2GyzzcKCBM8wjX3zzTfDcxYEMLvA+TOEEAGTzWeeeSbEi317hQD+H+McAhHbTv21115zl19+eVhUYF6BuTKLCGgOYzZLeQk7yyyzTJAvBP4mrEewgGkKGprgwqIiTD1pkQ8EMZZP0kbIgJ8mys9CCUIPFlsQxrAoglajETuqsaPd448/HjSbwRRhTxaG1IXtwoXpKHkwLchDDz00LEqyO5mZYPBe3k9d8n4WHsD64IMPLgmZLC86Ng6B1l8GblzZlbIQEAJCoGUQSHMKycBaqVkSkyB+WVQvTSiY/krzFuep1vikxc45MTGJKCJ8IA6TP1Zj0BJhMog6KKsg+HyQ2UWMqs6FgBAQAr0TAYQGbOsOk4pQmh/jDBoJaOIZ441AHdMMriG0DBhTEECgKWA7NPEMjQU0UWGq47Fmzz33DKaEOE9mIQGG2VbwERSwlbb5fUAwYWaRMPf//ve/SXoCQlOBfJAHhBVsdU/+MU9Ec5X0EBgw/yCv7KiFEAIBhJHly67tyBiOJoiZZKBNkHTyHOeTeJZPhBaYalP+WPhAGLQtwQsBBMILNCeIl4UhQgQIX1eM6RBmwwhaKG9siolwA+EDWhwbb7xxCIt56yWXXOJefvnlINQJN/Wv4QhIANFwiPUCISAEhIAQaEUEmPxgPiISAkJACAgBIZCGAIIGzB9R8cf0AXM/fpzvv//+4T7M+KhRo0rRMQU2/wWYNMDcYjKBKQIaDRAajkZoWNi29WgPkB4r+UaYZUI8g2LBBZoMCCzSyAQQCBY4x8wTpp700bbAsTVCA0w5MPdAkJKVr2T67GaF9gV+oDAZtXLF4WLmnwULhBvVUBEM0QgxQqgAsbgQ5wFhEkR+yTdEmSGexWmEm/rXMAQkgGgYtK2VMJ0L3uxRsSpHfLioeqFmJmoeArc/3adiPwX4jRCVR0DYlsdIIYSAEBACQkAI9GYEMCuAcUYTAm0Bfpjo4f+InZ9gWllVh6GPt7VHeGDaC5gTwOSTDvNr0iDdmMzHEvcQXqDFGJt5xGE5T2r/JZ/bNf4Y0HpE8IB2IGaKtp23mWPwbhxWo62QND2M82Vp2hE/DuzORXkoK9ofSQFDmmanxa/kWARDEySQLvXBL0nUFYSQJa4vtCfASjTxEJAAYuJh3dQ3IXxghwKc0JUj1JMIj52WqHkIFHWSSA7ZrpPw9XZa2bzSN/bNwrax+Cp1ISAEhIAQEALtjgCMO4wqAoiY8GWAAAKmF8aerd0333zzktCAeKy+w+hiUoGZAA6dIUwp8giNBkwOzAE1YTEnwJcDpguVEgIHTA3RdkDTwswrzPcBTi8ReKBFgVlEEUITA+ED2iE4s4SKxi2SfhymKIamHUJczqmbpFDBTDQx0cD3BET9gk+9zFNDovpXFoEJxUNloyhAOyKA5kPRjotwRTQlYhyQKqLihVM4UX0QQKhQji7bo59bYkifEKxI+HLpFXnO1qvY1RWh+733YyTXrUZFsGoEtkxI2O0CR5C1EhMe9r7GEWQrESsg2L8WpXJtpNzzou+JwzGhYScY29s9ftaK55Vi2oplUJ6EgBAQAu2GAGYLtuUmTC3jLnNdNAXQDsA/Ab4JYHZxksgYzyIeO1gwzqPFwO4WzI0RKOD3gd0yIPwypBHpQewAgVkEggK0FcxEIy1O3j38HUA4gbS0uUYYQb4pHyYJvA+CIWeMzCPbMQI8mP8zRlFuKKtcyfTQUECIwZabScJPBYRZB4KRIhgi6MEpJnMi6of0kzthUV8Ich5++OFQj+B71VVXBX8TVqZkXnTdGAQkgGgMrr0mVTzNrrXWWuGDppNDBWuDDTYInUqvAaFJBd1jjb5B+ACjXJRwvLfUUksVDZ4aji1RkRYXoWuuuSYwyUXCtlKYSrH99NNPwwqCOYDkyHdx/PHHh4HdysYWp3hlxm6yVmLCwFaYDOCtRHgMxxkXE5MiVK6NlHte5B3JMExQ2OIUNdp2oEoxzSsTKsNMuFDzFQkBISAEhEA2Asstt5xbeumlA4PNrg6nnHJK2BWDlXS0hGGUYXJZuGNbxzPOOCPs7oCwYO211w4J49gY3w8nn3yyu+iii4IDS5hdGH5j1mNzC5xU/vKXvwzM9/nnnx/MNxCEkI9qiHm5+ZMgHSNMLjDlwMElu0QgdGBnCOb1CFmgOF8WjyNpMpckLDttMJbiJwOm33a/iMNznkwLngFNDxPIxOExVwFjtvlE0yQPQ1v4xAyE+cLpp58eHGyyNSmON2MiD1tvvXXIC7t4gC9CIbbsRCAjmngIaBvOiYd1U990+OGHh46zaCYOOuigwDzlhX/wwQeD+tU222zj8JKLHRgrinRkMFpIItnuT1QdAosd3KnZkBYbBnm31Tvlhzue/4N78rVOafXTJ+dLrRFA4CiJ+qmW8P681VZbuQMPPLBsEgzQMDomWS8bYSIFqDe2CCDwmcLK/9ChQ0urJHwLSPdxPsXADrEqUg9Vv//85z+OfcQRZmC/2CrEigplZoJShMq1kXLPi7wjLQwCElRS0+xE08I3816lmObllVUi2igramxZJhICQkAICIF8BGBy8aUGweimjRtoAhCGsQ8zipjYqYLtOVmBR2hhmgd5cwH8NSAshxlPe1+cfi3naHagFWDMOvMZ5u79+pVf3KLMaHbY1qRoK6BBUXTuD668J8tXBPNH8kX5i2DI+9FCIT95+Qd/6orwCHxYNBJNXASkATFx8e5Rb2M/YOy/cESDfwk63OWXXz6sruFlF7UxI6SSOO5BoonAgsmvEQwtquQ4vuQ5UlVT5SLM2LFj3RFHHBGkxnT8MCRvv/22RXfDhw93CFiwryM+ewazpzKq7oTnR15jyssPcVm9Jq3555/fnX322WXV0eK0G3FuZhakzbkJHy68Z1xJ+FDNe8thz+CC92UwxObxiiuumOA1eVjGgenwTzrppIAp2LLlIUx0s6me2LLKgGdpJPvsmX3vvfeGyQZaIxBes1nFsPab19Zo1+xtjcYEeFFXtOksIm0wJSzbWiFEZAKDgIJr2/6K+AhBaNtJh1M8Y3Wc9s9KDfFIjzZAWkZ8n3jA5hnfLeU0QvWUfBghiGRrLMLybWMnSrqxGQ9t45BDDgntjHylmXCgTUI75DnfM3GMmEDx/fMcnBC0MbGAEIryPtouz+iHIPYxRzAEgTV9DEdr65dddll4Zv9YVWH1iHLg+Zw8mE2vheHI5Iv3XH/99fHtgBPtH2JVh36KtEgTzI3IA/nfbrvtwnOclyUxLRc/qyx4/d50003Dq/Aqvuuuu4bzvHZo+dJRCAgBIdBbEUBTgEU2flnCAEwycDKZFD6AGcww8wMzLSCNPOEDcQgLI531PsLUg9DWMOED6SGMyGPe43eaGYppN8DIFxU+kA64ZgkfeI6miJW/CIa8n/KUyz9pInigPiV8AOmJTxJATHzMe8QbkUQiYGAPYOtQrWB0GDixRIUM4px9hlFRQ90JRgmmBakpBIOCKjkT8tNOOy2snjIBN9p3332D9gZpoML25JNPuvXWW69kNwYzd+qppwZ7PPZWxp4NtTkm7WeddVZglGAWHnnkkZBkXn5QQRs2bFjokPB1ABMAs8GEv1mEtoP5I4BZNpMLhA/n3z2eCasmf+WwhxmiznBSdNRRRwUBAnGM8rC0MHZEC4I0YIyvvfbaIH2GSW8mNRJbysVEBEbd2h7Sfvbehjku19Zo19beqQO+J4QR7FedJJhxvgkk+ggEEdKhfUE81EMx16CujBAY4AgrzS8Mqx9oyVBffDeoK5IONpMQQiP7tnkXTDSqpub9mvhsOQZxjjCCawR5OOkiPTAw1VPCoYKJ3xnUV3EOhQkH37kR3yKCC75z0iBf9BUQQjLeAcYnnnhiKDv5ou1ChjkmFwg/aX8Q/ZD1QWCNai15QtCAkAOHvWabikAEAQn7rnNOeciDCZJCgl3/sFUlfqz1Q3kR9tDHsZUb/SZCEQSv2ORSX2aWQ5qUjZUf+i+2BYsxLRI/qyy8y3BBSEGfWq4dxmXTuRAQAkJACAgBISAEakWg08tHrakofq9DAGYBivfMZUXT7MZ4xrY+rHgymWalEDsxiBVIJMGYcJiN3F577eUOPvjg8Bw7LFZMYaaQyrISy0Qd5gFCywJ1duKzUgsxscaODUI4AmPLiif523DDDd15553nnn/++SCYyMsP+cUpDczF0KFDww+mAxW4ZtFiXU4mETyYqQXHWoUPVp4s7JEKX3311Q6hDoIYCM2W2IlRHpZWtyGi/0f7QIsCBpK0l1xyyW6aMBZuYh4bjS1lgRmlTSUdT6ImWK6tsfqBbSXfxGabbRbUMWnnO++8czeYEEDQ3vnmbFXl8ccfD5pE1O+OO+4Y0kGQBMHwb7zxxrk2j6zgoy7KijkCizvvvDN8D7QJVg6wn2Tlgu8LLQOubYXfMoeQEuY7VvdHKJPMP/0B6SLMRACIUAN7WtsuzPJMfiCECjiRRJiA4AEbUfJA+SHi02YR4BiBD4x/FtFXINxhJQftALyWo32AsBSBRtyHkQ5aX1mE7Sm4IDhgNQnhCXVJ34XpB3lGS4byIsyhjrGl5fuC8LgO/rbyE78Hs5Fy8fPKgt0vhAYIJhgIp8q1w/j9OhcCQkAICAEhIASEQC0ISABRC3q9OK7tDQwjYMS+xqweQggPdtttt6BejL8BfphSxIRHXmNSmYwb2TnO9VBnhmAIjFBxg5GFoTUBRMwMGAMGAwHBUBCetFAjL5cfzEpYGWTVdY011ghONY3xsTxMzCM+HmINCN7NvXqR4U16dg72tkKNIMaI1XRTryuCpcXjCCPHii4rv2gFUHdxvcZhJ9Z5o7GlHCZ4gHGMCaFWubaGhkLsGInwmAaw6h8TjCzfBVo/qNPzTswLTEAI3ggucLbEe2nbqONnEcxy3ObJhwkdYeRhWPH7YoQAgL4gKYBgKzK+vdjXQNyeLD6Mt2lS0b5gwBHQGMG4x/lBiInwC4eK9CMIMEz4QBx7B0JH82wd9xGWbnxE+GhqpKhwggHaCGCN1g/mMEYIRnkH2ghpRD1RDnaAwbkVeG+77bZBLZT+iWdoqFAf1BWaGHGdkpc04QPvKho/rSxpeS3SDtPi6Z4QEAJCQAgIASEgBKpBQCYY1aCmOCVmgAm+0T777BMm7Ezambyz/64JHeaee+6S/Rw2V6xOxqubsQ1YbLtlAgjUmmPCbszS5n7WZD2Ow7nFycsPq8OoT7NyyeoxzFMtThuTeajmOnY0WU/hA3kph33SPs5sBYtgGZeVlVdWqVmNZ2Ucr8as6jebGoktZXv66aeDJlCMs5W5XFtLxrG6iP0xkBbMOsIjfE3wDCEPZI4g0VpBEIAWA4JCGGCY5Cwypt2ex9+k3TN7WPueY4GEheH9rLDHlLY7hpXLwpkwwq753mOy8LRB+gi7tjAW3/xAcL9cH5FMw7C3vieZ7+S1vZsj+UWLC8EDjscwX0EIBKG9gCAPrQfqIU0wkpfXIvGzyhIykPKvXDtMiaJbQkAICAEhIASEgBCoCgFpQFQFmyIxQUaVnlVVbKVjhgXVaTQgWMWEWWUVlq05Y78OrLAnV4TTUEV9HSJNU5/GKzArkkziK6Vy+YFhYUUS9WlWLikfDAI+C1iVbCbBKOOzwMwwGp0XWz2HgZ5lllnC61jxpW6hcliGQNE/mGRWsXEYyg9bemz9cSDIinMzqVHYImj5/e9/X/I7EJexSFvD2SNMtjHDqOmz2p9k7GFKqRf8GiAQgDBRMkac1XBMmNB6mHPOOQNznGTq47zlnWO6gXlH/D1Tt5g+JQmhCKv77I9ujimztuhKxo2vEQCiIWCaV+AA8w4WfJ/HHHNM2OYMoSdk/hToP2rdphThy0orrRTMMxAqgBuCNIQLeUIcTFd4juANgacJGsCCfIMh9YLmCL49ilKt8e09Jpwp0g4tjo5CQAgIASEgBISAEKgVAWlA1Ipgm8RHPTveWSIv24QjfDlCJRmmB3VoVI1xzIYdOGYLCB/MUR3aDjiHQ3UbwQHO6Eg/djKX9S5zcIeQAydu5A1GCg0Ls2XOipt1Py8/qJnDwFE2NDlQ72al05jxrDQbcX+a7hr74RV5fh/SwteSL4QFK6+8chAwgT2ruDgQjCkPyzgc5zjzBEeYSZhCTGhgIo1pTIZv5HUaVvXAFkEZ/hJuuOGG4OwPQRbMa8ysW7mKtDVMHWj7CIHYKQHngttvv70lUTqaecKIESPCbhc4a8SRZEyswJtAxFbj4+dFzykTdYj/BRxP2hajaTtX8I3iDwFmnH4CgQCMd6WEEAPNGfoMsMUhpO3ljXkIDD1+X+gfcBgLZmjY4KuiHoRpGXWLmQemQwgTyvUJOMKln0LYZv5ryAt5ol5HjhwZ8EvbSSMvz7XGRxMGwpQH/xxF2mFefvRMCAgBISAEhIAQEAKVICANiErQauOw2N/jBZ/Vs3KEcIDw5YjJOIwRTCiO44zQVMBBnK2+soUfath4oYeRgOnEF8Cqq65qUbodzXbZ1JCvvPLK4LTO8oQmwh133FFyDGnhuiXiLywdu2/X5fJD3lmRhNmDYBCw357YtLeH9KyR3ofAl+XfDENN+FrJMDJMcQxI/Rr2MJ1oiFi4cliSHwuLY08EDyY4ghGiPVo7qTXvlcRvFLa23SZ5YfUbsyR2HTBtA8OC5zhSLdfW0DJCu8B2q6AukqvlpImJBdokpl2C0AOm33ZxsPfBqMP84lMhi+I8WhhrD1yTF7ZeResAPwYQuzjY7hJxfM5x4IjA49FHHw1bXCIYSDLvcZyQoP8X36NPwRQCph5CC8McTOLUEiEIQgfKDRHettGM0wkPU/7F5YsfW1y0rdB6wISFvuzoo48OZlrx1qZxPM7RnKDfwFdFLPDBvwwCW9MIwcwLDQl7V1pe7BnpVhOfeJYG2kbsOsSWrvjooC8t1w6JLxICQkAICAEhIASEQD0Q6OPVLzvqkZDS6N0IYBaBKj2OH7NMK9juDWYKhiFtkl0OQVTRUReOzT3Kxcl7Xi4/5BUHgM1gkPPy3YxnMLIwVOb/IZmHcljG4XFuST3GzhXj573xPK2tYbaEwACTBbaWRYhh/giyMMIfAqYK5og1DsczGGnMCGCg60EIo/jezUQkmSbCDvKPrw8zQTDTG4RRle4uQzvkezSBTvJ9bF1JXpI+EJLhKr1GCPrhhx8GISqMPG0YoQHaHWibVEMIY/mmzKyk0jRqjU9/Slni/i2tHVaaL4UXAkJACLQqAvUeG1q1nMpX70OAeXU7kQQQ7VRbyqsQEAK9BoFYAFFroXEyiNYRJhOs5JvJRq3plovP9qCYXvBedjxB6MCOGWhroBnQLoQJElpAmJbhQ4PtORHGYW6T1OZolzIpn0JACAgBISAEhIAQaAYCEkA0A3W9UwgIASFQBgHU/Vkdxw9HrXTrrbcG3xD4b4i3q6w13SLx2ZWD9yP4gGnHXIQtLNuNMLfArwa7WrDLCCY29fIx0W5YKL9CQAgIASEgBISAEKgWAQkgqkVO8YSAEBACQkAICAEhIASEgBAQAkJACAiBwghoF4zCUCmgEBACQkAICAEhIASEgBAQAkJACAgBIVAtAhJAVIuc4gkBISAEhIAQEAJCQAgIASEgBISAEBAChRGQAKIwVAooBISAEBACQkAICAEhIASEgBAQAkJACFSLgAQQ1SKneEJACAgBISAEhIAQEAJCQAgIASEgBIRAYQQkgCgMlQIKASEgBISAEBACQkAICAEhIASEgBAQAtUiIAFEtcgpnhAQAkJACAgBISAEhIAQEAJCQAgIASFQGAEJIApDpYBCQAgIASEgBISAEBACQkAICAEhIASEQLUISABRLXKKJwSEgBAQAkJACAgBISAEhIAQEAJCQAgURkACiMJQKaAQEAJCQAgIASEgBISAEBACQkAICAEhUC0CEkBUi5ziCQEhIASEgBAQAkJACAgBISAEhIAQEAKFEZAAojBUCigEhIAQEAJCQAgIASEgBISAEBACQkAIVIvA/wOkDhV15NIjKwAAAABJRU5ErkJggg=="
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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "164a07b7",
+   "metadata": {},
+   "source": [
+    "### On region view (the interval of a gene)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "
   
  
"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9f8e1ba4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of variants in the DRD2 interval chr11:113409605-113475691 is: 8126\n"
+     ]
+    }
+   ],
+   "source": [
+    "drd2_interval = \"chr11:113409605-113475691\"\n",
+    "drd2_interval_ht = filter_by_intervals(drd2_interval)\n",
+    "print(f\"The total number of variants in the DRD2 interval {drd2_interval} is: {drd2_interval_ht.count()}\")"
+   ]
+  },
+  {
+   "attachments": {
+    "Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "ac393319",
+   "metadata": {},
+   "source": [
+    "### On gene page\n",
+    "\n",
+    "To get the number of variants shown on the gene page, if you click 'all' and check 'exomes', 'SNVs', 'Indels', and 'Filtered variants' as this: \n",
+    "\n",
+    "![Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png](attachment:Screenshot%202024-11-01%20at%209.28.50%E2%80%AFPM.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e6bf7236",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of variants in DRD2 is:  1764\n"
+     ]
+    }
+   ],
+   "source": [
+    "drd2_ht = filter_by_gene_symbol('drd2')\n",
+    "print(\"The total number of variants in DRD2 is: \", drd2_ht.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bff63bb",
+   "metadata": {},
+   "source": [
+    "## Filter to variants by Ensembl Variant Effect Predictor (VEP)\n",
+    "\n",
+    "The examples below show the VEP based filtering using the Table filtered to DRD2."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "756a996c-bad2-4d24-a304-6eb5e3efca65",
+   "metadata": {},
+   "source": [
+    "### Filter to `lof`, `missense`, and `synonymous` variants passing filters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "700582e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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,"1"),("ENSP_mappings","5aer"),("ENSP_mappings","5aer"),("ENSP_mappings","6cm4"),("ENSP_mappings","6luq"),("ENSP_mappings","6vms"),("ENSP_mappings","7dfp"),("ENSP_mappings","7jvr"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000542968.5:c.1323C>A","ENSP00000442172.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,441,441,"ENSP00000442172","Ensembl",-1,"ENST00000542968",1,["P14416-1"],"T"),(1,"L","A1","protein_coding",NA,NA,1353,1353,1320,1320,"ctC/ctA",["synonymous_variant"],NA,[("Gene3D","1"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000544518.5:c.1320C>A","ENSP00000441068.1:p.Leu440=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,440,440,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"T"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["intron_variant","non_coding_transcript_variant"],NA,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,"ENST00000546284.1:n.245-811G>T",NA,NA,"MODIFIER","2/3",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"T"),(1,"L",NA,"protein_coding",1,NA,1677,1677,1323,1323,"ctC/ctA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.1323C>A","NP_000786.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,441,441,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"T"),(1,"L",NA,"protein_coding",NA,NA,1590,1590,1236,1236,"ctC/ctA",["synonymous_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.1236C>A","NP_057658.2:p.Leu412=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,412,412,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"T"),(1,"L",NA,"protein_coding",NA,NA,1405,1405,1323,1323,"ctC/ctA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.1323C>A","XP_016872785.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,441,441,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"T")]"SNV"5.00e+00"AS_MQ"FalseFalseFalseFalseFalseTrueFalseFalse"multi-snv"2False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,2955,2914,2974,2923,2564,2680]07685[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,2955,2914,2974,2923,2564,2680]07685[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,1,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]002.96e+001.86e-01NA0.00e+003.00e-02-2.55e-01NANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+\n",
+       "| locus           | alleles    |\n",
+       "+-----------------+------------+\n",
+       "| locus   | array |\n",
+       "+-----------------+------------+\n",
+       "| chr11:113410731 | [\"C\",\"A\"]  |\n",
+       "| chr11:113410731 | [\"C\",\"T\"]  |\n",
+       "| chr11:113410735 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410736 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410736 | [\"G\",\"T\"]  |\n",
+       "+-----------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(2,1.37e-06,1461866,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(2,4.... |\n",
+       "| [(1,6.84e-07,1461866,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(75,5.13e-05,1461876,0),(75,5.13e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
+       "| [(15,1.03e-05,1461882,0),(15,1.03e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
+       "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|                2 |         4.47e-05 |            44724 |\n",
+       "|                1 |         8.99e-07 |          1112010 |\n",
+       "|                8 |         7.19e-06 |          1112004 |\n",
+       "|               15 |         1.35e-05 |          1112010 |\n",
+       "|                1 |         8.99e-07 |          1112010 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                              0 | \"amr\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|          4.57e-05 |             43740 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          5.71e-06 |            350102 |                               0 |\n",
+       "|          2.86e-06 |            350106 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| \"amr\"                  |\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| \"nfe\"                  |\n",
+       "| NA                     |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(7.41e-06,2.77e-06),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(4.19e-05,3.82e-05),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(6.16e-06,4.89e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                7.41e-06 | \"amr\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                3.09e-06 | \"nfe\"                           |\n",
+       "|                8.10e-06 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                2.77e-06 | \"amr\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                2.24e-06 | \"nfe\"                           |\n",
+       "|                6.42e-06 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                 7.58e-06 | \"amr\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                 9.50e-07 | \"nfe\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                 2.84e-06 | \"amr\"                            |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      bool | set         | set |  float64 |  float64 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      True | {\"rs140938110\"}  | {}       | 5.56e+00 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.56e+00 | 6.00e+01 |\n",
+       "|      True | {\"rs201801648\"}  | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|      True | {\"rs1159504221\"} | {}       | 5.49e-01 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.49e-01 | 6.00e+01 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
+       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
+       "|       0.00e+00 |          127501 | 1.23e+01 |            3.70e-02 |\n",
+       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
+       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| info.SB               | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| array          |  float64 |      int32 |    float64 |    float64 |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| [134,83,109,52]       | 9.81e-01 |        378 |   1.31e+01 |   6.00e+01 |\n",
+       "| [134,83,109,52]       | 9.81e-01 |        378 |   0.00e+00 |   6.00e+01 |\n",
+       "| [3240,2211,2993,1962] | 7.34e-01 |      10404 |   0.00e+00 |   6.00e+01 |\n",
+       "| [386,195,316,166]     | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
+       "| [386,195,316,166]     | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|          0.00e+00 |        2.94e-01 |               1895 |   1.06e+01 |\n",
+       "|          0.00e+00 |        9.04e-01 |               1967 |   9.88e+00 |\n",
+       "|          0.00e+00 |        1.00e+00 |             127480 |   1.23e+01 |\n",
+       "|          0.00e+00 |        9.11e-01 |              11899 |   1.17e+01 |\n",
+       "|          0.00e+00 |        3.28e-02 |                408 |   8.16e+00 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE      | info.AS_SOR | info.AS_VarDP |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "|                float64 | array          |     float64 |         int32 |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "|               2.52e-01 | [134,83,56,19]        |    1.42e+00 |           179 |\n",
+       "|              -5.01e-01 | [134,83,53,33]        |    6.81e-01 |           199 |\n",
+       "|               1.60e-02 | [3240,2211,2991,1961] |    7.34e-01 |         10383 |\n",
+       "|               9.70e-02 | [386,195,304,161]     |    6.47e-01 |          1013 |\n",
+       "|              -9.23e-01 | [386,195,12,5]        |    7.90e-01 |            50 |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           bool |                       bool |                   bool |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                      False |                  False |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                      False |                  False |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|     False |      False |            False |         False |       5.27e+00 |\n",
+       "|     False |      False |            False |         False |       5.40e+00 |\n",
+       "|     False |      False |            False |         False |       8.85e+00 |\n",
+       "|     False |      False |            False |         False |       7.50e+00 |\n",
+       "|     False |      False |            False |         False |       5.00e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|             -1.37e-06 |\n",
+       "|             -6.84e-07 |\n",
+       "|             -5.13e-05 |\n",
+       "|             -1.03e-05 |\n",
+       "|             -6.84e-07 |\n",
+       "+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.ZonDIhdwyghm7SuWkX... |\n",
+       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.Hp20SlFHUdM7jlWmVV... |\n",
+       "| [\"ga4gh:VA.SBSoboxxiC0V-mhjjHg2geRlf3I0Ib2d\",\"ga4gh:VA.GzUD_86k_EDbtk-R58... |\n",
+       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.OXPDvbuulFZ9CSk4Xt... |\n",
+       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.4qhkDG1qrfqiD2TeOA... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"A\"]           |\n",
+       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"T\"]           |\n",
+       "| [113410734,113410734] | [113410735,113410735] | [\"G\",\"A\"]           |\n",
+       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"A\"]           |\n",
+       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"T\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| str               |     int32 | str    | str                            |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| \"C/A\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tA\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tT\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410735 | \".\"    | \"chr11\t113410735\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/T\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tT\t.\t.\tGT\" |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq              |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [0,0,0,0,12,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
+       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
+       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,0,65,332,11308,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
+       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                       7715 |\n",
+       "|                                       7715 |\n",
+       "|                                       7717 |\n",
+       "|                                       7685 |\n",
+       "|                                       7685 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                         32 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq   |\n",
+       "+----------------------------------------------+\n",
+       "| array                                 |\n",
+       "+----------------------------------------------+\n",
+       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0] |\n",
+       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]    |\n",
+       "+----------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq          |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [6,0,1,0,14,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
+       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
+       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,2,67,335,11310,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
+       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                           7715 |\n",
+       "|                                           7715 |\n",
+       "|                                           7717 |\n",
+       "|                                           7685 |\n",
+       "|                                           7685 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "|                                             32 |\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
+       "| [1,0,0,2,3,3,8,4,0,0]                      |\n",
+       "| [0,0,1,2,4,2,4,1,0,0]                      |\n",
+       "| [0,0,0,0,0,1,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           1 |\n",
+       "|                                           0 |\n",
+       "|                                           3 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        2.77e+01 |\n",
+       "|                                          0 |                        2.75e+01 |\n",
+       "|                                          0 |                        2.28e+01 |\n",
+       "|                                          0 |                        3.44e+00 |\n",
+       "|                                          0 |                        2.96e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            4.10e+00 |                       6.02e-01 |\n",
+       "|                            4.07e+00 |                       7.19e-01 |\n",
+       "|                            2.69e+00 |                       2.67e-01 |\n",
+       "|                            2.28e-01 |                             NA |\n",
+       "|                            1.86e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                 0.00e+00 |                    8.78e+00 |\n",
+       "|                                 2.00e-02 |                    8.78e+00 |\n",
+       "|                                 1.00e-02 |                    8.67e+00 |\n",
+       "|                                 1.00e-02 |                   -2.55e-01 |\n",
+       "|                                 3.00e-02 |                   -2.55e-01 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                      0.00e+00 |                          9.89e-01 |\n",
+       "|                      0.00e+00 |                          9.89e-01 |\n",
+       "|                      1.30e-01 |                          1.18e-01 |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of lof, missense, and synonymous variants passing filters in DRD2 is:  664\n"
+     ]
+    }
+   ],
+   "source": [
+    "var_ht = filter_by_csqs(['lof','missense','synonymous'], ht=drd2_interval_ht)\n",
+    "var_ht.show(5)\n",
+    "print(\"The total number of lof, missense, and synonymous variants passing filters in DRD2 is: \", var_ht.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35bc2c92-d90e-4a26-a04b-5bd7dc4e7d23",
+   "metadata": {},
+   "source": [
+    "### Filter to `lof` variants passing filters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9887fdb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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","HC","NAGNAG_SITE",NA,"INTRON_SIZE:1491",NA,NA,NA,NA,NA,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"C"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],768,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"C"),(1,NA,NA,"protein_coding",1,NA,NA,NA,NA,NA,NA,["splice_acceptor_variant"],NA,NA,NA,NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.811-2A>G",NA,NA,"HIGH","6/7","HC","NAGNAG_SITE",NA,"INTRON_SIZE:1491","ENST00000362072.8",NA,NA,NA,NA,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"C"),(1,NA,NA,"protein_coding",NA,NA,NA,NA,NA,NA,NA,["splice_acceptor_variant"],NA,NA,NA,NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.724-2A>G",NA,NA,"HIGH","5/6","HC","NAGNAG_SITE",NA,"INTRON_SIZE:2537",NA,NA,NA,NA,NA,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"C"),(1,NA,NA,"protein_coding",NA,NA,NA,NA,NA,NA,NA,["splice_acceptor_variant"],NA,NA,NA,NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.811-2A>G",NA,NA,"HIGH","6/7","HC","NAGNAG_SITE",NA,"INTRON_SIZE:1491",NA,NA,NA,NA,NA,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"C")]"SNV"5.84e+00"AS_MQ"FalseFalseFalseFalseFalseTrueTrueFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,929,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2699,3244,4728,36120,209411,224276,48878,7369,3147,3425,5116,7827,11506,15293,17615,18578,18759,20940]070149[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][1313,0,2,0,1481,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][618,1142,2737,3303,4730,36128,209411,224276,48878,7369,3147,3425,5116,7827,11506,15293,17615,18578,18759,20940]070149[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]003.50e+015.83e+00NA9.80e-01-7.90e-016.35e+00NANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+-------------+\n",
+       "| locus           | alleles     |\n",
+       "+-----------------+-------------+\n",
+       "| locus   | array  |\n",
+       "+-----------------+-------------+\n",
+       "| chr11:113412554 | [\"A\",\"G\"]   |\n",
+       "| chr11:113412612 | [\"CT\",\"C\"]  |\n",
+       "| chr11:113412614 | [\"ACG\",\"A\"] |\n",
+       "| chr11:113412865 | [\"G\",\"A\"]   |\n",
+       "| chr11:113412885 | [\"T\",\"C\"]   |\n",
+       "+-----------------+-------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(1,6.85e-07,1459578,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(2,1.37e-06,1461892,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(2,1.37e-06,1461894,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(1,6.85e-07,1459642,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33422,0),(0,0.... |\n",
+       "| [(1,6.86e-07,1458160,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33408,0),(0,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|                1 |         8.99e-07 |          1111998 |\n",
+       "|                2 |         1.80e-06 |          1112010 |\n",
+       "|                2 |         1.80e-06 |          1112012 |\n",
+       "|                1 |         8.99e-07 |          1111812 |\n",
+       "|                1 |         9.00e-07 |          1111636 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          5.71e-06 |            350106 |                               0 |\n",
+       "|          5.71e-06 |            350108 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| \"nfe\"                  |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                3.00e-07 | \"nfe\"                           |\n",
+       "|                3.00e-07 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                1.10e-07 | \"nfe\"                           |\n",
+       "|                1.10e-07 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                 9.50e-07 | \"nfe\"                            |\n",
+       "|                 9.50e-07 | \"nfe\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
+       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+----------+----------+----------+----------+----------------+\n",
+       "| was_split | rsid     | filters  |  info.FS |  info.MQ | info.MQRankSum |\n",
+       "+-----------+----------+----------+----------+----------+----------------+\n",
+       "|      bool | set | set |  float64 |  float64 |        float64 |\n",
+       "+-----------+----------+----------+----------+----------+----------------+\n",
+       "|      True | NA       | {}       | 2.83e+00 | 6.00e+01 |       0.00e+00 |\n",
+       "|     False | NA       | {}       | 5.48e-01 | 6.00e+01 |       0.00e+00 |\n",
+       "|      True | NA       | {}       | 2.32e+00 | 6.00e+01 |       0.00e+00 |\n",
+       "|      True | NA       | {}       | 1.02e+00 | 6.00e+01 |       0.00e+00 |\n",
+       "|     False | NA       | {}       | 2.45e+00 | 6.00e+01 |       0.00e+00 |\n",
+       "+-----------+----------+----------+----------+----------+----------------+\n",
+       "\n",
+       "+-----------------+----------+---------------------+---------------+----------+\n",
+       "| info.QUALapprox |  info.QD | info.ReadPosRankSum | info.SB       | info.SOR |\n",
+       "+-----------------+----------+---------------------+---------------+----------+\n",
+       "|           int64 |  float64 |             float64 | array  |  float64 |\n",
+       "+-----------------+----------+---------------------+---------------+----------+\n",
+       "|            1498 | 1.05e+01 |           -5.05e-01 | [63,17,52,10] | 1.04e+00 |\n",
+       "|            3458 | 1.79e+01 |            4.32e-01 | [65,38,58,32] | 7.50e-01 |\n",
+       "|            5336 | 1.56e+01 |            4.62e-01 | [99,81,94,68] | 8.22e-01 |\n",
+       "|             553 | 7.79e+00 |            2.96e-01 | [14,32,9,16]  | 4.68e-01 |\n",
+       "|             890 | 1.11e+01 |            1.80e-01 | [7,37,8,28]   | 3.79e-01 |\n",
+       "+-----------------+----------+---------------------+---------------+----------+\n",
+       "\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "| info.VarDP | info.AS_FS | info.AS_MQ | info.AS_MQRankSum | info.AS_pab_max |\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "|      int32 |    float64 |    float64 |           float64 |         float64 |\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "|        142 |   1.10e+00 |   6.00e+01 |          0.00e+00 |        1.00e+00 |\n",
+       "|        193 |   5.48e-01 |   6.00e+01 |          0.00e+00 |        5.90e-01 |\n",
+       "|        342 |   8.20e+00 |   6.00e+01 |          0.00e+00 |        6.65e-01 |\n",
+       "|         71 |   1.04e+00 |   6.00e+01 |          0.00e+00 |        3.92e-01 |\n",
+       "|         80 |   2.45e+00 |   6.00e+01 |          0.00e+00 |        4.53e-01 |\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "| info.AS_QUALapprox | info.AS_QD | info.AS_ReadPosRankSum | info.AS_SB_TABLE |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "|              int64 |    float64 |                float64 | array     |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "|                661 |   1.35e+01 |              -1.83e+00 | [63,17,21,4]     |\n",
+       "|               3458 |   1.79e+01 |               4.32e-01 | [65,38,58,32]    |\n",
+       "|               3455 |   1.80e+01 |               4.62e-01 | [99,81,58,32]    |\n",
+       "|                531 |   8.56e+00 |               2.96e-01 | [14,32,8,15]     |\n",
+       "|                890 |   1.11e+01 |               1.80e-01 | [7,37,8,28]      |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "| info.AS_SOR | info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "|     float64 |         int32 |           bool |                       bool |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "|    9.29e-01 |            49 |           True |                      False |\n",
+       "|    7.50e-01 |           193 |          False |                         NA |\n",
+       "|    1.15e+00 |           192 |          False |                         NA |\n",
+       "|    5.03e-01 |            62 |           True |                      False |\n",
+       "|    3.79e-01 |            80 |          False |                      False |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "| info.sibling_singleton | info.omni | info.mills | info.monoallelic |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                   bool |      bool |       bool |             bool |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                     NA |     False |      False |            False |\n",
+       "|                     NA |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "\n",
+       "+---------------+----------------+-----------------------+\n",
+       "| info.only_het | info.AS_VQSLOD | info.inbreeding_coeff |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|          bool |        float64 |               float64 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|         False |       5.30e+00 |             -6.84e-07 |\n",
+       "|         False |       4.25e+00 |             -1.37e-06 |\n",
+       "|         False |       3.61e+00 |             -1.37e-06 |\n",
+       "|         False |       5.39e+00 |             -6.84e-07 |\n",
+       "|         False |       5.84e+00 |             -1.37e-06 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.mqU9WPvGr3hduyybbeN-AKJ1zN-kclsI\",\"ga4gh:VA.uvlicmv1vZnrBBqm0U... |\n",
+       "| [\"ga4gh:VA.NztLKtEJA7H0h_Tz31YRmTUR4hT2PNxO\",\"ga4gh:VA.5xtydtW8O1beQhV-1c... |\n",
+       "| [\"ga4gh:VA.Lx5RcGLe0vnqmstRd134JgqaJUNZIzCF\",\"ga4gh:VA.N3JXRaksYTV6CSLvZ_... |\n",
+       "| [\"ga4gh:VA.uAq5BRYMw_lO7PZcOF5TpbGC5hA8JCCE\",\"ga4gh:VA.rN4HBsKxAnhIfQxMXe... |\n",
+       "| [\"ga4gh:VA.Hj6Aa_k4TOMxqWIs6lE_cgxiRrPFWp8C\",\"ga4gh:VA.NDTpZX0YUzM19pYVv6... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113412553,113412553] | [113412554,113412554] | [\"A\",\"G\"]           |\n",
+       "| [113412611,113412612] | [113412613,113412613] | [\"CT\",\"\"]           |\n",
+       "| [113412613,113412614] | [113412616,113412616] | [\"ACG\",\"\"]          |\n",
+       "| [113412864,113412864] | [113412865,113412865] | [\"G\",\"A\"]           |\n",
+       "| [113412884,113412884] | [113412885,113412885] | [\"T\",\"C\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+----------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                        |\n",
+       "+-------------------+-----------+--------+----------------------------------+\n",
+       "| str               |     int32 | str    | str                              |\n",
+       "+-------------------+-----------+--------+----------------------------------+\n",
+       "| \"A/G\"             | 113412554 | \".\"    | \"chr11\t113412554\t.\tA\tG\t.\t.\tGT\"   |\n",
+       "| \"T/-\"             | 113412613 | \".\"    | \"chr11\t113412612\t.\tCT\tC\t.\t.\tGT\"  |\n",
+       "| \"CG/-\"            | 113412616 | \".\"    | \"chr11\t113412614\t.\tACG\tA\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113412865 | \".\"    | \"chr11\t113412865\t.\tG\tA\t.\t.\tGT\"   |\n",
+       "| \"T/C\"             | 113412885 | \".\"    | \"chr11\t113412885\t.\tT\tC\t.\t.\tGT\"   |\n",
+       "+-------------------+-----------+--------+----------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"splice_donor_variant\"      |\n",
+       "| \"frameshift_variant\"        |\n",
+       "| \"frameshift_variant\"        |\n",
+       "| \"stop_gained\"               |\n",
+       "| \"splice_acceptor_variant\"   |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq                 |\n",
+       "+------------------------------------------------------------+\n",
+       "| array                                               |\n",
+       "+------------------------------------------------------------+\n",
+       "| [0,0,0,0,134,0,1469,0,311660,0,82,0,416443,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,1,0,2,0,314386,0,0,0,416555,0,0,0,0,0,0,2]        |\n",
+       "| [0,0,0,0,1,0,1,0,314388,0,0,0,416555,0,0,0,0,0,0,2]        |\n",
+       "| [0,0,0,0,547,0,1910,0,310815,0,33,0,416515,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,929,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1] |\n",
+       "+------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,1555,817,16216,229270,272936,45138,9370,3566,3616,4059,4521,5017,549... |\n",
+       "| [0,0,1,279,16685,230939,273577,45298,9412,3597,3653,4094,4550,5033,5504,5... |\n",
+       "| [0,0,0,278,16685,230938,273578,45300,9412,3597,3653,4094,4550,5033,5504,5... |\n",
+       "| [0,0,1141,1461,3821,36585,211757,225880,49280,7441,3163,3441,5118,7834,11... |\n",
+       "| [0,0,2699,3244,4728,36120,209411,224276,48878,7369,3147,3425,5116,7827,11... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                     102137 |\n",
+       "|                                     102187 |\n",
+       "|                                     102188 |\n",
+       "|                                      70182 |\n",
+       "|                                      70149 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          1 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                 |\n",
+       "+----------------------------------------------------------------+\n",
+       "| array                                                   |\n",
+       "+----------------------------------------------------------------+\n",
+       "| [441,0,0,0,850,1,1469,0,311660,0,82,0,416443,0,0,0,0,0,0,1]    |\n",
+       "| [1,0,0,0,1,0,2,0,314386,0,0,0,416555,0,0,0,0,0,0,2]            |\n",
+       "| [0,0,0,0,1,0,1,0,314388,0,0,0,416555,0,0,0,0,0,0,2]            |\n",
+       "| [890,0,0,0,783,0,1910,0,310815,0,33,0,416515,0,0,0,0,0,0,1]    |\n",
+       "| [1313,0,2,0,1481,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1] |\n",
+       "+----------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [244,858,1593,833,16218,229270,272936,45138,9370,3566,3616,4059,4521,5017... |\n",
+       "| [0,0,1,279,16685,230939,273577,45298,9412,3597,3653,4094,4550,5034,5504,5... |\n",
+       "| [0,0,0,278,16685,230938,273578,45300,9412,3597,3653,4094,4550,5033,5504,5... |\n",
+       "| [431,622,1175,1493,3823,36589,211757,225880,49281,7441,3163,3441,5118,783... |\n",
+       "| [618,1142,2737,3303,4730,36128,209411,224276,48878,7369,3147,3425,5116,78... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                         102137 |\n",
+       "|                                         102187 |\n",
+       "|                                         102188 |\n",
+       "|                                          70182 |\n",
+       "|                                          70149 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              1 |\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,1,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           1 |\n",
+       "|                                           1 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        3.40e+01 |\n",
+       "|                                          0 |                        3.20e+01 |\n",
+       "|                                          0 |                        3.30e+01 |\n",
+       "|                                          0 |                        4.50e+01 |\n",
+       "|                                          0 |                        3.50e+01 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            5.66e+00 |                             NA |\n",
+       "|                            4.56e+00 |                             NA |\n",
+       "|                            4.99e+00 |                             NA |\n",
+       "|                            8.76e+00 |                             NA |\n",
+       "|                            5.83e+00 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             9.60e-01 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             9.80e-01 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                -8.30e-01 |                    6.33e+00 |\n",
+       "|                                -4.00e-02 |                    8.89e+00 |\n",
+       "|                                -1.20e-01 |                    3.00e-01 |\n",
+       "|                                -1.10e-01 |                    8.79e+00 |\n",
+       "|                                -7.90e-01 |                    6.35e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of lof variants passing filters in DRD2 is:  17\n"
+     ]
+    }
+   ],
+   "source": [
+    "var_ht = filter_by_csqs(['lof'], ht=drd2_interval_ht)\n",
+    "var_ht.show(5)\n",
+    "print(\"The total number of lof variants passing filters in DRD2 is: \", var_ht.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "871c539b-a1c2-41a3-a33d-37649b603de2",
+   "metadata": {},
+   "source": [
+    "### Filter to `missense` variants passing filters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "86596aaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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A,[("Gene3D","1"),("ENSP_mappings","5aer"),("ENSP_mappings","5aer"),("ENSP_mappings","6cm4"),("ENSP_mappings","6luq"),("ENSP_mappings","6vms"),("ENSP_mappings","7dfp"),("ENSP_mappings","7jvr"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000542968.5:c.1319T>C","ENSP00000442172.1:p.Ile440Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,440,440,"ENSP00000442172","Ensembl",-1,"ENST00000542968",1,["P14416-1"],"G"),(1,"I/T","A1","protein_coding",NA,NA,1349,1349,1316,1316,"aTc/aCc",["missense_variant"],NA,[("Gene3D","1"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000544518.5:c.1316T>C","ENSP00000441068.1:p.Ile439Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,439,439,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"G"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["intron_variant","non_coding_transcript_variant"],NA,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,"ENST00000546284.1:n.245-807A>G",NA,NA,"MODIFIER","2/3",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"G"),(1,"I/T",NA,"protein_coding",1,NA,1673,1673,1319,1319,"aTc/aCc",["missense_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.1319T>C","NP_000786.1:p.Ile440Thr",NA,"MODERATE",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,440,440,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"G"),(1,"I/T",NA,"protein_coding",NA,NA,1586,1586,1232,1232,"aTc/aCc",["missense_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.1232T>C","NP_057658.2:p.Ile411Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,411,411,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"G"),(1,"I/T",NA,"protein_coding",NA,NA,1401,1401,1319,1319,"aTc/aCc",["missense_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.1319T>C","XP_016872785.1:p.Ile440Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,440,440,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"G")]"SNV"4.72e+00"AS_MQ"FalseFalseFalseFalseFalseTrueFalseFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,12,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,49,301,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,2954,2914,2973,2922,2564,2680]07683[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][4,0,0,0,13,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,50,304,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,2954,2914,2973,2922,2564,2680]07683[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]002.72e+014.02e+006.41e-010.00e+003.00e-026.25e+000.00e+005.08e-01
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+\n",
+       "| locus           | alleles    |\n",
+       "+-----------------+------------+\n",
+       "| locus   | array |\n",
+       "+-----------------+------------+\n",
+       "| chr11:113410731 | [\"C\",\"A\"]  |\n",
+       "| chr11:113410731 | [\"C\",\"T\"]  |\n",
+       "| chr11:113410735 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410738 | [\"G\",\"T\"]  |\n",
+       "| chr11:113410740 | [\"A\",\"G\"]  |\n",
+       "+-----------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(2,1.37e-06,1461866,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(2,4.... |\n",
+       "| [(1,6.84e-07,1461866,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(75,5.13e-05,1461876,0),(75,5.13e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
+       "| [(2,1.37e-06,1461886,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(1,6.84e-07,1461884,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(1,2.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|                2 |         4.47e-05 |            44724 |\n",
+       "|                1 |         8.99e-07 |          1112010 |\n",
+       "|                8 |         7.19e-06 |          1112004 |\n",
+       "|                2 |         5.04e-05 |            39700 |\n",
+       "|                1 |         2.24e-05 |            44724 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                              0 | \"amr\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 2 |\n",
+       "|                              0 | \"eas\"                 |                 1 |\n",
+       "|                              0 | \"amr\"                 |                 1 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|          4.57e-05 |             43740 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          5.71e-06 |            350102 |                               0 |\n",
+       "|          2.77e-05 |             36070 |                               0 |\n",
+       "|          2.29e-05 |             43740 |                               0 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| \"amr\"                  |\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| \"eas\"                  |\n",
+       "| \"amr\"                  |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(7.41e-06,2.77e-06),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(4.19e-05,3.82e-05),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(8.35e-06,3.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                7.41e-06 | \"amr\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                3.09e-06 | \"nfe\"                           |\n",
+       "|                8.35e-06 | \"eas\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                2.77e-06 | \"amr\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                2.24e-06 | \"nfe\"                           |\n",
+       "|                3.12e-06 | \"eas\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                 7.58e-06 | \"amr\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                 9.50e-07 | \"nfe\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                 2.84e-06 | \"amr\"                            |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      bool | set         | set |  float64 |  float64 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      True | {\"rs140938110\"}  | {}       | 5.56e+00 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.56e+00 | 6.00e+01 |\n",
+       "|      True | {\"rs201801648\"}  | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|     False | {\"rs1223741945\"} | {}       | 1.38e+01 | 6.00e+01 |\n",
+       "|     False | NA               | {}       | 3.13e+00 | 6.00e+01 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
+       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
+       "|       0.00e+00 |          127501 | 1.23e+01 |            3.70e-02 |\n",
+       "|       0.00e+00 |            3253 | 1.30e+01 |           -1.33e+00 |\n",
+       "|       0.00e+00 |             402 | 1.01e+01 |            7.13e-01 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| info.SB               | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| array          |  float64 |      int32 |    float64 |    float64 |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| [134,83,109,52]       | 9.81e-01 |        378 |   1.31e+01 |   6.00e+01 |\n",
+       "| [134,83,109,52]       | 9.81e-01 |        378 |   0.00e+00 |   6.00e+01 |\n",
+       "| [3240,2211,2993,1962] | 7.34e-01 |      10404 |   0.00e+00 |   6.00e+01 |\n",
+       "| [63,66,76,46]         | 1.28e+00 |        251 |   1.38e+01 |   6.00e+01 |\n",
+       "| [17,8,12,3]           | 1.29e+00 |         40 |   3.13e+00 |   6.00e+01 |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|          0.00e+00 |        2.94e-01 |               1895 |   1.06e+01 |\n",
+       "|          0.00e+00 |        9.04e-01 |               1967 |   9.88e+00 |\n",
+       "|          0.00e+00 |        1.00e+00 |             127480 |   1.23e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |               3253 |   1.30e+01 |\n",
+       "|          0.00e+00 |        1.54e-01 |                402 |   1.01e+01 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE      | info.AS_SOR | info.AS_VarDP |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "|                float64 | array          |     float64 |         int32 |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "|               2.52e-01 | [134,83,56,19]        |    1.42e+00 |           179 |\n",
+       "|              -5.01e-01 | [134,83,53,33]        |    6.81e-01 |           199 |\n",
+       "|               1.60e-02 | [3240,2211,2991,1961] |    7.34e-01 |         10383 |\n",
+       "|              -1.33e+00 | [63,66,76,46]         |    1.28e+00 |           251 |\n",
+       "|               7.13e-01 | [17,8,12,3]           |    1.29e+00 |            40 |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           bool |                       bool |                   bool |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                      False |                  False |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                      False |                  False |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|     False |      False |            False |         False |       5.27e+00 |\n",
+       "|     False |      False |            False |         False |       5.40e+00 |\n",
+       "|     False |      False |            False |         False |       8.85e+00 |\n",
+       "|     False |      False |            False |         False |       3.73e+00 |\n",
+       "|     False |      False |            False |         False |       4.72e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|             -1.37e-06 |\n",
+       "|             -6.84e-07 |\n",
+       "|             -5.13e-05 |\n",
+       "|             -1.37e-06 |\n",
+       "|             -6.84e-07 |\n",
+       "+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.ZonDIhdwyghm7SuWkX... |\n",
+       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.Hp20SlFHUdM7jlWmVV... |\n",
+       "| [\"ga4gh:VA.SBSoboxxiC0V-mhjjHg2geRlf3I0Ib2d\",\"ga4gh:VA.GzUD_86k_EDbtk-R58... |\n",
+       "| [\"ga4gh:VA.cROnHWMnTSTuzMfVIhSezHexCPbFdF3z\",\"ga4gh:VA.WAkJ8ZSeVrsh6ThoH7... |\n",
+       "| [\"ga4gh:VA.gXv9EmAG-Lbw5gSNFxiBHWB_WT8twCnG\",\"ga4gh:VA.S-YUvVERFiE1cgkxu2... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"A\"]           |\n",
+       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"T\"]           |\n",
+       "| [113410734,113410734] | [113410735,113410735] | [\"G\",\"A\"]           |\n",
+       "| [113410737,113410737] | [113410738,113410738] | [\"G\",\"T\"]           |\n",
+       "| [113410739,113410739] | [113410740,113410740] | [\"A\",\"G\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| str               |     int32 | str    | str                            |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| \"C/A\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tA\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tT\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410735 | \".\"    | \"chr11\t113410735\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/T\"             | 113410738 | \".\"    | \"chr11\t113410738\t.\tG\tT\t.\t.\tGT\" |\n",
+       "| \"A/G\"             | 113410740 | \".\"    | \"chr11\t113410740\t.\tA\tG\t.\t.\tGT\" |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq              |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [0,0,0,0,12,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
+       "| [0,0,0,0,12,0,66,0,314321,0,31,0,416511,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,12,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]  |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,0,65,332,11308,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
+       "| [0,0,57,319,11282,107355,345042,189122,36330,5605,2379,2756,3073,2932,295... |\n",
+       "| [0,0,49,301,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,295... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                       7715 |\n",
+       "|                                       7715 |\n",
+       "|                                       7717 |\n",
+       "|                                       7684 |\n",
+       "|                                       7683 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                         32 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq   |\n",
+       "+----------------------------------------------+\n",
+       "| array                                 |\n",
+       "+----------------------------------------------+\n",
+       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0] |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]    |\n",
+       "+----------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq          |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [6,0,1,0,14,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
+       "| [3,0,0,0,13,0,66,0,314321,0,31,0,416511,0,0,0,0,0,0,2]  |\n",
+       "| [4,0,0,0,13,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]  |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,2,67,335,11310,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
+       "| [0,1,58,321,11282,107355,345042,189122,36330,5605,2379,2756,3073,2932,295... |\n",
+       "| [0,1,50,304,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,295... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                           7715 |\n",
+       "|                                           7715 |\n",
+       "|                                           7717 |\n",
+       "|                                           7684 |\n",
+       "|                                           7683 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "|                                             32 |\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
+       "| [1,0,0,2,3,3,8,4,0,0]                      |\n",
+       "| [0,0,0,1,0,0,0,1,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           1 |\n",
+       "|                                           0 |\n",
+       "|                                           3 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        2.77e+01 |\n",
+       "|                                          0 |                        2.75e+01 |\n",
+       "|                                          0 |                        2.28e+01 |\n",
+       "|                                          0 |                        2.53e+01 |\n",
+       "|                                          0 |                        2.72e+01 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            4.10e+00 |                       6.02e-01 |\n",
+       "|                            4.07e+00 |                       7.19e-01 |\n",
+       "|                            2.69e+00 |                       2.67e-01 |\n",
+       "|                            3.67e+00 |                       2.65e-01 |\n",
+       "|                            4.02e+00 |                       6.41e-01 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                 0.00e+00 |                    8.78e+00 |\n",
+       "|                                 2.00e-02 |                    8.78e+00 |\n",
+       "|                                 1.00e-02 |                    8.67e+00 |\n",
+       "|                                 1.00e-02 |                    4.85e+00 |\n",
+       "|                                 3.00e-02 |                    6.25e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                      0.00e+00 |                          9.89e-01 |\n",
+       "|                      0.00e+00 |                          9.89e-01 |\n",
+       "|                      1.30e-01 |                          1.18e-01 |\n",
+       "|                      3.00e-02 |                          9.36e-01 |\n",
+       "|                      0.00e+00 |                          5.08e-01 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of missense variants passing filters in DRD2 is:  409\n"
+     ]
+    }
+   ],
+   "source": [
+    "var_ht = filter_by_csqs(['missense'], ht=drd2_interval_ht)\n",
+    "var_ht.show(5)\n",
+    "print(\"The total number of missense variants passing filters in DRD2 is: \", var_ht.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b980b4f-13bd-4bb4-bdbc-5584a6a59ca5",
+   "metadata": {},
+   "source": [
+    "### Filter to `synonymous` variants passing filters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b7e4368b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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],3110,NA,NA,NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000540600",1,NA,"T"),(1,"K","P2","protein_coding",NA,"CCDS8361.1",1626,1626,1305,1305,"aaG/aaA",["synonymous_variant"],NA,[("Gene3D","1"),("ENSP_mappings","5aer"),("ENSP_mappings","5aer"),("ENSP_mappings","6cm4"),("ENSP_mappings","6luq"),("ENSP_mappings","6vms"),("ENSP_mappings","7dfp"),("ENSP_mappings","7jvr"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321"),("CDD","cd15309")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000542968.5:c.1305G>A","ENSP00000442172.1:p.Lys435=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,435,435,"ENSP00000442172","Ensembl",-1,"ENST00000542968",1,["P14416-1"],"T"),(1,"K","A1","protein_coding",NA,NA,1335,1335,1302,1302,"aaG/aaA",["synonymous_variant"],NA,[("Gene3D","1"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321"),("CDD","cd15309")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000544518.5:c.1302G>A","ENSP00000441068.1:p.Lys434=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,434,434,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"T"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["intron_variant","non_coding_transcript_variant"],NA,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,"ENST00000546284.1:n.245-793C>T",NA,NA,"MODIFIER","2/3",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"T"),(1,"K",NA,"protein_coding",1,NA,1659,1659,1305,1305,"aaG/aaA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.1305G>A","NP_000786.1:p.Lys435=",NA,"LOW",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,435,435,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"T"),(1,"K",NA,"protein_coding",NA,NA,1572,1572,1218,1218,"aaG/aaA",["synonymous_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.1218G>A","NP_057658.2:p.Lys406=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,406,406,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"T"),(1,"K",NA,"protein_coding",NA,NA,1387,1387,1305,1305,"aaG/aaA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.1305G>A","XP_016872785.1:p.Lys435=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,435,435,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"T")]"SNV"8.87e+00"AS_MQ"FalseFalseFalseFalseFalseTrueFalseFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,266,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,2971,2945,3008,2939,2587,2701]07737[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27]0207[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][1,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,267,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,2971,2945,3008,2939,2587,2701]07737[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27]0207[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][3,2,30,42,65,52,79,55,7,3]153[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,1,0,0,0,1,0]009.48e+008.09e-01NA0.00e+001.00e-025.82e+00NANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+\n",
+       "| locus           | alleles    |\n",
+       "+-----------------+------------+\n",
+       "| locus   | array |\n",
+       "+-----------------+------------+\n",
+       "| chr11:113410736 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410736 | [\"G\",\"T\"]  |\n",
+       "| chr11:113410739 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410751 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410754 | [\"C\",\"T\"]  |\n",
+       "+-----------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(15,1.03e-05,1461882,0),(15,1.03e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
+       "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33478,0),(0,0.... |\n",
+       "| [(1,6.84e-07,1461894,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(576,3.94e-04,1461892,2),(576,3.94e-04,1461894,2),(12,3.58e-04,33480,1),... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|               15 |         1.35e-05 |          1112010 |\n",
+       "|                1 |         8.99e-07 |          1112010 |\n",
+       "|               NA |               NA |               NA |\n",
+       "|                1 |         8.99e-07 |          1112012 |\n",
+       "|                8 |         1.39e-03 |             5768 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                              0 | \"mid\"                 |                 4 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|          2.86e-06 |            350106 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          2.86e-06 |            350108 |                               0 |\n",
+       "|          9.64e-04 |              4148 |                               0 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| \"nfe\"                  |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| \"mid\"                  |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(6.16e-06,4.89e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(3.67e-04,3.57e-04),(2.06e-04,1.62e-04),(7.33e-04,6.53e-04),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                8.10e-06 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                7.33e-04 | \"amr\"                           |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                6.42e-06 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                6.53e-04 | \"amr\"                           |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                 6.90e-04 | \"amr\"                            |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                 6.12e-04 | \"amr\"                            |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      bool | set         | set |  float64 |  float64 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      True | {\"rs1159504221\"} | {}       | 5.49e-01 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.49e-01 | 6.00e+01 |\n",
+       "|     False | NA               | {}       | 4.26e+00 | 6.00e+01 |\n",
+       "|     False | NA               | {}       | 8.26e+00 | 6.00e+01 |\n",
+       "|     False | {\"rs77930100\"}   | {}       | 1.45e-15 | 6.00e+01 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
+       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
+       "|       0.00e+00 |             574 | 1.10e+01 |            5.32e-01 |\n",
+       "|       0.00e+00 |            1098 | 2.03e+01 |           -7.69e-01 |\n",
+       "|       0.00e+00 |          947678 | 1.23e+01 |           -7.00e-03 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| info.SB                   | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| array              |  float64 |      int32 |    float64 |    float64 |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| [386,195,316,166]         | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
+       "| [386,195,316,166]         | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
+       "| [23,8,13,8]               | 2.93e-01 |         52 |   4.26e+00 |   6.00e+01 |\n",
+       "| [5,11,21,18]              | 4.64e-01 |         54 |   8.26e+00 |   6.00e+01 |\n",
+       "| [20804,19237,19605,17401] | 7.35e-01 |      77027 |   1.45e-15 |   6.00e+01 |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|          0.00e+00 |        9.11e-01 |              11899 |   1.17e+01 |\n",
+       "|          0.00e+00 |        3.28e-02 |                408 |   8.16e+00 |\n",
+       "|          0.00e+00 |        2.12e-01 |                574 |   1.10e+01 |\n",
+       "|          0.00e+00 |        3.84e-03 |               1098 |   2.03e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |             947678 |   1.23e+01 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE          | info.AS_SOR |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "|                float64 | array              |     float64 |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "|               9.70e-02 | [386,195,304,161]         |    6.47e-01 |\n",
+       "|              -9.23e-01 | [386,195,12,5]            |    7.90e-01 |\n",
+       "|               5.32e-01 | [23,8,13,8]               |    2.93e-01 |\n",
+       "|              -7.69e-01 | [5,11,21,18]              |    4.64e-01 |\n",
+       "|               7.00e-03 | [20804,19237,19605,17401] |    7.35e-01 |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "\n",
+       "+---------------+----------------+----------------------------+\n",
+       "| info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "|         int32 |           bool |                       bool |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "|          1013 |          False |                         NA |\n",
+       "|            50 |           True |                      False |\n",
+       "|            52 |           True |                      False |\n",
+       "|            54 |           True |                      False |\n",
+       "|         77027 |          False |                         NA |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "| info.sibling_singleton | info.omni | info.mills | info.monoallelic |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                   bool |      bool |       bool |             bool |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                     NA |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                     NA |     False |      False |            False |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "\n",
+       "+---------------+----------------+-----------------------+\n",
+       "| info.only_het | info.AS_VQSLOD | info.inbreeding_coeff |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|          bool |        float64 |               float64 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|         False |       7.50e+00 |             -1.03e-05 |\n",
+       "|         False |       5.00e+00 |             -6.84e-07 |\n",
+       "|         False |       5.73e+00 |             -6.84e-07 |\n",
+       "|         False |       2.35e+00 |             -6.84e-07 |\n",
+       "|         False |       8.87e+00 |              6.55e-03 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.OXPDvbuulFZ9CSk4Xt... |\n",
+       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.4qhkDG1qrfqiD2TeOA... |\n",
+       "| [\"ga4gh:VA.uv4czuJ2nBK7lcR9wkK2XxHxMSui2-ME\",\"ga4gh:VA.bA0gzyCzudiyDon8QV... |\n",
+       "| [\"ga4gh:VA.euCgAx3O0dYWO86rUZqEKUoRwnhnhr-k\",\"ga4gh:VA.XQ_C7TKjlSKQw_ABwx... |\n",
+       "| [\"ga4gh:VA.8tDWBt70tPv5g5-MDqwnSl1-pElrIl1E\",\"ga4gh:VA.f3fUPUzazutlUq0Gul... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"A\"]           |\n",
+       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"T\"]           |\n",
+       "| [113410738,113410738] | [113410739,113410739] | [\"G\",\"A\"]           |\n",
+       "| [113410750,113410750] | [113410751,113410751] | [\"G\",\"A\"]           |\n",
+       "| [113410753,113410753] | [113410754,113410754] | [\"C\",\"T\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| str               |     int32 | str    | str                            |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| \"G/A\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/T\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tT\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410739 | \".\"    | \"chr11\t113410739\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410751 | \".\"    | \"chr11\t113410751\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113410754 | \".\"    | \"chr11\t113410754\t.\tC\tT\t.\t.\tGT\" |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq             |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
+       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
+       "| [0,0,0,0,9,0,68,0,314320,0,24,0,416519,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,1,0,28,0,314363,0,2,0,416552,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574] |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,0,55,309,11275,107353,345050,189133,36332,5605,2379,2755,3073,2932,295... |\n",
+       "| [0,0,18,272,11233,107401,345096,189157,36337,5604,2379,2755,3073,2932,295... |\n",
+       "| [0,0,16,266,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,297... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                       7685 |\n",
+       "|                                       7685 |\n",
+       "|                                       7683 |\n",
+       "|                                       7683 |\n",
+       "|                                       7737 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq  |\n",
+       "+---------------------------------------------+\n",
+       "| array                                |\n",
+       "+---------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574] |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq             |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27] |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                        207 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq          |\n",
+       "+-----------------------------------------------------+\n",
+       "| array                                        |\n",
+       "+-----------------------------------------------------+\n",
+       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0] |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq         |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
+       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
+       "| [5,0,0,0,10,0,68,0,314320,0,24,0,416519,0,0,0,0,0,0,1] |\n",
+       "| [0,0,0,0,1,0,28,0,314363,0,2,0,416552,0,0,0,0,0,0,1]   |\n",
+       "| [1,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574] |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,1,56,312,11275,107353,345051,189133,36332,5605,2379,2755,3073,2932,295... |\n",
+       "| [0,0,18,272,11233,107401,345096,189157,36337,5604,2379,2755,3073,2932,295... |\n",
+       "| [0,0,16,267,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,297... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                           7685 |\n",
+       "|                                           7685 |\n",
+       "|                                           7683 |\n",
+       "|                                           7683 |\n",
+       "|                                           7737 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574]    |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq         |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27] |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                            207 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq      |\n",
+       "+-----------------------------------------------------+\n",
+       "| array                                        |\n",
+       "+-----------------------------------------------------+\n",
+       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0] |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,1,2,4,2,4,1,0,0]                      |\n",
+       "| [0,0,0,0,0,1,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [3,2,30,42,65,52,79,55,7,3]                |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                          15 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          3 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,0,0,0,1,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        3.44e+00 |\n",
+       "|                                          0 |                        2.96e+00 |\n",
+       "|                                          0 |                        1.03e+01 |\n",
+       "|                                          0 |                        1.20e+01 |\n",
+       "|                                          0 |                        9.48e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            2.28e-01 |                             NA |\n",
+       "|                            1.86e-01 |                             NA |\n",
+       "|                            8.89e-01 |                             NA |\n",
+       "|                            1.03e+00 |                             NA |\n",
+       "|                            8.09e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                 1.00e-02 |                   -2.55e-01 |\n",
+       "|                                 3.00e-02 |                   -2.55e-01 |\n",
+       "|                                 0.00e+00 |                    3.26e+00 |\n",
+       "|                                 0.00e+00 |                    8.67e+00 |\n",
+       "|                                 1.00e-02 |                    5.82e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of synonymous variants passing filters in DRD2 is:  238\n"
+     ]
+    }
+   ],
+   "source": [
+    "var_ht = filter_by_csqs(['synonymous'], ht=drd2_interval_ht)\n",
+    "var_ht.show(5)\n",
+    "print(\"The total number of synonymous variants passing filters in DRD2 is: \", var_ht.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbcd2fa0-5108-4d94-a681-493882c295bf",
+   "metadata": {},
+   "source": [
+    "### Filter to 'Other' variants passing filters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4141ccb3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
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showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+\n",
+       "| locus           | alleles    |\n",
+       "+-----------------+------------+\n",
+       "| locus   | array |\n",
+       "+-----------------+------------+\n",
+       "| chr11:113409636 | [\"G\",\"C\"]  |\n",
+       "| chr11:113409693 | [\"G\",\"A\"]  |\n",
+       "| chr11:113409717 | [\"C\",\"T\"]  |\n",
+       "| chr11:113409758 | [\"C\",\"T\"]  |\n",
+       "| chr11:113410002 | [\"C\",\"A\"]  |\n",
+       "+-----------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(1,1.87e-03,534,0),(5,7.95e-06,628788,2),(0,0.00e+00,8,0),(0,NA,0,0),(0,... |\n",
+       "| [(1,1.20e-03,834,0),(16,2.54e-05,628790,3),(0,0.00e+00,6,0),(0,0.00e+00,2... |\n",
+       "| [(1,1.01e-03,990,0),(5,7.95e-06,628794,2),(0,0.00e+00,10,0),(0,0.00e+00,4... |\n",
+       "| [(1,1.23e-03,810,0),(4,6.36e-06,628786,1),(0,0.00e+00,12,0),(0,0.00e+00,4... |\n",
+       "| [(128,1.34e-01,952,15),(18463,2.94e-02,628994,6837),(0,0.00e+00,6,0),(17,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|               NA |               NA |               NA |\n",
+       "|                1 |         2.87e-03 |              348 |\n",
+       "|                1 |         2.17e-03 |              460 |\n",
+       "|               NA |               NA |               NA |\n",
+       "|                2 |         5.00e-01 |                4 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                              0 | \"eas\"                 |                 2 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          2.87e-03 |               348 |                               0 |\n",
+       "|          2.17e-03 |               460 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          5.00e-01 |                 4 |                               0 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| \"nfe\"                  |\n",
+       "| NA                     |\n",
+       "| \"eas\"                  |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(1.16e-01,1.08e-01),(0.00e+00,0.00e+00),(1.23e-01,1.01e-01),(8.88e-02,3.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                1.23e-01 | \"amr\"                           |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                1.02e-01 | \"nfe\"                           |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                 1.23e-01 | \"amr\"                            |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                 1.02e-01 | \"nfe\"                            |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+-----------------+----------+----------+----------+\n",
+       "| was_split | rsid            | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+-----------------+----------+----------+----------+\n",
+       "|      bool | set        | set |  float64 |  float64 |\n",
+       "+-----------+-----------------+----------+----------+----------+\n",
+       "|      True | {\"rs200733424\"} | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|      True | NA              | {}       | 5.32e+00 | 6.00e+01 |\n",
+       "|      True | NA              | {}       | 4.82e-16 | 6.00e+01 |\n",
+       "|      True | {\"rs200557458\"} | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|      True | {\"rs6278\"}      | {}       | 2.80e+00 | 6.00e+01 |\n",
+       "+-----------+-----------------+----------+----------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|       0.00e+00 |             582 | 1.94e+01 |           -2.11e+00 |\n",
+       "|       0.00e+00 |             898 | 9.76e+00 |            0.00e+00 |\n",
+       "|       0.00e+00 |             209 | 9.09e+00 |            8.42e-01 |\n",
+       "|       0.00e+00 |             947 | 1.26e+01 |           -6.74e-01 |\n",
+       "|       0.00e+00 |         1998239 | 1.99e+01 |            0.00e+00 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| info.SB                   | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| array              |  float64 |      int32 |    float64 |    float64 |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| [8,2,15,5]                | 5.82e-01 |         30 |   0.00e+00 |   6.00e+01 |\n",
+       "| [31,19,21,21]             | 3.30e-01 |         92 |   4.52e+00 |   6.00e+01 |\n",
+       "| [6,7,4,6]                 | 9.17e-01 |         23 |   0.00e+00 |   6.00e+01 |\n",
+       "| [16,19,18,22]             | 7.22e-01 |         75 |   0.00e+00 |   6.00e+01 |\n",
+       "| [19752,12842,45058,22649] | 9.83e-01 |     100292 |   2.80e+00 |   6.00e+01 |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|          0.00e+00 |        5.41e-01 |                582 |   1.94e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |                553 |   8.01e+00 |\n",
+       "|          0.00e+00 |        2.67e-01 |                178 |   1.05e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |                703 |   1.12e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |            1998092 |   1.99e+01 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE          | info.AS_SOR |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "|                float64 | array              |     float64 |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "|              -2.11e+00 | [8,2,15,5]                |    5.82e-01 |\n",
+       "|               0.00e+00 | [31,19,15,15]             |    3.30e-01 |\n",
+       "|               7.20e-02 | [6,7,3,5]                 |    1.00e+00 |\n",
+       "|              -1.13e+00 | [16,19,14,17]             |    7.13e-01 |\n",
+       "|               0.00e+00 | [19752,12842,45055,22646] |    9.83e-01 |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "\n",
+       "+---------------+----------------+----------------------------+\n",
+       "| info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "|         int32 |           bool |                       bool |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "|            30 |          False |                      False |\n",
+       "|            69 |          False |                      False |\n",
+       "|            17 |          False |                      False |\n",
+       "|            63 |          False |                      False |\n",
+       "|        100286 |          False |                         NA |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "| info.sibling_singleton | info.omni | info.mills | info.monoallelic |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                   bool |      bool |       bool |             bool |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                     NA |      True |      False |            False |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "\n",
+       "+---------------+----------------+-----------------------+\n",
+       "| info.only_het | info.AS_VQSLOD | info.inbreeding_coeff |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|          bool |        float64 |               float64 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|         False |       4.51e+00 |              8.00e-01 |\n",
+       "|         False |       5.16e+00 |              3.75e-01 |\n",
+       "|         False |       6.19e+00 |              8.00e-01 |\n",
+       "|         False |       6.36e+00 |              5.00e-01 |\n",
+       "|         False |       5.57e+00 |              7.33e-01 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.BlCz08hG7ZEQzvW20V8Kp4xJMx-pMKes\",\"ga4gh:VA.U4eD7PXtXRCClN6FQt... |\n",
+       "| [\"ga4gh:VA.aXxME51cngsJBnH_3WX01B0Ms-SLay9X\",\"ga4gh:VA.ynryFZCH9dBU67nwPr... |\n",
+       "| [\"ga4gh:VA.Q6p6okAv6qHSPSV8MOvy8Hhl0kZ6tQ4U\",\"ga4gh:VA.WIfYWxgEc5ICUUByiD... |\n",
+       "| [\"ga4gh:VA.k_gesWx8TbLs23MHIkc3NaVscBWVDrSE\",\"ga4gh:VA.NJz3hsVBrqbTn17g5T... |\n",
+       "| [\"ga4gh:VA.HjAJ4gj43VVFJQmV-h5WuPZZPBmNwSFZ\",\"ga4gh:VA.sT14AsCSWlf2AqH0wh... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113409635,113409635] | [113409636,113409636] | [\"G\",\"C\"]           |\n",
+       "| [113409692,113409692] | [113409693,113409693] | [\"G\",\"A\"]           |\n",
+       "| [113409716,113409716] | [113409717,113409717] | [\"C\",\"T\"]           |\n",
+       "| [113409757,113409757] | [113409758,113409758] | [\"C\",\"T\"]           |\n",
+       "| [113410001,113410001] | [113410002,113410002] | [\"C\",\"A\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| str               |     int32 | str    | str                            |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| \"G/C\"             | 113409636 | \".\"    | \"chr11\t113409636\t.\tG\tC\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113409693 | \".\"    | \"chr11\t113409693\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113409717 | \".\"    | \"chr11\t113409717\t.\tC\tT\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113409758 | \".\"    | \"chr11\t113409758\t.\tC\tT\t.\t.\tGT\" |\n",
+       "| \"C/A\"             | 113410002 | \".\"    | \"chr11\t113410002\t.\tC\tA\t.\t.\tGT\" |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq      |\n",
+       "+-------------------------------------------------+\n",
+       "| array                                    |\n",
+       "+-------------------------------------------------+\n",
+       "| [0,0,0,0,28,0,76,0,162,0,0,0,0,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,25,0,186,0,206,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,95,0,197,0,202,0,0,0,0,0,0,0,1,0,0,0]  |\n",
+       "| [0,0,0,0,100,0,111,0,193,0,0,0,0,0,1,0,0,0,0,0] |\n",
+       "| [0,0,0,0,120,1,223,5,34,1,5,0,2,5,7,1,3,2,1,66] |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq       |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,101,28,30,82,26,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,211,29,38,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "| [0,0,286,30,40,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "| [0,0,200,35,36,102,32,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "| [0,0,438,30,5,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq  |\n",
+       "+---------------------------------------------+\n",
+       "| array                                |\n",
+       "+---------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,1,1,11,5,2,1,5,0,2,5,7,1,3,2,1,66] |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq  |\n",
+       "+---------------------------------------------+\n",
+       "| array                                |\n",
+       "+---------------------------------------------+\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,108,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq  |\n",
+       "+---------------------------------------------+\n",
+       "| array                                |\n",
+       "+---------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,3,8,0,4,8,6,30,15,8,9,3,1,3,0,0,0] |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [313709,2,2,0,442,0,76,0,162,0,0,0,0,0,0,0,0,0,0,1]                          |\n",
+       "| [313080,4,2,0,906,6,189,2,206,0,0,0,0,0,0,0,0,0,0,0]                         |\n",
+       "| [313071,2,3,0,920,0,198,0,202,0,0,0,0,0,0,0,1,0,0,0]                         |\n",
+       "| [313441,3,2,1,640,0,111,1,193,0,0,0,0,0,1,0,0,0,0,0]                         |\n",
+       "| [301848,5644,543,415,1889,1584,436,212,123,210,485,568,76,48,44,53,135,57... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq            |\n",
+       "+-----------------------------------------------------------+\n",
+       "| array                                              |\n",
+       "+-----------------------------------------------------------+\n",
+       "| [240827,73298,103,28,30,82,26,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [240299,73676,214,29,38,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "| [240226,73668,294,30,40,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "| [240307,73671,209,36,36,102,32,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "| [238198,75776,482,33,5,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
+       "+-----------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]                                    |\n",
+       "| [0,3,0,0,1,5,3,1,0,0,0,0,0,0,0,0,0,0,0,0]                                    |\n",
+       "| [0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]                                    |\n",
+       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]                                    |\n",
+       "| [398,5642,541,415,716,1584,224,212,91,210,485,568,76,48,44,53,135,57,19,108] |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq    |\n",
+       "+---------------------------------------------------+\n",
+       "| array                                      |\n",
+       "+---------------------------------------------------+\n",
+       "| [2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
+       "| [8,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
+       "| [2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
+       "| [1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
+       "| [9330,2176,115,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq                   |\n",
+       "+------------------------------------------------------------------+\n",
+       "| array                                                     |\n",
+       "+------------------------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]                        |\n",
+       "| [0,0,0,0,1,1,2,0,1,0,1,0,0,4,0,0,0,0,0,0]                        |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]                        |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]                        |\n",
+       "| [0,0,4,4,27,107,217,41,416,6,1084,414,43,1634,53,495,208,34,2,0] |\n",
+       "+------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,1,1,5,4,3,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,2,2,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        8.46e+00 |\n",
+       "|                                          0 |                        9.08e+00 |\n",
+       "|                                          0 |                        1.51e+01 |\n",
+       "|                                          0 |                        9.13e+00 |\n",
+       "|                                          0 |                        2.02e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            7.01e-01 |                             NA |\n",
+       "|                            7.68e-01 |                             NA |\n",
+       "|                            1.40e+00 |                             NA |\n",
+       "|                            7.73e-01 |                             NA |\n",
+       "|                            9.23e-02 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             1.00e-02 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                 2.00e-02 |                    1.94e+00 |\n",
+       "|                                 0.00e+00 |                    5.70e-01 |\n",
+       "|                                 0.00e+00 |                    1.05e+00 |\n",
+       "|                                 3.00e-02 |                    2.58e+00 |\n",
+       "|                                 0.00e+00 |                    1.61e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of other variants passing filters in DRD2 is:  2075\n"
+     ]
+    }
+   ],
+   "source": [
+    "var_ht = filter_by_csqs(['other'], ht=drd2_interval_ht)\n",
+    "var_ht.show(5)\n",
+    "print(\"The total number of other variants passing filters in DRD2 is: \", var_ht.count())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b104a39b",
+   "metadata": {},
+   "source": [
+    "## Get frequency information for specific genetic ancestry groups\n",
+    "\n",
+    "The examples below show frequency filtering using the Table filtered to DRD2 `synonymous` variants."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a25ddd1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drd2_synonymous_ht = filter_by_csqs(['synonymous'], ht=drd2_interval_ht)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69814beb",
+   "metadata": {},
+   "source": [
+    "### Single genetic ancestry group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4f78166f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
afr
locus
alleles
filters
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64
chr11:113410736["G","A"]{}00.00e+00334800
chr11:113410736["G","T"]{}00.00e+00334800
chr11:113410739["G","A"]{}00.00e+00334780
chr11:113410751["G","A"]{}00.00e+00334800
chr11:113410754["C","T"]{}123.58e-04334801
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| locus           | alleles    | filters  | afr.AC |   afr.AF | afr.AN |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| locus   | array | set |  int32 |  float64 |  int32 |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| chr11:113410736 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410736 | [\"G\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410739 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33478 |\n",
+       "| chr11:113410751 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410754 | [\"C\",\"T\"]  | {}       |     12 | 3.58e-04 |  33480 |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "\n",
+       "+----------------------+\n",
+       "| afr.homozygote_count |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    1 |\n",
+       "+----------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var_ht = get_ancestry_callstats(gen_ancs='afr', ht=drd2_synonymous_ht)\n",
+    "var_ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e741e138",
+   "metadata": {},
+   "source": [
+    "### Multiple genetic ancestry groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e3a07848",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
afr
amr
eas
mid
nfe
sas
locus
alleles
filters
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64
chr11:113410736["G","A"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+0057620151.35e-051112010000.00e+00862540
chr11:113410736["G","T"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005762018.99e-071112010000.00e+00862540
chr11:113410739["G","A"]{}00.00e+0033478000.00e+0044724000.00e+0039700000.00e+005764000.00e+001112010000.00e+00862560
chr11:113410751["G","A"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005768018.99e-071112012000.00e+00862580
chr11:113410754["C","T"]{}123.58e-04334801439.61e-0444724000.00e+0039700081.39e-03576803282.95e-041112012000.00e+00862580
chr11:113410757["G","A"]{}00.00e+0033480000.00e+0044724012.52e-0539700000.00e+005766000.00e+001112012000.00e+00862580
chr11:113410757["G","T"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005766000.00e+001112012033.48e-05862580
chr11:113410763["C","T"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005768000.00e+001112010011.16e-05862580
chr11:113410769["G","A"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005768021.80e-061112012000.00e+00862580
chr11:113410775["G","A"]{}00.00e+0033480000.00e+0044724012.52e-0539700000.00e+005768018.99e-071112010000.00e+00862580
showing top 10 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| locus           | alleles    | filters  | afr.AC |   afr.AF | afr.AN |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| locus   | array | set |  int32 |  float64 |  int32 |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| chr11:113410736 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410736 | [\"G\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410739 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33478 |\n",
+       "| chr11:113410751 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410754 | [\"C\",\"T\"]  | {}       |     12 | 3.58e-04 |  33480 |\n",
+       "| chr11:113410757 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410757 | [\"G\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410763 | [\"C\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410769 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410775 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "| afr.homozygote_count | amr.AC |   amr.AF | amr.AN | amr.homozygote_count |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "|                int64 |  int32 |  float64 |  int32 |                int64 |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    1 |     43 | 9.61e-04 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "\n",
+       "+--------+----------+--------+----------------------+--------+----------+\n",
+       "| eas.AC |   eas.AF | eas.AN | eas.homozygote_count | mid.AC |   mid.AF |\n",
+       "+--------+----------+--------+----------------------+--------+----------+\n",
+       "|  int32 |  float64 |  int32 |                int64 |  int32 |  float64 |\n",
+       "+--------+----------+--------+----------------------+--------+----------+\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      8 | 1.39e-03 |\n",
+       "|      1 | 2.52e-05 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      1 | 2.52e-05 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "+--------+----------+--------+----------------------+--------+----------+\n",
+       "\n",
+       "+--------+----------------------+--------+----------+---------+\n",
+       "| mid.AN | mid.homozygote_count | nfe.AC |   nfe.AF |  nfe.AN |\n",
+       "+--------+----------------------+--------+----------+---------+\n",
+       "|  int32 |                int64 |  int32 |  float64 |   int32 |\n",
+       "+--------+----------------------+--------+----------+---------+\n",
+       "|   5762 |                    0 |     15 | 1.35e-05 | 1112010 |\n",
+       "|   5762 |                    0 |      1 | 8.99e-07 | 1112010 |\n",
+       "|   5764 |                    0 |      0 | 0.00e+00 | 1112010 |\n",
+       "|   5768 |                    0 |      1 | 8.99e-07 | 1112012 |\n",
+       "|   5768 |                    0 |    328 | 2.95e-04 | 1112012 |\n",
+       "|   5766 |                    0 |      0 | 0.00e+00 | 1112012 |\n",
+       "|   5766 |                    0 |      0 | 0.00e+00 | 1112012 |\n",
+       "|   5768 |                    0 |      0 | 0.00e+00 | 1112010 |\n",
+       "|   5768 |                    0 |      2 | 1.80e-06 | 1112012 |\n",
+       "|   5768 |                    0 |      1 | 8.99e-07 | 1112010 |\n",
+       "+--------+----------------------+--------+----------+---------+\n",
+       "\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "| nfe.homozygote_count | sas.AC |   sas.AF | sas.AN | sas.homozygote_count |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "|                int64 |  int32 |  float64 |  int32 |                int64 |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "|                    0 |      0 | 0.00e+00 |  86254 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86254 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86256 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "|                    0 |      3 | 3.48e-05 |  86258 |                    0 |\n",
+       "|                    0 |      1 | 1.16e-05 |  86258 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "showing top 10 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var_ht = get_ancestry_callstats(gen_ancs=['afr', 'amr', 'eas', 'mid', 'nfe', 'sas'], ht=drd2_synonymous_ht)\n",
+    "var_ht.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe2e98b8",
+   "metadata": {},
+   "source": [
+    "## Get frequency information for a specific genetic ancestry group at a specific variant"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a253b3db-0a50-4c71-ae3d-dd7d5e0bfde0",
+   "metadata": {},
+   "source": [
+    "### Example when the variant exists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4845be1d-d4c0-4b83-9e92-bd72379b8a99",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "
afr
locus
alleles
filters
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64
chr22:15528692["C","G"]{}6351.90e-02333806
"
+      ],
+      "text/plain": [
+       "+----------------+------------+----------+--------+----------+--------+\n",
+       "| locus          | alleles    | filters  | afr.AC |   afr.AF | afr.AN |\n",
+       "+----------------+------------+----------+--------+----------+--------+\n",
+       "| locus  | array | set |  int32 |  float64 |  int32 |\n",
+       "+----------------+------------+----------+--------+----------+--------+\n",
+       "| chr22:15528692 | [\"C\",\"G\"]  | {}       |    635 | 1.90e-02 |  33380 |\n",
+       "+----------------+------------+----------+--------+----------+--------+\n",
+       "\n",
+       "+----------------------+\n",
+       "| afr.homozygote_count |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "|                    6 |\n",
+       "+----------------------+"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var_ht = get_single_variant_ancestry_callstats(gen_ancs='AFR', contig='chr22', position=15528692, ref='C', alt='G')\n",
+    "var_ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c60cef2a-ae30-459b-a96a-82e754710bb2",
+   "metadata": {},
+   "source": [
+    "### Example when the variant *doesn't* exist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "bee28829",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "
afr
locus
alleles
filters
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64
"
+      ],
+      "text/plain": [
+       "+---------------+------------+----------+--------+---------+--------+\n",
+       "| locus         | alleles    | filters  | afr.AC |  afr.AF | afr.AN |\n",
+       "+---------------+------------+----------+--------+---------+--------+\n",
+       "| locus | array | set |  int32 | float64 |  int32 |\n",
+       "+---------------+------------+----------+--------+---------+--------+\n",
+       "+---------------+------------+----------+--------+---------+--------+\n",
+       "\n",
+       "+----------------------+\n",
+       "| afr.homozygote_count |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "+----------------------+"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var_ht = get_single_variant_ancestry_callstats(gen_ancs='AFR', contig='chr22', position=15528692, ref='C', alt='A')\n",
+    "var_ht.show(5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {
+    "height": "613.99px",
+    "width": "526.312px"
+   },
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "241.438px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/gnomad_toolbox/notebooks/needs_a_name.ipynb b/gnomad_toolbox/notebooks/needs_a_name.ipynb
new file mode 100644
index 0000000..c4b688a
--- /dev/null
+++ b/gnomad_toolbox/notebooks/needs_a_name.ipynb
@@ -0,0 +1,602 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8e609a46",
+   "metadata": {
+    "toc": true
+   },
+   "source": [
+    "
Table of Contents
\n",
+    "
"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e713032",
+   "metadata": {},
+   "source": [
+    "## Import modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e69953f7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T18:04:56.165634Z",
+     "start_time": "2024-12-06T18:04:55.603516Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "    \n",
+       "    
\n",
+       "        \n",
+       "        Loading BokehJS ...\n",
+       "    
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  const force = true;\n",
+       "\n",
+       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+       "    root._bokeh_onload_callbacks = [];\n",
+       "    root._bokeh_is_loading = undefined;\n",
+       "  }\n",
+       "\n",
+       "const JS_MIME_TYPE = 'application/javascript';\n",
+       "  const HTML_MIME_TYPE = 'text/html';\n",
+       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
+       "\n",
+       "  /**\n",
+       "   * Render data to the DOM node\n",
+       "   */\n",
+       "  function render(props, node) {\n",
+       "    const script = document.createElement(\"script\");\n",
+       "    node.appendChild(script);\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when an output is cleared or removed\n",
+       "   */\n",
+       "  function handleClearOutput(event, handle) {\n",
+       "    function drop(id) {\n",
+       "      const view = Bokeh.index.get_by_id(id)\n",
+       "      if (view != null) {\n",
+       "        view.model.document.clear()\n",
+       "        Bokeh.index.delete(view)\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    const cell = handle.cell;\n",
+       "\n",
+       "    const id = cell.output_area._bokeh_element_id;\n",
+       "    const server_id = cell.output_area._bokeh_server_id;\n",
+       "\n",
+       "    // Clean up Bokeh references\n",
+       "    if (id != null) {\n",
+       "      drop(id)\n",
+       "    }\n",
+       "\n",
+       "    if (server_id !== undefined) {\n",
+       "      // Clean up Bokeh references\n",
+       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+       "      cell.notebook.kernel.execute(cmd_clean, {\n",
+       "        iopub: {\n",
+       "          output: function(msg) {\n",
+       "            const id = msg.content.text.trim()\n",
+       "            drop(id)\n",
+       "          }\n",
+       "        }\n",
+       "      });\n",
+       "      // Destroy server and session\n",
+       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+       "      cell.notebook.kernel.execute(cmd_destroy);\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when a new output is added\n",
+       "   */\n",
+       "  function handleAddOutput(event, handle) {\n",
+       "    const output_area = handle.output_area;\n",
+       "    const output = handle.output;\n",
+       "\n",
+       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
+       "      return\n",
+       "    }\n",
+       "\n",
+       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+       "      // store reference to embed id on output_area\n",
+       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "    }\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "      const bk_div = document.createElement(\"div\");\n",
+       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "      const script_attrs = bk_div.children[0].attributes;\n",
+       "      for (let i = 0; i < script_attrs.length; i++) {\n",
+       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
+       "      }\n",
+       "      // store reference to server id on output_area\n",
+       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function register_renderer(events, OutputArea) {\n",
+       "\n",
+       "    function append_mime(data, metadata, element) {\n",
+       "      // create a DOM node to render to\n",
+       "      const toinsert = this.create_output_subarea(\n",
+       "        metadata,\n",
+       "        CLASS_NAME,\n",
+       "        EXEC_MIME_TYPE\n",
+       "      );\n",
+       "      this.keyboard_manager.register_events(toinsert);\n",
+       "      // Render to node\n",
+       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "      render(props, toinsert[toinsert.length - 1]);\n",
+       "      element.append(toinsert);\n",
+       "      return toinsert\n",
+       "    }\n",
+       "\n",
+       "    /* Handle when an output is cleared or removed */\n",
+       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
+       "    events.on('delete.Cell', handleClearOutput);\n",
+       "\n",
+       "    /* Handle when a new output is added */\n",
+       "    events.on('output_added.OutputArea', handleAddOutput);\n",
+       "\n",
+       "    /**\n",
+       "     * Register the mime type and append_mime function with output_area\n",
+       "     */\n",
+       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "      /* Is output safe? */\n",
+       "      safe: true,\n",
+       "      /* Index of renderer in `output_area.display_order` */\n",
+       "      index: 0\n",
+       "    });\n",
+       "  }\n",
+       "\n",
+       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+       "  if (root.Jupyter !== undefined) {\n",
+       "    const events = require('base/js/events');\n",
+       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  }\n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
+       "     \"
\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"
    \\n\"+\n",
+       "     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n",
+       "     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"from bokeh.resources import INLINE\\n\"+\n",
+       "     \"output_notebook(resources=INLINE)\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"
\"}};\n",
+       "\n",
+       "  function display_loaded() {\n",
+       "    const el = document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\");\n",
+       "    if (el != null) {\n",
+       "      el.textContent = \"BokehJS is loading...\";\n",
+       "    }\n",
+       "    if (root.Bokeh !== undefined) {\n",
+       "      if (el != null) {\n",
+       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+       "      }\n",
+       "    } else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(display_loaded, 100)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls == null || js_urls.length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    function on_error(url) {\n",
+       "      console.error(\"failed to load \" + url);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < css_urls.length; i++) {\n",
+       "      const url = css_urls[i];\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < js_urls.length; i++) {\n",
+       "      const url = js_urls[i];\n",
+       "      const element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
+       "  const css_urls = [];\n",
+       "\n",
+       "  const inline_js = [    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
+       "    },\n",
+       "function(Bokeh) {\n",
+       "    }\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    if (root.Bokeh !== undefined || force === true) {\n",
+       "          for (let i = 0; i < inline_js.length; i++) {\n",
+       "      inline_js[i].call(root, root.Bokeh);\n",
+       "    }\n",
+       "if (force === true) {\n",
+       "        display_loaded();\n",
+       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    } else if (force !== true) {\n",
+       "      const cell = $(document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\")).parents('.cell').data().cell;\n",
+       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  if (root._bokeh_is_loading === 0) {\n",
+       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+       "    run_inline_js();\n",
+       "  } else {\n",
+       "    load_libs(css_urls, js_urls, function() {\n",
+       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "      run_inline_js();\n",
+       "    });\n",
+       "  }\n",
+       "}(window));"
+      ],
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import hail as hl\n",
+    "\n",
+    "from gnomad_toolbox.analysis.general import get_variant_count_by_freq_bin\n",
+    "from gnomad_toolbox.filtering.variant import filter_by_intervals\n",
+    "from gnomad_toolbox.filtering.vep import filter_by_csqs\n",
+    "from gnomad_toolbox.load_data import get_gnomad_release"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b3c44396-5ee1-4263-91f8-78bdb9417bab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Welcome to\n",
+      "     __  __     <>__\n",
+      "    / /_/ /__  __/ /\n",
+      "   / __  / _ `/ / /\n",
+      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241210-2229-0.2.132-678e1f52b999.log\n"
+     ]
+    }
+   ],
+   "source": [
+    "hl.init(backend=\"local\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d36c12aa-d395-4f6c-891c-77caa4779e47",
+   "metadata": {},
+   "source": [
+    "## Get variant count by allele frequency bin\n",
+    "\n",
+    "The examples below show variant counts using the Table filtered to DRD2."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dec1dc9c-f145-46cb-8d33-ca43d24dd06a",
+   "metadata": {},
+   "source": [
+    "### Counts for AF bins: *0.1% - 1%* and *>1.0%*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "261a3380-8dba-41cb-b51b-033b23c963d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'0.1% - 1.0%': 49, '>1.0%': 28, 'AC0 - 0.1%': 2662}\n"
+     ]
+    }
+   ],
+   "source": [
+    "drd_interval_ht = filter_by_intervals(\"chr11:113409605-113475691\")\n",
+    "af_bin_ht = get_variant_count_by_freq_bin(ht=drd_interval_ht)\n",
+    "print(af_bin_ht)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d05fb1c6-91a9-42cf-87bf-eccb50f6e9b4",
+   "metadata": {},
+   "source": [
+    "### Counts for *singletons*, *doubletons*, and AF bins: *doubletons - 0.05%*, *0.05% - 0.1%*, *0.1% - 1%*, and *>1%*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0a07a84-584e-4b08-b356-9d38767a9c50",
+   "metadata": {},
+   "source": [
+    "#### All DRD2 variants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "aaa8dfac-f7b3-4ce2-b13d-b86a1648b7b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'0.05% - 0.1%': 11, '0.1% - 1.0%': 34, '>1.0%': 26, 'doubletons': 384, 'doubletons - 0.05%': 894, 'singletons': 1390}\n"
+     ]
+    }
+   ],
+   "source": [
+    "af_bin_ht = get_variant_count_by_freq_bin(\n",
+    "    af_cutoffs=[0.0005, 0.001, 0.01], \n",
+    "    singletons=True, \n",
+    "    doubletons=True, \n",
+    "    ht=drd_interval_ht,\n",
+    ")\n",
+    "print(af_bin_ht)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fcabb6e-12b5-4a51-aa06-da280ec8a654",
+   "metadata": {},
+   "source": [
+    "#### All DRD2 coding variants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a0fd192a-b1f3-4726-96cd-e88c0d8baf08",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'0.05% - 0.1%': 4, '0.1% - 1.0%': 8, '>1.0%': 4, 'doubletons': 111, 'doubletons - 0.05%': 291, 'singletons': 365}\n"
+     ]
+    }
+   ],
+   "source": [
+    "af_bin_ht = get_variant_count_by_freq_bin(\n",
+    "    af_cutoffs=[0.0005, 0.001, 0.01], \n",
+    "    singletons=True, \n",
+    "    doubletons=True, \n",
+    "    ht=filter_by_csqs(['coding'], ht=drd_interval_ht),\n",
+    ")\n",
+    "print(af_bin_ht)"
+   ]
+  },
+  {
+   "attachments": {
+    "Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "f9b2d921",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Filter to pLOF variants that we used to compute constraint metrics\n",
+    "pLOF variants meets the following requirements:\n",
+    "* High-confidence LOFTEE variants (without any flags),\n",
+    "* Only variants in the MANE Select transcript,\n",
+    "* PASS variants that are SNVs with MAF ≤ 0.1%, and\n",
+    "* Exome median depth ≥ 30 (**This is changing in v4 constraint?**)\n",
+    "\n",
+    "**Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**\n",
+    "\n",
+    "![Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png](attachment:Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6ce87a77",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'coverage' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[7], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# TODO: add function for this\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m coverage_ht \u001b[38;5;241m=\u001b[39m coverage(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexomes\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mht()\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m#Filter to PASS SNVs with AF <= 0.1% and median exome depth ≥ 30.\u001b[39;00m\n\u001b[1;32m      6\u001b[0m ht \u001b[38;5;241m=\u001b[39m ht\u001b[38;5;241m.\u001b[39mfilter(\n\u001b[1;32m      7\u001b[0m     (hl\u001b[38;5;241m.\u001b[39mlen(ht\u001b[38;5;241m.\u001b[39mfilters) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m) \n\u001b[1;32m      8\u001b[0m     \u001b[38;5;241m&\u001b[39m (ht\u001b[38;5;241m.\u001b[39mallele_info\u001b[38;5;241m.\u001b[39mallele_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msnv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;241m&\u001b[39m (ht\u001b[38;5;241m.\u001b[39mfreq[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mAF \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.001\u001b[39m)\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;241m&\u001b[39m (coverage_ht[ht\u001b[38;5;241m.\u001b[39mlocus]\u001b[38;5;241m.\u001b[39mmedian_approx \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m30\u001b[39m)\n\u001b[1;32m     11\u001b[0m )\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'coverage' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: add function for this\n",
+    "\n",
+    "coverage_ht = coverage(\"exomes\").ht()\n",
+    "\n",
+    "#Filter to PASS SNVs with AF <= 0.1% and median exome depth ≥ 30.\n",
+    "ht = ht.filter(\n",
+    "    (hl.len(ht.filters) == 0) \n",
+    "    & (ht.allele_info.allele_type == \"snv\")\n",
+    "    & (ht.freq[0].AF <= 0.001)\n",
+    "    & (coverage_ht[ht.locus].median_approx >= 30)\n",
+    ")\n",
+    "\n",
+    "\n",
+    "print(f\"Number of variants: {ht.count()}\")\n",
+    "ht.select(\n",
+    "    freq=ht.freq[0],\n",
+    "    csq=ht.vep.transcript_consequences[0].consequence_terms,\n",
+    "    coverage=coverage_ht[ht.locus],\n",
+    ").show(-1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {
+    "height": "613.99px",
+    "width": "526.312px"
+   },
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "219.438px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 710bdcf5919f196d2a63ae671ffb98b9ca6fc952 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Fri, 13 Dec 2024 13:07:59 -0500
Subject: [PATCH 029/121] Fix unterminated string error

---
 gnomad_toolbox/load_data.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index d591bed..f0b9d95 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -165,8 +165,7 @@ def _get_gnomad_release(
         )
     else:
         raise ValueError(
-            f"Version {version} is not available for {
-                data_type} in the {dataset} dataset. "
+            f"Version {version} is not available for {data_type} in the {dataset} dataset. "
             f"Available versions: GRCh38 - {data_type_releases['GRCh38']}, "
             f"GRCh37 - {data_type_releases['GRCh37']}."
         )

From 4bfc305cdbddf5f4492a8550028e15e3bd20e107 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 18 Dec 2024 09:01:14 -0700
Subject: [PATCH 030/121] Apply suggestions from code review

Co-authored-by: Qin He <44242118+KoalaQin@users.noreply.github.com>
---
 gnomad_toolbox/analysis/general.py |  6 ++++--
 gnomad_toolbox/load_data.py        | 11 ++++-------
 2 files changed, 8 insertions(+), 9 deletions(-)

diff --git a/gnomad_toolbox/analysis/general.py b/gnomad_toolbox/analysis/general.py
index bcdde4a..391f9e4 100644
--- a/gnomad_toolbox/analysis/general.py
+++ b/gnomad_toolbox/analysis/general.py
@@ -36,7 +36,9 @@ def freq_bin_expr(
 
     :param freq_expr: Array of structs containing frequency information.
     :param index: Which index of freq_expr to use for annotation. Default is 0.
-    :param ac_cutoffs:
+    :param ac_cutoffs: List of AC cutoffs to use for binning. 
+    :param af_cutoffs: List of AF cutoffs to use for binning.
+    :param upper_af: Upper AF cutoff to use for binning.
     :return: StringExpression containing bin name based on input AC or AF.
     """
     if isinstance(freq_expr, hl.expr.ArrayExpression):
@@ -46,7 +48,7 @@ def freq_bin_expr(
         ac_cutoffs = [(c, f"AC{c}") for c in ac_cutoffs]
 
     if af_cutoffs and isinstance(af_cutoffs[0], float):
-        af_cutoffs = [(c, f"{c*100}%") for c in af_cutoffs]
+        af_cutoffs = [(f, f"{f*100}%") for f in af_cutoffs]
 
     if isinstance(upper_af, float):
         upper_af = (upper_af, f"{upper_af*100}%")
diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index f0b9d95..bc5a57d 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -83,8 +83,7 @@ def set_default_data(
         # Validate data type.
         if data_type and data_type not in DATA_TYPES:
             raise ValueError(
-                f"Data type {data_type} is invalid. Choose from 'exomes', 'genomes', "
-                f"or 'joint'."
+                f"Data type {data_type} is invalid. Choose from {DATA_TYPES}"
             )
 
         # Get all possible versions.
@@ -101,8 +100,7 @@ def set_default_data(
         # Check version availability.
         if version not in possible_versions:
             raise ValueError(
-                f"Version {version} is not available"
-                f"{'' if data_type else f' for {data_type}'}. "
+                f"Version {version} for {data_type} is not available."
             )
 
         self.data_type = data_type
@@ -142,7 +140,7 @@ def _get_gnomad_release(
 
     # Validate dataset.
     if releases is None:
-        raise ValueError(f"{dataset} is invalid. Choose from {RELEASES.keys()}")
+        raise ValueError(f"{dataset} is invalid. Choose from {list(RELEASES.keys())}")
 
     # Get all releases for the given dataset and data_type.
     data_type_releases = releases.get(data_type)
@@ -150,8 +148,7 @@ def _get_gnomad_release(
     # Validate data type.
     if data_type_releases is None:
         raise ValueError(
-            f"Data type {data_type} is invalid. Choose from 'exomes', 'genomes', or "
-            "'joint'."
+            f"Invalid data_type '{data_type}' for dataset '{dataset}'."
         )
 
     # Check version availability for GRCh38 and GRCh37.

From ecb664ba88f1902948eff17d685731bbe0fa0953 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 18 Dec 2024 09:08:23 -0700
Subject: [PATCH 031/121] format

---
 gnomad_toolbox/analysis/general.py | 2 +-
 gnomad_toolbox/load_data.py        | 8 ++------
 2 files changed, 3 insertions(+), 7 deletions(-)

diff --git a/gnomad_toolbox/analysis/general.py b/gnomad_toolbox/analysis/general.py
index 391f9e4..704173f 100644
--- a/gnomad_toolbox/analysis/general.py
+++ b/gnomad_toolbox/analysis/general.py
@@ -36,7 +36,7 @@ def freq_bin_expr(
 
     :param freq_expr: Array of structs containing frequency information.
     :param index: Which index of freq_expr to use for annotation. Default is 0.
-    :param ac_cutoffs: List of AC cutoffs to use for binning. 
+    :param ac_cutoffs: List of AC cutoffs to use for binning.
     :param af_cutoffs: List of AF cutoffs to use for binning.
     :param upper_af: Upper AF cutoff to use for binning.
     :return: StringExpression containing bin name based on input AC or AF.
diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index bc5a57d..de29e85 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -99,9 +99,7 @@ def set_default_data(
 
         # Check version availability.
         if version not in possible_versions:
-            raise ValueError(
-                f"Version {version} for {data_type} is not available."
-            )
+            raise ValueError(f"Version {version} for {data_type} is not available.")
 
         self.data_type = data_type
         self.version = version
@@ -147,9 +145,7 @@ def _get_gnomad_release(
 
     # Validate data type.
     if data_type_releases is None:
-        raise ValueError(
-            f"Invalid data_type '{data_type}' for dataset '{dataset}'."
-        )
+        raise ValueError(f"Invalid data_type '{data_type}' for dataset '{dataset}'.")
 
     # Check version availability for GRCh38 and GRCh37.
     if data_type_releases["GRCh38"] and version in data_type_releases["GRCh38"]:

From 06de9807d30e3afafa155b573c5342e1cac72e63 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Wed, 18 Dec 2024 11:22:31 -0500
Subject: [PATCH 032/121] Put back setup.py

---
 gnomad_toolbox/setup.py | 40 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)
 create mode 100644 gnomad_toolbox/setup.py

diff --git a/gnomad_toolbox/setup.py b/gnomad_toolbox/setup.py
new file mode 100644
index 0000000..8bc0d5d
--- /dev/null
+++ b/gnomad_toolbox/setup.py
@@ -0,0 +1,40 @@
+"""Setup script."""
+
+import setuptools
+
+with open("README.md", "r") as readme_file:
+    long_description = readme_file.read()
+
+install_requires = []
+with open("requirements.txt", "r") as requirements_file:
+    for req in (line.strip() for line in requirements_file):
+        if req != "hail":
+            install_requires.append(req)
+
+
+setuptools.setup(
+    name="gnomad_toolbox",
+    version="0.0.1",
+    author="The Genome Aggregation Database",
+    author_email="gnomad@broadinstitute.org",
+    description="Toolbox to help users process gnomAD release files",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/broadinstitute/gnomad-toolbox",
+    packages=setuptools.find_namespace_packages(include=["gnomad_toolbox.*"]),
+    project_urls={
+        "Documentation": "https://broadinstitute.github.io/gnomad-toolbox/",
+        "Source Code": "https://github.com/broadinstitute/gnomad-toolbox",
+        "Issues": "https://github.com/broadinstitute/gnomad-toolbox/issues",
+        "Change Log": "https://github.com/broadinstitute/gnomad-toolbox/releases",
+    },
+    classifiers=[
+        "Topic :: Scientific/Engineering :: Bio-Informatics",
+        "Intended Audience :: Science/Research",
+        "License :: OSI Approved :: BSD 3-Clause License",
+        "Programming Language :: Python :: 3",
+        "Development Status :: 4 - Beta",
+    ],
+    python_requires=">=3.9",
+    install_requires=install_requires,
+)

From c37ad6cc016b2841384c95eedf05d48cc9e4b360 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 18 Dec 2024 11:52:30 -0700
Subject: [PATCH 033/121] move setup to the correct location

---
 gnomad_toolbox/setup.py => setup.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename gnomad_toolbox/setup.py => setup.py (100%)

diff --git a/gnomad_toolbox/setup.py b/setup.py
similarity index 100%
rename from gnomad_toolbox/setup.py
rename to setup.py

From 24b78ab3e6a32faa18d669646275878d05f08259 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Wed, 18 Dec 2024 17:25:12 -0500
Subject: [PATCH 034/121] Add instructions to set up the toolbox and use
 notebooks

---
 README.md                                     |  79 +++
 .../intro_to_filtering_variant_data.ipynb     | 621 +++++++++++++++++-
 gnomad_toolbox/notebooks/needs_a_name.ipynb   |   2 +-
 3 files changed, 693 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index 08eecb8..fe98f7b 100644
--- a/README.md
+++ b/README.md
@@ -31,3 +31,82 @@ pip install -r requirements.txt
 
 ### Opening the notebooks
 jupyter lab
+
+# Setting Up Your Environment for Hail and gnomAD Toolbox
+
+This guide provides step-by-step instructions to set up a working environment for using Hail and the gnomad_toolbox. The steps include installing Miniconda, creating a Conda environment, installing the necessary dependencies, and configuring the service account.
+
+Prerequisites
+
+Before starting, ensure you have the following:
+	•	Administrator access to your system to install software.
+	•	Internet connection for downloading dependencies.
+
+## Step 1: Install Miniconda
+Miniconda is a lightweight distribution of Conda that includes just Conda and its dependencies.
+1. Download Miniconda for your system from the [official website](https://docs.anaconda.com/miniconda/install/).
+2. Follow the installation instructions for your operating system:
+	•	Linux/macOS: Run the installer script in your terminal:
+```
+bash Miniconda3-latest-Linux-x86_64.sh
+```
+    •	Windows: Run the installer executable and follow the installation wizard.
+3. Confirm the insstallation by running:
+```
+conda --version
+```
+
+## Step 2: Install Google Cloud SDK (gcloud)
+
+The Google Cloud SDK is required to interact with Google Cloud services, including setting up authentication for Hail.
+1. Follow the official Google Cloud SDK installation guide for your operating system.
+2. After installation, initialize gcloud:
+```
+gcloud init
+```
+3. Authenticate with your Google account:
+```
+gcloud auth login
+```
+4. Set the default project:
+```
+gcloud config set project broad-mpg-gnomad
+```
+
+## Step 43: Configure the Service Account
+```commandline
+gcloud iam service-accounts keys create hail-local-sa-key.json --iam-account hail-local-sa@broad-mpg-gnomad.iam.gserviceaccount.com
+export GOOGLE_APPLICATION_CREDENTIALS=path-to-your-key/hail-local-sa-key.json
+```
+
+## Step 4: Create a Conda Environment
+1. Create a new Conda environment with a specific version of Hail (here we use Hail
+   0.2.132 and Python 3.11):
+```commandline
+conda create -n gnomad-toolbox hail=0.2.132,python=3.11
+```
+2. Activate the Conda environment:
+```commandline
+conda activate gnomad-toolbox
+```
+3. Clone the gnomad-toolbox repository:
+```commandline
+cd /path/to/your/directory
+git clone https://github.com/broadinstitute/gnomad-toolbox.git
+cd gnomad-toolbox
+pip install -r requirements.txt
+pip install git+https://github.com/broadinstitute/gnomad-toolbox.git
+```
+
+## Step 5: Verify the Setup
+1.	Start a Python shell and test if Hail and gnomad_toolbox are working:
+```commandline
+import hail as hl
+from gnomad_toolbox.analysis.general import get_variant_count_by_freq_bin
+hl.init()
+print("Hail and gnomad_toolbox setup is complete!")
+```
+Or open the notebooks:
+```commandline
+jupyter lab
+```
diff --git a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
index e6dd8fd..6ee51f0 100644
--- a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
+++ b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
@@ -51,7 +51,7 @@
        "    \n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -223,7 +223,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\");\n",
+       "    const el = document.getElementById(\"ac20726a-f588-48d8-a740-bc1df9e390c1\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -329,7 +329,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"ac20726a-f588-48d8-a740-bc1df9e390c1\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -345,7 +345,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"ac20726a-f588-48d8-a740-bc1df9e390c1\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"ac20726a-f588-48d8-a740-bc1df9e390c1\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -355,7 +355,7 @@
     "import hail as hl\n",
     "\n",
     "from gnomad_toolbox.load_data import get_gnomad_release\n",
-    "from gnomad_toolbox.filtering.variant import filter_by_intervals, filter_by_gene_symbol\n",
+    "from gnomad_toolbox.filtering.variant import get_single_variant,filter_by_intervals, filter_by_gene_symbol\n",
     "from gnomad_toolbox.filtering.frequency import (\n",
     "    get_ancestry_callstats, \n",
     "    get_single_variant_ancestry_callstats,\n",
@@ -378,8 +378,9 @@
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
       "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241210-2227-0.2.132-678e1f52b999.log\n",
-      "2024-12-10 22:28:45.710 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
+      "LOGGING: writing to /Users/heqin/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241218-1316-0.2.132-678e1f52b999.log\n",
+      "2024-12-18 13:16:59.591 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n",
+      "2024-12-18 13:18:09.999 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
      ]
     }
    ],
@@ -387,6 +388,610 @@
     "hl.init(backend=\"local\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fad2569e-601e-4ce6-8267-219963c7e6ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
"
+      ],
+      "text/plain": [
+       "+---------------+------------+\n",
+       "| locus         | alleles    |\n",
+       "+---------------+------------+\n",
+       "| locus | array |\n",
+       "+---------------+------------+\n",
+       "+---------------+------------+\n",
+       "\n",
+       "+---------------------------------------------------------------------------+\n",
+       "| freq                                                                      |\n",
+       "+---------------------------------------------------------------------------+\n",
+       "| array |\n",
+       "+---------------------------------------------------------------------------+\n",
+       "+---------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+-----------------------------------------------+\n",
+       "| grpmax.non_ukb.gen_anc | faf                                           |\n",
+       "+------------------------+-----------------------------------------------+\n",
+       "| str                    | array |\n",
+       "+------------------------+-----------------------------------------------+\n",
+       "+------------------------+-----------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+----------+----------+---------+---------+----------------+\n",
+       "| was_split | rsid     | filters  | info.FS | info.MQ | info.MQRankSum |\n",
+       "+-----------+----------+----------+---------+---------+----------------+\n",
+       "|      bool | set | set | float64 | float64 |        float64 |\n",
+       "+-----------+----------+----------+---------+---------+----------------+\n",
+       "+-----------+----------+----------+---------+---------+----------------+\n",
+       "\n",
+       "+-----------------+---------+---------------------+--------------+----------+\n",
+       "| info.QUALapprox | info.QD | info.ReadPosRankSum | info.SB      | info.SOR |\n",
+       "+-----------------+---------+---------------------+--------------+----------+\n",
+       "|           int64 | float64 |             float64 | array |  float64 |\n",
+       "+-----------------+---------+---------------------+--------------+----------+\n",
+       "+-----------------+---------+---------------------+--------------+----------+\n",
+       "\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "| info.VarDP | info.AS_FS | info.AS_MQ | info.AS_MQRankSum | info.AS_pab_max |\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "|      int32 |    float64 |    float64 |           float64 |         float64 |\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "| info.AS_QUALapprox | info.AS_QD | info.AS_ReadPosRankSum | info.AS_SB_TABLE |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "|              int64 |    float64 |                float64 | array     |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "| info.AS_SOR | info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "|     float64 |         int32 |           bool |                       bool |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "| info.sibling_singleton | info.omni | info.mills | info.monoallelic |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                   bool |      bool |       bool |             bool |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "\n",
+       "+---------------+----------------+-----------------------+\n",
+       "| info.only_het | info.AS_VQSLOD | info.inbreeding_coeff |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|          bool |        float64 |               float64 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "+---------------+----------------+-----------------------+\n",
+       "\n",
+       "+-------------------------+---------------------+-------------------+\n",
+       "| info.vrs.VRS_Allele_IDs | info.vrs.VRS_Starts | info.vrs.VRS_Ends |\n",
+       "+-------------------------+---------------------+-------------------+\n",
+       "| array              | array        | array      |\n",
+       "+-------------------------+---------------------+-------------------+\n",
+       "+-------------------------+---------------------+-------------------+\n",
+       "\n",
+       "+---------------------+-------------------+---------+--------+-----------+\n",
+       "| info.vrs.VRS_States | vep.allele_string | vep.end | vep.id | vep.input |\n",
+       "+---------------------+-------------------+---------+--------+-----------+\n",
+       "| array          | str               |   int32 | str    | str       |\n",
+       "+---------------------+-------------------+---------+--------+-----------+\n",
+       "+---------------------+-------------------+---------+--------+-----------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                              |\n",
+       "+-----------------------+---------------------------------------------+\n",
+       "+-----------------------+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges |\n",
+       "+---------------------------------------------+\n",
+       "| array                              |\n",
+       "+---------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges |\n",
+       "+---------------------------------------------+\n",
+       "| array                              |\n",
+       "+---------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges |\n",
+       "+---------------------------------------------+\n",
+       "| array                              |\n",
+       "+---------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges |\n",
+       "+---------------------------------------------+\n",
+       "| array                              |\n",
+       "+---------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges |\n",
+       "+-------------------------------------------------+\n",
+       "| array                                  |\n",
+       "+-------------------------------------------------+\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges |\n",
+       "+-------------------------------------------------+\n",
+       "| array                                  |\n",
+       "+-------------------------------------------------+\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges |\n",
+       "+-------------------------------------------------+\n",
+       "| array                                  |\n",
+       "+-------------------------------------------------+\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges |\n",
+       "+-------------------------------------------------+\n",
+       "| array                                  |\n",
+       "+-------------------------------------------------+\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges |\n",
+       "+-------------------------------------------------+\n",
+       "| array                                  |\n",
+       "+-------------------------------------------------+\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges |\n",
+       "+---------------------------------------------+\n",
+       "| array                              |\n",
+       "+---------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges |\n",
+       "+---------------------------------------------+\n",
+       "| array                              |\n",
+       "+---------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "+-------------------------------+-----------------------------------+"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht = get_single_variant(contig='chr22', position=15528692, ref='C', alt='A')\n",
+    "ht.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "725f9a57",
@@ -6252,7 +6857,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.9"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/gnomad_toolbox/notebooks/needs_a_name.ipynb b/gnomad_toolbox/notebooks/needs_a_name.ipynb
index c4b688a..cc43a46 100644
--- a/gnomad_toolbox/notebooks/needs_a_name.ipynb
+++ b/gnomad_toolbox/notebooks/needs_a_name.ipynb
@@ -566,7 +566,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.9"
   },
   "toc": {
    "base_numbering": 1,

From 1b9f98dd9446cd69b4d2048a013122e343cc959c Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Wed, 18 Dec 2024 17:27:30 -0500
Subject: [PATCH 035/121] small edit

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index fe98f7b..e0e9e0c 100644
--- a/README.md
+++ b/README.md
@@ -83,7 +83,7 @@ export GOOGLE_APPLICATION_CREDENTIALS=path-to-your-key/hail-local-sa-key.json
 1. Create a new Conda environment with a specific version of Hail (here we use Hail
    0.2.132 and Python 3.11):
 ```commandline
-conda create -n gnomad-toolbox hail=0.2.132,python=3.11
+conda create -n gnomad-toolbox hail=0.2.132 python=3.11
 ```
 2. Activate the Conda environment:
 ```commandline

From 62b7fd89cff9c1817eae646cc1b706f20bbe9562 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Thu, 19 Dec 2024 08:35:41 -0700
Subject: [PATCH 036/121] use updated freq_bin_expr in gnomad_methods

---
 gnomad_toolbox/analysis/general.py | 70 +-----------------------------
 1 file changed, 1 insertion(+), 69 deletions(-)

diff --git a/gnomad_toolbox/analysis/general.py b/gnomad_toolbox/analysis/general.py
index 704173f..be9fb7e 100644
--- a/gnomad_toolbox/analysis/general.py
+++ b/gnomad_toolbox/analysis/general.py
@@ -3,79 +3,11 @@
 from typing import Dict, List, Optional, Tuple, Union
 
 import hail as hl
+from gnomad.assessment.summary_stats import freq_bin_expr
 
 from gnomad_toolbox.load_data import _get_gnomad_release
 
 
-# TODO: Modify this function in gnomad_methods.
-def freq_bin_expr(
-    freq_expr: Union[hl.expr.StructExpression, hl.expr.ArrayExpression],
-    index: int = 0,
-    ac_cutoffs: Optional[List[Union[int, Tuple[int, str]]]] = [
-        (0, "AC0"),
-        (1, "singleton"),
-        (2, "doubleton"),
-    ],
-    af_cutoffs: Optional[List[Union[float, Tuple[float, str]]]] = [
-        (1e-4, "0.01%"),
-        (1e-3, "0.1%"),
-        (1e-2, "1%"),
-        (1e-1, "10%"),
-    ],
-    upper_af: Optional[Union[float, Tuple[float, str]]] = (0.95, "95%"),
-) -> hl.expr.StringExpression:
-    """
-    Return frequency string annotations based on input AC or AF.
-
-    .. note::
-
-        - Default index is 0 because function assumes freq_expr was calculated with
-          `annotate_freq`.
-        - Frequency index 0 from `annotate_freq` is frequency for all pops calculated
-          on adj genotypes only.
-
-    :param freq_expr: Array of structs containing frequency information.
-    :param index: Which index of freq_expr to use for annotation. Default is 0.
-    :param ac_cutoffs: List of AC cutoffs to use for binning.
-    :param af_cutoffs: List of AF cutoffs to use for binning.
-    :param upper_af: Upper AF cutoff to use for binning.
-    :return: StringExpression containing bin name based on input AC or AF.
-    """
-    if isinstance(freq_expr, hl.expr.ArrayExpression):
-        freq_expr = freq_expr[index]
-
-    if ac_cutoffs and isinstance(ac_cutoffs[0], int):
-        ac_cutoffs = [(c, f"AC{c}") for c in ac_cutoffs]
-
-    if af_cutoffs and isinstance(af_cutoffs[0], float):
-        af_cutoffs = [(f, f"{f*100}%") for f in af_cutoffs]
-
-    if isinstance(upper_af, float):
-        upper_af = (upper_af, f"{upper_af*100}%")
-
-    freq_bin_expr = hl.case().when(hl.is_missing(freq_expr.AC), "Missing")
-    prev_af = None
-    for ac, name in sorted(ac_cutoffs):
-        freq_bin_expr = freq_bin_expr.when(freq_expr.AC == ac, name)
-        prev_af = name
-
-    for af, name in sorted(af_cutoffs):
-        prev_af = "<" if prev_af is None else f"{prev_af} - "
-        freq_bin_expr = freq_bin_expr.when(freq_expr.AF < af, f"{prev_af}{name}")
-        prev_af = name
-
-    if upper_af:
-        freq_bin_expr = freq_bin_expr.when(
-            freq_expr.AF > upper_af[0], f">{upper_af[1]}"
-        )
-        default_af = "<" if prev_af is None else f"{prev_af} - "
-        default_af = f"{default_af}{upper_af[1]}"
-    else:
-        default_af = f">{prev_af}"
-
-    return freq_bin_expr.default(default_af)
-
-
 def get_variant_count_by_freq_bin(
     af_cutoffs: List[float] = [0.001, 0.01],
     singletons: bool = False,

From 8f49b091cc96cf735869ddb667c2e9e156b88708 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Thu, 19 Dec 2024 11:48:18 -0500
Subject: [PATCH 037/121] Reorganize steps

---
 README.md | 140 ++++++++++++++++++++++++++++++------------------------
 1 file changed, 79 insertions(+), 61 deletions(-)

diff --git a/README.md b/README.md
index e0e9e0c..2a6a811 100644
--- a/README.md
+++ b/README.md
@@ -24,81 +24,64 @@ ggnomad_toolbox/
 │   ├── intro_to_release_data.ipynb    # Jupyter notebook introducing the loading of gnomAD release data.
 ```
 
-# TODO: Add fully detailed info about how to install and open the notebooks.
-## Getting started
-### Install
-pip install -r requirements.txt
+## Setting Up Your Environment for Hail and gnomAD Toolbox
 
-### Opening the notebooks
-jupyter lab
-
-# Setting Up Your Environment for Hail and gnomAD Toolbox
-
-This guide provides step-by-step instructions to set up a working environment for using Hail and the gnomad_toolbox. The steps include installing Miniconda, creating a Conda environment, installing the necessary dependencies, and configuring the service account.
+This guide provides step-by-step instructions to set up a working environment for
+using Hail and the gnomad_toolbox.
 
 Prerequisites
 
 Before starting, ensure you have the following:
-	•	Administrator access to your system to install software.
-	•	Internet connection for downloading dependencies.
+* Administrator access to your system to install software.
+* Internet connection for downloading dependencies.
+
 
-## Step 1: Install Miniconda
+## Part 1: Setting Up Your Environment
+
+### Install Miniconda
 Miniconda is a lightweight distribution of Conda that includes just Conda and its dependencies.
 1. Download Miniconda for your system from the [official website](https://docs.anaconda.com/miniconda/install/).
 2. Follow the installation instructions for your operating system:
+
 	•	Linux/macOS: Run the installer script in your terminal:
-```
-bash Miniconda3-latest-Linux-x86_64.sh
-```
+    ```
+    bash Miniconda3-latest-Linux-x86_64.sh
+    ```
     •	Windows: Run the installer executable and follow the installation wizard.
-3. Confirm the insstallation by running:
-```
-conda --version
-```
-
-## Step 2: Install Google Cloud SDK (gcloud)
-
-The Google Cloud SDK is required to interact with Google Cloud services, including setting up authentication for Hail.
-1. Follow the official Google Cloud SDK installation guide for your operating system.
-2. After installation, initialize gcloud:
-```
-gcloud init
-```
-3. Authenticate with your Google account:
-```
-gcloud auth login
-```
-4. Set the default project:
-```
-gcloud config set project broad-mpg-gnomad
-```
-
-## Step 43: Configure the Service Account
-```commandline
-gcloud iam service-accounts keys create hail-local-sa-key.json --iam-account hail-local-sa@broad-mpg-gnomad.iam.gserviceaccount.com
-export GOOGLE_APPLICATION_CREDENTIALS=path-to-your-key/hail-local-sa-key.json
-```
+# TODO: Will we encourage users to use Windows?
+3. Confirm the installation by running:
+   ```
+   conda --version
+   ```
 
-## Step 4: Create a Conda Environment
-1. Create a new Conda environment with a specific version of Hail (here we use Hail
-   0.2.132 and Python 3.11):
-```commandline
-conda create -n gnomad-toolbox hail=0.2.132 python=3.11
-```
+### Create a Conda Environment
+1. Create a new Conda environment with a specific version of Python:
+   ```commandline
+   conda create -n gnomad-toolbox python=3.11
+   ```
 2. Activate the Conda environment:
-```commandline
-conda activate gnomad-toolbox
-```
-3. Clone the gnomad-toolbox repository:
-```commandline
-cd /path/to/your/directory
-git clone https://github.com/broadinstitute/gnomad-toolbox.git
-cd gnomad-toolbox
-pip install -r requirements.txt
-pip install git+https://github.com/broadinstitute/gnomad-toolbox.git
-```
+   ```commandline
+   conda activate gnomad-toolbox
+   ```
+3. Clone the gnomad-toolbox repository and install the dependencies:
+   ```commandline
+   cd /path/to/your/directory
+   git clone https://github.com/broadinstitute/gnomad-toolbox.git
+   cd gnomad-toolbox
+   pip install -r requirements.txt
+    ```
+   You might encounter errors when installing the dependencies, such as `pg_config
+   executable not found`. If so, you may need to install additional system packages.
+   For example, on Ubuntu, you can install the `libpq-dev` package:
+   ```commandline
+    sudo apt-get install libpq-dev
+    ```
+   or on macOS, you can install the `postgresql` package:
+   ```commandline
+    brew install postgresql
+    ```
 
-## Step 5: Verify the Setup
+### Verify the Setup
 1.	Start a Python shell and test if Hail and gnomad_toolbox are working:
 ```commandline
 import hail as hl
@@ -110,3 +93,38 @@ Or open the notebooks:
 ```commandline
 jupyter lab
 ```
+
+## Part2: Accessing gnomAD Data Locally with example notebooks
+If you already have experience with gcloud and have no problem running these notebooks,
+you can skip this section.
+
+### Install Google Cloud SDK (gcloud)
+
+The Google Cloud SDK is required to interact with Google Cloud services and access gnomAD public data locally.
+1. Follow the official Google Cloud SDK installation [guide](https://cloud.google.
+   com/sdk/docs/install) for your operating system.
+2. After installation, initialize gcloud to log in and set up your default project:
+   ```
+   gcloud init
+   ```
+3. You can check your gcloud config by:
+   ```
+   gcloud config list
+   ```
+   or set the default project:
+   ```
+   gcloud config set project {YOUR_PROJECT_ID}
+   ```
+
+## Configure a Service Account
+You will need to create a service account in gcloud console IAM & Admin or using
+gcloud CLI. Then you can create a key for service account and set the GOOGLE_APPLICATION_CREDENTIALS
+variable to the path of the key file.
+   ```commandline
+    gcloud iam service-accounts keys create hail-local-sa-key.json --iam-account {YOUR_SERVICE_ACCOUNT}
+
+    export GOOGLE_APPLICATION_CREDENTIALS=./hail-local-sa-key.json
+   ```
+Now, you can access gnomAD data locally using the gnomad_toolbox functions, however,
+avoid running queries on the full dataset as it may take a long time and consume a
+lot of resources, and most importantly, it may incur costs.

From 1d811e6a5a137c6b42a4963832259fa84075c6b9 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Thu, 19 Dec 2024 12:36:44 -0500
Subject: [PATCH 038/121] small edit

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 2a6a811..46a6004 100644
--- a/README.md
+++ b/README.md
@@ -48,7 +48,7 @@ Miniconda is a lightweight distribution of Conda that includes just Conda and it
     bash Miniconda3-latest-Linux-x86_64.sh
     ```
     •	Windows: Run the installer executable and follow the installation wizard.
-# TODO: Will we encourage users to use Windows?
+        (# TODO: Will we encourage users to use Windows?)
 3. Confirm the installation by running:
    ```
    conda --version

From 18d0a85e88f50ff2c7f8899de7026278a988fe00 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Thu, 19 Dec 2024 12:37:40 -0500
Subject: [PATCH 039/121] small edit 2

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 46a6004..caff74f 100644
--- a/README.md
+++ b/README.md
@@ -116,7 +116,7 @@ The Google Cloud SDK is required to interact with Google Cloud services and acce
    gcloud config set project {YOUR_PROJECT_ID}
    ```
 
-## Configure a Service Account
+### Configure a Service Account
 You will need to create a service account in gcloud console IAM & Admin or using
 gcloud CLI. Then you can create a key for service account and set the GOOGLE_APPLICATION_CREDENTIALS
 variable to the path of the key file.

From 4ddde29ee36747f32223df2c1bd67767e8d42df1 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Thu, 19 Dec 2024 10:59:29 -0700
Subject: [PATCH 040/121] Use `parse_variant` in gnomad_methods

---
 gnomad_toolbox/filtering/variant.py | 19 ++++---------------
 requirements.txt                    |  3 ++-
 setup.py                            |  2 +-
 3 files changed, 7 insertions(+), 17 deletions(-)

diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index ec00bc7..0de3172 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -3,6 +3,7 @@
 from typing import Optional, Union
 
 import hail as hl
+from gnomad.utils.parse import parse_variant
 from gnomad.utils.reference_genome import get_reference_genome
 
 from gnomad_toolbox.load_data import _get_gnomad_release
@@ -47,21 +48,8 @@ def get_single_variant(
     # Determine the reference genome build for the ht.
     build = get_reference_genome(ht.locus).name
 
-    # TODO: Move this to gnomad_methods.
-    # Parse the variant string if provided.
-    try:
-        if variant and ":" not in variant:
-            contig, position, ref, alt = variant.split("-")
-        if all([contig, position, ref, alt]):
-            variant = f"{contig}:{position}:{ref}:{alt}"
-        variant = hl.eval(hl.parse_variant(variant, reference_genome=build))
-    except ValueError:
-        raise ValueError(
-            f"Invalid variant format: {variant}. Expected format: chr12-235245-A-C "
-            f"or chr12:235245:A:C"
-        )
-
     # Filter to the Locus of the variant of interest.
+    variant = parse_variant(variant, contig, position, ref, alt, build)
     ht = hl.filter_intervals(
         ht, [hl.interval(variant.locus, variant.locus, includes_end=True)]
     )
@@ -72,7 +60,8 @@ def get_single_variant(
     # Check if the variant exists.
     if ht.count() == 0:
         hl.utils.warning(
-            f"No variant found at {variant.locus} with alleles {variant.alleles}"
+            f"No variant found at {hl.eval(variant.locus)} with alleles "
+            f"{hl.eval(variant.alleles)}"
         )
 
     return ht
diff --git a/requirements.txt b/requirements.txt
index 32706b5..3410cd0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,6 @@
 # We're using the main branch of gnomad_method on github rather than the pip version
-git+https://github.com/broadinstitute/gnomad_methods@main
+# TODO: Decide on how to handle this. We might just need to have more releases of gnomad_methods.
+#git+https://github.com/broadinstitute/gnomad_methods@main
 hail
 jupyter
 jupyter_contrib_nbextensions
diff --git a/setup.py b/setup.py
index 8bc0d5d..78ab1cb 100644
--- a/setup.py
+++ b/setup.py
@@ -21,7 +21,7 @@
     long_description=long_description,
     long_description_content_type="text/markdown",
     url="https://github.com/broadinstitute/gnomad-toolbox",
-    packages=setuptools.find_namespace_packages(include=["gnomad_toolbox.*"]),
+    packages=setuptools.find_namespace_packages(include=["gnomad_toolbox*"]),
     project_urls={
         "Documentation": "https://broadinstitute.github.io/gnomad-toolbox/",
         "Source Code": "https://github.com/broadinstitute/gnomad-toolbox",

From a68b7fdd0a690f6df1535875a607f56d4d09bb5c Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Thu, 19 Dec 2024 13:16:23 -0500
Subject: [PATCH 041/121] Specify Java version

---
 README.md | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/README.md b/README.md
index caff74f..6c97519 100644
--- a/README.md
+++ b/README.md
@@ -70,6 +70,18 @@ Miniconda is a lightweight distribution of Conda that includes just Conda and it
    cd gnomad-toolbox
    pip install -r requirements.txt
     ```
+   **Note:** Hail 0.2.127+ requires Java 8 or Java 11 and jupyter labs requires Java
+   11+, and if you have an Apple M1 or M2, you must have arm64 Java installed, you
+   can first check your Java version by running:
+   ```commandline
+    java -version
+    ```
+   and check if you have the arm64 Java by running:
+   ```commandline
+    file $JAVA_HOME/bin/java
+    ```
+   If you don't have the arm64 Java, you can find it [here](https://www.azul.com/downloads/?os=macos&architecture=arm-64-bit&package=jre#zulu)
+
    You might encounter errors when installing the dependencies, such as `pg_config
    executable not found`. If so, you may need to install additional system packages.
    For example, on Ubuntu, you can install the `libpq-dev` package:

From 7248a81c1bc3b1197470d2e3fa428b14fd1b13b8 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Thu, 19 Dec 2024 13:22:17 -0500
Subject: [PATCH 042/121] Undo weird changes to notebooks

---
 .../intro_to_filtering_variant_data.ipynb     | 621 +-----------------
 gnomad_toolbox/notebooks/needs_a_name.ipynb   |   2 +-
 2 files changed, 9 insertions(+), 614 deletions(-)

diff --git a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
index 6ee51f0..e6dd8fd 100644
--- a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
+++ b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
@@ -51,7 +51,7 @@
        "    \n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -223,7 +223,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"ac20726a-f588-48d8-a740-bc1df9e390c1\");\n",
+       "    const el = document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -329,7 +329,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"ac20726a-f588-48d8-a740-bc1df9e390c1\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -345,7 +345,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"ac20726a-f588-48d8-a740-bc1df9e390c1\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"ac20726a-f588-48d8-a740-bc1df9e390c1\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -355,7 +355,7 @@
     "import hail as hl\n",
     "\n",
     "from gnomad_toolbox.load_data import get_gnomad_release\n",
-    "from gnomad_toolbox.filtering.variant import get_single_variant,filter_by_intervals, filter_by_gene_symbol\n",
+    "from gnomad_toolbox.filtering.variant import filter_by_intervals, filter_by_gene_symbol\n",
     "from gnomad_toolbox.filtering.frequency import (\n",
     "    get_ancestry_callstats, \n",
     "    get_single_variant_ancestry_callstats,\n",
@@ -378,9 +378,8 @@
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
       "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/heqin/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241218-1316-0.2.132-678e1f52b999.log\n",
-      "2024-12-18 13:16:59.591 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n",
-      "2024-12-18 13:18:09.999 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241210-2227-0.2.132-678e1f52b999.log\n",
+      "2024-12-10 22:28:45.710 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
      ]
     }
    ],
@@ -388,610 +387,6 @@
     "hl.init(backend=\"local\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "fad2569e-601e-4ce6-8267-219963c7e6ae",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "
histograms
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in_silico_predictors
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non_ukb
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singleton
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AS_VQSLOD
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end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
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start
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transcript_consequences
variant_class
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AS_culprit
positive_train_site
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locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
"
-      ],
-      "text/plain": [
-       "+---------------+------------+\n",
-       "| locus         | alleles    |\n",
-       "+---------------+------------+\n",
-       "| locus | array |\n",
-       "+---------------+------------+\n",
-       "+---------------+------------+\n",
-       "\n",
-       "+---------------------------------------------------------------------------+\n",
-       "| freq                                                                      |\n",
-       "+---------------------------------------------------------------------------+\n",
-       "| array |\n",
-       "+---------------------------------------------------------------------------+\n",
-       "+---------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+\n",
-       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
-       "+------------------+------------------+------------------+\n",
-       "|            int32 |          float64 |            int32 |\n",
-       "+------------------+------------------+------------------+\n",
-       "+------------------+------------------+------------------+\n",
-       "\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                          int64 | str                   |             int32 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|           float64 |             int32 |                           int64 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "\n",
-       "+------------------------+-----------------------------------------------+\n",
-       "| grpmax.non_ukb.gen_anc | faf                                           |\n",
-       "+------------------------+-----------------------------------------------+\n",
-       "| str                    | array |\n",
-       "+------------------------+-----------------------------------------------+\n",
-       "+------------------------+-----------------------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                  float64 | str                              |   int32 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "\n",
-       "+-----------+----------+----------+---------+---------+----------------+\n",
-       "| was_split | rsid     | filters  | info.FS | info.MQ | info.MQRankSum |\n",
-       "+-----------+----------+----------+---------+---------+----------------+\n",
-       "|      bool | set | set | float64 | float64 |        float64 |\n",
-       "+-----------+----------+----------+---------+---------+----------------+\n",
-       "+-----------+----------+----------+---------+---------+----------------+\n",
-       "\n",
-       "+-----------------+---------+---------------------+--------------+----------+\n",
-       "| info.QUALapprox | info.QD | info.ReadPosRankSum | info.SB      | info.SOR |\n",
-       "+-----------------+---------+---------------------+--------------+----------+\n",
-       "|           int64 | float64 |             float64 | array |  float64 |\n",
-       "+-----------------+---------+---------------------+--------------+----------+\n",
-       "+-----------------+---------+---------------------+--------------+----------+\n",
-       "\n",
-       "+------------+------------+------------+-------------------+-----------------+\n",
-       "| info.VarDP | info.AS_FS | info.AS_MQ | info.AS_MQRankSum | info.AS_pab_max |\n",
-       "+------------+------------+------------+-------------------+-----------------+\n",
-       "|      int32 |    float64 |    float64 |           float64 |         float64 |\n",
-       "+------------+------------+------------+-------------------+-----------------+\n",
-       "+------------+------------+------------+-------------------+-----------------+\n",
-       "\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "| info.AS_QUALapprox | info.AS_QD | info.AS_ReadPosRankSum | info.AS_SB_TABLE |\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "|              int64 |    float64 |                float64 | array     |\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "| info.AS_SOR | info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "|     float64 |         int32 |           bool |                       bool |\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "| info.sibling_singleton | info.omni | info.mills | info.monoallelic |\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "|                   bool |      bool |       bool |             bool |\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "\n",
-       "+---------------+----------------+-----------------------+\n",
-       "| info.only_het | info.AS_VQSLOD | info.inbreeding_coeff |\n",
-       "+---------------+----------------+-----------------------+\n",
-       "|          bool |        float64 |               float64 |\n",
-       "+---------------+----------------+-----------------------+\n",
-       "+---------------+----------------+-----------------------+\n",
-       "\n",
-       "+-------------------------+---------------------+-------------------+\n",
-       "| info.vrs.VRS_Allele_IDs | info.vrs.VRS_Starts | info.vrs.VRS_Ends |\n",
-       "+-------------------------+---------------------+-------------------+\n",
-       "| array              | array        | array      |\n",
-       "+-------------------------+---------------------+-------------------+\n",
-       "+-------------------------+---------------------+-------------------+\n",
-       "\n",
-       "+---------------------+-------------------+---------+--------+-----------+\n",
-       "| info.vrs.VRS_States | vep.allele_string | vep.end | vep.id | vep.input |\n",
-       "+---------------------+-------------------+---------+--------+-----------+\n",
-       "| array          | str               |   int32 | str    | str       |\n",
-       "+---------------------+-------------------+---------+--------+-----------+\n",
-       "+---------------------+-------------------+---------+--------+-----------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.intergenic_consequences                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, impact: st... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------+\n",
-       "| vep.most_severe_consequence |\n",
-       "+-----------------------------+\n",
-       "| str                         |\n",
-       "+-----------------------------+\n",
-       "+-----------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.motif_feature_consequences                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, high_inf_p... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.regulatory_feature_consequences                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                              |\n",
-       "+-----------------------+---------------------------------------------+\n",
-       "+-----------------------+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_edges |\n",
-       "+---------------------------------------------+\n",
-       "| array                              |\n",
-       "+---------------------------------------------+\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_edges |\n",
-       "+---------------------------------------------+\n",
-       "| array                              |\n",
-       "+---------------------------------------------+\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_edges |\n",
-       "+---------------------------------------------+\n",
-       "| array                              |\n",
-       "+---------------------------------------------+\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "+---------------------------------------------+\n",
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-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_edges |\n",
-       "+---------------------------------------------+\n",
-       "| array                              |\n",
-       "+---------------------------------------------+\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_edges |\n",
-       "+-------------------------------------------------+\n",
-       "| array                                  |\n",
-       "+-------------------------------------------------+\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_edges |\n",
-       "+-------------------------------------------------+\n",
-       "| array                                  |\n",
-       "+-------------------------------------------------+\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges |\n",
-       "+-------------------------------------------------+\n",
-       "| array                                  |\n",
-       "+-------------------------------------------------+\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges |\n",
-       "+-------------------------------------------------+\n",
-       "| array                                  |\n",
-       "+-------------------------------------------------+\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges |\n",
-       "+-------------------------------------------------+\n",
-       "| array                                  |\n",
-       "+-------------------------------------------------+\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_edges |\n",
-       "+---------------------------------------------+\n",
-       "| array                              |\n",
-       "+---------------------------------------------+\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_edges |\n",
-       "+---------------------------------------------+\n",
-       "| array                              |\n",
-       "+---------------------------------------------+\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                      int64 |                         float32 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "+-------------------------------+-----------------------------------+"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht = get_single_variant(contig='chr22', position=15528692, ref='C', alt='A')\n",
-    "ht.show()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "725f9a57",
@@ -6857,7 +6252,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/gnomad_toolbox/notebooks/needs_a_name.ipynb b/gnomad_toolbox/notebooks/needs_a_name.ipynb
index cc43a46..c4b688a 100644
--- a/gnomad_toolbox/notebooks/needs_a_name.ipynb
+++ b/gnomad_toolbox/notebooks/needs_a_name.ipynb
@@ -566,7 +566,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,

From e542c1b70fd1e1718f7159a1d3f8d8f482407de5 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Thu, 19 Dec 2024 11:56:37 -0700
Subject: [PATCH 043/121] Uncomment

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 3410cd0..702bdab 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
 # We're using the main branch of gnomad_method on github rather than the pip version
 # TODO: Decide on how to handle this. We might just need to have more releases of gnomad_methods.
-#git+https://github.com/broadinstitute/gnomad_methods@main
+git+https://github.com/broadinstitute/gnomad_methods@main
 hail
 jupyter
 jupyter_contrib_nbextensions

From 056ab1c2d1f7957989cc39880dc612558ffbd48b Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Thu, 19 Dec 2024 16:27:55 -0500
Subject: [PATCH 044/121] Use the filter CDS function from gnomad_methods

---
 gnomad_toolbox/filtering/variant.py | 62 ++++-------------------------
 1 file changed, 7 insertions(+), 55 deletions(-)

diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index ec00bc7..ba1f403 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -3,6 +3,7 @@
 from typing import Optional, Union
 
 import hail as hl
+from gnomad.utils.filtering import filter_to_gencode_cds
 from gnomad.utils.reference_genome import get_reference_genome
 
 from gnomad_toolbox.load_data import _get_gnomad_release
@@ -120,6 +121,11 @@ def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.
            gnomAD browser, which only focus on variants in "CDS" regions plus
            75bp (default of `exon_padding_bp`) up- and downstream.
 
+           However, gnomAD browser used a preprocessed Gencode file which excluded
+           46 genes on chrY that share the same gene id as chrX. For example,
+           if you use this function to filter "ASMT" gene, you will get more variants
+           than shown in the gnomAD browser.
+
     :param gene: Gene symbol.
     :param exon_padding_bp: Number of base pairs to pad the CDS intervals. Default is
         75bp.
@@ -129,60 +135,6 @@ def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.
     # Load the Hail Table if not provided
     ht = _get_gnomad_release(dataset="variant", **kwargs)
 
-    # Determine the reference genome build for the ht.
-    build = get_reference_genome(ht.locus).name
-
-    # Make gene symbol uppercase
-    gene = gene.upper()
-
-    # TODO: Create a resource for this in gnomad_methods (is it different from our
-    #  current gencode resources?
-    # gene_ht = hl.read_table(
-    #    f"gs://gcp-public-data--gnomad/resources/{build.lower()}/browser/gnomad"
-    #    f".genes.{build}.GENCODEv{'19' if build == 'GRCh37' else '39'}.ht"
-    # )
-
-    # TODO: This actually takes a while to run locally for a single gene. Is there a
-    #  way to speed this up?
-
-    # Filter to the gene of interest.
-    # gene_ht = gene_ht.filter(gene_ht.gencode_symbol == gene)
-
-    # Get the CDS intervals for the gene.
-    # chrom_expr = hl.if_else(build == "GRCh38", "chr" + gene_ht.chrom, gene_ht.chrom)
-    # intervals = gene_ht.aggregate(
-    #    hl.agg.explode(
-    #        lambda exon: hl.agg.collect(
-    #            hl.locus_interval(
-    #                chrom_expr,
-    #                exon.start - 75,
-    #                exon.stop + 75,
-    #                reference_genome=build,
-    #                includes_end=True,
-    #            )
-    #        ),
-    #        gene_ht.exons.filter(lambda exon: exon.feature_type == "CDS"),
-    #    )
-    # )
-
-    # TODO: Consider this alternative approach to get the intervals from gencode. That
-    #  is not too bad time wise
-
-    from gnomad.resources.grch38.reference_data import gencode
-
-    gencode_ht = gencode.ht()
-    gencode_ht = gencode_ht.filter(
-        (gencode_ht.gene_name == gene) & ((gencode_ht.feature == "CDS"))
-    )
-    intervals = hl.locus_interval(
-        gencode_ht.interval.start.contig,
-        gencode_ht.interval.start.position - exon_padding_bp,
-        gencode_ht.interval.end.position + exon_padding_bp,
-        includes_start=gencode_ht.interval.includes_start,
-        includes_end=gencode_ht.interval.includes_end,
-        reference_genome=build,
-    ).collect()
-
-    ht = hl.filter_intervals(ht, intervals)
+    ht = filter_to_gencode_cds(ht, genes=gene, padding=exon_padding_bp)
 
     return ht

From 752c123691642e66ad95a7595df040704f9023f8 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Thu, 19 Dec 2024 17:21:08 -0500
Subject: [PATCH 045/121] small edits

---
 gnomad_toolbox/filtering/variant.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index ba1f403..386e83c 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -134,7 +134,6 @@ def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.
     """
     # Load the Hail Table if not provided
     ht = _get_gnomad_release(dataset="variant", **kwargs)
-
     ht = filter_to_gencode_cds(ht, genes=gene, padding=exon_padding_bp)
 
     return ht

From 7776c23abf5aa086fdde037cc9162f3beb147744 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Thu, 19 Dec 2024 17:34:37 -0500
Subject: [PATCH 046/121] Formatting

---
 README.md | 48 +++++++++++++++++++++++-------------------------
 1 file changed, 23 insertions(+), 25 deletions(-)

diff --git a/README.md b/README.md
index 6c97519..ae3d9fb 100644
--- a/README.md
+++ b/README.md
@@ -69,17 +69,17 @@ Miniconda is a lightweight distribution of Conda that includes just Conda and it
    git clone https://github.com/broadinstitute/gnomad-toolbox.git
    cd gnomad-toolbox
    pip install -r requirements.txt
-    ```
+   ```
    **Note:** Hail 0.2.127+ requires Java 8 or Java 11 and jupyter labs requires Java
    11+, and if you have an Apple M1 or M2, you must have arm64 Java installed, you
    can first check your Java version by running:
    ```commandline
     java -version
-    ```
+   ```
    and check if you have the arm64 Java by running:
    ```commandline
     file $JAVA_HOME/bin/java
-    ```
+   ```
    If you don't have the arm64 Java, you can find it [here](https://www.azul.com/downloads/?os=macos&architecture=arm-64-bit&package=jre#zulu)
 
    You might encounter errors when installing the dependencies, such as `pg_config
@@ -87,24 +87,24 @@ Miniconda is a lightweight distribution of Conda that includes just Conda and it
    For example, on Ubuntu, you can install the `libpq-dev` package:
    ```commandline
     sudo apt-get install libpq-dev
-    ```
+   ```
    or on macOS, you can install the `postgresql` package:
    ```commandline
     brew install postgresql
-    ```
+   ```
 
 ### Verify the Setup
-1.	Start a Python shell and test if Hail and gnomad_toolbox are working:
-```commandline
-import hail as hl
-from gnomad_toolbox.analysis.general import get_variant_count_by_freq_bin
-hl.init()
-print("Hail and gnomad_toolbox setup is complete!")
-```
-Or open the notebooks:
-```commandline
-jupyter lab
-```
+   Start a Python shell and test if Hail and gnomad_toolbox are working:
+   ```commandline
+   import hail as hl
+   from gnomad_toolbox.analysis.general import get_variant_count_by_freq_bin
+   hl.init()
+   print("Hail and gnomad_toolbox setup is complete!")
+   ```
+   Or open the notebooks:
+   ```commandline
+   jupyter lab
+   ```
 
 ## Part2: Accessing gnomAD Data Locally with example notebooks
 If you already have experience with gcloud and have no problem running these notebooks,
@@ -113,8 +113,7 @@ you can skip this section.
 ### Install Google Cloud SDK (gcloud)
 
 The Google Cloud SDK is required to interact with Google Cloud services and access gnomAD public data locally.
-1. Follow the official Google Cloud SDK installation [guide](https://cloud.google.
-   com/sdk/docs/install) for your operating system.
+1. Follow the official Google Cloud SDK installation [guide](https://cloud.google.com/sdk/docs/install) for your operating system.
 2. After installation, initialize gcloud to log in and set up your default project:
    ```
    gcloud init
@@ -130,13 +129,12 @@ The Google Cloud SDK is required to interact with Google Cloud services and acce
 
 ### Configure a Service Account
 You will need to create a service account in gcloud console IAM & Admin or using
-gcloud CLI. Then you can create a key for service account and set the GOOGLE_APPLICATION_CREDENTIALS
-variable to the path of the key file.
-   ```commandline
-    gcloud iam service-accounts keys create hail-local-sa-key.json --iam-account {YOUR_SERVICE_ACCOUNT}
+cloud CLI. Then you can create a key for service account and set the key.
 
-    export GOOGLE_APPLICATION_CREDENTIALS=./hail-local-sa-key.json
+   ```commandline
+   gcloud iam service-accounts keys create hail-local-sa-key.json --iam-account {YOUR_SERVICE_ACCOUNT}
+   export GOOGLE_APPLICATION_CREDENTIALS=./hail-local-sa-key.json
    ```
 Now, you can access gnomAD data locally using the gnomad_toolbox functions, however,
-avoid running queries on the full dataset as it may take a long time and consume a
-lot of resources, and most importantly, it may incur costs.
+you should avoid running queries on the full dataset as it may take a long time and
+consume a lot of resources, and most importantly, it may generate costs.

From 7a29ba58e3ffb02719faae2016b1fb05417023cc Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Thu, 19 Dec 2024 17:40:26 -0700
Subject: [PATCH 047/121] Change `filter_by_csqs` to
 `filter_by_consequence_category` and clean it up and use gnomad_methods
 function

---
 gnomad_toolbox/filtering/vep.py               | 137 +++++++++++-------
 .../intro_to_filtering_variant_data.ipynb     |  78 +++++-----
 2 files changed, 130 insertions(+), 85 deletions(-)

diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py
index a8b8793..b54098f 100644
--- a/gnomad_toolbox/filtering/vep.py
+++ b/gnomad_toolbox/filtering/vep.py
@@ -1,78 +1,117 @@
 """Functions to filter gnomAD sites HT by VEP annotations."""
 
-from functools import reduce
-
 import hail as hl
-from gnomad.utils.vep import CSQ_CODING, LOF_CSQ_SET
+from gnomad.utils.vep import LOF_CSQ_SET, filter_vep_transcript_csqs_expr
 
 from gnomad_toolbox.load_data import _get_gnomad_release
 
-# TODO: I haven't looked over this function yet. Is there anything in gnomad_methods
-#  that could be used here? If not, is there anything here that should be moved to
-#  gnomad_methods?
-
 
-def filter_by_csqs(
-    csqs: list[str],
+# TODO: Check these csq sets, the ones in the code don't match what is listed on the
+#  browser. We should make sure they are consistent.
+def filter_by_consequence_category(
+    plof: bool = False,
+    missense: bool = False,
+    synonymous: bool = False,
+    other: bool = False,
     pass_filters: bool = True,
     **kwargs,
 ) -> hl.Table:
     """
-    Filter variants by VEP transcript consequences.
-
-    :param csqs: List of consequences to filter by. It can be specified as the
-         categories on the browser: pLoF, Missense / Inframe indel, Synonymous, Other.
+    Filter gnomAD variants based on VEP consequence.
+
+    https://gnomad.broadinstitute.org/help/consequence-category-filter
+
+    The [VEP](https://useast.ensembl.org/info/docs/tools/vep/index.html) consequences included in each category are:
+
+        pLoF
+
+            - transcript_ablation
+            - splice_acceptor_variant
+            - splice_donor_variant
+            - stop_gained
+            - frameshift_variant
+
+        Missense / Inframe indel
+
+            - stop_lost
+            - start_lost
+            - inframe_insertion
+            - inframe_deletion
+            - missense_variant
+
+        Synonymous
+
+            - synonymous_variant
+
+        `Other
+
+            - protein_altering_variant
+            - incomplete_terminal_codon_variant
+            - stop_retained_variant
+            - coding_sequence_variant
+            - mature_miRNA_variant
+            - 5_prime_UTR_variant
+            - 3_prime_UTR_variant
+            - non_coding_transcript_exon_variant
+            - non_coding_exon_variant
+            - NMD_transcript_variant
+            - non_coding_transcript_variant
+            - nc_transcript_variant
+            - downstream_gene_variant
+            - TFBS_ablation
+            - TFBS_amplification
+            - TF_binding_site_variant
+            - regulatory_region_ablation
+            - regulatory_region_amplification
+            - feature_elongation
+            - regulatory_region_variant
+            - feature_truncation
+            - intergenic_variant
+            - intron_variant
+            - splice_region_variant
+            - upstream_gene_variant
+
+    :param plof: Whether to include pLoF variants.
+    :param missense: Whether to include missense variants.
+    :param synonymous: Whether to include synonymous variants.
+    :param other: Whether to include other variants.
     :param pass_filters: Boolean if the variants pass the filters.
     :param kwargs: Arguments to pass to _get_gnomad_release.
     :return: Table with variants with the specified consequences.
     """
+    if not any([plof, missense, synonymous, other]):
+        raise ValueError(
+            "At least one of plof, missense, synonymous, or other must be True."
+        )
+
     # Load the Hail Table if not provided
     ht = _get_gnomad_release(dataset="variant", **kwargs)
 
-    missense_inframe = ["missense_variant", "inframe_insertion", "inframe_deletion"]
+    lof_csqs = list(LOF_CSQ_SET)
+    missense_csqs = ["missense_variant", "inframe_insertion", "inframe_deletion"]
+    synonymous_csqs = ["synonymous_variant"]
+    other_csqs = lof_csqs + missense_csqs + synonymous_csqs
 
-    filter_expr = []
-    if "lof" in csqs:
-        filter_expr.append(
-            hl.literal(LOF_CSQ_SET).contains(ht.vep.most_severe_consequence)
-        )
+    csqs = (
+        (lof_csqs if plof else [])
+        + (missense_csqs if missense else [])
+        + (synonymous_csqs if synonymous else [])
+    )
 
-    if "synonymous" in csqs:
-        filter_expr.append(ht.vep.most_severe_consequence == "synonymous_variant")
+    filter_expr = hl.bool(True)
 
-    if "missense" in csqs:
-        filter_expr.append(
-            hl.literal(missense_inframe).contains(ht.vep.most_severe_consequence)
-        )
+    if csqs:
+        filter_expr &= filter_vep_transcript_csqs_expr(ht.vep, csqs=csqs)
 
-    if "other" in csqs:
-        excluded_csqs = hl.literal(
-            list(LOF_CSQ_SET) + missense_inframe + ["synonymous_variant"]
+    if other:
+        filter_expr |= filter_vep_transcript_csqs_expr(
+            ht.vep, csqs=other_csqs, keep_csqs=False
         )
-        filter_expr.append(~excluded_csqs.contains(ht.vep.most_severe_consequence))
-
-    if "coding" in csqs:
-        filter_expr.append(
-            hl.literal(CSQ_CODING).contains(ht.vep.most_severe_consequence)
-        )
-
-    if len(filter_expr) == 0:
-        raise ValueError(
-            "No valid consequence specified. Choose from 'lof', 'synonymous', 'missense', 'other'."
-        )
-
-    # Combine filter expressions with logical OR
-    if len(filter_expr) == 1:
-        combined_filter = filter_expr[0]
-    else:
-        combined_filter = reduce(lambda acc, expr: acc | expr, filter_expr)
-
-    ht = ht.filter(combined_filter)
 
     if pass_filters:
-        ht = ht.filter(hl.len(ht.filters) == 0)
+        filter_expr &= hl.len(ht.filters) == 0
 
-    return ht
+    return ht.filter(filter_expr)
 
 
 # TODO: The following was in one of the notebooks, and I think we should add a wrapper
diff --git a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
index e6dd8fd..4f75e9e 100644
--- a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
+++ b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
@@ -41,7 +41,7 @@
     {
      "data": {
       "text/html": [
-       "    \n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -90,22 +90,14 @@
        "   * Handle when an output is cleared or removed\n",
        "   */\n",
        "  function handleClearOutput(event, handle) {\n",
-       "    function drop(id) {\n",
-       "      const view = Bokeh.index.get_by_id(id)\n",
-       "      if (view != null) {\n",
-       "        view.model.document.clear()\n",
-       "        Bokeh.index.delete(view)\n",
-       "      }\n",
-       "    }\n",
-       "\n",
        "    const cell = handle.cell;\n",
        "\n",
        "    const id = cell.output_area._bokeh_element_id;\n",
        "    const server_id = cell.output_area._bokeh_server_id;\n",
-       "\n",
        "    // Clean up Bokeh references\n",
-       "    if (id != null) {\n",
-       "      drop(id)\n",
+       "    if (id != null && id in Bokeh.index) {\n",
+       "      Bokeh.index[id].model.document.clear();\n",
+       "      delete Bokeh.index[id];\n",
        "    }\n",
        "\n",
        "    if (server_id !== undefined) {\n",
@@ -114,8 +106,11 @@
        "      cell.notebook.kernel.execute(cmd_clean, {\n",
        "        iopub: {\n",
        "          output: function(msg) {\n",
-       "            const id = msg.content.text.trim()\n",
-       "            drop(id)\n",
+       "            const id = msg.content.text.trim();\n",
+       "            if (id in Bokeh.index) {\n",
+       "              Bokeh.index[id].model.document.clear();\n",
+       "              delete Bokeh.index[id];\n",
+       "            }\n",
        "          }\n",
        "        }\n",
        "      });\n",
@@ -223,7 +218,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\");\n",
+       "    const el = document.getElementById(\"e2e202d6-58aa-44df-bd42-63e8ab96953f\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -306,7 +301,7 @@
        "    document.body.appendChild(element);\n",
        "  }\n",
        "\n",
-       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
+       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n",
        "  const css_urls = [];\n",
        "\n",
        "  const inline_js = [    function(Bokeh) {\n",
@@ -329,7 +324,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"e2e202d6-58aa-44df-bd42-63e8ab96953f\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -345,7 +340,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"e2e202d6-58aa-44df-bd42-63e8ab96953f\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"e2e202d6-58aa-44df-bd42-63e8ab96953f\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -360,7 +355,7 @@
     "    get_ancestry_callstats, \n",
     "    get_single_variant_ancestry_callstats,\n",
     ")\n",
-    "from gnomad_toolbox.filtering.vep import filter_by_csqs"
+    "from gnomad_toolbox.filtering.vep import filter_by_consequence_category"
    ]
   },
   {
@@ -377,9 +372,8 @@
       "     __  __     <>__\n",
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
-      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241210-2227-0.2.132-678e1f52b999.log\n",
-      "2024-12-10 22:28:45.710 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
+      "  /_/ /_/\\_,_/_/_/   version 0.2.133-4c60fddb171a\n",
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241219-1733-0.2.133-4c60fddb171a.log\n"
      ]
     }
    ],
@@ -1577,7 +1571,12 @@
     }
    ],
    "source": [
-    "var_ht = filter_by_csqs(['lof','missense','synonymous'], ht=drd2_interval_ht)\n",
+    "var_ht = filter_by_consequence_category(\n",
+    "    plof=True, \n",
+    "    missense=True,\n",
+    "    synonymous=True, \n",
+    "    ht=drd2_interval_ht\n",
+    ")\n",
     "var_ht.show(5)\n",
     "print(\"The total number of lof, missense, and synonymous variants passing filters in DRD2 is: \", var_ht.count())"
    ]
@@ -2654,7 +2653,7 @@
     }
    ],
    "source": [
-    "var_ht = filter_by_csqs(['lof'], ht=drd2_interval_ht)\n",
+    "var_ht = filter_by_consequence_category(plof=True, ht=drd2_interval_ht)\n",
     "var_ht.show(5)\n",
     "print(\"The total number of lof variants passing filters in DRD2 is: \", var_ht.count())"
    ]
@@ -3743,7 +3742,7 @@
     }
    ],
    "source": [
-    "var_ht = filter_by_csqs(['missense'], ht=drd2_interval_ht)\n",
+    "var_ht = filter_by_consequence_category(missense=True, ht=drd2_interval_ht)\n",
     "var_ht.show(5)\n",
     "print(\"The total number of missense variants passing filters in DRD2 is: \", var_ht.count())"
    ]
@@ -4832,7 +4831,7 @@
     }
    ],
    "source": [
-    "var_ht = filter_by_csqs(['synonymous'], ht=drd2_interval_ht)\n",
+    "var_ht = filter_by_consequence_category(synonymous=True, ht=drd2_interval_ht)\n",
     "var_ht.show(5)\n",
     "print(\"The total number of synonymous variants passing filters in DRD2 is: \", var_ht.count())"
    ]
@@ -5916,12 +5915,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The total number of other variants passing filters in DRD2 is:  2075\n"
+      "The total number of other variants passing filters in DRD2 is:  2739\n"
      ]
     }
    ],
    "source": [
-    "var_ht = filter_by_csqs(['other'], ht=drd2_interval_ht)\n",
+    "var_ht = filter_by_consequence_category(other=True, ht=drd2_interval_ht)\n",
     "var_ht.show(5)\n",
     "print(\"The total number of other variants passing filters in DRD2 is: \", var_ht.count())"
    ]
@@ -5938,12 +5937,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "a25ddd1b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "drd2_synonymous_ht = filter_by_csqs(['synonymous'], ht=drd2_interval_ht)"
+    "drd2_synonymous_ht = filter_by_consequence_category(synonymous=True, ht=drd2_interval_ht)"
    ]
   },
   {
@@ -5956,7 +5955,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "4f78166f",
    "metadata": {},
    "outputs": [
@@ -6017,7 +6016,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "e3a07848",
    "metadata": {},
    "outputs": [
@@ -6152,7 +6151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "4845be1d-d4c0-4b83-9e92-bd72379b8a99",
    "metadata": {},
    "outputs": [
@@ -6200,10 +6199,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "bee28829",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-19 17:34:53.716 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -6252,7 +6258,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.5"
   },
   "toc": {
    "base_numbering": 1,

From bc2120ac16bac46fcd58c06e2f7726078a3dbb9f Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Fri, 20 Dec 2024 12:32:00 -0700
Subject: [PATCH 048/121] Fix other filter in `filter_by_consequence_category`

---
 gnomad_toolbox/filtering/vep.py               |  10 +-
 .../intro_to_filtering_variant_data.ipynb     | 695 +++++++++---------
 2 files changed, 371 insertions(+), 334 deletions(-)

diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py
index b54098f..adbd6d3 100644
--- a/gnomad_toolbox/filtering/vep.py
+++ b/gnomad_toolbox/filtering/vep.py
@@ -98,18 +98,20 @@ def filter_by_consequence_category(
         + (synonymous_csqs if synonymous else [])
     )
 
-    filter_expr = hl.bool(True)
+    filter_expr = None
 
     if csqs:
-        filter_expr &= filter_vep_transcript_csqs_expr(ht.vep, csqs=csqs)
+        filter_expr = filter_vep_transcript_csqs_expr(ht.vep, csqs=csqs)
 
     if other:
-        filter_expr |= filter_vep_transcript_csqs_expr(
+        other_expr = filter_vep_transcript_csqs_expr(
             ht.vep, csqs=other_csqs, keep_csqs=False
         )
+        filter_expr = other_expr if filter_expr is None else (filter_expr | other_expr)
 
     if pass_filters:
-        filter_expr &= hl.len(ht.filters) == 0
+        pass_expr = hl.len(ht.filters) == 0
+        filter_expr = pass_expr if filter_expr is None else (filter_expr & pass_expr)
 
     return ht.filter(filter_expr)
 
diff --git a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
index 4f75e9e..ae240c0 100644
--- a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
+++ b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
@@ -51,7 +51,7 @@
        "    \n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -218,7 +218,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"e2e202d6-58aa-44df-bd42-63e8ab96953f\");\n",
+       "    const el = document.getElementById(\"d5c4232b-0e66-4b61-983c-ba8423f8e445\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -324,7 +324,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"e2e202d6-58aa-44df-bd42-63e8ab96953f\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"d5c4232b-0e66-4b61-983c-ba8423f8e445\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -340,7 +340,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"e2e202d6-58aa-44df-bd42-63e8ab96953f\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"e2e202d6-58aa-44df-bd42-63e8ab96953f\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"d5c4232b-0e66-4b61-983c-ba8423f8e445\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"d5c4232b-0e66-4b61-983c-ba8423f8e445\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -373,7 +373,7 @@
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
       "  /_/ /_/\\_,_/_/_/   version 0.2.133-4c60fddb171a\n",
-      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241219-1733-0.2.133-4c60fddb171a.log\n"
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241220-1230-0.2.133-4c60fddb171a.log\n"
      ]
     }
    ],
@@ -488,11 +488,18 @@
    ]
   },
   {
+   "attachments": {
+    "78f1b020-4f45-4800-82fa-0bdffb877962.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "id": "756a996c-bad2-4d24-a304-6eb5e3efca65",
    "metadata": {},
    "source": [
-    "### Filter to `lof`, `missense`, and `synonymous` variants passing filters"
+    "### Filter to `lof`, `missense`, and `synonymous` variants passing filters\n",
+    "\n",
+    "![Screenshot 2024-12-20 at 11.12.17 AM.png](attachment:78f1b020-4f45-4800-82fa-0bdffb877962.png)"
    ]
   },
   {
@@ -1575,18 +1582,25 @@
     "    plof=True, \n",
     "    missense=True,\n",
     "    synonymous=True, \n",
-    "    ht=drd2_interval_ht\n",
+    "    ht=drd2_ht,\n",
     ")\n",
     "var_ht.show(5)\n",
     "print(\"The total number of lof, missense, and synonymous variants passing filters in DRD2 is: \", var_ht.count())"
    ]
   },
   {
+   "attachments": {
+    "7348e149-4f8f-4eca-aa55-6234293c1f3d.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "id": "35bc2c92-d90e-4a26-a04b-5bd7dc4e7d23",
    "metadata": {},
    "source": [
-    "### Filter to `lof` variants passing filters"
+    "### Filter to `lof` variants passing filters\n",
+    "\n",
+    "![Screenshot 2024-12-20 at 11.12.35 AM.png](attachment:7348e149-4f8f-4eca-aa55-6234293c1f3d.png)"
    ]
   },
   {
@@ -2653,17 +2667,24 @@
     }
    ],
    "source": [
-    "var_ht = filter_by_consequence_category(plof=True, ht=drd2_interval_ht)\n",
+    "var_ht = filter_by_consequence_category(plof=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
     "print(\"The total number of lof variants passing filters in DRD2 is: \", var_ht.count())"
    ]
   },
   {
+   "attachments": {
+    "440e8e62-f912-4c99-9687-12d655742ab5.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "id": "871c539b-a1c2-41a3-a33d-37649b603de2",
    "metadata": {},
    "source": [
-    "### Filter to `missense` variants passing filters"
+    "### Filter to `missense` variants passing filters\n",
+    "\n",
+    "![Screenshot 2024-12-20 at 11.12.46 AM.png](attachment:440e8e62-f912-4c99-9687-12d655742ab5.png)"
    ]
   },
   {
@@ -3742,17 +3763,24 @@
     }
    ],
    "source": [
-    "var_ht = filter_by_consequence_category(missense=True, ht=drd2_interval_ht)\n",
+    "var_ht = filter_by_consequence_category(missense=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
     "print(\"The total number of missense variants passing filters in DRD2 is: \", var_ht.count())"
    ]
   },
   {
+   "attachments": {
+    "f42fc1f8-afe3-4684-9fee-66582eba9080.png": {
+     "image/png": 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AoBUFPvjgAzvrrLPsrfmv2MgNhtmIzYfYorlLfA5uefAG36r18ccfnzNHCugdOWlYznk0sc+gHnnnyTfDlVdeaVM+esEGLqiyTz6rtYEDB/pFBg4YaB++/rG9+8Z7Nnxif7PaGqteXG09hnaxT5a+a58tet/ueXqe9RrZxWobam3Oh/Otc6+qJpur6lJpSxcu8+P7Duptby6Z1mQeRiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCLSdQOKA3H322cfnVq1UqsVbtTKbnm699VY/SgFz0RZo9Yr12bNn22mnneZbhowuN3fuXHvsscdMrUx+8skntuOOO/rWeLX+EPS7dOlSHwRcXl5uv/3tb00tR4akVnv/+c9/2iabbGJHH310GO27l156qSnA78gjj/Qt477yyit23XXX2UYbbWTHHHOMn0fbP++886xHjx72i1/8wm655RZ74okn/HLrrbeeHXLIIbbDDjs0Wq8GGhoaTK+qVxCy8t+vXz/7yle+Yocffrjfxv/+9z/79re/nTcQOVOetP5zzjnH5s+fbz/96U9N8/z73//2LWuqtWIFOx922GE+OFHzZkta5r777rNnn33Wz6K/z1tvvWUKAk5/7b3m1Uf57tSpk02cONF/n0mTJmVbvS1cuNC3ZKr162+1++67+2VGjx6ddZn0CfX19TZlypSU41prreW/n1pkVuvKpDVL4PHHH7dn3n7S9jx2svXstypgNnQlcdP919nYsWMbBfynC6nV2wn9xqaPbjLcs193X84nT57cZFqcEVdddZX95je/sX5jetjC5fNsZU2t1c+rdd2VpscVnv77FBuz6wDrt3Yv++CZ6fbZrPk2fEJ/W3entayqW4XN+3ixdR/Y2RqszpbMrbZ+45oGCCsgd1HdMlu2bJn3mL9sns/zmDFj4mSReRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBFpYIHFArlq9VbCpWsj929/+1iQgd9GiRXb77bf7bB988MGNsn/99debAnkVMKtXtYf00Ucf2YQJE3yrumHcww8/7Ht//vOf25lnnun7FUB6xx13+KDeo446qlGQ65///Gc/bdCgQaZpIYi3trbWB7Wqxd5TTz3Vr+fdd9+1K664wvbbb79UQK6CXjVOy8+bN8+uvvrqkBV76qmn7JprrrHLL7/cjj322NR49SgwOOQvTLjzzjvt73//u1VXV5sCCxWgm69l4Ex50vr0vRTELPdf//rXYRP2zDPP2F//+lcfwHzTTTelxmfqee211/x3C9O0rD5aZwjIVWDxCSecYH/5y1/CbL57//33+0BleXzrW99qNE0DM2bMsPHjx/s8hokK/lV68sknLVcgb5hfXbWGKstoUl7093j++edt2LD8LZ1Gl6W//QoouP3hlx60bQ+YkArGjX6b4esM8YHw559/fs6AXAXhT7D8Abl9XIuzn376aXQTsfu1X7jxvuts/op5Nmqt/tajb3e/bK0LyrWyBnv9+TdtWd0SF4w7xj6fOt8WzF5kIzYZ7INzw0YaVpZbj2FdrapzlS2attzW3mlV67phurp6CKGqa4VVL6/2gfS9B/bwDy4QkBtVoh8BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQaDuB8kI2/c1vftMvdvPNN5sCXqMpBGMqAFWt1eZLCxYssH333dcH46pl1QceeMC3lHrhhRf6Rc8999xUgK+CbDWvkgJKQ1IeHnroIT+o4FW17hqS+hWMu84665haXc2XtLyCTxV8q0BWBbtus802frGf/OQnvjXYsI5//OMfqWDcH/zgBz4A9bnnnvMt7D744IM+GDfM29yugnG1DQWnat2HHnqoX6WCnxX4mispAPrtt9/2gcGa7/TTT/fDL7/8cmoxtUwcgnH13V9//XVTULSCm5XUkrBa50xPah1UgcfBS4HIatVW6atf/arNnDkzfZEmw9puCMa95JJLfEvA+h2pxVL9PQ444ABT68ikNUNAZXva51NtxLpDs35hTXtt6qs+wD/bTGppOz0t/HyRffTGp/7z6mNv+u78zxbkXE/6OsLwI488YtfdfY2NnDjQevTvZgPG9AmTrLJTpXXt0dk+/t8sG7jhqiDdudMWuVZw662qR6V9/t4C/5n2/CxbPm+ldelTaXW1dVa70E3vWeEDjlMrW91TXlWeKoO9+ncvKM/p62QYAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgOAKJW8jVZnfYYQffcqmCJdV67I477pjKzW233eb7jzzyyNS4XD233nqrvfXWW7b55pv7Fl/VCq6SWl1V/3e+8x375S9/aV//+tf9+F133dUHzKrl2eOPP96Pe+GFF3zQ7fDhw02vqVcLm6FF2hC4q9Zw46Zrr73WDjvsMD/7+uuv71vv3XDDDf02FNgaWn39/e9/7+dRPkJAqUbou/Tu3dtOOeUUP70Y/6l12ug2FPSqgFn9DRSku/3222fdTN++fU0ftYirpNaJ01vWrKystN/85jfeXcZK6623nm255Za+5WEFNctZQbLp6ZZbbrHddtvNj5bXdtttZ1tttZUPGLzhhht8AHD6MmG4vr7eTjvtND+o34KCeJXkrW1vu+22PkBbf9MQjO1n4L8OKzBt2jQbOLqfLateZtUuCLxmaa0PRK2s7+S/88LPF1vvgT2tx4Bupt/XEUccYWuvvXbKQ8uHNHfuHL+s9iXT//e5zfzkMx9A3r1fF5s7Y771H9bXPn5lts3ttsTOPvtsvy/T8tH1jR492q9OrXsrheGnn37aKgfUWtcBVbb486XWuZcLqq2rs4qKCj+f/lv8yQrrv1F3W+Smf/7eIqvs0WAfvzjLt+BdVl5m1QtW2MoFqx5qqFmx0i+n1qpVLtQqbmjpWxPUX+1MQsoUcBym0UUAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEGhdgYICchW8qVZXf/WrX/nWa0NAroJD77//fv8NDjrooFjf5MUXX/TzHXfccT4AN7rQIYcc4gNyFbCrFlK7d++eCghVi7gKXFOQmoI1ldS6qrar1l5PPvlkP+7RRx/13S9/+cu+G+e/Pffcs9Fs48aN80F4CshTwK+SWuUNwb5quTY9KUi3mAG5aiU2mvS9999/f1MLtcUIzJNPJqOuXbvazjvvbP/85z9T3z2aDwUnh2DcMF7ByKeeeqp997vf9a3dhvGZujJVsK+ChUMwbpivT58+9o1vfMPOP/98vx4CcoNMx+4+8cQTtt5hg+3Djz6wz6fNtyWfVfvg3J69eljn3pXmA3KX9LT6rjV2279usdruy61qWVdT0PpHH31kdz1wRwpo5twZPgB36Rs19un/PrPynnXWpW8nq+9bZr1cC7YLZs63FSuX2/QFn9olf73QXp/7gi2ct9h61Pe1lXU1fj0bbL6OLVu8wge/1y6tt2EjVrXcO/WV923lBtU2bfqHtnSOCx5eXu3nLy8r9wG1KjsKtl30qZs2a6Yt+XC5Dd2xp2sBt9yq59VYzZI6P39FnzJTcK659nPVr1RXX7eqlVw3WFFe4fdzFa6F3JB6D+wVeukigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgiUgEBBAbnKt4JlFZCrFnF/97vfWZcuXXzQpqYpoHXo0Oyvm9c8Iem170onnniib6E1jE/v/u9///Mt0/br18+22WYbHwyr1mo32GCDVBDwHnvs4VtnVcuxS5YsMQXEhfVvvfXW6avMOKzAUG0jPW200Ua+xVcFASu9++67qVlCi5mpEa5HQcshn9Hxhfavs846TRbVd1dSa5rFSAo2VuDts88+a++8844PQFy0aJEPmM22/s022yzjJLVwq6R15UqvvPKKn6ygXLXIm55Cq6QhcDt9OsMdT0BBtb1n11tlp0r7+G3Xou3cldalT6U1dKqzPoO6W9dhlbaiYZl1Gmi2ZN58+/sDd9r0t2b7YH0F8/79gbus14DuHmalrbDKruX28eszzDrX2+Dxfa28stwqu5S7Fmg727ypS6zM7QW79aly2+hkb86ZYgN7DrYpz39g82bNt859q+ztBa9Y9ZLl1rtHb/vszYW2srbGj1/8QbWNX3+YrazuZJV9yq1m5QofRNvgimNdbZ116rS6Rd+Pq61b31XBt73W6mq9R3W3jx6bbcsXrPpeVb1dXlyelNTvYu39eurNBeW6dSnAVwH45RWrgnU1n1oIJiGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCJSOQMEBuQoG3XzzzW3KlCm+hdq99trLbr75Zv/NDj/88NjfUAGfISmoN1PStmpqVrVWqelqkVWt0+ozYMAAU7CmWsatqqqyvffe25566in/GT58uA8mVYCwXlkfJymIN1PS6+OjSa23hrR8+XLfem8YDt3PP/889Da7m759rVBBesVKstxnn31SwbcKTB4yZIiNGjUq1RJwpm2pNdxMqUePHn50aFE40zwaN3fu3NSkbH9/zRDWl5qZng4rMHLkSOvXfYAPoB06usHqhtdb/cpVgfCdVnZ2Qar17uNax16+0gZ1G2xbb76tjTxglP+tbrfddq4F3M726tsv27tPTrMq6+yd+g/qZ/UNdTbvvSVW1bXCyqvKrHu/rta5e5XZYLOln9fY8oXLbGC3MTZ80Gjru/5atqT/UuvUr8rGbbi2ffTxR1ZV0cmG1S3x2+nUt9L+c/ejLg8N1rO3C451RbFTVScfQKsNlnUus4qKCqtygcSDN+ppalm3qo8LD3Yt5i5oWOoCbt3X67mq5duVC1d9H30n3++mqWz7IFzXcm4o53UrXXTu6t3TojmLzQZ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"
+    }
+   },
    "cell_type": "markdown",
    "id": "5b980b4f-13bd-4bb4-bdbc-5584a6a59ca5",
    "metadata": {},
    "source": [
-    "### Filter to `synonymous` variants passing filters"
+    "### Filter to `synonymous` variants passing filters\n",
+    "\n",
+    "![Screenshot 2024-12-20 at 11.12.57 AM.png](attachment:f42fc1f8-afe3-4684-9fee-66582eba9080.png)"
    ]
   },
   {
@@ -4831,17 +4859,24 @@
     }
    ],
    "source": [
-    "var_ht = filter_by_consequence_category(synonymous=True, ht=drd2_interval_ht)\n",
+    "var_ht = filter_by_consequence_category(synonymous=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
     "print(\"The total number of synonymous variants passing filters in DRD2 is: \", var_ht.count())"
    ]
   },
   {
+   "attachments": {
+    "5565afe1-9dbe-42de-b70e-224b9d8651e4.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "id": "cbcd2fa0-5108-4d94-a681-493882c295bf",
    "metadata": {},
    "source": [
-    "### Filter to 'Other' variants passing filters"
+    "### Filter to 'Other' variants passing filters\n",
+    "\n",
+    "![Screenshot 2024-12-20 at 11.13.08 AM.png](attachment:5565afe1-9dbe-42de-b70e-224b9d8651e4.png)"
    ]
   },
   {
@@ -4854,11 +4889,11 @@
      "data": {
       "text/html": [
        "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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A,"A"),(1,NA,NA,"protein_coding",1,NA,1751,1751,NA,NA,NA,["3_prime_UTR_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.*65C>T",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,NA,NA,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"A"),(1,NA,NA,"protein_coding",NA,NA,1664,1664,NA,NA,NA,["3_prime_UTR_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.*65C>T",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"A"),(1,NA,NA,"protein_coding",NA,NA,1479,1479,NA,NA,NA,["3_prime_UTR_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.*65C>T",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"A")]"SNV"5.24e+00"AS_MQ"FalseFalseFalseFalseFalseNATrueFalse"multi-snv"2False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,4139,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,22477,65025,118275,313361,178106,20388,3276,1054,693,645,582,508,355,259,173,113,79,64]0265[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]01[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][653,0,1690,2,6865,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][264,3602,22767,65834,118294,313419,178113,20399,3282,1056,696,645,582,508,355,259,173,113,79,64]0265[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]01[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]006.25e+004.76e-01NA0.00e+002.10e-011.62e+00NANA
showing top 5 rows
\n"
       ],
       "text/plain": [
@@ -4867,11 +4902,11 @@
        "+-----------------+------------+\n",
        "| locus   | array |\n",
        "+-----------------+------------+\n",
-       "| chr11:113409636 | [\"G\",\"C\"]  |\n",
-       "| chr11:113409693 | [\"G\",\"A\"]  |\n",
-       "| chr11:113409717 | [\"C\",\"T\"]  |\n",
-       "| chr11:113409758 | [\"C\",\"T\"]  |\n",
-       "| chr11:113410002 | [\"C\",\"A\"]  |\n",
+       "| chr11:113410657 | [\"C\",\"T\"]  |\n",
+       "| chr11:113410658 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410658 | [\"G\",\"T\"]  |\n",
+       "| chr11:113410660 | [\"A\",\"G\"]  |\n",
+       "| chr11:113410662 | [\"G\",\"A\"]  |\n",
        "+-----------------+------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -4879,11 +4914,11 @@
        "+------------------------------------------------------------------------------+\n",
        "| array    |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [(1,1.87e-03,534,0),(5,7.95e-06,628788,2),(0,0.00e+00,8,0),(0,NA,0,0),(0,... |\n",
-       "| [(1,1.20e-03,834,0),(16,2.54e-05,628790,3),(0,0.00e+00,6,0),(0,0.00e+00,2... |\n",
-       "| [(1,1.01e-03,990,0),(5,7.95e-06,628794,2),(0,0.00e+00,10,0),(0,0.00e+00,4... |\n",
-       "| [(1,1.23e-03,810,0),(4,6.36e-06,628786,1),(0,0.00e+00,12,0),(0,0.00e+00,4... |\n",
-       "| [(128,1.34e-01,952,15),(18463,2.94e-02,628994,6837),(0,0.00e+00,6,0),(17,... |\n",
+       "| [(18,1.25e-05,1445518,0),(24,1.64e-05,1461322,0),(1,3.02e-05,33096,0),(0,... |\n",
+       "| [(23,1.59e-05,1448166,0),(23,1.57e-05,1461456,0),(0,0.00e+00,33180,0),(0,... |\n",
+       "| [(94,6.49e-05,1448162,0),(105,7.18e-05,1461456,0),(24,7.23e-04,33180,0),(... |\n",
+       "| [(1,6.89e-07,1450438,0),(2,1.37e-06,1461588,0),(0,0.00e+00,33208,0),(0,0.... |\n",
+       "| [(2,1.38e-06,1451396,0),(2,1.37e-06,1461538,0),(0,0.00e+00,33252,0),(1,2.... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
        "+------------------+------------------+------------------+\n",
@@ -4891,11 +4926,11 @@
        "+------------------+------------------+------------------+\n",
        "|            int32 |          float64 |            int32 |\n",
        "+------------------+------------------+------------------+\n",
-       "|               NA |               NA |               NA |\n",
-       "|                1 |         2.87e-03 |              348 |\n",
-       "|                1 |         2.17e-03 |              460 |\n",
-       "|               NA |               NA |               NA |\n",
-       "|                2 |         5.00e-01 |                4 |\n",
+       "|                4 |         1.01e-04 |            39606 |\n",
+       "|                3 |         7.57e-05 |            39630 |\n",
+       "|               24 |         7.23e-04 |            33180 |\n",
+       "|                1 |         9.07e-07 |          1103066 |\n",
+       "|                1 |         2.24e-05 |            44696 |\n",
        "+------------------+------------------+------------------+\n",
        "\n",
        "+--------------------------------+-----------------------+-------------------+\n",
@@ -4903,11 +4938,11 @@
        "+--------------------------------+-----------------------+-------------------+\n",
        "|                          int64 | str                   |             int32 |\n",
        "+--------------------------------+-----------------------+-------------------+\n",
-       "|                             NA | NA                    |                NA |\n",
+       "|                              0 | \"eas\"                 |                 4 |\n",
+       "|                              0 | \"eas\"                 |                10 |\n",
+       "|                              0 | \"afr\"                 |                15 |\n",
        "|                              0 | \"nfe\"                 |                 1 |\n",
-       "|                              0 | \"nfe\"                 |                 1 |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                              0 | \"eas\"                 |                 2 |\n",
+       "|                              0 | \"amr\"                 |                 1 |\n",
        "+--------------------------------+-----------------------+-------------------+\n",
        "\n",
        "+-------------------+-------------------+---------------------------------+\n",
@@ -4915,11 +4950,11 @@
        "+-------------------+-------------------+---------------------------------+\n",
        "|           float64 |             int32 |                           int64 |\n",
        "+-------------------+-------------------+---------------------------------+\n",
-       "|                NA |                NA |                              NA |\n",
-       "|          2.87e-03 |               348 |                               0 |\n",
-       "|          2.17e-03 |               460 |                               0 |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|          5.00e-01 |                 4 |                               0 |\n",
+       "|          1.11e-04 |             36050 |                               0 |\n",
+       "|          2.86e-05 |            349848 |                               0 |\n",
+       "|          8.50e-04 |             17650 |                               0 |\n",
+       "|          2.86e-06 |            349920 |                               0 |\n",
+       "|          2.29e-05 |             43728 |                               0 |\n",
        "+-------------------+-------------------+---------------------------------+\n",
        "\n",
        "+------------------------+\n",
@@ -4927,11 +4962,11 @@
        "+------------------------+\n",
        "| str                    |\n",
        "+------------------------+\n",
-       "| NA                     |\n",
+       "| \"eas\"                  |\n",
        "| \"nfe\"                  |\n",
+       "| \"afr\"                  |\n",
        "| \"nfe\"                  |\n",
-       "| NA                     |\n",
-       "| \"eas\"                  |\n",
+       "| \"amr\"                  |\n",
        "+------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -4939,11 +4974,11 @@
        "+------------------------------------------------------------------------------+\n",
        "| array                                |\n",
        "+------------------------------------------------------------------------------+\n",
+       "| [(7.78e-06,6.42e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(3.35e-05,1.... |\n",
+       "| [(1.06e-05,8.83e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(2.01e-05,1.... |\n",
+       "| [(5.41e-05,5.02e-05),(4.99e-04,4.24e-04),(2.99e-05,1.81e-05),(0.00e+00,0.... |\n",
        "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(1.16e-01,1.08e-01),(0.00e+00,0.00e+00),(1.23e-01,1.01e-01),(8.88e-02,3.... |\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
        "+-------------------------+---------------------------------+\n",
@@ -4951,11 +4986,11 @@
        "+-------------------------+---------------------------------+\n",
        "|                 float64 | str                             |\n",
        "+-------------------------+---------------------------------+\n",
+       "|                3.35e-05 | \"eas\"                           |\n",
+       "|                2.01e-05 | \"eas\"                           |\n",
+       "|                4.99e-04 | \"afr\"                           |\n",
        "|                      NA | NA                              |\n",
        "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                1.23e-01 | \"amr\"                           |\n",
        "+-------------------------+---------------------------------+\n",
        "\n",
        "+-------------------------+---------------------------------+\n",
@@ -4963,11 +4998,11 @@
        "+-------------------------+---------------------------------+\n",
        "|                 float64 | str                             |\n",
        "+-------------------------+---------------------------------+\n",
+       "|                1.99e-05 | \"eas\"                           |\n",
+       "|                1.06e-05 | \"eas\"                           |\n",
+       "|                4.24e-04 | \"afr\"                           |\n",
        "|                      NA | NA                              |\n",
        "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                1.02e-01 | \"nfe\"                           |\n",
        "+-------------------------+---------------------------------+\n",
        "\n",
        "+--------------------------+----------------------------------+\n",
@@ -4975,11 +5010,11 @@
        "+--------------------------+----------------------------------+\n",
        "|                  float64 | str                              |\n",
        "+--------------------------+----------------------------------+\n",
+       "|                 3.78e-05 | \"eas\"                            |\n",
+       "|                 1.54e-05 | \"nfe\"                            |\n",
+       "|                 5.23e-04 | \"afr\"                            |\n",
        "|                       NA | NA                               |\n",
        "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                 1.23e-01 | \"amr\"                            |\n",
        "+--------------------------+----------------------------------+\n",
        "\n",
        "+--------------------------+----------------------------------+---------+\n",
@@ -4987,119 +5022,119 @@
        "+--------------------------+----------------------------------+---------+\n",
        "|                  float64 | str                              |   int32 |\n",
        "+--------------------------+----------------------------------+---------+\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
+       "|                 2.24e-05 | \"eas\"                            |       3 |\n",
+       "|                 1.15e-05 | \"nfe\"                            |       1 |\n",
+       "|                 4.23e-04 | \"afr\"                            |       2 |\n",
        "|                       NA | NA                               |       2 |\n",
-       "|                       NA | NA                               |       2 |\n",
-       "|                 1.02e-01 | \"nfe\"                            |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
        "+--------------------------+----------------------------------+---------+\n",
        "\n",
-       "+-----------+-----------------+----------+----------+----------+\n",
-       "| was_split | rsid            | filters  |  info.FS |  info.MQ |\n",
-       "+-----------+-----------------+----------+----------+----------+\n",
-       "|      bool | set        | set |  float64 |  float64 |\n",
-       "+-----------+-----------------+----------+----------+----------+\n",
-       "|      True | {\"rs200733424\"} | {}       | 0.00e+00 | 6.00e+01 |\n",
-       "|      True | NA              | {}       | 5.32e+00 | 6.00e+01 |\n",
-       "|      True | NA              | {}       | 4.82e-16 | 6.00e+01 |\n",
-       "|      True | {\"rs200557458\"} | {}       | 0.00e+00 | 6.00e+01 |\n",
-       "|      True | {\"rs6278\"}      | {}       | 2.80e+00 | 6.00e+01 |\n",
-       "+-----------+-----------------+----------+----------+----------+\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      bool | set         | set |  float64 |  float64 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      True | {\"rs200559334\"}  | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|      True | {\"rs201475538\"}  | {}       | 8.65e-01 | 6.00e+01 |\n",
+       "|      True | {\"rs201475538\"}  | {}       | 8.65e-01 | 6.00e+01 |\n",
+       "|      True | {\"rs1950760213\"} | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.51e-01 | 6.00e+01 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
        "\n",
        "+----------------+-----------------+----------+---------------------+\n",
        "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
        "+----------------+-----------------+----------+---------------------+\n",
        "|        float64 |           int64 |  float64 |             float64 |\n",
        "+----------------+-----------------+----------+---------------------+\n",
-       "|       0.00e+00 |             582 | 1.94e+01 |           -2.11e+00 |\n",
-       "|       0.00e+00 |             898 | 9.76e+00 |            0.00e+00 |\n",
-       "|       0.00e+00 |             209 | 9.09e+00 |            8.42e-01 |\n",
-       "|       0.00e+00 |             947 | 1.26e+01 |           -6.74e-01 |\n",
-       "|       0.00e+00 |         1998239 | 1.99e+01 |            0.00e+00 |\n",
+       "|       0.00e+00 |           12992 | 1.27e+01 |            0.00e+00 |\n",
+       "|       0.00e+00 |          142805 | 1.36e+01 |           -1.80e-02 |\n",
+       "|       0.00e+00 |          142805 | 1.36e+01 |           -1.80e-02 |\n",
+       "|       0.00e+00 |            3619 | 1.78e+01 |            6.74e-01 |\n",
+       "|       0.00e+00 |            3070 | 9.27e+00 |            0.00e+00 |\n",
        "+----------------+-----------------+----------+---------------------+\n",
        "\n",
-       "+---------------------------+----------+------------+------------+------------+\n",
-       "| info.SB                   | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
-       "+---------------------------+----------+------------+------------+------------+\n",
-       "| array              |  float64 |      int32 |    float64 |    float64 |\n",
-       "+---------------------------+----------+------------+------------+------------+\n",
-       "| [8,2,15,5]                | 5.82e-01 |         30 |   0.00e+00 |   6.00e+01 |\n",
-       "| [31,19,21,21]             | 3.30e-01 |         92 |   4.52e+00 |   6.00e+01 |\n",
-       "| [6,7,4,6]                 | 9.17e-01 |         23 |   0.00e+00 |   6.00e+01 |\n",
-       "| [16,19,18,22]             | 7.22e-01 |         75 |   0.00e+00 |   6.00e+01 |\n",
-       "| [19752,12842,45058,22649] | 9.83e-01 |     100292 |   2.80e+00 |   6.00e+01 |\n",
-       "+---------------------------+----------+------------+------------+------------+\n",
+       "+---------------------+----------+------------+------------+------------+\n",
+       "| info.SB             | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+---------------------+----------+------------+------------+------------+\n",
+       "| array        |  float64 |      int32 |    float64 |    float64 |\n",
+       "+---------------------+----------+------------+------------+------------+\n",
+       "| [543,53,386,37]     | 7.04e-01 |       1019 |   4.82e-16 |   6.00e+01 |\n",
+       "| [4798,567,4576,579] | 6.27e-01 |      10518 |   1.55e+00 |   6.00e+01 |\n",
+       "| [4798,567,4576,579] | 6.27e-01 |      10518 |   0.00e+00 |   6.00e+01 |\n",
+       "| [83,21,79,20]       | 6.91e-01 |        203 |   4.82e-16 |   6.00e+01 |\n",
+       "| [174,36,99,22]      | 6.12e-01 |        331 |   1.21e+00 |   6.00e+01 |\n",
+       "+---------------------+----------+------------+------------+------------+\n",
        "\n",
        "+-------------------+-----------------+--------------------+------------+\n",
        "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
        "+-------------------+-----------------+--------------------+------------+\n",
        "|           float64 |         float64 |              int64 |    float64 |\n",
        "+-------------------+-----------------+--------------------+------------+\n",
-       "|          0.00e+00 |        5.41e-01 |                582 |   1.94e+01 |\n",
-       "|          0.00e+00 |        1.00e+00 |                553 |   8.01e+00 |\n",
-       "|          0.00e+00 |        2.67e-01 |                178 |   1.05e+01 |\n",
-       "|          0.00e+00 |        1.00e+00 |                703 |   1.12e+01 |\n",
-       "|          0.00e+00 |        1.00e+00 |            1998092 |   1.99e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |              12891 |   1.32e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |              19189 |   1.44e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |             123616 |   1.35e+01 |\n",
+       "|          0.00e+00 |        4.53e-01 |               3591 |   1.98e+01 |\n",
+       "|          0.00e+00 |        8.45e-01 |               2972 |   1.38e+01 |\n",
        "+-------------------+-----------------+--------------------+------------+\n",
        "\n",
-       "+------------------------+---------------------------+-------------+\n",
-       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE          | info.AS_SOR |\n",
-       "+------------------------+---------------------------+-------------+\n",
-       "|                float64 | array              |     float64 |\n",
-       "+------------------------+---------------------------+-------------+\n",
-       "|              -2.11e+00 | [8,2,15,5]                |    5.82e-01 |\n",
-       "|               0.00e+00 | [31,19,15,15]             |    3.30e-01 |\n",
-       "|               7.20e-02 | [6,7,3,5]                 |    1.00e+00 |\n",
-       "|              -1.13e+00 | [16,19,14,17]             |    7.13e-01 |\n",
-       "|               0.00e+00 | [19752,12842,45055,22646] |    9.83e-01 |\n",
-       "+------------------------+---------------------------+-------------+\n",
+       "+------------------------+---------------------+-------------+---------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE    | info.AS_SOR | info.AS_VarDP |\n",
+       "+------------------------+---------------------+-------------+---------------+\n",
+       "|                float64 | array        |     float64 |         int32 |\n",
+       "+------------------------+---------------------+-------------+---------------+\n",
+       "|              -3.90e-02 | [543,53,379,36]     |    7.13e-01 |           976 |\n",
+       "|               1.34e-01 | [4798,567,588,57]   |    8.94e-01 |          1334 |\n",
+       "|              -2.10e-02 | [4798,567,3988,522] |    5.96e-01 |          9184 |\n",
+       "|               9.51e-01 | [83,21,77,19]       |    7.15e-01 |           181 |\n",
+       "|               1.49e+00 | [174,36,87,20]      |    5.79e-01 |           216 |\n",
+       "+------------------------+---------------------+-------------+---------------+\n",
        "\n",
-       "+---------------+----------------+----------------------------+\n",
-       "| info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
-       "+---------------+----------------+----------------------------+\n",
-       "|         int32 |           bool |                       bool |\n",
-       "+---------------+----------------+----------------------------+\n",
-       "|            30 |          False |                      False |\n",
-       "|            69 |          False |                      False |\n",
-       "|            17 |          False |                      False |\n",
-       "|            63 |          False |                      False |\n",
-       "|        100286 |          False |                         NA |\n",
-       "+---------------+----------------+----------------------------+\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           bool |                       bool |                   bool |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                      False |                  False |\n",
+       "|          False |                         NA |                     NA |\n",
+       "+----------------+----------------------------+------------------------+\n",
        "\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "| info.sibling_singleton | info.omni | info.mills | info.monoallelic |\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "|                   bool |      bool |       bool |             bool |\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "|                  False |     False |      False |            False |\n",
-       "|                  False |     False |      False |            False |\n",
-       "|                  False |     False |      False |            False |\n",
-       "|                  False |     False |      False |            False |\n",
-       "|                     NA |      True |      False |            False |\n",
-       "+------------------------+-----------+------------+------------------+\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|     False |      False |            False |         False |       9.14e+00 |\n",
+       "|     False |      False |            False |         False |       6.96e+00 |\n",
+       "|     False |      False |            False |         False |       9.35e+00 |\n",
+       "|     False |      False |            False |         False |       4.34e+00 |\n",
+       "|     False |      False |            False |         False |       5.24e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
        "\n",
-       "+---------------+----------------+-----------------------+\n",
-       "| info.only_het | info.AS_VQSLOD | info.inbreeding_coeff |\n",
-       "+---------------+----------------+-----------------------+\n",
-       "|          bool |        float64 |               float64 |\n",
-       "+---------------+----------------+-----------------------+\n",
-       "|         False |       4.51e+00 |              8.00e-01 |\n",
-       "|         False |       5.16e+00 |              3.75e-01 |\n",
-       "|         False |       6.19e+00 |              8.00e-01 |\n",
-       "|         False |       6.36e+00 |              5.00e-01 |\n",
-       "|         False |       5.57e+00 |              7.33e-01 |\n",
-       "+---------------+----------------+-----------------------+\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|             -1.64e-05 |\n",
+       "|             -1.57e-05 |\n",
+       "|             -7.19e-05 |\n",
+       "|             -1.37e-06 |\n",
+       "|             -1.37e-06 |\n",
+       "+-----------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
        "| info.vrs.VRS_Allele_IDs                                                      |\n",
        "+------------------------------------------------------------------------------+\n",
        "| array                                                                   |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.BlCz08hG7ZEQzvW20V8Kp4xJMx-pMKes\",\"ga4gh:VA.U4eD7PXtXRCClN6FQt... |\n",
-       "| [\"ga4gh:VA.aXxME51cngsJBnH_3WX01B0Ms-SLay9X\",\"ga4gh:VA.ynryFZCH9dBU67nwPr... |\n",
-       "| [\"ga4gh:VA.Q6p6okAv6qHSPSV8MOvy8Hhl0kZ6tQ4U\",\"ga4gh:VA.WIfYWxgEc5ICUUByiD... |\n",
-       "| [\"ga4gh:VA.k_gesWx8TbLs23MHIkc3NaVscBWVDrSE\",\"ga4gh:VA.NJz3hsVBrqbTn17g5T... |\n",
-       "| [\"ga4gh:VA.HjAJ4gj43VVFJQmV-h5WuPZZPBmNwSFZ\",\"ga4gh:VA.sT14AsCSWlf2AqH0wh... |\n",
+       "| [\"ga4gh:VA.xwHxL6yO3I874HqhfGar1ZIaOp66ifsx\",\"ga4gh:VA.eglRJIN5-izMH1peMx... |\n",
+       "| [\"ga4gh:VA.3n-gYs3xsspnxwI91EMCEDsIcrj9bIqO\",\"ga4gh:VA.9Y9GDmOWCTqMks8dsN... |\n",
+       "| [\"ga4gh:VA.3n-gYs3xsspnxwI91EMCEDsIcrj9bIqO\",\"ga4gh:VA.R13f7bcCv-WGRkCM6W... |\n",
+       "| [\"ga4gh:VA.MHTiWiUO_hsQ_FAaIagdomN6DdnpjqRm\",\"ga4gh:VA.D8-MF1xUzSMIKncRc7... |\n",
+       "| [\"ga4gh:VA.oDr3967BFZ6uu2LJEc9_RVWIOnNqtSF-\",\"ga4gh:VA.N81rCvIHb-9PjhcHLL... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
        "+-----------------------+-----------------------+---------------------+\n",
@@ -5107,11 +5142,11 @@
        "+-----------------------+-----------------------+---------------------+\n",
        "| array          | array          | array          |\n",
        "+-----------------------+-----------------------+---------------------+\n",
-       "| [113409635,113409635] | [113409636,113409636] | [\"G\",\"C\"]           |\n",
-       "| [113409692,113409692] | [113409693,113409693] | [\"G\",\"A\"]           |\n",
-       "| [113409716,113409716] | [113409717,113409717] | [\"C\",\"T\"]           |\n",
-       "| [113409757,113409757] | [113409758,113409758] | [\"C\",\"T\"]           |\n",
-       "| [113410001,113410001] | [113410002,113410002] | [\"C\",\"A\"]           |\n",
+       "| [113410656,113410656] | [113410657,113410657] | [\"C\",\"T\"]           |\n",
+       "| [113410657,113410657] | [113410658,113410658] | [\"G\",\"A\"]           |\n",
+       "| [113410657,113410657] | [113410658,113410658] | [\"G\",\"T\"]           |\n",
+       "| [113410659,113410659] | [113410660,113410660] | [\"A\",\"G\"]           |\n",
+       "| [113410661,113410661] | [113410662,113410662] | [\"G\",\"A\"]           |\n",
        "+-----------------------+-----------------------+---------------------+\n",
        "\n",
        "+-------------------+-----------+--------+--------------------------------+\n",
@@ -5119,11 +5154,11 @@
        "+-------------------+-----------+--------+--------------------------------+\n",
        "| str               |     int32 | str    | str                            |\n",
        "+-------------------+-----------+--------+--------------------------------+\n",
-       "| \"G/C\"             | 113409636 | \".\"    | \"chr11\t113409636\t.\tG\tC\t.\t.\tGT\" |\n",
-       "| \"G/A\"             | 113409693 | \".\"    | \"chr11\t113409693\t.\tG\tA\t.\t.\tGT\" |\n",
-       "| \"C/T\"             | 113409717 | \".\"    | \"chr11\t113409717\t.\tC\tT\t.\t.\tGT\" |\n",
-       "| \"C/T\"             | 113409758 | \".\"    | \"chr11\t113409758\t.\tC\tT\t.\t.\tGT\" |\n",
-       "| \"C/A\"             | 113410002 | \".\"    | \"chr11\t113410002\t.\tC\tA\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113410657 | \".\"    | \"chr11\t113410657\t.\tC\tT\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410658 | \".\"    | \"chr11\t113410658\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/T\"             | 113410658 | \".\"    | \"chr11\t113410658\t.\tG\tT\t.\t.\tGT\" |\n",
+       "| \"A/G\"             | 113410660 | \".\"    | \"chr11\t113410660\t.\tA\tG\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410662 | \".\"    | \"chr11\t113410662\t.\tG\tA\t.\t.\tGT\" |\n",
        "+-------------------+-----------+--------+--------------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -5179,11 +5214,11 @@
        "+---------------------+-----------+------------+\n",
        "| str                 |     int32 |      int32 |\n",
        "+---------------------+-----------+------------+\n",
-       "| \"chr11\"             | 113409636 |          1 |\n",
-       "| \"chr11\"             | 113409693 |          1 |\n",
-       "| \"chr11\"             | 113409717 |          1 |\n",
-       "| \"chr11\"             | 113409758 |          1 |\n",
-       "| \"chr11\"             | 113410002 |          1 |\n",
+       "| \"chr11\"             | 113410657 |          1 |\n",
+       "| \"chr11\"             | 113410658 |          1 |\n",
+       "| \"chr11\"             | 113410658 |          1 |\n",
+       "| \"chr11\"             | 113410660 |          1 |\n",
+       "| \"chr11\"             | 113410662 |          1 |\n",
        "+---------------------+-----------+------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -5191,11 +5226,11 @@
        "+------------------------------------------------------------------------------+\n",
        "| array                                    |\n",
-       "+-------------------------------------------------+\n",
-       "| [0,0,0,0,28,0,76,0,162,0,0,0,0,0,0,0,0,0,0,1]   |\n",
-       "| [0,0,0,0,25,0,186,0,206,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,95,0,197,0,202,0,0,0,0,0,0,0,1,0,0,0]  |\n",
-       "| [0,0,0,0,100,0,111,0,193,0,0,0,0,0,1,0,0,0,0,0] |\n",
-       "| [0,0,0,0,120,1,223,5,34,1,5,0,2,5,7,1,3,2,1,66] |\n",
-       "+-------------------------------------------------+\n",
+       "+------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq                       |\n",
+       "+------------------------------------------------------------------+\n",
+       "| array                                                     |\n",
+       "+------------------------------------------------------------------+\n",
+       "| [0,0,0,0,4430,0,32721,0,357851,0,60423,0,267316,0,0,0,0,0,0,18]  |\n",
+       "| [0,0,0,0,4183,2,30033,1,354765,0,58262,0,276721,0,0,0,1,0,0,115] |\n",
+       "| [0,0,0,0,4183,0,30033,1,354765,0,58262,0,276721,0,0,0,1,0,0,115] |\n",
+       "| [0,0,0,0,3304,0,26636,0,351846,0,54948,0,288484,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,4139,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]   |\n",
+       "+------------------------------------------------------------------+\n",
        "\n",
        "+---------------------------------------------+\n",
        "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
@@ -5342,17 +5377,17 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq       |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,101,28,30,82,26,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,211,29,38,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "| [0,0,286,30,40,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "| [0,0,200,35,36,102,32,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "| [0,0,438,30,5,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
-       "+--------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,29987,74977,120278,300405,170342,18935,3117,1021,684,644,577,507,353... |\n",
+       "| [0,0,26988,70993,120130,305542,173013,19471,3177,1036,688,647,582,509,357... |\n",
+       "| [0,0,26986,70993,120130,305542,173013,19471,3177,1036,688,647,582,509,357... |\n",
+       "| [0,0,24253,67281,120581,309738,175490,19882,3222,1041,692,644,582,508,354... |\n",
+       "| [0,0,22477,65025,118275,313361,178106,20388,3276,1054,693,645,582,508,355... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
        "+---------------------------------------------+\n",
        "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
@@ -5371,11 +5406,11 @@
        "+--------------------------------------------+\n",
        "|                                      int64 |\n",
        "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
+       "|                                        252 |\n",
+       "|                                        265 |\n",
+       "|                                        265 |\n",
+       "|                                        264 |\n",
+       "|                                        265 |\n",
        "+--------------------------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -5390,17 +5425,17 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq  |\n",
-       "+---------------------------------------------+\n",
-       "| array                                |\n",
-       "+---------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
-       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]   |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,1,1,11,5,2,1,5,0,2,5,7,1,3,2,1,66] |\n",
-       "+---------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18] |\n",
+       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,21] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,94] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "+--------------------------------------------+\n",
        "\n",
        "+---------------------------------------------+\n",
        "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
@@ -5438,17 +5473,17 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq  |\n",
-       "+---------------------------------------------+\n",
-       "| array                                |\n",
-       "+---------------------------------------------+\n",
-       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,108,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+---------------------------------------------+\n",
+       "+-----------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq    |\n",
+       "+-----------------------------------------------+\n",
+       "| array                                  |\n",
+       "+-----------------------------------------------+\n",
+       "| [0,0,0,2,3,3,2,1,0,1,1,0,1,0,0,1,1,0,0,1]     |\n",
+       "| [0,0,1,2,4,3,4,1,1,0,0,1,0,1,0,0,0,2,0,1]     |\n",
+       "| [0,0,2,3,11,13,14,10,8,9,1,3,2,0,4,0,0,1,1,1] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
+       "+-----------------------------------------------+\n",
        "\n",
        "+---------------------------------------------+\n",
        "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
@@ -5467,11 +5502,11 @@
        "+--------------------------------------------+\n",
        "|                                      int64 |\n",
        "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
+       "|                                          1 |\n",
+       "|                                          2 |\n",
+       "|                                         11 |\n",
+       "|                                          1 |\n",
+       "|                                          1 |\n",
        "+--------------------------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -5486,17 +5521,17 @@
        "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq  |\n",
-       "+---------------------------------------------+\n",
-       "| array                                |\n",
-       "+---------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,3,8,0,4,8,6,30,15,8,9,3,1,3,0,0,0] |\n",
-       "+---------------------------------------------+\n",
+       "+-----------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq    |\n",
+       "+-----------------------------------------------+\n",
+       "| array                                  |\n",
+       "+-----------------------------------------------+\n",
+       "| [0,0,0,0,0,0,2,0,4,4,4,3,1,0,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,2,0,1,0,3,3,8,5,0,1,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,0,0,6,5,19,14,25,17,6,1,1,0,0,0,0,0] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]     |\n",
+       "+-----------------------------------------------+\n",
        "\n",
        "+---------------------------------------------+\n",
        "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
@@ -5534,17 +5569,17 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [313709,2,2,0,442,0,76,0,162,0,0,0,0,0,0,0,0,0,0,1]                          |\n",
-       "| [313080,4,2,0,906,6,189,2,206,0,0,0,0,0,0,0,0,0,0,0]                         |\n",
-       "| [313071,2,3,0,920,0,198,0,202,0,0,0,0,0,0,0,1,0,0,0]                         |\n",
-       "| [313441,3,2,1,640,0,111,1,193,0,0,0,0,0,1,0,0,0,0,0]                         |\n",
-       "| [301848,5644,543,415,1889,1584,436,212,123,210,485,568,76,48,44,53,135,57... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+-----------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                        |\n",
+       "+-----------------------------------------------------------------------+\n",
+       "| array                                                          |\n",
+       "+-----------------------------------------------------------------------+\n",
+       "| [979,2,2487,1,8860,1,32722,0,357851,0,60423,0,267317,0,0,0,0,0,0,18]  |\n",
+       "| [886,4,2000,0,7936,2,30033,1,354765,1,58262,0,276721,0,0,0,1,0,0,116] |\n",
+       "| [886,4,2000,0,7936,2,30033,1,354765,1,58262,0,276721,0,0,0,1,0,0,116] |\n",
+       "| [684,0,1637,0,6556,2,26636,0,351846,0,54948,0,288484,0,0,0,0,0,0,1]   |\n",
+       "| [653,0,1690,2,6865,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]   |\n",
+       "+-----------------------------------------------------------------------+\n",
        "\n",
        "+-------------------------------------------------+\n",
        "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
@@ -5582,17 +5617,17 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+-----------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq            |\n",
-       "+-----------------------------------------------------------+\n",
-       "| array                                              |\n",
-       "+-----------------------------------------------------------+\n",
-       "| [240827,73298,103,28,30,82,26,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [240299,73676,214,29,38,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "| [240226,73668,294,30,40,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "| [240307,73671,209,36,36,102,32,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "| [238198,75776,482,33,5,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
-       "+-----------------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [415,5663,30415,76214,120310,300505,170348,18947,3122,1023,686,644,577,50... |\n",
+       "| [362,4790,27388,71922,120182,305624,173020,19481,3184,1039,690,648,582,50... |\n",
+       "| [362,4790,27388,71922,120182,305624,173020,19481,3184,1039,690,648,582,50... |\n",
+       "| [296,4122,24622,67964,120609,309787,175498,19891,3228,1043,694,645,582,50... |\n",
+       "| [264,3602,22767,65834,118294,313419,178113,20399,3282,1056,696,645,582,50... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
        "+-------------------------------------------------+\n",
        "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
@@ -5611,11 +5646,11 @@
        "+------------------------------------------------+\n",
        "|                                          int64 |\n",
        "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
+       "|                                            252 |\n",
+       "|                                            265 |\n",
+       "|                                            265 |\n",
+       "|                                            264 |\n",
+       "|                                            265 |\n",
        "+------------------------------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -5630,17 +5665,17 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]                                    |\n",
-       "| [0,3,0,0,1,5,3,1,0,0,0,0,0,0,0,0,0,0,0,0]                                    |\n",
-       "| [0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]                                    |\n",
-       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]                                    |\n",
-       "| [398,5642,541,415,716,1584,224,212,91,210,485,568,76,48,44,53,135,57,19,108] |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [1,2,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,18]     |\n",
+       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,21]     |\n",
+       "| [2,4,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,95]     |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "+------------------------------------------------+\n",
        "\n",
        "+-------------------------------------------------+\n",
        "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
@@ -5678,17 +5713,17 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq    |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| [2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
-       "| [8,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
-       "| [2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
-       "| [1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
-       "| [9330,2176,115,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+---------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,1,3,6,4,2,1,0,1,1,0,1,0,0,1,1,0,0,1]      |\n",
+       "| [0,0,1,2,4,3,4,1,1,0,0,1,0,1,0,0,0,2,0,1]      |\n",
+       "| [0,2,4,4,17,13,14,10,8,9,1,3,2,0,4,0,0,1,1,1]  |\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
        "\n",
        "+-------------------------------------------------+\n",
        "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
@@ -5707,11 +5742,11 @@
        "+------------------------------------------------+\n",
        "|                                          int64 |\n",
        "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
+       "|                                              1 |\n",
+       "|                                              2 |\n",
+       "|                                             11 |\n",
+       "|                                              1 |\n",
+       "|                                              1 |\n",
        "+------------------------------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -5726,17 +5761,17 @@
        "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq                   |\n",
-       "+------------------------------------------------------------------+\n",
-       "| array                                                     |\n",
-       "+------------------------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]                        |\n",
-       "| [0,0,0,0,1,1,2,0,1,0,1,0,0,4,0,0,0,0,0,0]                        |\n",
-       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]                        |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]                        |\n",
-       "| [0,0,4,4,27,107,217,41,416,6,1084,414,43,1634,53,495,208,34,2,0] |\n",
-       "+------------------------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,1,3,2,0,0,2,0,4,4,4,3,1,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,2,0,1,0,3,3,8,5,0,1,0,0,0,0,0,0]      |\n",
+       "| [0,2,7,0,0,1,6,5,19,14,26,17,6,1,1,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
        "\n",
        "+-------------------------------------------------+\n",
        "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
@@ -5779,11 +5814,11 @@
        "+--------------------------------------------+\n",
        "| array                               |\n",
        "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,1,1,5,4,3,0,0]                      |\n",
+       "| [1,1,0,2,5,2,1,0,0,0]                      |\n",
+       "| [0,0,1,1,5,3,2,0,0,0]                      |\n",
+       "| [0,1,4,4,2,5,0,1,4,1]                      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,1,0,1,0,0,0,0,0]                      |\n",
        "+--------------------------------------------+\n",
        "\n",
        "+---------------------------------------------+\n",
@@ -5791,9 +5826,9 @@
        "+---------------------------------------------+\n",
        "|                                       int64 |\n",
        "+---------------------------------------------+\n",
+       "|                                           2 |\n",
        "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
+       "|                                           1 |\n",
        "|                                           0 |\n",
        "|                                           0 |\n",
        "+---------------------------------------------+\n",
@@ -5831,7 +5866,7 @@
        "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
        "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
        "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,1,2,2,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
        "+--------------------------------------------+\n",
        "\n",
        "+---------------------------------------------+\n",
@@ -5851,11 +5886,11 @@
        "+--------------------------------------------+---------------------------------+\n",
        "|                                      int64 |                         float32 |\n",
        "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        8.46e+00 |\n",
-       "|                                          0 |                        9.08e+00 |\n",
-       "|                                          0 |                        1.51e+01 |\n",
-       "|                                          0 |                        9.13e+00 |\n",
-       "|                                          0 |                        2.02e+00 |\n",
+       "|                                          0 |                        3.61e+00 |\n",
+       "|                                          0 |                        2.90e+00 |\n",
+       "|                                          0 |                        2.38e+00 |\n",
+       "|                                          0 |                        1.22e+00 |\n",
+       "|                                          0 |                        6.25e+00 |\n",
        "+--------------------------------------------+---------------------------------+\n",
        "\n",
        "+-------------------------------------+--------------------------------+\n",
@@ -5863,11 +5898,11 @@
        "+-------------------------------------+--------------------------------+\n",
        "|                             float32 |                        float64 |\n",
        "+-------------------------------------+--------------------------------+\n",
-       "|                            7.01e-01 |                             NA |\n",
-       "|                            7.68e-01 |                             NA |\n",
-       "|                            1.40e+00 |                             NA |\n",
-       "|                            7.73e-01 |                             NA |\n",
-       "|                            9.23e-02 |                             NA |\n",
+       "|                            2.42e-01 |                             NA |\n",
+       "|                            1.80e-01 |                             NA |\n",
+       "|                            1.31e-01 |                             NA |\n",
+       "|                           -1.85e-02 |                             NA |\n",
+       "|                            4.76e-01 |                             NA |\n",
        "+-------------------------------------+--------------------------------+\n",
        "\n",
        "+--------------------------------------+\n",
@@ -5878,7 +5913,7 @@
        "|                             0.00e+00 |\n",
        "|                             0.00e+00 |\n",
        "|                             0.00e+00 |\n",
-       "|                             1.00e-02 |\n",
+       "|                             0.00e+00 |\n",
        "|                             0.00e+00 |\n",
        "+--------------------------------------+\n",
        "\n",
@@ -5887,11 +5922,11 @@
        "+------------------------------------------+-----------------------------+\n",
        "|                                  float64 |                     float64 |\n",
        "+------------------------------------------+-----------------------------+\n",
-       "|                                 2.00e-02 |                    1.94e+00 |\n",
-       "|                                 0.00e+00 |                    5.70e-01 |\n",
-       "|                                 0.00e+00 |                    1.05e+00 |\n",
-       "|                                 3.00e-02 |                    2.58e+00 |\n",
-       "|                                 0.00e+00 |                    1.61e+00 |\n",
+       "|                                 1.20e-01 |                   -4.68e-01 |\n",
+       "|                                 5.00e-02 |                    7.10e-02 |\n",
+       "|                                 3.00e-02 |                    7.10e-02 |\n",
+       "|                                 4.00e-02 |                   -1.27e+00 |\n",
+       "|                                 2.10e-01 |                    1.62e+00 |\n",
        "+------------------------------------------+-----------------------------+\n",
        "\n",
        "+-------------------------------+-----------------------------------+\n",
@@ -5915,12 +5950,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The total number of other variants passing filters in DRD2 is:  2739\n"
+      "The total number of other variants passing filters in DRD2 is:  783\n"
      ]
     }
    ],
    "source": [
-    "var_ht = filter_by_consequence_category(other=True, ht=drd2_interval_ht)\n",
+    "var_ht = filter_by_consequence_category(other=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
     "print(\"The total number of other variants passing filters in DRD2 is: \", var_ht.count())"
    ]
@@ -6207,7 +6242,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-12-19 17:34:53.716 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
+      "2024-12-20 12:31:16.359 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
      ]
     },
     {

From f482dba8d010a8d90b514d00e525e07aa9c23453 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Fri, 20 Dec 2024 15:26:58 -0500
Subject: [PATCH 049/121] Draft code

---
 gnomad_toolbox/filtering/vep.py | 62 +++++++++++++++++++++++----------
 1 file changed, 44 insertions(+), 18 deletions(-)

diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py
index a8b8793..d37eed9 100644
--- a/gnomad_toolbox/filtering/vep.py
+++ b/gnomad_toolbox/filtering/vep.py
@@ -3,7 +3,7 @@
 from functools import reduce
 
 import hail as hl
-from gnomad.utils.vep import CSQ_CODING, LOF_CSQ_SET
+from gnomad.utils.vep import CSQ_CODING, LOF_CSQ_SET, filter_vep_transcript_csqs
 
 from gnomad_toolbox.load_data import _get_gnomad_release
 
@@ -75,23 +75,49 @@ def filter_by_csqs(
     return ht
 
 
-# TODO: The following was in one of the notebooks, and I think we should add a wrapper
-#  around this function to make it much simpler instead of using it in the notebook.
+def filter_to_plofs(gene: str, select_fields: bool = False, **kwargs) -> hl.Table:
+    """
+    Filter to observed pLoF variants that we used to calculate the gene constraint metrics.
+
+    .. note::
 
-# Filter to LOFTEE high-confidence variants for certain genes
+            pLOF variants meets the following requirements:
+            - High-confidence LOFTEE variants (without any flags),
+            - Only variants in the MANE Select transcript,
+            - PASS variants that are SNVs with MAF ≤ 0.1%,
+            - Exome median depth ≥ 30 (# TODO: This is changing in v4 constraint?)
 
-# In this example, we are filtering to variants in ASH1L that are LOFTEE high-confidence
-# (with no flags) in the MANE select transcript.
+    :param gene: Gene symbol.
+    :param select_fields: Boolean if the output should be limited to specific fields.
+    :return: Table with pLoF variants.
+    """
+    # TODO: need to think more how to optimize this so it won't use a lot of memory
+    var_ht = _get_gnomad_release(dataset="variant", **kwargs)
+    cov_ht = _get_gnomad_release(dataset="coverage", **kwargs)
+
+    var_ht = filter_vep_transcript_csqs(
+        var_ht,
+        synonymous=False,
+        mane_select=True,
+        genes=[gene],
+        match_by_gene_symbol=True,
+        additional_filtering_criteria=[
+            lambda x: (x.lof == "HC") & hl.is_missing(x.lof_flags)
+        ],
+    )
+
+    var_ht = var_ht.filter(
+        (hl.len(var_ht.filters) == 0)
+        & (var_ht.allele_info.allele_type == "snv")
+        & (var_ht.freq[0].AF <= 0.001)
+        & (cov_ht[var_ht.locus].median_approx >= 30)
+    )
+
+    if select_fields:
+        var_ht = var_ht.select(
+            freq=var_ht.freq[0],
+            csq=var_ht.vep.transcript_consequences[0].consequence_terms,
+            coverage=cov_ht[var_ht.locus],
+        )
 
-# from gnomad.utils.vep import filter_vep_transcript_csqs
-# ht = get_gnomad_release(data_type='exomes', version='4.1')
-# ht = filter_vep_transcript_csqs(
-#    ht,
-#    synonymous=False,
-#    mane_select=True,
-#    genes=["ASH1L"],
-#    match_by_gene_symbol=True,
-#    additional_filtering_criteria=[lambda x: (x.lof == "HC") & hl.is_missing(x.lof_flags)],
-# )
-# ht.show()
-# ht.count()
+    return var_ht

From 09551f89f2de83b5a88ec0ed13ce7dcde866025f Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Fri, 20 Dec 2024 14:45:01 -0700
Subject: [PATCH 050/121] Small docstring fix

---
 gnomad_toolbox/filtering/vep.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py
index adbd6d3..049e5db 100644
--- a/gnomad_toolbox/filtering/vep.py
+++ b/gnomad_toolbox/filtering/vep.py
@@ -23,7 +23,7 @@ def filter_by_consequence_category(
 
     The [VEP](https://useast.ensembl.org/info/docs/tools/vep/index.html) consequences included in each category are:
 
-        pLoF
+        pLoF:
 
             - transcript_ablation
             - splice_acceptor_variant
@@ -31,7 +31,7 @@ def filter_by_consequence_category(
             - stop_gained
             - frameshift_variant
 
-        Missense / Inframe indel
+        Missense / Inframe indel:
 
             - stop_lost
             - start_lost
@@ -39,11 +39,11 @@ def filter_by_consequence_category(
             - inframe_deletion
             - missense_variant
 
-        Synonymous
+        Synonymous:
 
             - synonymous_variant
 
-        `Other
+        Other:
 
             - protein_altering_variant
             - incomplete_terminal_codon_variant

From df9eb61f86460e96b4587cfacb0275d95481469d Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Fri, 20 Dec 2024 16:59:14 -0500
Subject: [PATCH 051/121] Add to-do

---
 gnomad_toolbox/filtering/variant.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index 386e83c..ca4ed39 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -115,6 +115,8 @@ def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.
     """
     Filter variants in a gene.
 
+    # TODO: Add a pre-processing step to filter out these genes on chrY to match the gnomAD browser.
+
     .. note::
 
            This function is to match the number of variants that you will get in the

From aa4e8e5fd7c1b45c1439f5a915f99a0b142206c0 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Fri, 20 Dec 2024 17:08:18 -0500
Subject: [PATCH 052/121] Move to-do

---
 gnomad_toolbox/filtering/variant.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index ca4ed39..e4f78ce 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -111,12 +111,12 @@ def filter_by_intervals(
     return ht
 
 
+# TODO: Add a pre-processing step to filter out these genes on chrY to
+# match the gnomAD browser.
 def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.Table:
     """
     Filter variants in a gene.
 
-    # TODO: Add a pre-processing step to filter out these genes on chrY to match the gnomAD browser.
-
     .. note::
 
            This function is to match the number of variants that you will get in the

From f3d459ba2f8016116f92bfba52be28a5c72f1eb6 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Fri, 20 Dec 2024 17:08:59 -0500
Subject: [PATCH 053/121] Change param name

---
 gnomad_toolbox/filtering/variant.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index e4f78ce..536c0bd 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -136,6 +136,6 @@ def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.
     """
     # Load the Hail Table if not provided
     ht = _get_gnomad_release(dataset="variant", **kwargs)
-    ht = filter_to_gencode_cds(ht, genes=gene, padding=exon_padding_bp)
+    ht = filter_to_gencode_cds(ht, genes=gene, padding_bp=exon_padding_bp)
 
     return ht

From 80b1af4de7faf352c14f73c7b45846def2bd3aac Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Thu, 2 Jan 2025 15:17:26 -0500
Subject: [PATCH 054/121] Get relevant coverage table for each version

---
 gnomad_toolbox/filtering/vep.py               | 90 ++++++++++++++-----
 .../notebooks/explore_release_data.ipynb      |  2 +-
 .../intro_to_filtering_variant_data.ipynb     |  2 +-
 3 files changed, 72 insertions(+), 22 deletions(-)

diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py
index d37eed9..9554bed 100644
--- a/gnomad_toolbox/filtering/vep.py
+++ b/gnomad_toolbox/filtering/vep.py
@@ -3,9 +3,11 @@
 from functools import reduce
 
 import hail as hl
+from gnomad.resources.grch37.reference_data import gencode as grch37_gencode
+from gnomad.resources.grch38.reference_data import gencode as grch38_gencode
 from gnomad.utils.vep import CSQ_CODING, LOF_CSQ_SET, filter_vep_transcript_csqs
 
-from gnomad_toolbox.load_data import _get_gnomad_release
+from gnomad_toolbox.load_data import _get_gnomad_release, gnomad_session
 
 # TODO: I haven't looked over this function yet. Is there anything in gnomad_methods
 #  that could be used here? If not, is there anything here that should be moved to
@@ -75,42 +77,90 @@ def filter_by_csqs(
     return ht
 
 
-def filter_to_plofs(gene: str, select_fields: bool = False, **kwargs) -> hl.Table:
+def filter_to_plofs(
+    gene_symbol: str, select_fields: bool = False, **kwargs
+) -> hl.Table:
     """
-    Filter to observed pLoF variants that we used to calculate the gene constraint metrics.
+        Filter to observed pLoF variants that we used to calculate the gene constraint metrics.
+
+        .. note::
 
-    .. note::
+                pLOF variants meets the following requirements:
+                - High-confidence LOFTEE variants (without any flags),
+                - Only variants in the MANE Select transcript,
+                - PASS variants that are SNVs with MAF ≤ 0.1%,
+                - Exome median depth ≥ 30 (# TODO: This is changing in v4 constraint?)
 
-            pLOF variants meets the following requirements:
-            - High-confidence LOFTEE variants (without any flags),
-            - Only variants in the MANE Select transcript,
-            - PASS variants that are SNVs with MAF ≤ 0.1%,
-            - Exome median depth ≥ 30 (# TODO: This is changing in v4 constraint?)
+    **Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**
 
-    :param gene: Gene symbol.
-    :param select_fields: Boolean if the output should be limited to specific fields.
-    :return: Table with pLoF variants.
+        :param gene_symbol: Gene symbol.
+        :param select_fields: Boolean if the output should be limited to specific fields.
+        :return: Table with pLoF variants.
     """
-    # TODO: need to think more how to optimize this so it won't use a lot of memory
-    var_ht = _get_gnomad_release(dataset="variant", **kwargs)
-    cov_ht = _get_gnomad_release(dataset="coverage", **kwargs)
+    var_version = kwargs.pop("version", gnomad_session.version)
+    var_ht = _get_gnomad_release(dataset="variant", version=var_version, **kwargs)
+
+    # Determine the version of the coverage table
+    if var_version.startswith("4."):
+        cov_ht = _get_gnomad_release(dataset="coverage", version="4.0", **kwargs)
+    elif var_version.startswith("3."):
+        cov_ht = _get_gnomad_release(dataset="coverage", version="3.0.1", **kwargs)
+    elif var_version.startswith("2."):
+        cov_ht = _get_gnomad_release(dataset="coverage", version="2.1", **kwargs)
+    else:
+        raise ValueError(
+            f"Unrecognized version: '{var_version}'. Please specify a valid version."
+        )
 
+    # Get the gene interval from gen_ht
+    gen_ht = (
+        grch37_gencode.ht() if var_version.startswith("2.") else grch38_gencode.ht()
+    )
+    interval = (
+        gen_ht.filter(
+            (gen_ht.feature == "gene")
+            & (gen_ht.gene_name.lower() == gene_symbol.lower())
+        )
+        .select()
+        .collect()
+    )
+
+    if not interval:
+        raise ValueError(f"No interval found for gene: {gene_symbol}")
+
+    # Convert to a list of intervals
+    interval = [row["interval"] for row in interval]
+    var_ht = hl.filter_intervals(var_ht, interval)
+    cov_ht = hl.filter_intervals(cov_ht, interval)
+
+    # Filter to high-confidence LOFTEE variants
     var_ht = filter_vep_transcript_csqs(
         var_ht,
         synonymous=False,
-        mane_select=True,
-        genes=[gene],
+        mane_select=True if var_version.startswith("4.") else False,
+        # TODO: When this function is applied to DRD2 gene in v4.1, it will get 7 pLoF
+        #  variants instead of 8 on the browser and the 4.1 constraint table,
+        #  because one of them is not in mane select, nor in canonical transcript.
+        genes=[gene_symbol.upper()],
         match_by_gene_symbol=True,
         additional_filtering_criteria=[
-            lambda x: (x.lof == "HC") & hl.is_missing(x.lof_flags)
+            lambda x: (x.lof == "HC")
+            & (hl.is_missing(x.lof_flags) | (x.lof_flags == ""))
         ],
     )
 
+    if var_version.startswith("2."):
+        allele_type_expr = var_ht.allele_type
+        cov_cut_expr = cov_ht[var_ht.locus].median
+    else:
+        allele_type_expr = var_ht.allele_info.allele_type
+        cov_cut_expr = cov_ht[var_ht.locus].median_approx
+
     var_ht = var_ht.filter(
         (hl.len(var_ht.filters) == 0)
-        & (var_ht.allele_info.allele_type == "snv")
+        & (allele_type_expr == "snv")
         & (var_ht.freq[0].AF <= 0.001)
-        & (cov_ht[var_ht.locus].median_approx >= 30)
+        & (cov_cut_expr >= 30)
     )
 
     if select_fields:
diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
index 1801596..a6032d1 100644
--- a/gnomad_toolbox/notebooks/explore_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -6582,7 +6582,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.9"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
index e6dd8fd..41f38ae 100644
--- a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
+++ b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
@@ -6252,7 +6252,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.9"
   },
   "toc": {
    "base_numbering": 1,

From 532fcc86af78c43cafcfca12b6bf49b3053919ad Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Fri, 3 Jan 2025 11:48:06 -0500
Subject: [PATCH 055/121] Revert notebook changes

---
 .../notebooks/explore_release_data.ipynb      |   2 +-
 .../intro_to_filtering_variant_data.ipynb     | 749 +++++++++---------
 2 files changed, 396 insertions(+), 355 deletions(-)

diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
index a6032d1..1801596 100644
--- a/gnomad_toolbox/notebooks/explore_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -6582,7 +6582,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
index 41f38ae..ae240c0 100644
--- a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
+++ b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
@@ -41,7 +41,7 @@
     {
      "data": {
       "text/html": [
-       "    \n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -90,22 +90,14 @@
        "   * Handle when an output is cleared or removed\n",
        "   */\n",
        "  function handleClearOutput(event, handle) {\n",
-       "    function drop(id) {\n",
-       "      const view = Bokeh.index.get_by_id(id)\n",
-       "      if (view != null) {\n",
-       "        view.model.document.clear()\n",
-       "        Bokeh.index.delete(view)\n",
-       "      }\n",
-       "    }\n",
-       "\n",
        "    const cell = handle.cell;\n",
        "\n",
        "    const id = cell.output_area._bokeh_element_id;\n",
        "    const server_id = cell.output_area._bokeh_server_id;\n",
-       "\n",
        "    // Clean up Bokeh references\n",
-       "    if (id != null) {\n",
-       "      drop(id)\n",
+       "    if (id != null && id in Bokeh.index) {\n",
+       "      Bokeh.index[id].model.document.clear();\n",
+       "      delete Bokeh.index[id];\n",
        "    }\n",
        "\n",
        "    if (server_id !== undefined) {\n",
@@ -114,8 +106,11 @@
        "      cell.notebook.kernel.execute(cmd_clean, {\n",
        "        iopub: {\n",
        "          output: function(msg) {\n",
-       "            const id = msg.content.text.trim()\n",
-       "            drop(id)\n",
+       "            const id = msg.content.text.trim();\n",
+       "            if (id in Bokeh.index) {\n",
+       "              Bokeh.index[id].model.document.clear();\n",
+       "              delete Bokeh.index[id];\n",
+       "            }\n",
        "          }\n",
        "        }\n",
        "      });\n",
@@ -223,7 +218,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\");\n",
+       "    const el = document.getElementById(\"d5c4232b-0e66-4b61-983c-ba8423f8e445\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -306,7 +301,7 @@
        "    document.body.appendChild(element);\n",
        "  }\n",
        "\n",
-       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
+       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n",
        "  const css_urls = [];\n",
        "\n",
        "  const inline_js = [    function(Bokeh) {\n",
@@ -329,7 +324,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"d5c4232b-0e66-4b61-983c-ba8423f8e445\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -345,7 +340,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"ab66414b-9a8d-4992-add5-5e50546bdda0\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"d5c4232b-0e66-4b61-983c-ba8423f8e445\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"d5c4232b-0e66-4b61-983c-ba8423f8e445\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -360,7 +355,7 @@
     "    get_ancestry_callstats, \n",
     "    get_single_variant_ancestry_callstats,\n",
     ")\n",
-    "from gnomad_toolbox.filtering.vep import filter_by_csqs"
+    "from gnomad_toolbox.filtering.vep import filter_by_consequence_category"
    ]
   },
   {
@@ -377,9 +372,8 @@
       "     __  __     <>__\n",
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
-      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241210-2227-0.2.132-678e1f52b999.log\n",
-      "2024-12-10 22:28:45.710 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
+      "  /_/ /_/\\_,_/_/_/   version 0.2.133-4c60fddb171a\n",
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241220-1230-0.2.133-4c60fddb171a.log\n"
      ]
     }
    ],
@@ -494,11 +488,18 @@
    ]
   },
   {
+   "attachments": {
+    "78f1b020-4f45-4800-82fa-0bdffb877962.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "id": "756a996c-bad2-4d24-a304-6eb5e3efca65",
    "metadata": {},
    "source": [
-    "### Filter to `lof`, `missense`, and `synonymous` variants passing filters"
+    "### Filter to `lof`, `missense`, and `synonymous` variants passing filters\n",
+    "\n",
+    "![Screenshot 2024-12-20 at 11.12.17 AM.png](attachment:78f1b020-4f45-4800-82fa-0bdffb877962.png)"
    ]
   },
   {
@@ -1577,17 +1578,29 @@
     }
    ],
    "source": [
-    "var_ht = filter_by_csqs(['lof','missense','synonymous'], ht=drd2_interval_ht)\n",
+    "var_ht = filter_by_consequence_category(\n",
+    "    plof=True, \n",
+    "    missense=True,\n",
+    "    synonymous=True, \n",
+    "    ht=drd2_ht,\n",
+    ")\n",
     "var_ht.show(5)\n",
     "print(\"The total number of lof, missense, and synonymous variants passing filters in DRD2 is: \", var_ht.count())"
    ]
   },
   {
+   "attachments": {
+    "7348e149-4f8f-4eca-aa55-6234293c1f3d.png": {
+     "image/png": 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LQOEFdrWqbqZW1U1f4JWM2V7xrtH70WqthdI0iOuqrnncZhrQba5TikviNajrqeESl6dQzsBB8hBwKu/u0vCuBXdTNcC7WqvvrtGqu3t0R332hdK0KrI7RSShu1sq/M4jcXUIYReKKwdBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHjKlAogV1fulbVXeGVtJleyVyoIc7dhXRPCRrUraeVV1tqSPcEDek212q69TWkW0GDnLTjJuBL0+DuJp1Wa3h3pYZ3V2gl5S16OZmFc0muGvrMT3ZJhZ5abbeFW1yJPO/CkeUoCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBwPASOObCbvccn6Yu1su7XXsnW4KZkFMJtJGml1SYa0m2lId0TddLQZlx1qq0WgmzhHkIr62ZZ1d2VGtTWZ5+13Cfe9foOpBXCaTSs7dHnn9jbLYkdtZpyVUK7haDKIRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA4DgIFDywq7nMzM1aVXe2VzK0sq5vk1699h1Tq6ghzaYu8bRxSUJbnVLc4q5EUPOYTItpZ+9+rbKcqtPPWnV3mQZ31+rLcOgYT66P3tVI34OeLknqpqHtem7tOMZjsjsCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBQzAIFC+xmayHdNV45PD1bMudpMHPPMV61VdRtppV022s4s51b4nXZnUwy8xhVj8vu3oNacXeNBneXatXdJUeDu8dacbe6SPypbkk60y0JTTW06zkut8ZJEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgQIJ5Duw68sQSf9FK+t+ni1ZPx5jFdUEDeo2Fonr6JZEneJTCOoW6CmWwJ0suJuxWqcfteKuTt4NepGZx3ChyfqedNJKu+d4JKGlW1z67tAQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB0iCQr8CuL80naT9pWHeaV7KXa1i3oAFMLZLqqmNBXZcknqxVUzWA6a5MRd3S8MLk9xq9+31OwDvjBw3uasVd33Y9gje/Rzk6XkO6nrYa2j1fA95aidmVyDtTQEl2QwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBYhSIObDrPaTBy8VeOTwtW7wr9AqzC3iVlTV02dolCado6LK9W+JqauiS3GUBMUvJbprtztqh788Sr2TMPxr2PlDAa4/T9+dEl1S44EhVZncSL08BJdkNAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSKSSCmwK73sFbWXaCVdadqWHe1XllBKqRq0NLdSCTegro6xTfSCqnaRys/Ar4sLcq8zivp87ySqcFd7wa994IEv7VCs/sEkaSLPJLYSaszE9otPy8Rd4oAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAKRTIM7BrYd30hVpZ9yMN667SO9RqqfluWlU3rr1LErtrWLetBiwrUxU134ZlaIfsfT7J+FmDu995JWupvlAFqbarr5BbK+0mXeyWCidp+LsC71QZekW4FQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTKlECugV1fhkjaomw5/KGGdVfofee3sq5mKF0NRBK6aqjytCNVdcVTpvy4mYIKaGXdDK22mzZbq+3O8Ypvix4ov2Fwq7TbSivtXuqRCh084koo6MWwHwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFB0AnE5HloDlenLNVD5iVe8K3VUfsO68RqmPEGr6vbWsG5nt3iqUQE1R+vyuEGD2wkpR96LtLr6rs3Q98wqOGfmA0PfSe+vGirXd9StFXYTW2uCl0B4PgAZigACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACxSEQPbCrlU4zUjWs+1m2ZP+iKxrezVerKBLX0SUVznJL4olucWmYkoZANAFPdZck9/CIp6ZL0r70StYSfd8ORRuZQ5++m9nLfHI4OVtcSUdCwMLrlgMW3QgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggcDwEogZ2s7Z45fBX2UfCk/mpeGp3UF0k/lSXJPXxSEJTrXiqv2gI5CZgge4KHTziruySw1WyJXOuhnb35rZH2LYMkazFPkmrqpV2K7okri6J3TAhVhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA4DgKRAR2s/dptdLvtdLpfA1NHs7flblqa1j3dLck9XJLfD1N6pKbzB9geR7t0eq4za0as752Fb2SOdMrvh35ANGqvJlztSp0LZHkM4+Ef/OxN0MRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBIhMIqX/r02q66Uu8kvGdhiV35++crnoauDzbLclneyS+PmHd/Okx2hHQgHd8Q32HztHqzGdpeLdO/lx8u0QyNOibvlTf3/xWhs7fqRiNAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIxCwQEtjNXKdhR6tsuiHm/Z2BFtZNtLBuL4/E1aKsbv70GB0uEFfbJUm9j4Z264ZvzX3du04kTd/hzA3e3AeyFQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEECgmgUBgN3uPT9LmeCV7uU8kH1lHV22trNvHLUmne8RTg7BuMT23Mn+auJouJwCe0Fsr7eYnBK7vbvbPPkmfp+/yXn2XaQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMBxFjgS2M0WSf9Zq5LO17TjwXxcUXUN656hYd2eGtatTlg3H3IMjUHAAuBJZ3gkvqe+W9Vi2ME/5IBI5jyfZCzX9zkf4XP/7swRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBwhRwArtZ27ySscArvs35OHSySHxXC1S6JS4/FVDzcQqGIhBXW98xrbIb30VDu/rOxdq8GzSw+4NXsrZRZTdWM8YhgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDRCMT5MrW67hKvZP+swUattBtTixOJ62hBSo/E1TtSpDem/crAoOnTp8trr70mffv2lSuvvDLHO1qyZIl8++23EdsrVaok9evXl44dO0rdunUjthdHx5w5c2TBggXOqVq3bi19+vTJ12k3btwokydPdvb5/e9/LzVr1oxpf6/XKxMmTJCPPvpIUlNTpXHjxnLuuedK//79JS5OX6ocWnx9reKsl+jbmy1ZC/U9zcphYHC3vstZP/kk/USveGp6xBUfvJFlBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoPgEXAdXpvkOvJct2fM0CBljMVJ3Sy12epVHKpzkEdFf5aVZ4LRFixZigVVrhw4dyjFo+vrrr8sdd9yRK81ll10mw4cPl9q1a+c6rjA37tixQ1q1aiX79+93Djto0CB5+eWXYz7FlClT5JZbbgnsv3DhQmnXrl2e+6enpzsh508++SRibNeuXWXatGliYeYcmwZwD/+QLYffzxbvqhxHhW7Qorxxp7mk4tUeiW9UvoLloRCsIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDA8RRwpy/zindl7GFdlxZTTejhloTWGoAsR2Fde0izZ88OhHVt/csvv7RZrq1ly5byr3/9y5kef/xxueqqq6Ry5crOPlal9uKLL5bdu3fneozC3HjvvfcGwrb5Oa6Fk//0pz/J1VdfXaD9hw4dKv6w7gMPPCAzZsyQv/zlL46FVfz9wx/+kPvl6LuW2Fbfu+5ucdXIfWhgq77W2b/4JEMnXyxVeQM7soAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggUnoA7a7mGGWPNi8ZrxdJOLqlwskfcyVq+tJy1sWPHhtzxO++8E7IebaVt27Zy3333OdOQIUPEjrF9+3b54osvnOFWofaKK66QAwcORNu9UPumTp0q48aNc0KyDz74YL6Ofcopp8irr77q7Dt+/Ph87WsViYcNG+bsY/N//OMfctpppzmBXTumtffee0+WLFniLOf0m7uivntd3BLX0Urn5jQqtN+3SyRzuVeyd8RYPjp0d9YQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBYxZwZ6+Kvbquu4lWOT1FA5N1fgvrrlq1SkaMGOFUjr300kudMOaePXvk66+/lsGDB8ukSZMCF7lz506nz6qtZmVlyVtvvSW33nqr9OnTR26//Xb55ptvAmPDF3799Vd5/fXX5frrrxc7z9NPPy1WmdXr9YYPlUcffdQ5z9atW2XKlCly2223Oeew6rIbNmxwxts1/vOf/3SOZcd74oknZP369RHH8ndYhVl/uNTOa83Cr3ac/Da32y1nnHFGoOLsrFmz5KWXXsrzMHbPZjpv3ryoY//3v/85261yb3jbt2+fDBo0yOl+5ZVXpG7duuFDcl1fsWKF9OzZUxYvXiyXXXZZYKzL9du7EOgMW7B3wVpKSooMHDjQWfb/duWVV0qXLl2c1c8++8zfneM8rq5W2dV30N04xyGhG6zK7gqfZKZGviehA1lDAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEikYgzrcjxgMnicR31rDkiW4R/WXt22+/lbPOOuvIytHfP/nkE/nvf/8rV199tQwfPlwqVqwol19+ubN19+7dTl+dOnVk165dTgDXv/PMmTNl5MiR8p///Ef+7//+z9/tzD/66COxYGdws/NYu/vuu53wbvC21157TbZt2yZJSUny7LPPBjbZOd5880359NNPpW/fvmKVX/3NjmfntzENGzb0dwfm06ZNc5Z79eolnTt3losuukg+/vhjsXDsTTfdFBiXnwULKt9///3yzDPPyNtvvy0PPfRQrrtbZV4ztWq8VvE2uFkA2ir57t+/PyRQ6x/z+OOPOyZ23XbvsQSE/fva/Pnnn3eCz3FxMZa2DdrZwr7WrrnmGklISAjaImKB3379+sn8+fPFQtl5Nn33Elu7JfMkn2Rs1hBuWp57iG+bVtlN9UliB5+4K+UdMM77iIxAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEYhdwS1Zsg90pLklor5VNjwYerbKuP4h7/vnnO5VslyxZEqhCa8HSnJqFaa1aroVzf/zxRyes2r17d2f4n//8Z9m7d29g19mzZwfCulYhdvr06bJgwQJ56qmnnDHDhg2TF154ITA+eMHCulZt96effnKq2VoQ1wKtdi6rODt27Fj55ZdfZMKECVK5cmUnwGtVf6O10aNHO91W4dfadddd58zfeOMNZ17Q3/zBWwu1WhA3t+Y/9wcffCDp6ekhQy1obPdmYWir3hvcLAxrTtbyG9T1H+ePf/yjRAvr+nxawjaPtmzZMmdE06ZNo45s0qSJ0//zzz9H3R7e6a6s72IHfRebxhi+1Xc8WwO7mZvzvtbwc7GOAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIHKtAbOVSK2h13XYuiW/2W0ByzJgxTkDUqs2OGzdOkpOTnWtp1aqVtG3bVnr27JnrtY0aNUr69+/vjGndurWcfPLJzn4WOl2+fLl07drV2WbVZ61de+21TtjUKrJa69Chg1SpUkVuv/12efDBB+XOO+8Uj8fjbPP/ZtV3/VVrTzzxRCdMbBVmrVkA98ILL3SWU1JSZOjQoTJkyBCZO3eu0xf82+bNm2Xq1KlO16WXXurM/fvOmjVLVq9eLc2bNw/eJebljh07BsZaBd1KlSoF1sMXOnXqJG3atBELwH711VdiQWl/mzRpkrNo1YmDHazyroVtrVlot1GjRs5ycf5mgWlrOZ27cePGzvZYA7s2OKG5SzLa6rRGQ7ih2WXnWOG/eTf4JHujjm0ZvoV1BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoGgF3LEc3t1Qw7ottaJp8m+B3Xnz5jm73nHHHYGwrv9Yp556aiAM6+8Ln/sDr/7+E044QSw4a23jxo3+bpkzZ46z/MADD4g/rOvfOHDgQKcyrq2vXLnS3x2YX3LJJYFlW2jfvn1gvXfv3oFlW/Bv27p1a0i/rYwfP97ps7BvzZo1nWUL1vor3lpguaDNKuL626FDh/yLOc4HDBjgbHv//fcDYyyU668AbMHm4Pbaa6/JwoULxYLVFuY9Hm3Lli3OaS1gHa1ZdWNrFtbOyMiINiSiz13R3kmXuOv/9k5GDArq8O0RydIKu97DVNkNYmERAQQQQAABBBBAAAEEEECgkATsJ/jYfzp+euhD8uGHHzo/0aeQDs1hEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEyoBA3oFdzUN6WmiF3SahwcjvvvvOuf127dpFZbCKuTk1C2jWqFEjYrM/NOvzHQlVbtq0SbZt2+aMa9kyemlU/3kWL14ccbwGDRqE9AUHfv0Vgf0DEhMT/YsR8xEjRjh9FipetGhRYGrRQmG0DR8+XLxer7Oc39+Cq8r6w8C5HeOaa65xNk+cOFHS04+UlrVnYWHXLl26iFU49jerDGxVhq3997//lbi42Aoq+/cvrLlVQ7YWHMQOPra/33f55bkAAEAASURBVALbCQkJwZtyXU5IcYunuQ4JfTWj75Mt4tXAbvYOArvRgehFAAEEEEAAAQQQQAABBBAoqMBHH30krz37bzlhW6qcsGeT1F652PkpPr/++mtBD8l+CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACZUwg78CuFj+N07Cup1poKrJWrVoOxd69e6OS7Nq1K2q/dSYlJUXd5naHXs6ePVoW9WjLKVDrr9q6e/du/9BCnVtAd8WKFc4xhw0bJlY92D89/vjjTr+Fir///vsCnTc4aOw3ze1A9evXd6oXW0D3q6++coZ+8MEHzvyWW24J2XXo0KHO+tVXXy1NmzaVnTt3BqYDBw442w4fPuyEou14RdXatm3rHHrdunVRT7F+/Xqn3z8u6qAonZ7q+l5akLxSlI1RurzbRbJ3RtlAFwIIIIAAAggggAACCCCAAALHIPDEE09I9/hMOTelgZzX7kTpWMElKYd3yciRI4/hqOyKAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBQlgRCE7JR7szdwCVxDXRY2Mju3bs7o+fOnRuxl1Wb/fbbbyP689sRXFV3w4YNUXf/6aefnP6OHTtG3X6sne+++27gEBaIDZ/8G8eMGeNfjHmemZkp48ePd8ZfddVVMVfAveGGG5x9LKiblZUlo0ePdtavvPLKkHNPnz7dWZ8wYYJY0Dd4+tvf/uZss30bNWok4fuGHOgYV0488UTnCP5nFX64pUuXOl1t2rQJ35T7ur6TcQ3d4q4XGibPaSffTp94d1NhNycf+hFAAAEEEEAAAQQQQAABBPIvMHXqVKmdtl/Ob//bT7yxo9x5ZneZP/3zwH8Czv+R2QMBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKAsCYTFcCNvzVNPxFMnMhDZrVs3Z7AFPxcuXBiyo1WWWbJkSUhfQVbi4+PFHwz+7LPPIg6xfPlySU1Ndfo7dOgQsf1YOyxQ++qrrzqHmTRpkowYMSJi+uKLL5ztY8eOlUOHDsV8Sgs1Dxo0SGbMmOHsc/PNN8e87wUXXCCVK1cWC+JalV2rjmuB35o1a4YcwwKwOU116tQJjLUxFtotqtauXTvn0O+8846E/zjQLVu2yDPPPONs94/Lz3XE1XWJu25se/i0qLB3jwZ2s2MbzygEEEAAAQQQQAABBBBAAAEE8hKwr4EMOb9XxLB6VSvL2U3ryahRoyK20YEAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFD+BHIP7OpWdy2XeKpFBnYHDBggZ599tiPWtWtXJzD6wAMPiC3bN6uCA6HHwnrppZc6u99+++0ye/bswKEs6GkhVWvnnnuuVKpUKbCtsBa+/PJLJwxr4Vj/vYYfu0ePHs69Wmh22rRp4Zsj1letWiUWXO3bt68ztwFWMfe8886LGJtTR3Jyslx33XXOtf3pT39yhvXv3z9iuFX5Wbx4cdTp/vvvd8ZbaNjGvPHGG866BYntmL1795ZFixZFHDOvDgs42zvw3nvvBYaeeeaZ4q+W/OCDD8revXudbRZwfuSRR5xlM77iiisC+8S64Kmugd2a+n7m/iYfOVyWBnb365SmoV0aAggggAACCCCAAAIIIIAAAsco8Mknn0i1g3vk5KYNox6p7+86yNwvP5OVK1dG3U4nAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFCSBOx7GjbZ90Bs4nschft0co85JmkOspqIKyHypFb91oKnFpa19uGHH8qwYcOcart33323DB48OHKnAvTcc889TqDVdu3Vq5e0atXKCYQ2adLE+bGSVh129OjRBThy3ru8/fbbzqDrr79eEhKiIOhWj8cjN910kzMu2nVMnjzZ2df2t8mu95ZbbnG8bKdbb71V/vOf/zj75+e3fv36OcOtwrCFXc8555z87J7jWKuWbKHbWbNmOdWEcxwYZUNWVpYT9rVjDBkyJDAiLi5Ohg8f7qw7Pyq0dm2xIHbjxo0Dz27kyJFiQeT8NlfikXdUKsS2p++gT7yxF0KO7aCMQgABBBBAAAEEEEAAAQQQKJcC8+bNk2u65PwTfxpUqyIpFeJk48aN5dKHm0YAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHSI/DZZ5/JqGHPydpPJ8vWbz6T7yeOc9atn1Y4ArkGdl1atNZVMbK6rv/U1atXl48++ki2b98ukyZNkvfff99JVD/99NNi4c3w5nLlfCwba8FOaxaC9Tfb57///a/85S9/kc6dO4sFVC0QmpKSIlYd9uOPPxa7jmgt+Di23X/8aGPDtx08eDBQJfaaa66Jtkugz1/p18Kou3btcvrd7ui0Fq610K5d+4wZM5wga4UKMaZNA2cU6datm2NgXVbtOKdAcdAuIYv+6wu/73bt2jnXZ4MvvvjikH2CV+w+rAXvb8t2X9ZuvvlmZ+7/zcLWFupu2PBI1SFL31tVYqvEPHbsWLn88sv9Q/M9d1dyib2rsTSfhnV9h6mwG4sVYxBAAAEEEEAAAQQQQAABBEIFVq9eLTZ9/vnnganHCU0Dg1J37BKbpi9f6Uy2wbZ/8cUXgTEsIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIlTcC+9zFrwhhpl75HmuzbKt0rx0nv6hWcdeu37bRjF3BtuvxAjulFd4pI0u89knTybwFa/ymXL18uNlWtWlXOPPNMf3dgfsUVVzhh2jfffFOuu+66QP+xLuzevVvS0tKkfv36x3oo9s9BwOfzSUZGhiQmavnaAjR7PjmFkNPT02XZsmWyadMmqVu3rhMOLkhl3eDLOjw3Ww69ly2+dcG90Zc9J7ukYl+PJLSIHqiOvhe9CCCAAAIIIIAAAggggAAC5V3AfhrNuqVLJKVWdYk7fECDubvl9XcnyKz7B8oB8cj0X1bLhu075Mym9WXD7r2yZs8BqVujuiRUSJTxm/bxhazy/gJx/wgggAACCCCAAAIIIIAAAggggAACCCCAAAIIlGABK8j5aNfWcmpKk4irnJu6Tt5YtVX+9vRz0rx584jtdMQucKSkbU7j47XCbg4jrDqqv/LsSy+9JH379pVq1arJokWLZMKECU5Y1w7bu3fvnI5eoP6cqukW6GDsFFXAqhoXNKxrB8wprGvb7LidOnVyJlsvjOay91SnHJPnwSfRws++7OAOlhFAAAEEEEAAAQQQQAABBBDIWcB+0s/06dNlzqTx0qtBTWmUkCGNqleV7C0HpXGVirJw2a/ybuoWaVUxQVpXrCBN4nzSpHYV8el/Zt2wdYtM37JbNmaK8xOD7KcF0RBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBkiTw5ZdfSuLOLRrWPTfqZVmI95sVa8SKtz766KNRx9AZm0DuFXZbi1S8xiOJ7SMr7NrhBw4cKKNHj87xTMOHD5dbb701x+1sQKAwBNIWH6mw612R99E87V2SbO90ayrs5q3FCAQQQAABBBBAAAEEEEAAgb///e/iXrVUetStLqe1aBoAmblyjYyZ/q0k6T8vD2Vkyt1d2kjLurXkm7Vb5Jv1W8T+1dm0crKMX75GvPqTbLqcf4lUqF3X+SlFffr0CRyHBQQQOP4CFsy3ydqaNWukWbNmzrL9xsdrgIIFBBBAAAEEEEAAAQQQQAABBBBAAAEEECijAmeeeaYM/V3LkO+DhN/qev3pgheNel9WrlwZvon1fAjkUD/36BG8Wo1Up5zayJEj5cILL5QxY8bIDz/8IBs3bpTOnTvLKaecIv369ZPTTjstp13pR6DwBKy0bi7vaciJ7DumNtEQQAABBBAoYwIWLPCHDKLdmn2CXRaa3ae/Rbtff+XC4JCFfzxzBBAoXQL+j/doH+t2J/bxnp+P9cI+XunS5GqPReDtt9+WjwdcIY2rVws5zNqdu6WRBnLHLF0tky/pLtUSPPLtuq0yW8O6SfqPVNvWuFIFebBLa9l9OE0W/zRX4mvVlW+2bRL7yTZl5e/mEBRWCl2gvHyOV+hw+TjgG2+8IWt/+lHkwD5pWrOarF+3TlxNmsjanXuco8yYMcP5CWJ8zOYDtRwPLY+fb/jv2R67//M2/l1Wjj8IuHUECkkg+HMgWw7/tx9/LxcSNIdBAAEESpFA8N8N0S47v18rjHYM+hBAAAEEEIhFwP+1EP/XQcL3KY1/J9k9bVi+VE675uzw2wlZb6w/fdB1cF9IHyv5F8g9sKs/stGXlftBr7rqKrGJhsDxEvDF8J4Grk3feFf0gtGBISwggAACCCBQmgTsk+evP5oia9euFd/+PdKkRmiYaN2uI0GDtUuXSEqHk6RXr16l6fYC12r3+dZbbzn3aZ1N9EeQ2z3bjyL3t8OZmVK3fkPZnpYuyXXqO/daWu/Xf0/MESivAhaOsj/bmuqfaV79sy24Bf5ca9pM1mX65MYbb4z45m3weFsOPt6atWtC/qwMPp67YVPneOH7s15+BezdaZLgigjrmsj6XXtl5oZtWkU3SZrp30efamXdRTv3S4961aVng1ohaNsOHJKalSpKj15nyHdamXf0f18msBsixEq4QHn5HC/8vot73T7Gv31vjH7cVnP+bmhc0S2ntWnmXEaj9CP/43vmolkyY+sGcbvdpfZz6eJ2La/nC/58ozA+fynpjna/qUsWO/8us8/ZrNnnWdbW6udpTqtcVdxVqvH51RENfkcAgRgEgv9s8X+da53+Zxqf/mcaa4F/v5Xyr3PFQMEQBBBAAIGjAvbv42+++cb53NP/d0Mwjv/vhm8qV5PeF1/Kv9uCcVhGAAEEECh0Aft+tX09JK/vX5W27zfZ9917BP2EwdzgLLRrfz+H/8fK3PZhW6hAroFd32EN7KaF7sAaAiVNwN5TsSmG5orXwK5ONAQQQAABBMqKgH3y/MQTT0jPE5o6IQNv+r6QINqaTeucb2bYF7TOvO7GUvvFKv99NqlRVe9nr/RoVMcJ62YHBXYPHjos83+YJz/v3i/Ldu13qhcS2C0rbzr3Ud4E7M+sGR9P0T/bmkkTrXYY/J8RvGn7nD/XvtZqiGPmLnb+XMvriwLBxzPLRhrI8rfg421MIlDid2F+RMD+/gl+/8Jd1u87KD3q13C6N2kod93e/TLk5JbhwyTe45bsjHSnv4e+17e/MzliDB0IBAv4P/cp65/jBd/z8Vi2vx8a6deJrju1U8Tp/R/79jH75LSvnW8Q87llBBMdQQLBn28UxucvQYcukYt2v1+Nfcv5ezI77D+Orj7671ALT3gaNiOwWyKfIBeFQMkUsD9b3vzPSyFf5+peV/9TgH69y1pZ+TpXydTnqhBAAIGSKeD/93H3OlWdzz3Dvwfi/9rezJXfOP9ZjH+3lcznyFUhgAACZUVg9OjRkr1xTZ7fvyrL32+yr5va3895fW+urDzzoriP3AO7+zWwe8BXFOflmAgUmoB3v098B2M7nCtZA7tJrtgGMwoBBBBAAIFSINC0aVPpd9vt0izRrZ8Yr5MNes2exCpi1UeaaPURd/0m0qxle2mm/T179iwFdxT9Eu0+H3roIefHFH+78Eenwq7s2RkyuG2DBnIoI1MaH06Xy2vXK9X3G3JjrCBQDgXsz6vsPbucH0/u/3PNz+BpUEXc+uec/dn20FkXiv35kFcLPp6N3aj7+5sdL+XE9uLWH33eq31HfzdzBByBM844Q/735ONRNRrrfyKx/0Cyfs+R8MApdWvIB6u3yKzNO6V7/ZoR+3gSEmX97j0ya6WGgNu2j9hOBwLBAuXlc7zgez4ey/b3w4wNa+SZT7+R7vof4KzZx7a/2certQ36051uKMWfS/vvh3nRCgR/vhHt8xf/5xuxfv5StFd77Ee3+21YtZK4Dhz5e9D+PWqtadMjVTDtc6szdNmllc5oCCCAQKwC9mdLVlaWuLbYn6T6d7BOZfHrXM7N8RsCCCCAQEwC9u/j6667LuJ7IP6dA1/b068V2t8jNAQQQAABBIpS4Prrr5e1WlBG9Osh/n+v+M8X+DuplH6/yV+13n8/Oc1dGruL5XtzOe1Pv2YXN12eSyJXiw4lXumWSpfHiSsBLgRKnoAvQ2T/hCzJmOIV0V95tYSL9X2+yiPuioR287JiOwIIIIBA6RJITU0Vm/wt+MdQpKSkON3+uX9MaZ3779M/999H8P0FL/u3M0cAgdIlEP7nWvjV28d5fj7Wczue/zj+efi5WC/fAmd17SLDL+sj9mOegtvMlfojGectkO/Wb5UXzugotZKTZPK6bbJp7wFJEJ90Cwrt7tQq8Ev2pUmFZidIdu2G0kt/RGOfPn2CD8cyAlEFwv/sKsuf40UFKIbO6dOny1dffSWebRudszWtWV3Wr18njRs3kTVp2fqf4BqLq0p1GTBgQDFcDaco7QLhH7PB9+P/PMM/D95Wmpftnq3558H3F7xcmu+Ra0cAgeIXsL+fgxufAwVrsIwAAgiUT4Hy+Ll2+XzS3DUCCCBQ8gXK4t9Jdk/ndu8qi/96d54P4KTHhsmKzdvyHMeAnAVyD+zqfvG9XVLx6jiJq1OyAo6bN2+WDRs2yCmnnJLz3ZWBLYcOHZLdu3dLrVq1JDExscjuKCMjQ7Zv3y7JyclSvXr1IjtPYR84a6tPDrybJVkzfXkf2qMB9L5uqXxZnP6X7LyHMwIBBBBAAAEEEEAAAQQQQACBkSNHyqavP5Ee9WpIt+ZHqgaaigV2x0z/VqolxsuuAwflvm4dpHmt6jJj7RaZsW6LtKiSLBv2HZIN+w/Kcv0f9T0vvkzanHqaE9QlwMR7hUDJE/jyyy8DF+UPBTVv3jxf/zkkcAAWEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBUiTQokULmTbwKmkUVrwk+Ba+X71OHpv3i3z99dfB3SznU0CTi7m37C0i2dt9JSqwu2fPnkBp5TfffNP5EQi530Xp3Tpw4ECZOHGizJo1S7p06RJxI3PnzpX58+dLlSpVpH///oHtM2bMkKVLlwbWc1po1aqVnHXWWXL48GHp2LGjcxzbz4K7paFZYNcbY2jfVVnEXU2D54R1S8Oj5RoRQAABBBBAAAEEEEAAgRIhYP8ufzU7W8ZMGi+b9uzTL1ZVkYbVqsqc1PXyy4498vc+p8rnW/fK5LXbpEd6liTFeWTgSSfKTK28u/FQmmRXSJYftq+VMUMeFgv/0RBAoGQK2NfHaAgggAACCCCAAAIIIIAAAggggAACCCCAQHkU6Nevn7w7f67cd87pOd7+U5/MkKHPv5zjdjbEJpBnYNe72SdZm3yS2EYP6I7toEU9KisrK3AKC5oWZ5s9e7ZMmzZN2rRpU+RBYUujW1jXPiDCw7pm8NRTT8mjjz7q3H7lypVDAruTJ0+Wl1/O+wPkkksucQK7VatWlWeffVYGDRokzz33nAwdOrQ4WQt2Lq9I1kaveLfEUF1Xz+Cq4RJP6SkeXDAT9kIAAQQQQAABBBBAAAEEECh0Afu3ss/nk2VLf5SVew5KSlyWpDVqIfLrWmnTormc3q22jJw5T1akp8vG3fskJU1/EkxSstSpXVua168jaytUJaxb6E+FAyKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBhCNx8883y6P2LZa4WKzk1pXHEIT9YuFQOV68jZ599dsQ2OvInkGdgV/ZpKHKdT7L3+jTsqNVJS0CrVauWLFiwQOzH011wwQXFekWLFy+WJ598Uq666qoiDexma/Weu+++27k3/9x/o+vWrZMbb7zRqbrr7wufX3zxxdK4ceQHj3/cuHHjZOHChVKtWjV/l1x//fVy3333yWOPPSY33XRTrvsHdjqOC9l79L3Ud1P2x3YR7tpaXLdmyXiHY7tiRiGAAAIIIIAAAggggAACCJQUgdtuu01WrVrlTHZNPfXHQ81d9qtkxCc6lziw5ymSumOXrN6+y1lvXruG2P9Dn7dmg5xzzjlOH78hgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAiVNwH5CYL8//FG+Hj9GNu9dKg30Jw02rFbFucx5a9bLp9sPlI4CoCUNNsr15B3Y1Txk9iqfZK7zamDXE+UQx6erQ4cOYlNZbfPnz5dly5ZJy5YtpVOnToHbtG8OnnrqqbJ//36nyu+DDz4ot9xyS2C7f6FPnz5iU7S2a9cueeKJJ5xNf/jDHwJDEhISnGq+r7zyiowfP17uvffewLaSuJCZ6pXs1XplsRTY1VfXXV8r7NYisFsSnyXXhAACCCCAAAIIIIAAAgiUBoEWGtK1yd9OOeUUeW/BQhl8Vk+nK6VWDbEpuD3/xUzp3uO84C6WEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIESJXDuueeK1+uVGR9Okl92HZLELbvkVP0+yI9xe+X6224U2047doG8A7t6Du8GDeyu8ElCS5+4k13y008/yeuvvy41atSQv/71rxFXsXnzZnnqqaec/n/+85+SnJzsLFsA1YKgVqV237590rlzZ+nWrZtceeWVIcfYuXOnPP7441KpUiUZMmSIjB49Wr788kux/s8++0wsWGrH3bZtm9x///3SqFGjwP5ZWVnyzjvvyLx58+Tnn3+WBg0aOOe5+uqrpWnTpoFxtjBmzBiZO3eu/P73v5fKlSvLBx98IDNnznTO26VLF7nzzjudZRubmpoqw4YNk0WLFtmqfP/99zJ48GBn2a6xfv36zrL9Zsf59ttvnbH16tWTjh07OqHaunXrBsbktfDJJ584Q6699tqQoTt27HDCuha0tUq/FuDNb3vppZecY1x44YVi31wMbpdddplYYHfKlCklOrDrPeiTDH0nvZtjSeuKuKpqdd0GLuf9Db5flhFAAAEEEEAAAQQQQAABBBAoqMCAAQNk0KUXBAK70Y6TeihT/nzGGdE20YcAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIlBiB888/X0444QRZuXKlMzXV9YHni9NXYi6ylF+Ia9PlB2JKPLrbuaTi7z2S2NrtBGX9Idnly5eLlUQObhb4vOuuu6R79+7y9ddfO5tGjRolwdVcg8cPGjRIXnjhBYmLO5Iftgfetm1bJ0RryeyJEycGhu/du1eSkpKkVatWTojWKtFaINaaBXgvv/xysb7wZoHcL774wgnv+rdZZVoL995xxx1OKNiq1ga3lJQU51i275w5c+T00+2HWUY2CyC3adNG0tLSxO5l3LhxEYPsGFOnTpWuXf+fvTuBk6Mu88f/zExuCCEc4QgkHAnhCiKnHMohKB7giSueKArqf13wpyuygtd6rKL+dHd1V5cVgRUE8aeAKIorh4ggICBguHNzhCsk5M7M/L9PhR4nk05mJukkk8n7+0qnqqurvlX1ru7p6u5PP33wCrfVm/CSl7ykqrCb4d/Xve51HbNMmzatCjwff/zx1bR77rkn9t9//8oqA83dtaeeeipGjx5dzfbHP/6xWrbzMll9N0PG2WbMmBGjRo3qfHOfGV94X1vMv7Q12u7v0d03WvZpimFvK/ff3Zr7zD7YEAIECBAgQIAAAQIECBDY8AVeeeB+8V9veVVsO2L4Cjtz57SZ8b2ZL8SFF164wm0mECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIbFwCzdGjGrulyu7kUtH0nrZoK/neDHHWAqNXXHHFCmKXXXZZNe3kk0+uhhkSrYV1M8ib1WkzVJtVcrN9//vfjz/84Q/VeOf/MkCbYd1Pf/rTVWXdrFqb1XVX1rJybPab25dB16zoe/nll8fhhx9eVZT9+Mc/XnfR7373u3HIIYfEtddeW1Xc/eQnP1nNl1V1s89sGaDNcPI555xTXT/iiCOq6zmt9nOYWf03w7q5/qyQO3v27Ljjjjsqq9yXs846q1q2u//mz59fbXvOt++++y43+5gxYzrsl7uhh1cyGJ0tQ8AZ9O3asmpyLdCbYeC+2PI+uPgv5b44tWdh3byPt+zcFANLhV2NAAECBAgQIECAAAECBAg0UuCAo4+Ny/+84uvnJ56fG1+95obqV30auT59ESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIbJgCA5q2jGh/sgcbPz9iyZ1tsXhcUwx5SUu85z3viauuuqoKqH7sYx/r6ODxxx+Pm266qbqe1W6zZdXWr33tazFixIjIEG9T07LgZFbGzaDu1VdfHRnGzRBs13beeedV6+o6vd71DM7mel796ldXFW9znpyWAdQM5OZ2ZRh22LBhyy0+fvz4yJBxbXqGZGfOnFlV381wcbYhQ4ZUlYS33nrr6vpWW221QmXhDPxme/e73x1HH310NT5x4sTI6sLnn39+1X9bW1s0N6+6ymvnSrm18GzV2Rr+l8fm3HPPrXqpBY/rdVnb/87bUW++9TKtLWLRA22x9O4ysqBnW9BUDtnAEtht3lRgt2di5iJAgAABAgQIECBAgACBngqccsop8fmPnxG/vu/BePVeu1WLPTFnbnzn+j9G89jxy/1qTk/7NB8BAgQIECBAgAABAgQIECBAgAABAgQIECBAgED/ExjQsmtTLJ1VKpX2oFhp29QSlry9LQaMbq5CscOHD48777wzshLtzjvvXOlceeWV1fDtb397bL755tX4hAkTIi/12qte9aoqsDt9+vR6N8eb3/zmutPrTaxV9O16W+dKslntd+zYscvNcsIJJ3SEdWs3vOY1r6kCu4888khtUrfDWr8XXXRRFT4+5phjYsCAAVVQ+Ywzzuh2+doMTz/9dDXayLBudvjNb36z6jerI++3337VeL3/ttyypLhLq21HvXnW17S8ry7+U6muW//usuJmlYxuy/imGFACuxoBAgQIECBAgAABAgQIEGi0wG677RZvP/XD8ePvfTeemjsvttls03j4hYXx0KARce5Xv9ro1emPAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgAxVoHrB7CTIuy9V2vwtLIpbe1R6L/twag9oHd1S+veKKKzqWvfTSS6vxd77znR3TciQr22aV2QzVHnrooVWAN4Ohp59++nLzdb4yatSo2HTTTTtP6nY8A8Rf+cpX4k1velNkUHeHHXaIQYMGrXK5/HCta6tNW7p0adebVno99zmDy7NmzYoMAW+zzTZx4oknxg9/+MOYPXv2SpfrekMtKLvFFlt0vWm1r2fF4G9/+9vV8mefffYq++mrgd22+e2x8I7WWPqXki4v98WetKaRpbpuuY8P2HrVVY170pd5CBAgQIAAAQIECBAgQIBAPYH8YuzfldDunwZuHjeUXyhq23P/6hduVvbl5Xp9mEaAAAECBAgQIECAAAECBAgQIECAAAECBAgQINC/BZoH7d4cLeNKaLeHBUjbnypVdv9Qqpw+0BbvePuyUO5ll11WKT3++ONx0003RQZtX/nKV3bIZfXcl7/85XHaaafFxRdfHLfffnt1W6M/uPrP//zPOPjgg+Ozn/1sVbX3ySefjAyfHnHEER3bUm+kubkxYc7tttsubr755upDuYkTJ8bcuXMjw8ynnnpqZXL11VfXW/0K0wYPHlxN601YeIVOukw499xzqyn5IeJLX/rSLrcuf3XJkmVp2KwO3Gdaa8TiSW2x+A9t0f5MD7eq3KdbdmuKQbs1R1Mf2pUebr3ZCBAgQIAAAQIECBAgQGADEsgv7p533nlx6j9+Kv7xH/9xpb80tAHtkk0lQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBooEDzwO2bY+DEpshKpD1tbY+USqfXt8VLRr00dhu/WxXAnTJlSlx11VVVF+9973ujc9jzBz/4Qdxzzz0xfvz4uPbaa6sg6wMPPFCFW7/+9a/3dLWrnG/OnDnxD//wD9U855xzTjz88MMxY8aMuPvuu+Oaa65Z5bKNvLFWNfiOO+6IDDBfcMEFHQHZrPo7b968ble31VZbVfM880xPk6mr7nLq1Knx3e9+t5opbbprtQq/GbzuE60U1F08ta26z7VN6fkWNW1ZquvuU6rrbteYQHbP12xOAgQIECBAgAABAgQIENhYBXbfffeNddftNwECBAgQIECAAAECBAgQIECAAAECBAgQIECAwCoEmpsGRQyeWKrs7pnlSFcxZ+eblkYsvbuEdm9oj4+edEZ1S1aS/clPflKNv/3tb+88d1x33XXV9Q996ENVtdtaBdmcWLttuQVW48ptt91WLTV8+PA466yzYsyYMR29ZGi30e35559focvHHnssJk+eHIsXL65uy/DuSSedFL/+9a875r333ns7xlc2kstlmzVrVjSiyu5Xv/rVqr83vOENse+++1bjq/ovKxNn6yuB3SVPtMWC61pj6T0luVvuez1q5b48YO+mGLxXqa47sEdLmIkAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsFYEqtKjA7dtjkH7lWDjtr1YRykUu+SWtnj9dm+O7Vt2qCq43nDDDTFx4sTq0rmncePGVVd/9atfLRdAzaqvV199dedZV3t85513rpadO3duVbm31lGGaD/4wQ/Wrq7xcMSIEVUfv/3tb6MWbK11mhV0J0yYEB/96Edrk6rhpEmTOq73pNJOLbCbCz344IMdy67OyCOPPFL9JGcu25Pquu3t7VXF5Jx/m222ycF6bUufbo8FN7bFkj+VsO78nm9K0/ZR3acHjFJdt+dq5iRAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTWhsCyNGOpRpqVSAfuX64O6/lq2p+NGHLX8DjzmHPihanL0pQf+MAHVujgrW99azUtQ64ZAj3hhBNihx12iDPOOCPGjx+/wvyrM2GXXXaJAw44oFr0mGOOiUMPPTSOPPLI2GmnneLZZ8uGNqjtt99+HT3tuOOOcfDBB8fMmTOraZ/4xCeq4fnnnx8ZID755JOrfT3iiCOq6R/5yEeiFvjt6KTOyIABA+K4446rbrnrrrvqzNHzSV/5yleqmTNMvM8++3S74JQpUyJDz9l6Uo232w7XYIbW50pY96bWWFICu/FcLzrapIR1Dywh9N3L/bmnVaN70b1ZCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQK9EVgW2C1LtIxsiiEHN0fLhKaIjqndd9X+ZHu8ZqsT4qSd3hujWraNN7/5zSsslOHTyy+/PEaPHl2FQa+55ppYsGBBnHvuuXH66adX8w8cOLBjuaamsg2r0bKC71ve8pZqydtvv72qtJth2WuvvTaGDx9eTc8wbK21tCxLc3aeVrutNq02rE3PCroXXXRRRxXhO++8M+bMmVPdfOKJJ0Zuwx577FGFeC+++OLIfc3w7ne+85341re+Veum2+HrX//6ap6bbrpplfOuymrq1Klx4YUXVsufffbZq+ynduMtt9xSjWbIevDgwbXJ63zY+mwJ6/6+NRb/ri3an+rF6jOju0dTDC6B3ZbNV+9+1Iu1mZUAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0K1A0+LFi9trc7UviVhwS2ss+FlrtM+oTe3ZsGm7EpJ8ZVMMPawlWrZceVDyySefjEWLFsX2228fXcOwPVtT93MtWbIkpk+fHiNHjqwu3S+xenPMmzcvMjA7bNiKZYkzkPzYY4/FqFGjOsLCvVnLtGnTYty4cdWy2c+6Cs++8Y1vjF/+8pdxwQUXxEknndSbTW7YvEufXlZZtwrrPtG7bpvGRAx9c0sMPbAlmv6WAe9dJ+YmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDRQYLnAbvbb+nx7zP9tqWz661LZdHbv1tS0TVMMekUJ7b6iJQaUcW3NBN73vvfFj370o/jZz34Wr3vd69assx4snWHqHXfcsaoIfPfdd8eQIUN6sFQDZynR8SVPtMXCG9ticbn0qrJu2YymkRGDX9McQ48uofHN3P8aeGR0RYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKyBQHPXZVtGNMWQQ5pjwP4l8NjLvGb7k+2x+H/bYv6vlsaSqW0R5Z+2+gJf+tKXqoU/85nPxNKlS1e/ox4u+ZWvfKWa85vf/Oa6D+uW+8riKXnfaY1Fv+t9WDeGRgw4sNx3D24W1u3h8TYbAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsG4EVgjs5moHbtccQ44qVUr3LqHdgb3bkPZnS/DyxvaYd2UJXv61NdoXlbKp2moJbL/99vFv//ZvMXz48LjjjjtWq4+eLvTcc8/FXXfdFaeccso6qebbebvyPrLw3taYf0VrLPl9ub881/nWHoyX++iAfUpl5yNLZedt696le9CJWQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECa0egafHixXUTte2tEYvuKSHKn7dF2/1llt5Wyx0U0bJbUww+sjkGv6RUPS2VezUCXQVaZ7fHorvaYuH1rdH2ULl1Sdc5urneUu5ne5aw7htbYsieJaxbrmsECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgb4kMGBlG9NUgo+D92iJ9oURC5aUMOUjZc7ehHYXR7Te1x4Lnm+L1ifbY8jBzTFwtEDlyrw3uuklEL5kRgnq3tIWi8ul/bEiUDc6vgqZcndqHhcx5LgSCp/gvrUKKTcRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC61FgpYHd3KamwSW0W6rjti+NWPiLEtqdXCb2JlRZ5m2fXiqoPt8erY+3x+BDy2X35mjeVLXd9XjM1/uq2+aW+8T9bbHoD22x9J5yJ5m7GptU7kLNu0YMfW2prDuxOZpKRWeNAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINAXBZoWL17cbQS3bX57LLy9VEP9ZQntTim70ZtKu7W9LtHg5rERAw9qjiH7N8eA7UvIcpVx4dqChv1FIIPfWVV30R1tseRPbdE2vexZmdbrlpV1dymVdTOsW+5LzUMFwHttaAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgnQn0KLCbW1OFdv9cQrvXlNDuI2VC62pu42YRA/ZqikEHNMfgPZujZYsStpS3XE3MDWSxEglf+kx7LP5rWyy+rVTV/etqVtXN3W0pYd3xpbLucS0xeN8S1h3mzrOB3AtsJgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ2GgFehzYTaG2he2x6O4M7bZF64MldLlkNd1KhdSmbZti4L4luPvSctm1BC83FbxcTc0+vVjb3BLUfbhU1b2zPZaW+077k3lHWs1NHljyurs3xZDXlCrNe5cKzUPcZ1ZT0mIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAOhToVWA3t6t9UcSiSa2x8NpSKfXeEtpdsAZbO6hUSx37YnB3YnMMLOPNQ4Uw10C0zyyaFZmXTClh3XvaYkkJ6rZNK5u2eA02b1ipzDyxhHWPLZV1dy9h3XLf0QgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECG4JArwO7uVPtraW47iNtseB3rbHkjhLafX4Nd3VoqZy6Swnu7lMue5bg7o4luDtMcHcNVdfL4lVQd1oJ6v61BLr/0h6tk9cw1J17sXnEwAOaYujRLTFo51KeuWW97JqVEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRWS2C1ArvVmkoOc8nMtlj4h7ZYfHNbtD9RppZpa9Q2WRbcHbBXUwwqVVQHjinB3U0Fd9fIdB0t3PZCCemWirpL7i9B3b++GNSdt4YrL4e+afuIQYc2x5DDyv1huxLWdXdYQ1SLEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAutaYPUDuy9uaetz7bHwz62x6Ia2aHukTFzSgF0YFtE8tlTb3aMpBowv4d1SVbVl85LULHlNrQ8JtEXk8V88uS2WPNQeSye1R9vUBlTUzV0cVMLb4yIGH9kSg/d98fj3oV23KQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBngqscWA3V9S+sD0WPVCq7d5UqqveXQKbs3u6+m7mK6HN5lJhtWVCCe+OKxVWdy4B3m2aommwMqvdyK3Vm/N4L3myXCaXkO7D5Zg/0B7tj5dVLm7QakdGDHxJUwx+eXMM3q3Z8W4Qq24IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB9SPQkMButeml2uqSx0to97ZSbfWPpdru9DJ1aYN2qlTWbdqyBHdLYLdl1xLe3akEd3dsjgEjS3C3pUHr0M0qBdpbSzXdZ0tId3oJ6E4pQd1HSjXdR0tQ97myWDn2DWkDsrJyCeu+rDmGHFgC2tuWA6+qckNodUKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAisP4HGBXZf3Ie2F0q13UltsaiEdlvvezHQ2cj9G1oynNuV4O7OEQPGvhjcHV2ub1bCu8KdjZSugritz5dw7mPlMq0Edae2R+vkMvnxUkV5YWNX1bRFOZ57N8WgQ0pV3d2bo3kTVZQbK6w3AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYH0JNDywW+1IVtt9rIR272yLxbeVSqyTS8Bz0VrYxeGlwO4OTdE8poQ9y3DA6FJ1t4R5m0c0RVOp1qr1XqC9VEVunV0CuiWU21qO4dLpZTithHRnlGP4Qu/763aJISVnvUsJ6h7YFINfqqput15mIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBDY4gbUT2H2RoW1Beyx+pFxuL8HPu9ui7YlyQ+taMMpirJuW4Of2pdLuDiXEm8NtS4B3mzLcqgR4h5YZFGytD19yuHmcWp8qId0ny/CJDOqWy4wXK+lmSLfM0/DWkserBK1fUirq7t8cg3YtIeshDlLDnXVIgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIrHeBtRrYre1d67MltDupVNstFXeX/rU92p8pt6yNEGhthaVqa9PWy4K7zSW42zKqXPJ6Ce+2bLGRB3gzoDu/BHLLMWl9ulxKULd1Vpn2YlC3/emCuLAGuRaGmZ3eqgR19ypVdfctQd09mqNlpKDuWpDWJQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINBHBNZJYLfa17YoFVzbYtG9bbHkLyUk+mAJ7s4ut6zN4G6uOLOgWX23hHabR5Xh1mW4ZQnvblnCuyUo2rx5GW5WqrsOzpn7X2tfVIoaz2mPttnFPEO65dJWAtNtJajb9uSyYaytKrqdOTOoO7K4T2iKgfs0xeC9WmJAOSbR3Hkm4wQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB/iew7gK7L9q1Ly3B3cdKcPe+Ety9rwRIHyqJ3XUR3K0duwzwDi3h0S1KVrSEdptzWIK7VZh0RLlewrvNw8u0TcuwXJoG1hbs28P2xSV8O6+EcOfm5cWQ7vNlvNi2P1eGz5bbn3mxunFW0F3bQekaVy2oO74EdfcuVXX3ao4B2zZH04DaDIYECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgf4tsM4DuzXO9iURS0pwd/Gktlg6aVlwt72ESqNU4l3nrRbi3bwEeTO0O6IMNyuh3eElsJvh3WFluMmLwwz7lkvzkDKtXJoHla1tWctb3FpYSqXc9kUleLughG5L4LZ9QZk2v4zPK8MS1G0vVXLbSiXd9jk5LLeXsG5VwbjMt87CuZ0ZSuXcpqxkXIK6A/YoFXX3KEHd7UpQdwMJQHfeFeMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgTURWG+B3dpGVxV3nyzB3ftLxd0HSnD34RI0fbLcWgK9671lkLcEcjOsG+XSlBV3hy0L7GZot2lohnbLMEO7Od/Acr0EUqvqsVlBtlyaMsyb/WSl2RJircbLoArRlnBye1a6zWEJ5UZeSgXiNGlfUhxK1dx0aK/CumWYYd0SwK0u88uwhHQjw7oltBs577qqmltWtdKW+79t2fVxJai7e6moO6EEdUepqLtSLzcQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC/V5gvQd2O4RLaHXp0+2x5JES3C2h3aXl0ja9JFAzlNrXW4ZyO0K7ZTyryLaU8O6Asv0Z3K0FdavQbvmvTG7PpG75VwvuLgvqltuWlolLm6rAbhXCzSBuBnn7etu07PKYpmgZV3Z/XHMM3LUEdbcs+5P7rhEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIENmKBvhPY7XQQ2uaW4O60ZeHdpY+WqrtTShXZp8oMfaHqbqft3OhHs6rw1iWou1OpprtLUxXSHVhCu82lErFGgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwTKBPBnZrB6e9BHSXPlWCu1PaYunkEtyduqzqbvvsMseGUHW2tiP9aViqCTeNLIVzdyzVdMeWkO7OJay7U6mmu3VWFO5PO2pfCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKNEejTgd3Ou9g2vz2WPpaVd9uidXoJ706PaJtZKu8K73ZmWjvjtZDu6BLS3bFU1N2xOQaWsO6A7Uo13aGq6a4ddL0SIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC/UVggwnsdgZve6EEdx8vAd4M7pbQbmsZbyth3vZnylyLOs9pfLUFBpdKuluVcO72JZRbgrktoyMGlqBuFdLdREh3tV0tSIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGx0AhtkYLfzUcrKu62zSoC3BHZb8/JkCe8+GdFeprXPKXMu7Ty38ZUKDCgB3RHlMqoEdEeVoO62pYJuCevmpSWnqaS7Ujo3ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRWJbDBB3Y771z7kojW2csCvEufaCtB3oi2p0qA9+kS4H2mBHhnl7nLPFoRGPRiQHerEsbdsgR0q6BuCehuUwK6edm8KZpKiFcjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYM4F+FdjtStG+qAR4nysB3qf/dmkrwd2250qANy8l3Ns+tyzV36vwDizh3E3LZWQJ4Y6MaM7LFiWUu3W5lMBudcnbSohXI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaKxAvw7sdqVqL8HcthdKeDdDvM9mcHdZeLft+RLcnVMCvNVlWYi3fX5ZekOrxpvB3E1eDOduVgK4I8r4ZiWcO6JU0d28VNEtId3mEsythpuW21u6CrlOgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQaIGNKrBbF6+1hHjnlwDvnBLefb6M5zAvL5QA77xyKQHfalgCvO0LyviC0svCMl4usbhc1lV13gFlXaUCbtOQpoih7dE0tIwPLaHbYWWYId1NyngZNg/PSwnmZki3hHVbcljmC+HcAqgRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBNa9gMDuyszbSyi3BHLb5pXwbl5eDOwuG2Zgt4R3F5VhuWRwt31xuV4L8JYQb3sJAldh3hxmX+USbTmeI6U1lxBt/iuXHFaB2hLKrareZjg3xzOgO6gEccswBpfxvJTAboZ1mzOoW4K4OWwuYd28VPNlXxoBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECfERDYXdNDkRV6l5Qc7qIS2C3DWFqGWXU3Q7t5yZBuFdQtwyq0u3xgtwrrNpfwbV5qQd0BJXX7YmC3uRbYLbdrBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECG55ARkS1NRFoKRVuyyVK5dtlrTZck04tS4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0F8E1G3tL0fSfhAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECPRJAYHdPnlYbBQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEB/ERDY7S9H0n4QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAj0SQGB3T55WGwUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAfxEQ2O0vR9J+ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9EkBgd0+eVhsFAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQH8RENjtL0fSfhAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECPRJAYHdPnlYbBQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEB/ERDY7S9H0n4QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAj0SQGB3T55WGwUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAfxEQ2O0vR9J+ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9EkBgd0+eVhsFAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQH8RENjtL0fSfhAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECPRJAYHdPnlYbBQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEB/ERDY7S9H0n4QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAj0SQGB3T55WGw1ycnaAABAAElEQVQUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAfxEQ2O0vR9J+ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9EkBgd0+eVhsFAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQH8RENjtL0fSfhAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECPRJAYHdPnlYbBQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEB/ERDY7S9H0n4QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAj0SQGB3T55WGwUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAfxEQ2O0vR9J+ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9EkBgd0+eVhsFAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQH8RENjtL0fSfhAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECPRJAYHdPnlYbBQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEB/ERDY7S9H0n4QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAj0SQGB3T55WGwUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAfxEQ2O0vR9J+ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9EkBgd0+eVhsFAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQH8RENjtL0fSfhAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECPRJAYHdPnlYbBQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEB/ERDY7S9H0n4QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAj0SQGB3T55WGwUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAfxEQ2O0vR9J+ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9EkBgd0+eVhsFAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQH8RENjtL0fSfhAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECPRJAYHdPnlYbBQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEB/ERjQX3bEfhAgQIAAAQIECBAgQIAAAQIE1pfAAw88EFdddVW0z34mdj/40Nhtt91iwoQJ62tzrJcAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCPCaiw28cOiM0hQIAAAQIECBAgQIAAAQIENiyBX/ziF/Ff3/hajJs1OcbNfiy2fvjuOPPMM+PBBx/csHbE1hIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKw1AYHdtUarYwIECBAgQIAAAQIECBAgQGBjEPjiF78Yhw5cEq/aeft49V67xT5DmmLnBc/Gf//3f28Mu28fCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEeiCwXgK7jz/+eNx222092Ly1O0tra2vcdNNN8dxzz63dFfWi9764Tava/NmzZ8fMmTNjwYIFq5ptudty3lxm1qxZy013hQABAgQIECBAgAABAgQIbGgCv/zlL2PrhXPjuL0nLLfpHz3q0Lj9d9fGQw89tNx0VwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBDZOgdUK7D7wwAPx3e9+N37wgx90q5Y//5jzXnzxxdW8GfAcO3ZsHHbYYXHJJZd0u/zanOHLX/5yHH300fGyl70s2tra1uaqetx3X9ymVW38N77xjdh5553j29/+9qpmW+62G2+8sVrmne9853LTXSFAgAABAgRWT+Dhhx+OvFxzzTXVpV4vebtGgAABAgQINF4gq+ueedwRK3S87YjhcczYbXv03skKC5tAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ7wQGrM4eDR06NM4444xq0UMPPTR23333lXbzrW99K84777x4+9vfHu94xzti6dKlHfP2piprx0INHFm4cGHV27x586rAbnPzauWXG7hFEetimzIoPWnSpHjd614XBx98cEO2v68EnhuyMzohQIAAAQIbiEAGdH/9619Xletzk7OC/bhx4+K4445bYQ/yi1Z57pW377rrrtUwxzUCBAgQIEBg9QXyuXjzebNjv7Gj63Zy4v4T47Sf/aZ8seaU6rm37kwmEiBAgAABAgQIECBAgAABAgQIECBAgAABAgT6iECtGFhtmLkC2YLGHZzVCuyOGTMmjjrqqLjuuuvi8ssvj7PPPrvuFmX49NJLL61uy7Butq222iruuOOOmDJlSrzmNa+ppq2v/z796U/HvvvuGwcccEAMGLBaFA3f9HWxTVdeeWX89Kc/jdGjRzcssNtwCB0SIECAAAEC3Qr8+Mc/jltvvbWaL0O4eU6Tz+/12jPPPBNZ5X7RokXVPDvssEO8733vi1e96lX1ZjeNAAECBAgQ6IHAbbfdFm87YOJK59x+881i5yEDOr5Us9IZ3UCAAAECBAgQIECAAAECBAgQIECAAAECBAgQWM8Cv/nNb+L6q34er9x913iyFAyb/NSzceOQYXHk8W+ULWjQsVntlOp73/veKrB70UUXRYZMm5qaVtik//3f/425c+fG8OHlZyCPOabj9okTJ0Ze1ncbNmxYnHjiiet7M5Zbf1/cpuU20BUCBAgQIECgzwi8/vWvj7xkywBuXlbWMpx77LHHxowZM+Laa6+NOXPmxGabbbay2U0nQIAAAQIE6gg8+uij1dRHHnmkGuZz6n+/5mUdc05++tlqvDY8evdxcdi4sfHb3/42jjjiiI75jBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE+pJAfuZx8+UXx16xOMbMeTLGDB8QY5cOiZmzZ1fTMx+amQNtzQRWO7B7/PHHV2uePHlyVTE3K7p1bZdcckk16bTTTluugu2Xv/zlmDVrVnziE59YIViS1d+uv/76yLT29OnT48gjj6yq+Wb/tVDwvHnzqpBwc3NznHvuudHS0tKx6qz6e8UVV8Q+++wT73//+zum58i//uu/Rn64dvLJJ1eVde+666744Q9/GHvvvXd84AMfqObN9f/zP/9zbLrppvG5z30uLr744qoaXS43YcKEOOmkk+IVr3jFcv3mlfb29sjKOhlSzu3fYostqlT5u9/97mod9957b5xyyindBpXrbVP2//nPfz6ee+65OOussyLnyQfI7bffHlntOMPQ73rXuyI9VtVymauvvjpuueWWarY8PpMmTYoMCecx6dxy3rzkdg8aNCj233//an8OPvjgzrMtN/7888/Hf/zHf1T957HKn+POqn0777zzcvOt6kpbW1vceeedHY477rhjtX9Z0TmrM2sECBAgQGBjFMiQT7bOX4DK629961tz0KP2spf9LUxUez7vPK1HnZiJAAECBAhsxALf//73Y9p998TOW42MAQteiMlPPxeP3n1nDD52v5hSgrq/e+DRmPHU03HU2O3iyeeejymzX4gpTzwVg4YM7ngdvhHz2XUCBAgQIECAAAECBAgQIECAAAECBAgQIECgDwt88YtfjM8fvHsctPP4jq0cvfmIavxPk6fF+f/5nchf/t1ll106bjfSe4GmxYsXt/d+sWVLZAA1K+x+7GMfi69+9avLdZNV22oBywyWZoC21jL4mkHfrtOnTp0a++23X1WVtzZvbfiZz3wmzj777OpqhmMzyJmh3zvuuGO5EOw73vGOuPzyy2PUqFFV4LcW8l26dGlss802Vd9ZCSeXv+yyy6qg6xve8Ib4yU9+UvX98MMPx5577lktf8IJJ8R5551X24SO4Xe+85344Ac/2HE9R772ta91bF/nGzK0umDBgrjhhhvi5z//ebz2ta/tfPMK4/W2KWfKinm5v2eeeeYK1nn72972tvif//mfHF1p+8Y3vlEFfrvOkBWQM6icLW0/9KEPxfnnn991tup6erznPe/puO2cc86ptufUU0+t9i+3sWv7/e9/H7VgUN7261//OjLwndWFMhTcuWW15gxhd215PP/0pz/F9ttv3/Um1wkQIECAQL8WyC8D5ZeJXvnKV0Y+72oECBAgQIDAuhXI9y9+97vfxc0//XEcsf2WscPIEdXlpoenxH9de2N86uX7xaWTn4gJmwyKsZsMiYNHb11t4B+mz4oZc+fF7554LmYuiarKbm++0Lpu99LaCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgY1VIHMJXz39I/Hz/+9vucCuFl//zY3Rus/LqsKjXW9zvecCqy7J2k0/73znO6s5fvSjH0UGYju3rOSabeLEicuFdTvP03l8dimdnAHZuXPnVpVZf/WrX1WVVr/+9a9Xs33hC1+oArZ5JUO4OW+2m2++uRrmf7kNGQbNlsHRrA5bazmefY8fP74K69amr2yYy2c4NcO5f/nLX6ow7KGHHlrN/qlPfSqymmytZRC3FiY+/fTTIwOqt956a1Wh95prrqnCurV513SYwehcR4ZXs+8MKGfLoG+ud1UtKw7ff//9VdXbnC/Dv3n9z3/+c8diWdm4FtbNfb/nnnuqDxXzZ7SzZSXi+fPnd8xfG8lKQxlMrnn97Gc/qyoj5+1vectb4vHHH6/NutJhrrcW1v32t79dVRLO+1EGe/N4vOlNb4qsrqwRIECAAIGNRSDDQfkttnwezWrza7v94Ac/iLxoBAgQIECAwN8ELrjggph5fXn9vefO8db9J8bLdhnTEdqdsNXmcU2prDvwhefj+B23irfssVM8+tzc+OFfHopHSpXdbYcOioGLFsSY1vnx3a9+pfryTT6/awQI9C2BWjA/H595PpzD2qVvbamtIUCAAAECBAgQIECAAAECBAgQIECAQOMFMpdw5nFHrLLjvzvwJZE5UW3NBAasyeKveMUrqkq0Gaa86aab4sgjj+zo7tJLL63GTz755I5pqxq55JJLYtKkSfHSl740fvzjH8ewYcOq2ffaa69q/CMf+Uh86UtfqirJ5g3HHntsFajNyrWnnXZaNe9tt91WhXJHjx4dM2fOjEx+Z2A4Wy3Ym9V0e9ryDfp3vetd1ey77757Vf03q+9m8DeDrrWqsd/61reqeXI7aoHTnJD7MmLEiKoCcTVDA/7L6rad15HhnfyZ7DwGGeJ9+ctfvtK1jBw5MvKSFXWzZdXeriWqBwwYUFULTvc0zpYVkQ888MCqcnHuezpniLZru/jii+PVr351NTm9Dj/88DjooIOqasoXXnhhFRDuukzteltbW3ziE5+oruZ9IUO+2dI7133YYYdVAe48prWwdjWD/wgQIECAQD8W+MMf/hB5+exnP1s9r65qV6dMmVI9565snp4EfvM5OMMKRx99dOy0004r62q9Ts/9rLXc1q6tVrmwr25/1+11nQCBlQvUHu/1Huu5VD7ee/NYb3R/K99yt/Q3gfw1m6vf/6bYceTmy+3a1Geeix2GD4uL73s0rjj+0Nh8UEv8ftqT8cfpT8TQaKtu23HTIfHJA3aP5xYsjLvv/VMM3GqbuHHWY9UXkXvy3LzcCl3ZKAUacY63UcL1YqfzC+RT7/1LxAtzYuyWm8f0adOiacyYmPrM7KqXfO8x3/P0mO0F6kY868Z4vlHb5zzstfM2r8s24geBXSfQIIHO50A53vW1n+flBkHrhgABAhuQQOfnhnqb3dv3Cuv1YRoBAgQIEOiJQO29kNr7IF2X2RCfk3KfZtx/XxzytmO67s5y13csv0DYNG/OctNc6b3AGgV2M9yZVVv/5V/+parwWgvsZnj0l7/8ZbU1b33rW3u0Vbfffns136mnntoR1q0teNJJJ0UGdjPQmxVWN9lkk47AaFbUbW9vrz7syjBntqzOmuvNarFnnHFGNe26666rhvlz0j1tr33ta5ebddy4cdWHwvmAy0BwtqzqWwsDZ+Xbri1DvB/72Me6Tl7t61lltnPLasNvfOMbIyvcTp8+vfNNqzWePvWMhg4dWoV3rrjiio5977yCDC/Xwrq16RlW/vjHPx5///d/X1XLrU2vN0zTDANnmLgW1q3Nt/nmm8ff/d3fVRUG77rrLoHdGowhAQIECPR7gQwHZKt9gajeDufJ8/W/uDKmTp0a7XNnx5gtlg8TTXt2WdBg6n33xM4TX9JxDlWvr/xCTq7zxhtvXOGDkHrzr8tpuZ/5BaDcz2xjNtukGt+xDGttwZIlsc12o+OphYti2Kjtqn2t9yWj2vyGBAj0XYH8W5R/28aWv2lt5W9b59bxd23sTjFtSXvklxq7fnjbef4c79zflKlTlvtb2bm/5tFjq/66Lu/6xiuQ950xg5pWCOumyPRnn4+bZsyKscOHxk7l+ejXU5+Iu56ZG4dtOzIO336r5dBmvTA/ttx0kzjsiFfEHx6eEhf9x78L/y0n5EpXgUae43Xt2/W/CeRj/PeXXVwet5tXzw07btIch5RK2dl2WLTsh8luuuvmuOHJGdHc3LzKc+lqIf9t1AKdzzcacf7S1zFzfyffc3f1uizP2bLleVa2qeU8rWrDR0TzZps7v1qm4X8CBHog0PlvS+19rmnlyzTt5cs02Tpev/Xgfa4erM4sBAgQILABCOTr4/zMIs89a88NnTe79txw4/DN48jXn+B1W2cc4wQIECDQcIH8vDqfk7r7/GpD+7wpP4M/bNexPfLK0G4+P3f32VyPOttIZ1qjwG6aZZg2A7tZUfeb3/xmDBkyJDLUmS0Dr9ttt1013t1/tZ+E/PCHP1xVeF3Z/Pfee29V2XaLLbaIQw89tArLZrXbPfbYoyMk/JrXvKaqQpeVZ1944YXIsGmt/0MOOWRlXS83PYOjuY6ube+9964qBWRIONuDDz7YMUutckDHhDKSoebadnaevrrj48ePX2HR3PdsWaW2ES3DyHkMb7nllnjggQeq6r1z5sypArUr63/fffete1NWyM2Wfa2qZRA3W4Z2s6Jv11b7VkIt2N31dtcJECBAgEB/FBg7dmy8+93vjhyurOXJc/48xeHjxlYhg7ZFc5YLok15bFr1YUa+oXXUSe9Z5ZtVY1788KMWil3ZOtfH9Np+jtliRNmf5+OwHUZFhnVbOwV2581fELf/+bb4a/kp8knPzq2+0CWwuz6OlnUSWHOB/Jt1w9VXlr9tO8WYUu2w85cR2hbOqf6uXV+qIV78p7urv2vdvSnQub/cuh1KIKvWOvc3c6hASc3FcJlAPv90vv91dZk+Z14ctt2y9w4eK6Hcac/PjTP3W/F1+8CW5mhdvKha/LByv/7wj5a9b9K1P9cJ1ARq5z6NOMer9Wm4okA+P+wwsLy/edCK72vVHvv5mP2XX11ffUDs3HJFQ1P+JtD5fKMR5y9/67lvjuX+XnfJhdXzZGuXL44++uLr0AxPtIzeSWC3bx5CW0WgTwrk35YLvvNvy73Pdeg25UsB5f2ubL15n6tP7qCNIkCAAIFeC9ReHx86akR17tn1M5Dae3s3PXxj9WUxr9t6TWwBAgQIEOiFwEUXXRStM6d0+/lVf/68Kd83zefn7j6b6wXrRjfrGgd2Myz60pe+NO68887ICreve93r4kc/+lEFmQGTnrYMhNZahn7rtVzX4sWLO27Kiq5Z3TYvW221VWSYMyvrDhw4MF7/+tfHTTfdVF1Gjx5dBUEzQDxs2LCO5Vc1kiHfei2raXRuWf211hYuXFhV/61drw2feuqp2ugaD7uuPzvMKruNaml5/PHHd4RzM7i87bbbVkGhWiXheuvKarr12qabblpNrlUkrjdPTnvmmWc6blrZ8c8Zav11zGyEAAECBAj0Y4FddtklsopIXmph2q67m2Hed5724dhpcHM5MZ4WM8oMLYM361imebsxsdP4vWOnMv3www/vuvhy1/PEOlt38y230Dq6kvt51llnVT9T/Ps7/1JV2I3Zfzt/yM3Yc/vtY/7iJbHjgkXxxq237ZP7sY64rIbABi+Qf4daZz9b/Tx57e9abadatt8smsvfufzbdtYrX7vKLzXUluncX06bWZavtexv5932juby0+dH7L1PbbIhgUrgFa94RfzwX/65rsaO5Usk+QWS6bOXvZ9x4DZbxM8efSJufvyZOHS7LVdYpmXQ4Jj+3Oy4+eESAt5z7xVuN4FAZ4FGnuN17tf48gL5/HDDjCnx9V/fGIeWL8Bly8d2reXjNduMJRHv7uZcuraM4cYr0Pl8o975S+18o6fnL31dMvd39IhNo+mFZc+D+Xo029ixy6pg5rnVK8p4U6l0phEgQKCnAvm3JX/ZsumJ/EtanoPLZXXf56o68B8BAgQIbPAC+fo4i8h1/QyktmMd7+2V9wrzeUQjQIAAAQJrU+Ad73hHTC0FZaK8H1J7vVJbX8dz0gb6eVOtan1tf1Y2zJhiPj9rqy8wYPUX/duS73//++OjH/1oXHLJJZEVaDPYmUHPrHTb03bwwQdHVsS9/PLL44QTTujRYsccc0x89rOfrX7etBaczVButmOPPTY+9alPVX3WqtJmwLfRbfsSDBk1alRVhTZDy11PAmfNmhUPPfRQo1e71vo788wzq7DuG97whjjnnHOq41kLCb/pTW+Kq6++uu66s/JxvVarQNzdN9kmTpxYLZ6h7LvvvrteV6YRIECAAIGNTmDJkiVx/vnnVye8//RP/1R3//Oba5///OerXwCoVaTPGdvLz1CMLbflqXLtVwBqw7odlYmHHXZYdTnqqKNWNst6m17bz9yAoyZPrraj8/7mhM7713m8mtl/BAhsUAL5dygf910f57WdqP1t6+ljvbv+av3UhrX1GBLI+2FT+Tnv6c89H/kzT53b2C1HxtRS6X368y/Eo+UyZsTwOHHPneP3jz8bt5bQ7ss6hXafKVXg75mzMIYMnBKtW4+Os8/+UOeujBNYQSDve406x1uhcxM6BPL5IX9F67rrroubZs2spo8d1BbTp0+LHXccE1M23758cW7H8n7fyOiL58gdO2KkTwhsbOcbub95qZ2v1Yadz6c6j/eJg2QjCBDo8wK1vy21X82sbfDqvM9VW9aQAAECBDZsgZW9Pu68V7Xzztqw823GCRAgQIBAIwXe9773rfC5fOf+a89FtWHn2/ryeBYPy89BetJuKkUO8vlZW32BhgR23/zmN1eB3csuuyzGjRtXbU1+y6mn1WxzgYMOOqgK1+aL8HqB3fvvvz9aW1tj1113jVoF1qzsm8HgXGbQoEHVel/5yldWwwwOZ5D2qquuiv3333+526orDfzv5S9/efz0pz+N0047raroO3LkyKr3rAbcmyrDDdykHnX1/PPLP9DmzZsXt956a7VshoL22edv1aUyMJQ/RbSy9vvf/74KLad55/aLX/yiurrffvt1nrzCeB6vbJMmTYrHHnssMgjduWUF3ieeeCKyku8OO+zQ+SbjBAgQIECg3wq8973vjfxZjQsuuCCy2u7b3/72le5rnvSv6Yn/0UcfXQUWVrqSPnJDbT9rwz6yWTaDAIEGCzTi71rnTWp0f537Nt6/Bd5x6ofjsuuvicO23SJetsuyqoG1PZ4xd368pFTW/cYdD8THXzYx3j1xXNww9Ym4YdoT8egLC2PGnPkxY+68uL98o/7w178hdj3okMjnW89hNUHD7gT87epOaM1vz8dkXvKXw2qtrXz5bUx50/nIcg7u8VpTMeyJwMb4mK09RmrDnjiZhwABAt0J5HOzRoAAAQIEOgtsjOfanfffOAECBAj0HYH++JyU+9Q2bHjMKKHdHboUL+ksf8uj02L0hD07TzK+GgLNq7HMCotsvfXWkRVZs335y1+uhlkCujetVv32u9/9bvzgBz+Itra2avEM6f7f//t/q/Bo/hRlXq+1AQMGxPHHH18FRS+88MLIKq3bbbdddXNTU1NkRdj8Zn9W7R09enTstttutUUbOvz6179ehYOzkm5uwwc+8IH4yEc+EhMmTKgqdGSouC+1WjXia665JhYsWNCxaZtsskm1Hznhl7/8Zcf0RYsWxQc/+MGq8m7HxC4jc+fOrcLJtRBwHqfvfe978eMf/7ias3Z8uyzWcTWNXvWqV1XXa8e0duOU8iFJHvsMaF966aW1yYYECBAgQKDfC+SJcVa/33bbbeNzn/tc/PGPf1zr+1z78tNaX5EVECBAgACBDUTglFNOiW1efmxcfN+j8f/+fG/8afK0mFnetLp18vR44OnZ8ZoJO8eoHXaMK6bOij/NfCqGDmiJU16yWwwYMDBmzl8YrUOGxZ+fej4+cuY/RfaVz+8aAQJ9TyDPg2uXfKzmuMdr3ztOtogAAQIECBAgQIAAAQIECBAgQIAAgcYLvPOd74xLb//LKjv+6jU3lF8QPHuV87ixe4GGBHZzNe95z3s61pbh2EMOOaTjek9Gcv4M62b70Ic+FBkCfu1rX1v9BHQGVbL927/9W2SotHOrhTxz2hvf+MbON8Vxxx3Xcb0WKO6Y0MCR3N+s5LvHHnt0hIfPO++8mDNnTvzwhz+sflq6gatb464OPvjgqo+bbrqpqljb2S1Ld2fLn13MDyVe//rXV9VuL7744lV+SHHAAQdU4eQ8bllxOCvtfvSjH636Ouecc6pKJdWVVfyXFQTHjx8f99xzT1VFN7cz+8qgdS0MncFhjQABAgQIbEwCp556anXSm9XqX3jhhYbs+owZM6ovNOWXmjQCBAgQIECge4F8Pj7wDW+NSUO3iOtnL4qbX1gaC3fYNWLEyNhj113iC294VYwctU08tKgtrn9idtxcArpLhw6LUeU18uF7jIuXvexlVbX87tdkDgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAuhU4+eST4+EYXIqWTK+74p/deV8sGDkqjjnmmLq3m9hzgQE9n3XVc2ZwNqukZqXVDFVmhdvetqxMm1VzL7vssvjtb39bXbKPrKz6hS98IepVaT3qqKM6VtP1DpFhz1o79thja6Mdw5aWlmp84MCBHdO62+7cvmy1ZWsL5jbefffdMXPmzLjlllsiq9hmsGbkyJHVz1jX5utuWOu38zZ1XqZ2e71ptW3rfFu98Xe9610xffr0qgLurFmz4ve//33HbBnUHTRoUHzrW9+q9iX3J4O7WSn3/PPPryoW11tPBns/9alPxemnnx633npr1V9WGz7xxBPjk5/8ZEf/OdLcvCwn3nUf0+pXv/pVfO1rX4urr7467rzzzo7lPvaxj1X9b7bZZh3TjBAgQIAAgY1FIM9jdik/x7vrrruusMu1593aDflFoh122KF2dbnhT3/60+r5/ZFHHonf/OY31XwZIFrZ/Mst7AoBAgQIENjIBU477bTI59C8ZDu8PC//adKDsXjg4Or6KYcfGJOffjYeferZ6vouW28R+a7EbVNmRL33JKqZ/EeAAAECBAgQIECAAAECBAgQIECAAAECBAgQWM8CmUd454f+v7j+JxfH48/fF9tvPiJGb74sp3fblOnx66deUF23QceoafHixe0N6quh3SxYsCAee+yx6iegu1bVbeiKGtBZVqp9+umnY9y4cbH33nsv12PuR4ZgMsj88MMPx5gxY5a7fX1eaW1tjfnz50cGZ4cMGbLcprS3t1eBngwI589wdxdkri2cyz3xxBNVoDmr7K5Je/zxxyP7y/XXQr5r0p9lCRAgQIBAfxQ4/vjjl9utPB856aST4qCDDlpuen4JJ8NCeW5y4IEHVrcdccQR1ReichmNAAECBAgQ6L3AP/3TP8WmD94ZZ7zy8JUu/A8/vjIOfe9p8f73v3+l87iBAAECBAgQIECAAAECBAgQIECAAAECBAgQILC+Ba655pq44aqfx+DFC8plYRxUsgW/nfRwHHH8G+O4445b35vXL9bfsAq7jdYYOnRo3SpyjV5PI/q77bbb4swzz6wqDOdPSx988MFVwPXmm2+Of//3f6/CuuPHj+9TYd3c7wzjZlXkei0DuqtTbS+X22677ep12etpjeqn1yu2AAECBAgQ2IAE8ptu+SWnbBnKfeaZZ6ovEHUN7GZQd6+99ooM6dZOpAV1N6ADbVMJECBAoE8KZAj31BNes8rA7uT5S+JTr3hFn9x+G0WAAAECBAgQIECAAAECBAgQIECAAAECBAgQqAlkliBzBFmYNC9jy/VTSk5XtqAmtObDPlthd813bd31MHv27Cr8MmnSpLorzUqz+RPUGeTVCBAgQIAAAQKNFnjooYeqLmvD/KJQXrq2vL3e9K7zuU6AAAECBAj0XOCVB+4X//WWV8W2I1b8Quyd02bG92a+EBdeeGHPOzQnAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAvxQQ2G3QYV20aFF8+9vfjv/93/+N22+/vep1v/32iwMOOCD+/u//PkaPHt2gNemGAAECBAgQIECAAAECBAgQ6CsC+Ys7W06+N/7+qEOX26Qnnp8bZ1x6VZz2xa/F6173uuVuc4UAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDjExDY3fiOuT0mQIAAAQIECBAgQIAAAQIEGiTw4IMPxuc/fka8dezW8eq9dqt6fWLO3PjOdX+MR4aMjJ///OcNWpNuCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIENmSBARvyxtt2AgQIECBAgAABAgQIECBAgMD6FNhtt93i7ad+OH78ve/GU3PnxTabbRoPv7AwHho0Is796lfX56ZZNwECBAgQIECAAAECBAgQIECAAAECBAgQIECAQB8SUGG3Dx0Mm0KAAAECBAgQIECAAAECBAhsmAJXXnll5GWz9qWx3e57xQknnBATJkzYMHfGVhMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDRcQGC34aQ6JECAAAECBAgQIECAAAECBDZWgfvvvz923333jXX37TcBAgQIECBAgAABAgQIECBAgAABAgQIECBAgMBKBAR2VwJjMgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFGCDQ3ohN9ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQX0Bgt76LqQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQaIiCw2xBGnRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoLyCwW9/FVAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQINERDYbQijTggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUFxDYre9iKgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGGCAjsNoRRJwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTqCwjs1ncxlQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBDBAR2G8KoEwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL1BQR267uYSoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAhAgK7DWHUCQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIH6AgK79V1MJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINAQAYHdhjDqhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEB9AYHd+i6mEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGiIgMBuQxh1QoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKC+gMBufRdTCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDREQGC3IYw6IUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBfQGC3voupBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBBoiILDbEEadECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKgvILBb38VUAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAg0RENhtCKNOCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNQXENit72IqAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgYYICOw2hFEnBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOoLCOzWdzGVAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQEMEBHYbwqgTAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAvUFBHbru5hKgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoCECArsNYdQJAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgfoCArv1XUwlQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0BABgd2GMOqEAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQH0Bgd36LqYSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaIiAwG5DGHVCgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoL6AwG59F1MJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINERAYLchjDohQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUF9AYLe+i6kECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEGiIgsNsQRp0QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqC8gsFvfxVQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECDREQ2G0Io04IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1BcQ2K3vYioBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBhgi0nHPOOZ9rSE86IUBgvQnMmDEj5s2bF8OHD19v21Bb8ezZs+Mvf/lLPPXUU7HZZpvFwIEDazf1aPjcc8/FfffdF08//XRsvvnmMWDAgB4tZyYCqxJYsGBBTJ48OTbZZJP1fp9qb2+PRx55JCZNmhRNTU0xYsSIVW36Crfl8g8++GDk474nj7HePiYXLlwYjz76aJ+wWmHnTSDQC4G+9LjPzZ46dWr12B05cmSvnxvb2tqqx2X+3cjn1b7wfN+LQ2FWAutUoC+dF8+fPz/uvPPOWLp0aXVe21uIOXPmxF133VUtlo/7PG9YWVvT84uV9Ws6gb4q0Nee5xvhtLb+fi1atKh67TFlypQYNmxYDB06tBGbqw8C61wg3y967LHHqtfQzc0bVw2K3r6u749/I9f5Hc4K+4RAf7ove13fJ+5SNmIDEugPz/uPP/543HPPPZHvDeT7gWt6/vLMM8/E3XffXZ3T53m9RqC/CKyt18Kr65OvOebOndtn3oPvT+dDq3tMLNc/BNblc/uavi/fV8T72t/HvuLSX7djXf6976uPkd58ll471168eHH1+duqPj/rq/eZjevdzb56FGzXRilw1VVXxZZbbhmDBg2Ke++9dwWD/GD/y1/+cnV7zte1tba2xuc///mqj1122SXGjh1bjecy+QZgb9ub3vSmal25PU8++eQKi3e3PbnAv/7rv8aoUaPi8MMPj4MOOih23HHH+MUvfrFCX/UmZMjg05/+dGyzzTZx6KGHxgEHHFC9GMp9zHV3bg8//HDHtub2dr1Mmzat8+yxww47rDBPbZlLLrlkuXld6TsCzz77bJx00knVscthvfbXv/419t9//2qe733veyvMkuHvvD9lKHbvvfeuAq4vf/nLq9DcCjN3M+G2227ruB/9n//zf+rO3d325In1YYcdFnvuuWccddRRseuuu8Zb3vKW6k27uh12mfilL30pttpqq5g4cWL1GMtQ+xe/+MVYsmRJlzmXXe3NYzLfPEybDAF3tsrpGoF1JdDXHvff/va3Ox73P//5z+sydPe4z+XyeWj8+PHV82M+bk855ZTIcHxPWn4J5qUvfWnsscce1d+NfL5/yUteUvfcoXN/+aF+7fkv5+/cbr311o79qj0fdh1eeeWVnRcxTmCtCvSl8+JVPXZqCN2dF2fQNp/f8/Ge58X5+M/H4w033FDrYpXD/GJPPifnc34O81x/3333rT7oq7dgb88vuvu7VW8dphFohEB3j51cR3f3z3V9ft+Tc5OaTU/+ftTmrQ3X5HV9T16fXHfdddVrjnzNlH9Ptt122/jnf/7n2uoNCax1gZ48hro7D/h//+//xYQJE6r3i/KcOEPn73nPeyKfb3vbenJ+v6rtueCCC1Z6Hr3zzjv3dnOiu/fissPevK5v5N/IXu+MBQi8KNCTx31fe75f1fb09n3oeneE3ryur72O7/oaPa93fh/7hBNOWOnfo5y36/sA9bbLNAKNFljVYynX1ZPXA4183u/J8+zKnvfX5DGW79W/733vqz6zO/LII6v38vfbb7+VfibR3d/N+++/v/oMZLvttosjjjgicpifeeR7ARqB9SWwssdObXu6u1+vyWvh2jpqw568Fu/u70/e/rnPfa76nH2nnXaKvORn8zktb+tJ6+5vYOc+erLNzu07ixlfXwLdPZZzu7r7e9DI5/buXtOv7vvyPXlMdncMVnbe0dvP4xr597G7bXb7mgs04jHSyL/3a+sxklIru4/XU6z3erbztH/8x39cbrHefJae4f/clvy8PM+1873CrbfeOvJvzYbWlK7c0I6Y7d3gBV544YX45Cc/Geedd17HvmRYtXPLyjcnn3xy3HzzzdXk/CZf13baaafFhRdeWL0wz0BAPnlfdNFF1YuH/EZEhvh62i6++OK4+uqrO2Zfne258cYb4xOf+ESMHj06PvShD1UfnPznf/5nvPnNb46HHnqo+oPZsYI6I//1X/8V5557buQHHO9+97urNx2+//3vRwYUn3/++fjmN7/ZsdSsWbOq8de+9rXVi6aOG14c2XTTTTsm5QupnD8DjvkBZdc2bty4rpNc7wMC119/fbzrXe+qjl1uTtcQet5H8/51+umnd2xt13ny5CZDbtnyvpnHOsOn3/nOd+KQQw6pwm755lZPWn6jKR+Ttdb1MdKT7cntO/XUU+P222+vQjwZ2P3JT35SvZD5zGc+E1//+tdr3dcdnnXWWfGNb3yjCv2cffbZ1Tfp/+d//ie+8IUvxMyZM+M//uM/lluuN4/J/OAgQ/LZzjjjjOrEJt9cyJO6/HD/jjvuqB5Dy63AFQINFuhrj/usZN35BcPqPO6vuOKKeNvb3lY9t+XjKQPx+dydz9d5PnDppZeuUjHfXDjxxBOrCuH54iOf9x544IHq70W+0Z+hvvxyTL125plndvwN7fqmYn655iMf+Ui9xeLaa6+tnrdVuK/LY2KDBfriefGqHju5+92dp+fj7dhjj60q62Y4/+ijj64eU/kGf06/5ZZbIh+/K2sZCMg3GfL8NZfPwG9+uS/PAfI5OStt5xd+aq035xc9OV+p9WtIoNEC3T12enL/XJfn97n/3Z2bdDXq7u9H1/nz+uq+ru/u9Un2nb9689a3vrWqDPQP//AP1Wv1fA2Vgd199tkn3vCGN+RsGoG1JtDdY6gn5wH5mvf9739/df/N177/P3vnAS9HVbf/s7u3pZFOQgoQaiD0SBMIHZGigAVFeCki6gtYULAg8oJ8FPXFRnkpf0QEVBARFVS69FBDJyEJAUII6YWUW7b8n+fcPcvczZaZ3dm7s/c+J5k77czMme/OmTnnd57zOyNGjDD/+te/zJ/+9CfDb6azm/m5iXLlez/pef/99+2l+I1ubW3tcdlCne17RMhbKWeLY/Qg9fow35F5SdWqCPgmUC7fR+177yc9QezQhUAFqdcHsWN//OMftx378q/JdxntDm1tbfm7tC4CNSPgJy+Vqw8wcWF+98t9Z8t996vJY2znuuWWW6y9nZ2MKNThOtu9HnvssR4jb5V7b3IkPDoAYRvhhRdeaLbZZhvz4IMPmuuvv97stdde1k7IUQUVRKC3CJTLO0xHueeacSqtC/PY/FCuLu7n/XP66acbvjfYTva5z33OXoJ1DjrJore/G264If+yuXU/78Bc5OxCuTSrbJ9PTOv1IFAuL/t5H4T5bS9Xp6/GLl8uT5bjX6rcEbQ9Lsz3Y7l0a391BMLII2G+72uZR0o944UonnnmmYbfx/zANN5///2286nbF7Qtneemto0d2I4//njrIIvaOH6/b7vtNnPMMce4U0d/DvfAGU1ioGeg954BKPz5Zsqgop5BL1u7DDFcLh+iwcFuYxxU7DMuvvc3ggdZG+eQQw7JQJybOxY9ODIYLjeDD38Gnvty273H5i+jomGPgeevDF5i9rw8v4vnJz3odZSBUNceCxFB7lh457PbIEzMbXPn9c4hFrTxINbNwPCQi4uCXu7+mU53DESONv6jjz6a2+b25c/Ry9jG/clPflI2bv6xWu+9fOFlffvtt9vfjM8xRG12GUK1Hr8fhLp2OyrPGf62zC8QwxWM88ADD/TYjgq2jQ9BeI/t3jTkL3//+9+3x1xwwQV2joJAj2P9pOeqq66yxzLfu/NjeNoM74Hpz0+ni8M5XPrbOMynGI4ndzyM/hnmLx7/9NNP57YHzZMQJdpzwACROwevC88ddjs6GfTY7k2bluuTT/oa96jle4hgMijo555/5jGIa3vkAz/5/vDDD7fngFEwdyy/c8zLPCc82ue2F/pNWVZgvN/+9rc94sHIYbczDYWOQ0XF7ufx/D7zeoXi5W/ju4PvXk7e73F+PK0r34f1DLhyblTKxeXyjp9yMcunzLcQx/XId/DIZ7eX+6Z+85vftPHyyzUubRDY9ThvkPKFn/dWWL+tzqP3hPcZ8JN3/DyfLk5+ubkW5Xs/ZRPvPbo8GuTbW029vlz9hGlDpyH7Pvnd736Xe2/Mnz/fbqPdgHUM7z1oWfk2zGfATx4qVw5gehiHzyuEsrnnlfVoV85Gp5bc9lLp91O+95Meft/5nfdrcyuWpnK2OB4XtF4f5juyWLq1Xe+JUs+An3zvntNGsucFsUMX4hOkXh+GHZv1CL6n0LHf1/uxUJq1TXk96DNQLm/7qQ/wmmF99/18Z/189wtxKJfH2NbHPMi2rrVr1+byIW0B3I4RJXPb/Lw3XdsH30Xe9KDzkD0f2+C827Ws/FvrZ6Bc3vHzXFdTF86/v3J1cT/vH9Y1mD/h+KmHTZz2cW7jvlJ2/HLvwKBpZnx3zjDsH/nX17reE36eAT95udz7gNcJ69vup05fqV2+3HukHC8/5Y5C5yjUHhfm+7HQNbUtvPwfVh4J631fyzxS6TNe6Hlj+xa/qy+88EKuDOtsfH7a0tlezuNZz/aeHyNS2O1853i3R305jptREAER6EUCy5Ytsz2F6WF36NChG1yZ3mTpZZa9bvGCNoU83KGib3v10fusdz89y3KYDvb8z/emt8GFshvOOecc2zuX6SnUE9dPeuj1ix4+6a3P6/ELL1c7RA+H4aRr8mLBDbsNY0cPDyV0i/74448bNCpaN+bueFSM7OKYMWPcpqJzehVi8OtJteiJtKPXCPBZohfJ559/3kCUXvC6HO6JnqrZc4lDYxYK9Kr8y1/+cgPPyvhQ2+jOQ0ahY73b6H2WvWj5POPj792VW/aTHvecsye8C7FYzMDoZlfpJahYcF6DTj75ZDs8totHbxlf/epX7Sp7NrkQNE+SOQM993mDW3f7vfu0LAJhEohavuc3kfmOeX/KlCkFb7VcvqfXy3g8br3Ojxs3LncOeuLi8HUM9JBbLPA7Tq847NV/wgkn9IhGr7v8Np5//vk9tnOFZQR684bo1nqu3yBCiQ0Q9NgyBD2B5nsMK3GYdolAxQSiVC72k3f8lIv5PqMXffbs9QZ6ymXgN7pU+Pe//213c/hMb6B3XojvDXsbk5sLQcoX5d5b7pyai0DYBPzkHT/PZ2+W7/2UTRwnP+8PF9c7r7Re76d+whF46FGAdR/v+4h1aI7ugYZHa3PwpkfLIhAmAT95qFw5AMJc64mOZVR61nWB9WhXRvd+E93+QnM/5fty6eF50YHWevtlOb+aUM4Wx3MHrdeH9Y6s5r50bP8m4CffR+177yc9QezQ+U9A0Hp9tXZsjrrH9gLaAzhymYII9BaBcnnJT30gzO++n++sn+9+Pj8/eYwjVzHQ5t/c3Jw7xbe+9S27zOF+XfDz3qT3QraR0CbgDRyZh4Gj8CiIQG8SKJd3/DzXldaF8+/TT13cz/tn6dKl9tS0w3tt4lzmNgb3jbYreX/KvQO90f2kmfFVtvdS03I9CPjJy+XeB2F+2/3U6ZnmoHZ5v3my1G/gp9xR6PhC7XFhvR8LXU/bwiUQRh5hisJ639cqjzCNlT7jPNYbINK17VscmXb77be3u4K2pTs9DOu93rDFFlsYjorlyuLefVFebopy4pQ2EeiLBDi0fCmhKQWvjEPxbbHAON/+9rc32P3kk0+al19+2YodKXYtFygI+Mtf/mKFr9N9tgAAQABJREFUtnvvvbcVBuUf4yc96KFoD6Pb8fyw3377mYcffthw6B4n/suPQ9fnDDQyME3oAWXQC8QKmihqyuflhJasLF1zzTV22B82RDIuhwTyBleJYuPOHXfcYYchHj16tI3L9CQSCW90LUeAAIdt/cpXvmLYCMfCfqHwq1/9aoPnIj9eIXEtetEY9LyxUVloLxfY0M20MPzmN7+xaSp0jJ/0MG9SjO8V7vFcu+++uz1lKeMavGfbOMOGDbNz7x/eE8Ps2bNzm4PmyU996lN2uJ8rr7zSXHrppVZkyAIS1xmOO+643Lm1IAK1IBClfA9vc+bss8+2lSR2nEEvyYK3XC7fsxHfa4R3J+EQIPzOM3g7ubj9bo4ei3aRHRj4LYOHX4MegvY7x+/lLrvs4qL2mFNkzIoiPGSb4cOH99hXaoWNDjxWDXulKGlf2ASiVC72k3f8lIv5zSz03WQ5gMEZIoqxZNkHngR7NBC4uO5bz/eDG3Y7SPmi3HvLXUdzEQibgJ+84+f57M3yvZ+yiePk5/3h4nrn5BK0Xu+3fsIGQwbWx/PrvKz7M7h3il3RHxEImYCfPFSuHECbT6E8wvI6h6fl97JYmdh7O37L9+XSw3PS+L/ZZpuZt956y6aB3+099tjD2phYjvYT/NjieJ6g9fow3pF+0q84IlCMgJ98H7XvvZ/0BLFD57MJWq+v1o59/fXX2064tOd5BUf56dK6CIRNoFxe8lMfCOu77/c76+e7n8/JTx6bNWuWPYzlA2+gjY7tZ3QQ4IQJft6bXucf3vOxEx9DMWcm3rhaFoEwCZTLO36e60rqwoXuwU9d3M/7h+3KbGNmOwDF9a6dmfmZ27jPbSuUjnLvQO8xftLM+Crbe6lpuR4E/OTlcu+DsL7tfuv0ldjl/ebJYr+B33JH/vHF2uPCej/mX0/r4RMII48wVWG872uZRyp9xgsR/9GPfmQ3f+9738vtDtqWzjIwneNQM0OnVzNmzLDL1ImxrO1HI5e7eAQWJNiNwI+gJPQvAvni0/y7HzhwYP6mkusUH7Ixjo319Li15557muuuu67kMdzJggA9c7JBgZ70igU/6XEeAidMmLDBaSZOnGi3MU4xwe5rr71mhYxnnnmmuemmm3LnuOKKK+x2euj1ihydZwP2LM5vZLz22mvNKaeckjuHM3SykpUflx9Axq/WM0ruYloIhQAF1eVCuXzkPZ7PHnvb0MvzPffcY5+Dq6++OieU9cbNX2bvtmeffdaKWOm9GkNR5Eex6+XSw/xGAV0hj8HMY2xoZD4oFvbaay+766677jKnn356j2f2mWeesfuc8J0rQfPkxz/+ccNC0gUXXGDfIzvssIP15jNv3jy7nfsVRKCWBKKU77/zne/YW8Uw8z08YeTff7l8nx/frd944422AoGh60oKat03i14AKPCjJzxvuOSSS6ynce+2V1991XrVpUdwCvGDBPa+ZEMkG/bovVtBBHqDQLl85Kcc6k1npeViv3knaHpc2ph/zz33XLvKvF8qHHTQQVYANH36dONEdYxPo4sLbPjfbbfdbHk+SPmiHG93fs1FIGwCfvJOkOezN8r3fsom5OT3/VGOqd/3l9/6iasPuPq49/pumytrePdpWQTCIuAnDwXJ9xgK1gpc+A3861//agYMGGA4Sg3n5UKY5Xt+d+nxJr/BnnV6prGcgNivLY735PJxUFtbNe/Iciy1XwRKEQg731fzLPv9Xvp5DwWxQ+fzcd9av/X6auzYfDfRxk9b/0knnZSfFK2LQE0JlMtLfuoD3gRW+t0P8p0tl2ZverjsN485we7YsWPzT2HYvkDBLuv348eP7zGq5AaRS2xwdRCOwlOovaHEodolAlUTKJd3/JQHvInwWxf2HsNllw/K2cH9vn/YhnjaaacZto05Zz9sm2Y+Y3tiqVCOiTvWb5pdfM6rKQ95z6NlEQhKwE9e9vvs89qVftt5rN86PePmh1J2+UrypPf8Qcod3uO47Kc9rtL3Y/61tF4bAmHnkWre97XKI9U84/nUKfD/xz/+YSh0dqNm5cfxrhdqS1+9erVtx542bZrtPO91hkePu0ceeaR1ftVIol0Jdr2/upZFoAEJ8INOQaELFKH6EdpcdtllVkB48803214H7vhK5s57j/P05T2H+1hx2MBigUYMFpgoDqRQiC9qfgB+/etfG/ZMpFjwxRdfzB3uzrXTTjsZDgPMAuFjjz1mvvCFL9hhwFmB2meffWx85wWBTOjpd+edd7YeCs866yzb64Jx3UcsdwEt9CkC9Erjeuzwxj75yU+WbURjPBrJzzvvPNt7lt42qwnuORw1alTB09BY5zzvFYrAxkAOicBeTBS2c2Jhgx43mU8YaPx3oZI8SW9dDMyHnFxw29265iLQCAQqzfc0wtFb1xlnnGE7wIR9r48//rg9N43yF198ccnT02sXA/M4ewVefvnlZquttjIU6Z9wwgnmBz/4gfWiwXcaAz33uneVey/YHT7+sNHhoosuUsOeD1aKEm0ClZSLq8k7fmhwGFyWz9lYzyEx2Uu+VOBwQHwPHXXUUYZCAw69x049X/va13KHOQF/teWL3Am1IAINRqDS73yY5XsiC/P94ef9FST99ALKUMjjp6u3uzqDjag/IhBxAv/5z3/MT3/601wq2QHd27E7tyNvIezyvasr09sd6wx8D3DkKo7McfDBB1uvuKVGuQhii3N51OVZ762VsrVV+o70nl/LIhAFApU+y0G+l37uM4gdOv98Qev1rnxfiR2bDjxYT2Cdw0/bQH5atS4CUSJQ6Xc/yHc26P36zWPsXMSOPE1NGza/u7K5E+wGTQPjsxMB7QUMTFMjCRJsovVHBPII+KkL5x0Sal3cnbu9vT3XGZB1CBfY/hyG1/pK7QeVlodc+jUXgagQqPTbXk2dvpRdvtI86eVZabnDb3tcJe9Hb/q03FgEKn3f1yqPkF6lz3gh8k6r8/3vf7/Q7h7birWluxFs6P2e5W125KfTG9a7v/nNb5q7777b8Pz/+7//2+N8UV6JRzlxSpsIiEB5AhShUqjHOT13UcjHhn1+7IsF9tDgS5G9BD/zmc8Ui+Z7u/PQ44yK3gNdQ6GL493nljfaaCO7yDR96Utfsh4HKWb62c9+Ztg7kr0jmGYXOKQBjY8UG1P0MHjwYBvvtttus1H++c9/uqhWoMuGFHom5QubvSnpkYzrDHfccUcurhb6JoFDDz3UsMcNP+LsjUYvufRYy4JuqcDnhoZuenpubm4uFbXsPtej3usdz3sQCxIUjxcLsVjMFjoYh0Nv8RlmT1/mmfPPP982xHt7I7n85jdPcugBeuE44ogjzNNPP22WLl1q51zndj+Fp2Jp13YRqAeBSvJ9Z2enYWcOFvLLiWkruSd+yz7xiU/Y8/M75RrZi53L29DPzikU7dODGHsOUhTAwO0u8BvIzivMs5MmTXKbfc3pbZ7vO3rZVsOeL2SKFFEClZSLq8k75TDQ8OcMBRT1cMSHcoENbxTdM0/SUzaH3WbnNXZmc8OCO9FvteWLcmnRfhGIKoFKvvO8lzDL9zxfmO8PP++vIOlnfZqhUP3D1RHYaVBBBBqFAJ9/1uvZ0fXnP/+5+b//+z+zxRZblOz4Gnb5nsNXs6x9yy232Ho4y/MU3lA8zHo5v91PPfVUUaRBbXFB6/XuwpW+I93xmotAVAhU+iwH+V76udcgduj88wWt19PRBNMf1I69Zs0aayPkO6nQkKr56dK6CESdQCXf/aDf2SAMguQx2uRYJujq6trgEq4DgCurbxChzAael7ZFevznSJXs4K8gAo1OwE9dOP8ew6yL89zslHfYYYdZD9hsQ2RbomtPpL2dZRLXcS8/LX7XK01zpeUhv+lSPBHoLQKVfNurqdOXs8tXmicdr2rKHX7b4yp5P7r0ad54BCp539cyj1TzjOfTpyMqtouzvYv6llKhVFv6sGHDcofSWy87zlP7xVFq2W7O9n06w2mk0NRIiVVaRUAENiRAISEnilE5zZgxw9x///3m3nvvNccee+yGB2ALvfIxfOxjHzMPPvigXeaf+fPn22X2xKDRgKIgP8ENBei8f3iPoTiSwQkLvPvcMoWGNDLsscceblNuzjTSqyhFhO4c9KRbKOy77752M3tduMBtbrvbxvmIESOsYJn3SnHzoEGDvLu13IcIxONxK0DbZJNNzIknnmifo/3339/88pe/NKeffnrBO2UjIAW92223nWFhh0N1MLgGb1bO77nnHiug8+PVhwI4GuvYOyo/0OBHY9sBBxyQv6vHOo3u9KbNvPDoo4/aQsd+++1nz0s3/17xfZA86bxZM30szCQSCXtdDuXJdXr0pKdret/0M9xoj0RrRQTqRKCSfH/DDTdYD5j0XstvqQsvvfSSXeRwHSz4M9/5HUrLnYPfV1a2mNf5PuG7pVzYdNNNc1Fcxxa3Yffdd7eL7hvO95QTAlLY695ZjERhAxsJ+M7itz2/MsQ0XXLJJVZscPLJJ7tLaC4CDUkgaLm4mrzjBxDz1jXXXGOOPvpo+y31cwzjUPhDke4jjzxi3nzzTetNm41yPB+DKxOHUb6wJ9QfEWgwApV858Mu34f9/ij3/gqafveecHV870/s6u2lOgx642tZBKJAgF7qOG277bZ26ujosLYtNqizbl8ohF2+57unWGdWNhKwXs76OjueFwpBbXFB6vXe61XyjvQer2URiAqBSp7loN9LP/caxA6df74g9XoeW6kdm3UO1u1//OMfqxNu/o+g9YYkUMl3P+h3NgiYIHmMggEK/NiO4H0H8Hqs3zO4zrd2xecfln2OP/54a7OkMxsuK4hAXyBQri6cf49h18V5/j/84Q/2O8qRrtg24ALbE+lM59RTTzV//OMfi9YFXPxi82rSXEl5qFg6tF0E6kmgkm97NXX6Unb5avKkY1hpuSNIe1zQ96NLm+aNSaCS932t8ggJVvqMF6Lv17tuubZ0an1c2HPPPd2indPBI7U2FPJSn5ZfDu8ROUIrEuxG6MdQUkTAL4G//e1vhkJTetmaMGFCj8MoCKBgl8N/FQvOMPDd7363YBQKdigOdA15BSN5NtKrCQOHM/j85z+f28PeS0wLg4uT2+lZoCCQAmMKB/OD89DrfQF/5zvfMRyG5JxzzukRnYUcBm9cii5nzpxpvQzkiw15bva0yN/e46RaaUgC69evN/QaS2FaviiX3nX5u7tnq9ANuoZt9uKhYCY/sBcQJz5ffj1XUKDHYyj29Xq/dALzUgI+5g326B01apSh2JiTCzQUMLjGPC67/OYnT7p8TmOiE+vyHAxcp3cPCuo5Ma8qiEBUCVSb75nHGGig45Qf6PWdE98LTgSTH6fQ+rJly2yjPb3ZsaJAwa+f4LzeUYCbHyjCZXDvEnY8cd7y2EMxP/D7yPLBSSedZL10e/e7RgcKDORd10tGy41CoJpycTV5pxwf5i0aIg455BDriY9GST+BxgSma/LkyT3KGBTe06s2yzDexr1qyhd+0qM4IhAVAtV+58Mu34fx/gjy/gqaftepkJ17OASg9x3EzgAMQcozUXkOlI7+ReDVV1+1Qz0fc8wx1ujuvXvW09l4wGGhi4Wwy/fMhxQHH3TQQbmhqN21V6xYYRdpSysWgtrigtTrq31HFkuztotAbxOo9lkO+r30c39B7ND55wtSr+exldixaR+gUFfedfPpa73RCFT73Q/6nfXLJ2gec/Zzina9wj+219EZB0fHpAAwSEilUnZkSra1sfzjOu0HOYfiikCUCASpC+enO4y6eP45naMddg7MD24bPQ1WGoKmudryUKXp1HEiEDaBar/tldbpy9nlg+bJQlwqLXeUa4+r5v1YKJ3aFm0C1b7va5VHSK3SZzyfOEeionNGtl0Xau928f20pdO+TQcUs2fPtiPQ00GjNzjHe9TTNErw12rYKHejdIpAPyHAHjVXXXWVYU+Lyy67LFfBZ48gioEYvIIgCme9RgAOH7hq1aoNaP3mN78xdK9PV/xBhuWhKJIvR/bioIjWVWDuvvtua4Q44ogjjNc7YH56KHjk/dCLJxs+nICWQkKek8HrffeNN96wjST0Vuh9sbOQw+AVM7LQxfti+r785S/b/fzD+6Toih8HclToWwT4DD355JP2uaJQxhnIeZcUsVK89tnPfrbHTXufy9122y035Lw3Ej/0Z511lh0eh88TvdD6DfSAS8HuxRdfnHuuOaSm89BDz3ne4E3P8uXLrXCWohx67GltbbVReR/nnnuuFe9QjOdCkDzJvEGjPvMrxe0UCLnAdaaZ+9Wg76hoHlUC1eZ79pinp/r8QKHLlVdeaYe1p7cbb6eQ/Lj56/wGUWDAygOH2KHX+FLBm+95HeZrftdZmfF66rr88svtafjNZKCAjx6xC4VTTjnFflc5bHB+Jx82OtArB/O4vOsWoqdtjUCgmnJxpXmnHJc77rjDnH322XZ4yj//+c8lxfDefM/z/uIXv7Dll5tvvrlHWYV5mO+S/Ia5oOWLcmnXfhGIKoFqv/Nhl+8reX/k5/cg76+g6Wcdl2UAegji+4TLDKzP/PSnP7XffnZkVBCBKBNgBxXailgHpkDFO8rFnXfeaZPuysPuPrz5LOzyPcvMN954o6070+Y2dOhQe1na4pw9ap999nFJMd60cGNQW1yQen0l78hcQrUgAhEiUMmz7M1rQb+Xfm49iB2a5/OmJ0i9nsdWYsf2NvqTn4IINCqBoN99b17jPQf9zvrlVC6P5aeDbWHnnXeeOf/8823bk7PjM30M+W0SftJB+z+H9/36179ufvjDH/o5RHFEINIEgtSFeSPefFZJXbwcjAMPPNB2tr/++uttm59zbEOxPEf0YPC2O3vTU+7c3B80zZWUh/ykQ3FEoLcJBP22M33e/FVJnd6PXT5onsxPF9crKXf4aY8L+n5kWhQal0Al7/veyCMkGvQZ96bL+4v48a4bpC39K1/5im0jYzuaG5GS1+PIs+wcx3Z8r/3Qm5YoLkuwG8VfRWkSgTIEKECkUPWKK66wAjsKV+l1iw0WfBHRKOC8dVIgxAINvelSKMgwbdq0gle46667rJCVoiCv566CkT0b6ZGPHgBYYeEwgHxRstBBAS4LPRQ6uUARLr2EsCfxE088YTfTmMrhxW655RYrhGTPYwqKaQyhx8CLLrqox4v1v//7v20DyQEHHGAFVHRpTo/DPJ7iw+OOO85dzhpAeN8UTXBYNDZKUqjrDCTf+MY3cnG10LcI0DBGj89Tp041Z5xxhhXt8rdngx8Dn1MXKDKn59ulS5dacTmff68A1sV766237CKfs0L7XbxCcz7Xt956q31OV65caT3u0dDG55Lesr2idIqCKZyniJb5myI73gO3Mf+edtpphl5D2ADPPMJGeK8QL0iepJifBkQa/HbaaSdz5pln2jzKnlMu73K/V/Rf6P60TQSiQKCafE8v05zyg/Pezu9H0HzPDinsPcjwr3/9y07e83/605+2eZzb8t9D3EavtxTsUtDPPDplyhRDATHfHexY48Q37FVYLG3sMMPKSaH9FADy/lipUcMeiSs0IoFqysWV5p1SnCgq+tznPmejjBw5coMRIWh0YxmeoVA5nXmdZRWW35977jnr3Z496zkaBcv3F154oT3W/QlSvnDHaC4CjUogyHc+vzwddvk+6PujUH4P8v6qJP2XXnqp7XzHesTzzz9vyw5XX321/flZrxg2bFijPgpKdz8hwO8o66esl+6+++62fj98+HDbCZffRo424S3j5penwy7fU3DD7zTLzh/96EetTYHfdeZvetFjh3CW1xkK2b6C2uKC1Ot5zSDvSMZXEIGoEgjyLOfn+0q+l+U4BLFD56eH5/Zbr2dcCvmC2LFpP+f3Xp1wSU+h0QkE+e6H8Z31w6tcHitUxucoePR6TScdLC+wg/z06dPN7bffbtvEWE8JEthp39kQ6NHf65SG53H2/CDnVFwRqDeBIHXh/HwWtC7u516PPPJI27bMdu7XXnst5ziDTjTYjs16B+MwFHr/lLtGJWkOUh4qd33tF4F6EQjybWca88vSQev0fu3yQfNkoXwftH7P+/PTHhfk/chzKjQ+gSDv+97KI6Qa5BkvlEd4Dn5D2a517LHH9nDCyH3eEKQt/Utf+pItG3NEXGpZqKOhAzqOiMXA0SgaKUiw20i/ltLa5wi4XnrlhHAUvXpDS0uL4TCWFPmxskJPIww0zrG3LQWuLtALAIO395/bV2weND08D70S0tsvvX45YTDTTe8jXm+9bmiRfC+DFOdScEhvP07cxONpdKTXXm/gi5dCxlNPPTV3Le5nL0gKGMnBBXoyoIiZgmA2SnJioODyuuuuM3vuuaeLqnkECTjvx25eKInuec2Pw8YyCuT4/PDZdIG/OQ1dzjtue3u7Fetyu9cTtIvvnbtrubl3n1t2+/LTw+0U4ND4zueXEwMbGPMLDzQKMHifTwrg6cGHYnMK0F244IILCvbM95sneZ6vfvWrNv8xHU6ky+0UBrEx0tsIyu0KIlBLAi7vuHmhaxXLZ7XI9+767ppu3Tt3+/LTTI/eLjiP8W6dczbo85tW7D1Ej9f8JlK49+tf/zp3KA3+N910Uw+hfm6nz4V169YZVmj4zXTCX5+HKpoI1IRAVMvF5W42v5z+0ksv5Q5xI1/kNmQXXGNboXI6GwFoXGTZ1RkZeBg767GDDsU73hCkfJF/HNfz31veOFoWgVoSyM873msV+676/c7zXIXK095ruGV3LTd3271zl0/c3LvP73Kh/B6kXl/sOi7dbu6Nx2HB+N5gOcIJdbmfXrnYyVdBBHqLgMs7bl7ousXKAfSYsdlmm9mysLM18Xga+1l3dcPcFStPF7qW21Yo37h9xdLD/MOObuzU6vV6T2E8v9vuHovZvtz5C80LpSdIvT7IO7LQ9bVNBMIk4PKCmxc6t3vm8+P4fZaD5Ht3LTcPkh6/duhi6QlSrw9qx6awSJ1wC/2a2lZPAi6f5eft/DQVqg/4/e6H9Z11aSr23S+XxwqV8XlOOonhMOB0MMM2PAba2dl2RZFQoeB4ubmL49rKuP773//ebc7NKSJkBycFEagHgWJ5x6XFPc9u7rYHqQsXy2fuXJXM898/FBU+/vjj1kHU3//+95yzKcZz9jnX4bXc+8fvO7Bcuv2Wh8qdR/tFIAwCLg+7eaFzFnsf+P22FytLF7qW2+bym1sPYpd3x/iZl8v3hc6Rnza/7XFB3o+Frqtt9SHg8oabF0pFsTzi930ftTzifcaL5RHqvBhoQysV/Lal8xxsH6NOjo752CGOEwO1Xxx1PX8kLrszwn9iGLYrE+H0KWkiIAJlCNC9+HvvvWcbBWjUyw9snKOolx49e8P9N3v58qXMjw4NkvnXpJGDngkoMi7UM4Muz9kLghUhvli9L/v8e+P6O++8Y4f15FCBgwYNKhQlt2358uVm1qxZ1nvomDFjctu10PcJsCDM3j30xpwvdKHX3Z133tl6rCtXYAiDVDqdtnlkyZIlZuLEiTZN+eelgYCep++77778XVbYRwECh+NhHnGGgg0iZjeUy5P5xzlWFNDn59/8uFoXgSgTcM9yFPJ9OU7l3kP81r/99ttm0aJF1gtwvlGx3Pm1XwT6C4GolYvLcS9XTl+wYIHtJcwyPkeoKGXw8VO+KJce7ReBRiJQ6jvP+yhVnq7HfZbL7+XeX9WkmQZd1rE7Ojqs126yURCBRiSwbNkyO5oTy/euocPdR7nytIsX1pz1ceYrzun1J198U872FTQdldbrC9WFgl5b8UWgngRKfe97O9+TQyk7dLn0BK3Xy45dzydP144CgVLf/bC/s5Xeb7kyPtvtOGIfRwigHT+/vFDpdXWcCPQlAuXqwuXyWS1Y0B7H4HVG5a5Tj/dPqfKQS5fmItAIBEp928uVpet5f/XI97zfcu/HejLRtWtDoNT7XnlkQ+bU2lCXtvnmm5tCOrkNj4jelsJd+aKXTqVIBESgCAEKWgtVGlx0vrwPO+ywXhPe0fjAoQqLBdcb0us51BuXotupU6d6N5VcZuMDJz+BHobonUSh/xGg8JTDURUKc+fOtZt7q8cNxTZMS7H0sMJC7xjMt4UCBccU8/oN5fJk/nlKscqPq3URiDKBUs9yb+f7cpzKpYff+s1R4eCkIAIiUJxA1MrFxVPavadcOZ1l/FLlfO/5y5UvvHG1LAJ9gUCp73y58nQ97r9cfi/3/qomzaw/uNFFqjmPjhWBehOg2LyY4LxceTrstFMwzNEyioVytq9ixxXbrnp9MTLa3tcJlPre93a+J+tSduhy6Qlar5cdu68/3bq/cgRKfffD/s6WS0ux/eXK+OPGjTOcFERABIoTKFcXLpfPip+58j2lbHH1eP+UKg9Vfpc6UgR6n0Cpb3u5snTvp/bDK9Yj3/Pq5d6PH6ZQS32FQKn3vfLIhr/y6NGjDadGDvFGTrzSLgIiUJoAPX28/PLLRYV/pY+uzV56IOEwPa2trbW5gM4qAgEJsKBNb5WlhOYBT1lVdFfgOvjgg6s6jw4WAREoTiBq+T5q6SlOTntEoHEJRK1cHLX0NO4vq5SLwIYEolaeVn7f8DfSFhEIm0DUytOyfYX9C+t8IrAhgajl+6ilZ0Ni2iICfYdAFL6zKuP3nedJdxJdAlHMZ1F4/0T3F1PKRKByAlEuSyvfV/676sjwCCiPhMcySmeKdXZ2ZqKUIKVFBEQgPAJ0lT9jxgyz5ZZbmqFDh4Z34irOxI8JveiW6qFYxel1qAgEJrBw4UKzatUqM3ny5MDH1uIAetedPXu22XnnnTcY5rMW19M5RaA/Eohavo9aevrjM6F77vsEolYujlp6+v4ToDvsTwSiVp5Wfu9PT5/utV4Eolaelu2rXk+CrtufCEQt30ctPf3pWdC99j8CUfjOqozf/5473XHvE4hiPovC+6f3fwldUQRqTyDKZWnl+9r//rpCeQLKI+UZNWIMCXYb8VdTmhuWQEtLi9l///3Nfffd17D3oISLgAj4I/CjH/3IcLr//vvNtGnT/B2kWCIgAg1NQN/5hv75lHgRCExA3/rAyHSACDQ0gUceecQccsghqtM39K+oxItAMAKHHnqoefjhhw0cXgQ7ULFFQAQakoD71l9wwQWGk4IIiEDfJ6Bvfd//jXWHIuAl4Gx5+tZ7qWhZBPo2AbXb9e3fV3cnAl4C7jvfKHa8uDfxWhYBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABEQiXgAS74fLU2URABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABESgBwEJdnvg0IoIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIhEugKdzT6WwiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAJ1IZA2JtPFKdM9TyIVKaynMtk51hkHk8GmDyeuxOx/ztwUo9sXTLEE5phiCexEy1LMTc0xE2vujoO/CiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiUISLBbAo52iYAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEBdCUBcm+6AtnZ9xqQxZdZjmevtWOfcThDcdmKZYl3Mu5exDYLdTFa0a5ch3qWAl6LdD8W62WXMnFA3N6dg1wp1MWeLEpc5p2C3BeJdiHVjLZgo2uW8FQLeVmh82z5cjg3A+gBsxxTHPgqAFURABERABERABERABERABERABERABESgPxKQYLc//uq6ZxEQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQgcgQoKg2vRYCXEyZtVheg+U13dusQHcd9LXruvfZdYh2DQS7mXbOsQ+iXYp0rRgXs94J9MqbDfTAS8EuhLrGinU5p0gX2wa6CetYjnN9EMS7g7A8GNuy8/hALKvVKgtUMxEQAREQAREQAREQAREQAREQAREQgb5IINbZ2emxqPTFW9Q9iUB0CLS0wGKJsP/++0cnUQFSkoD7hGZYXTm1wG1CE5YS+NuE8dA459+4neJwwOD955w1wICc/ZeGC4c0rMfd/5ImmeFS0nTBqtwFFxD4i7VOuz9AEhVVBCJF4OGHH27Y/B4pkEqMCDQIAeZ5hkb9zjcIZiVTBCJFQN/6SP0cSowI1JyAvvU1R6wLiECkCCjPR+rn6FOJoR11UGywGRYbYYbGhpmNYkPNEEx2Ht8Iy0PMoPgQMxhxBtnlQaYVKthErG+4pU3BDtwOlfG69FqzJrPGrM18YNZAmbwm/YFZnVllPsC0OrMa00o7rcyssHFoPa51UL6vNWGdXwSiRUB5Plq/h1IjAr1BQPm+NyjrGiIQHQLK89H5LZQSEegNAszz0MH2xqWqvoYEu1Uj1AlEwD8BJ9j1f0TvxITk1gyNDzfDm4abjZo5bWQGNQ3CNNgMbBqIaZAZkBhoWhOtmNpMGyYut2D8sqZ4s2mOU7SLCcsJzOMxinXj9l/MLmcFuxmKdCnZxTyTMSm4jUimIcxNQ6iLsdq43JnqNO3pdtOR6sDUbtoxrU+tN+uTa81aTOswrUmuMR90rTarulaalckVZmV6hRX79g4tXUUEREAEREAEREAEREAEREAEREAEREAEREAEREAEyhOglZR214mtm5lxA8ebMQPGmo3buqfRAzY2I1tHYRpphmMa3DQE9lW6qe2/IZlOwfb7gVnescxOy9qXmCXti83i9vfNovWLMC00761bYOZ3vmVWpVcBlPzR9N+nRXcuAiIgAiIgAiIgAiIgAiIgAiIgAj0JSLDbk4fWREAE6kkAdksOiZayw6h1D6WW4fBqGFLNzr3DqWHZcJ3DqGFItdxwahxSrfZODDakRBt1a8+h1GIDYsZwKDUMlRbjMGluGDUMn8Zh1GLZeWII1jmkGh0b4xAFERABERABERABERABERABERABERABERABERABEagVAfgkMKmVcFSwDNNy2F8xTy8zJr0U8+Wwt66AM4PVuDriKQQg0Azz7kaYRsDeO8KY+ChMI7GMKYFtCc6Hwg6MeAoiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEGUC8rAb5V9HaROBCgjAaS2EuDAIwzCcXoUJBuD06u55husfwDBsJ4hxrWAXF8ExDR9otIVw1wzBnCJdzDGCHAy5MNwOxTLFuzDacpnGWyvkbWr4u9YNiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAI1IkAbbFWoAtBbmoJpsWwv2KeXtwt0s2sQMLoCEEhfAJtsP0Oh52X4t3RmDaGaHd099SEOW3BGAxOQQREQAT6BgE6FE+jfQ8TnevYeRqdQOhoh9uz8+59iMx4nLDIQ+0fLriJi27ZRnD7uAIPOHSC4xzhZJcxoGZ3cPsw77Etjt2YYpwnsJPLdMqTm+O9bPdl43DZnROLCiIgAiIgAiIgAiIgAiLQXwhIsNtffmndZ98kgMo2xblJeGZIw2NDCgbgNJfthMo21jMYGSwDwW6/NQyzsk8PvRTyDoMxYBhsA/TCQGMu1hPDYTegFwZ6ZxjUbUDomw+L7koEREAEREAEREAEREAEREAEREAEREAEREAERKAqAjCzptfBHktx7vuYY0ovwvQ+tkOsm1mJs8t7blWIKz6YDh1o+x0DO+8Y2HzHwubLCetWwIuR2iQMq5iuDhQBEaiEAIW0+CZkuvB9sPMPl+lIx25Ldu+zjnW4LTuZFLZj2W53Al2Kcr3LPD/i2dExvdsZj2JdTtTfMmDeQ6DL7flTNh5nhQS7uW1sd+MrNTvPxacANyvINRDqOrFu9zIiO7FuVsTL7TkxLzpYxJoQh3Meyw4X2Yne07nPelHnfq43d697l+35bWL0RwREQAREQAREQAREQASiTUCC3Wj/PkqdCPQgYL3nQnybpMcGDqOWHVKN3hsyWM5geLUMvObaynmPI7WyAQFW+CnihXg3NgqGW0xuKDUuN3Ed3nmtQWGDg7VBBERABERABERABERABERABERABERABERABESgXxCA4ClFm+xCTO/BLot5CvP0QthiYaPtt44Sov7j04kDbbzjIN4dB/vvJrD5Yt6EeQKjsUnYFfUfUOkTgYgRoDiWotsOvP/hOZ3zDOednLzL3euG2xkfc7vMOFzOCndt5w67jO+I25bEu8kJdiHkdWJd2+ZH8W1fDB6Bb7dAF+1yTbh3J95txnJWoMt59zLmLXjHczvmpgXHZNftdrfO7wC3c90tt+K7QA/t3Mbz8foKIiACIiACIiACIiACItDLBCTY7WXgupwIBCJAYzA86HIoNTekmh1WbVHWY4OGVAuEs2xkO4wa6ucYPi2+MQy58L5gh1HDehO3yQNvWYSKIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAINT4B22ZXdAt3kgrRJvgt77Luw0S6AcGg17q6vCqca/ocrcgMQZMWGwu47HvbeCZygBRsftwLexFCJd4tQ02YR6B8E8D63Atx2CHHXQ1TbDpGtnbieXaY4l9sg0DXZfbl1j3iXHThsnKxIV851evkRokdeinAp0oVA146+SaEuRLq5ZbYDtmHdzrvjcT2GdSvkzS7HBnAd7YIDusXAEvb28m+py4mACIiACIiACIhAHycgwW4f/4F1e41HgD1sUytgDH4/bYdVSy3EOrw2pDG8Gj3osieuQi8RYEUeHnjj9LrAaSyHUoMhl0Opjcj2vu2lpOgyIiACIiACIiACIiACIiACIiACIiACIiACIiACNSQALW7qg6xIdz5Eum9DoPs2hFzwpms4qhlmCn2AAHRaBiOvxel1dzM4auA0MWaasR4fjJ3cryACItBnCHDkygyEuOm1eJ+vw3xd93oGcyvQtevYhn0ZCnSzk6F41y1DpGs94qqzRp95LuyN0LsuBb4U70KgG6M4F3Njl7E+MLvNzvHdGNi9n/Hi2EYnP5zb4ygWVhABERABERABERABERABnwQk2PUJStFEoJYEOHROEsOncUi1JLw0pOCtIf0ejAUQ6RoYEWQMriV9n+emoXYwKuDwuhsfTw8MMOhi4rxpJCrp7KGrIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi0FAErG12UcZ0vQXbLKYUp/mwy9KTbqOKdJ0IiUIkDCse45DiCSxnpxjnjEOTpnfCqr1n3rebIFDLpLDuJjtcOw7qQgQsN7SIDbdhPe9CsJvYHHZeTM2cj4nbYdRxdwoiIAJRJ4BXER3hpNeiXQ0jVlphLuYZCnSxjfPcMjpfUKibwXazLmZFuvSKK6/pUf+R65w+fi/p4IeC3UFYpkgXc05xtBvGINy1y9zPdkQKed2c++Dx135r63wburwIiIAIiIAIiIAIiEB0CEiwG53fQinpZwQyXbBxQqTbRW8NMADTCJyaD0PBYhgKaCBQiDYBDo+zMSrdE2HnhkHXemGYGDcJindpCFcQAREQAREQAREQAREQAREQAREQAREQAREQARGIJgGYYFMrYZt9B9M8jHQ2F7bZNyHsWo7kUpgaxeAEQ/DsZwZkPvQGCBGR9Q5IMRGdCnAocE60UWKKtcBeSc9/TqybXY75FOxm6FESTOil0ol2advOULDLOYRyXOfIcBQ/54aJd8PGw0OlyXqwtKPHRdVDJfjERgLTFrD1bolpUtw0bwoh71CqenEPCiIgAnUlYD3lwgtuiqLcD6CxhUf0DOdcpxA3u56xy1jPinbtewevKwURqBkBfiP43aWIlwLeIfjuwpO7W7biXW6jiBfzBKY491Pgy2+zggiIgAiIgAiIgAiIQL8jIMFuv/vJdcN1JQBjpDMEJ9+BUJfeGjC0WmYRUgWDpkKDEqAxfAyMuRw+jR4YNvUYc2n4VhABERABERABERABERABERABERABERABERABEag7AQq+kkvSpgsC3a7ZEOrOhtgLI54ZikqjEGhL5FDcG1Ho0y3u6eHBj179IPDpHoIbyxyWm0N5Q6gby87j9OTXywIgeuC1wt122Lop2oVDijQFuxyG3jvcfJ7Hy25hXbfozv4GURHz4jeIT4Cdd+vuqWVLOGoYBcYa8jwKuURp6OsE8B5I892xCu1n8HSeXo3l7Dyzqns9g/UMtts5BLrW03df56L7a1wC7DxDIS+/7RvhWzIU3xjOsc55PLue4DK3sWOO2hYb9/dWykVABERABERABETABwEJdn1AUhQRqJYAjQvJhTACQ6CbfBOG4DdhVIiSITjIDdLYS8Ok9dBAIyUM2lxn5RFz65mBy5zYq9TrfYC9mGl0zc57DKVmvTQgctYzQ8MOpQajeXw8esjSEwMmDqHWvAk4tXlBgIGCCIiACIiACIiACIiACIiACIiACIiACIiACIhArxCgiLTrPUxvQKz7BkRgmDJLcel6edOlqZACW4p3hmXFOxDsUMhDr3tWwEMPfPTGB+FunMNuU8DTy0Lc0H8c8LZCPDt0PZadp8ysII8CPArzMisxQaxHcZ4djY725HoE8I6NBvZtYOO1Exw1jIt3D29ej/TomiLQxwiwE0Ua7wM6ukkj31Okm3bLmLt3AffRU3fd3tl9jLtuJyIE+E3n953ff1cWwDw+DNvsHG2NXIand5YF5I03Ir+bkiECIiACIiACIiACIRCQYDcEiDqFCBQkACNiCsaFrrdhBJ4DoS6m9LxuA4MVrRY8qI4bOVwLPAcYemngHAZgzmN2KDXMKThlHE4U69o54nCZhktWLD3znFjXq1OlYdWJdSncpUGcQl03t0OoIQLndhn74Y3BeWWww6nBM4PBsEcZer3AZOdR9E4MwXJsOCrSkyDc3QoG3a1gzIUHXvaQ7SFixm0oiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIhE/AOlJ4N2M6Z8JG+zpEYbNhe6QItLcDbawQ3cSGQ3QzEjZDzinCyc4TFOZQkMPhsfupF1cn3LNeNT2CvfQK2NVXQMi3HLbg5R4Rb2/+hrRxQ1CVgMfd5u1jpmVb2HrhgVdOGnrzR9C1Gp4APeeibSeFPJ1inuYceTrNCesZOyGPQ7SvESkb/tfWDVRDgO2v7NDDMgLaGWMjUF4YgfZFrts5vkcsN6DMIE+81YDWsSIgAiIgAiIgAiJQPwIS7NaPva7cVwnA6JBcCqHu3G5vDclZMDbAKGwoNK13cMOuwEtDDB4aYpyj92ZsECp2HI6lx5Bq2M+hvyDU7RbtIg4My73pxcEOpeYEu+CX5nBqEOnSqMN5BvM0hlHL2AnbMPRRbhg1emLA9kgMhUSOEyHcdZ4YMIRa00hVpOudHXR9ERABERABERABERABERABERABERABERCBvkkg0w777HwIdV+FjfZViMLehH32g166V4o76QiBwtxRsKnCQ2sctsCEmyi8oeimFREZV6E4AfxsafyW9LiZWgbfD8uyAj/Y39PwkJxeApswhLx07GAdRRQ/U3h7YFNP0EHDFAh3p2SFu/wtFURABHoQsCJ8eM22+ZZ5F5PNt5wjP1sBPsW5cN6iIAIiUIYA23cp4kVZwnb8yc4TKGckKOLlOhwG9ddOP2XoabcIiIAIiIAIiIAIRI6ABLuR+0mUoEYlQHFpanHadMJLQ9draZOcCWPhYtwNhvTp9QDvrtZTLnr9c9gUem+wQ6pwSDVU6OyQalx2Q6rRcwN6bDZqyMDDLodNshMM7+kPsOyGT+MQSvCawaGT6J2Bc2vApYff3g7wjhEbA+HudsY0bxc3LVtDuDsaxlx6J1YQAREQAREQAREQAREQAREQAREQAREQAREQARGoigDthF0LYKOlUPdFCMQw6plhp/5aB3rQpUAXtr/4xjD3bQzxDCfY/qyYBkNZywteSD8C7Lop2H9TEO2mINi102LYgBfB/rsoK+CFE4iaB9jWrcfdnbLC3XHxhrax15yXLtDnCTiBbtLlS8wpqk8vQd7E3Irro+DYptF/Cbb/cWK7knceRzuf3YbvntvH/exP4Jm46F3PLXu3cxmnyU3569yHkPHG4TLb3ThxVM10rHt0zTR25La5fdl4mClUQYCdg9gJCGUN2znIlTlQ/mhihyEJeKuAq0NFQAREQAREQAREoLYEJNitLV+dvT8QQMUzSaEuBLpWqIuh1TLo3W8roL11/zQI22FRUAFj5cxOMAbbIdUwt4Jd7BuAqritjfdWwup8HRgBrAcGCHU5xFKKQ6nZIdQwh+eFXC9uinh7w4jrcMBIEoNnjabtOIQahLuTJdx1aDQXAREQAREQAREQAREQAREQAREQAREQAREQgcAEYAdMLkqbjldgp30BtkCMemboubFWgSIoeFq1At1x0EaNhQ0WUxM760MoE6dAtz/ZYWvF2c958dun1uA3h2g3CcFu6v3uKb0Q9l8sW8/KFJLVKsBpRmJbiHZ3iZvWHWDnxe8vcXatYOu8kSJAHSYcqSSR91LIe8x/aa94Hm0y8p4LBvxewDuqgdOcWDPeD80AR2+p3AYnLwbb3LL1jsptdP6Sm+MYCnAxxXiurBjXinN7bO9+93SLdj3xsTknyuUyA+ax/O2efTYO35vu3emWMc+JdBnJs53LGY8wl06OuoW7aDO1At7u9R7LLj4cH2VSOAHndIKUnewyPDB3z7P76ZHZThAEd2KbW+e5+nvgc4W24jjKI/ExeFTYcQjlEk50HhTHaKsqm/T3h0T3LwIiIAIiIAIiEBUCEuxG5ZdQOhqPACp/SQzd0zkT3hpegkfd17JCXdQPax7aUKeiKJfG31GYc8gT9py0w6qhEkavuhqGq+jPwCHxKN5NQbRrh2NyPb3dMGoYjqlXBLysG1O4i+HTmnfMCncxdI0MukV/Ou0QAREQAREQAREQAREQAREQAREQAREQAREQgR4EUuig3zkLQt3nYaN9GTZa2vZqESiW4ohm42GTHQ+bHuYJeFVt2gRz2mMprlKoOwEKu/hMJBfieXgPNmBM6XexbQEM9xiJrSaONmjnhYflph0h3N0Ndt5t4/aZqDsMJUAEQiZg8xfbVhYij1Ecj7nNY5jbdy+8nPfp4MS3bKODIx1DZzqct7BNzi13z2PYZoW5FOpyhMusYJfLVqCbFe3GmrKCXa5boS6Os/PudZPo/r7khLh9CTDaWSngtYJcCHY5t8tJzzIEuRk7dW+jQNeu41lzgl161+dksttyy3TUg4nxMm6ZXp65jPPU5HuA00Ym8Jmk93+WU8ZhwryJHYxcuYWCbwUREAEREAEREAEREIG6EJBgty7YddFGJ5CCx9bON+itIWsEXow7qmXvTRqD4SU3xooUekXScwO9NdjekRTrDkEFXhWryh8rGATsMGoQ7rre4Bw+LYVh1DKYZ+iBlz2AaxVg5OFv27QDbDbwxNCyDQy6GKpGQQREQAREQAREQAREQAREQAREQAREQAREQAREoDABim8634JX3WfhUAFi3cx7iBe2DY/iLHpQnQAb7ETMJ8KGNzFumiF2kRfdwr9LpLZCo0u7b5Liwvmw5c/Pincxt+LdsJ1vwEZvxdxT4W0XU8vm8W6hXqSgKDEiEIyAFekuRx6C+D25IG1S76LtBCL4NPJVzT1YB0tq5bH5rqe4kaNUDsiYGAW5XLZzLHPelhXl2mWsw2mO3W6Futl9FONye3Yepxddtd1V/rsEONIKfz2C3UxHt0jXinndMsS6dOiT4ZxCXk5ufT0uxu3rsT+3jG0U99ay/TfAPVYVlU2OG2XFu+Oz5Znx6HREIS9Ga1Wno6ro6mAREAEREAEREAERCExAgt3AyHRAfybAilvn2xnTQW8NM9K2Z74dmqUWUFjJh/dc9nqMj8OcPR83QeWJQt2hqFmpkl8L6vacrNinIcrmEE70xJCCsT/XSxxeeGvmfRe9puMw+jfvBk8Mu8KguxmNPKxFK4iACIiACIiACIiACIiACIiACIiACIiACIiACFgC0Igl0fG+A6OedU6H7W42NlBcE2YYROEl7LCbwdHh5rDLbgaRLu20Gk46TMq9ey48Juk1GdNF0eHbsO+/BdHh29iGdbM25KQMgPl+W9h494JwdyfY9DEynoYhD5mxTldbAhAoplYjr0Cc28X88g7E7u8gv1Cku662l67J2dH2YgYiG3LC+z02EG0vbh3C3B7LyL9xTBTs2jmFutzGOQW4arKpyU/U6yfFo0wxb5piXpQhKNRNZwW7abueFe/iec9wopDXLa/DMr4bnGz5A56BGy6wnENPu5vie7Up2iVRzrEjB9CZEAXsCiIgAiIgAiIgAiIgAjUlIMFuTfHq5H2GACpuFG92vJgynU/DkDcHG8I2AhMWRbobo4I0ARUkeG1o4kTD8GgaBmQFqNfzlEZFPLUYhikMm9bde5xzVMbRMFAT8S4MRYmtYNDdM2Zad06YJvz+MgLV69fXdUVABERABERABERABERABERABERABERABKJCwHrVfTNt2p9MmeTzEMwsQcpgogslQNBlHShMgk12C0yTINJF53rrPEHilVAQR+YkFCPCYUPXOxAizoMo8U3YezHP0FlDWF6aadLd2JhmetvdG84Z8DzR66aCCESZAL2SdkGU28V8MQ8dIuZB0AjRbuii9rAhsPmMXm0HY6IgdzDEtVy261imOJHb0ekiN0c7TJzCXQpz0TYnkWLYP0ofOR++F2l42U1TpIspbUW7mFOwi04gds5ta7F/Dbd1b7dzCnohCg6tnFIrpMgncbZHo/yTwNTCchBGBZVDoVoB13lFQAREQAREQAREAHWTzs7OsMxZ4ikCfZIAK2GdszG02lP0qosK17KQb5OG4JEQaMKbKqcm9GTksGpNo1AZkgEvZNjVn46NAsmlMFZxCDX0Kk/C43IaU2Y5zh1yL9rYaHjw2BWi3T1h0N0qLtF29T+fziACIiACIiACIiACIiACIiACIiACIiACItCgBFIr6FABdtpHISJ7A80aHKY6jEDPiRSqbAVbHGxwzRTrbgxxJT0pKvR5ApkuOutImy6IdpNzYPOdkxUowtNiKKENtn94223dr9vbbmKYHHOEwlUnCZVA+oPu0SW7mAfgtTyN/JBZgUtArBipwJEn6f12I0xD0IY2BO9vTFzOzSk+hGCX63YOka4840bqV+xbiUFxxHrqhWCXXtzTEOzaOfIURbvpD7Afy3a+uns5g23WU3XU8hc6J8VG4JvFctDW8LrLMhHarOPIXwoiIAIiIAIiIAIiIALhEpBgN1yeOltfIoBKVhJeVdufh1fdx9MmPQ83B+NdaAHGAg4zwt6KHFatGVPTGPVYDI1vL5yIvc2T76O3OYZPS74FQxaekTREvAaV8NACRNusHLd8FEbdqfC2CyG3vO2GRlcnEgEREAEREAEREAEREAEREAEREAEREAERiDoBCFq63oVX3SfSpnN62mQWIsEwwVUdIPiyttltYJvlBA+oCYpSpEupGm1DngDPVGo1PYtCvDsL9l6IFul111BYVW3gYzUONt69Y6btoxBAjYMqSl6bq6Wq40MgYD1Nw2t55+t45jGl5+OZh7fQuge+hyF2jw3lhHYzvK/jXN4I4kHM45zbCe9xinXpUVcOcOr+sykBGxKwYl4Iefl9oTA+DdFumsurUJSBp3euZ7LLnNvOSGGUcTZMSrAt9EBNJ1OT8e3anl534VQIeU5BBERABERABERABEQgHAIS7IbDUWfpYwRYgeqEYa79MQyt9hx6QS4N6QbR+5deUxNbopKDqXnL7mHVaExQaGwCrGh3wZjVNaQ8mtgAAEAASURBVBfC3bmofGMKcwg1PjfNH4EXhn2yw6fJw0djPzBKvQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQFkC1k6L0c/WPwQ7LUY/C0U8OazbPttMAcpkjHQG77pxDImuIAKOQHp91tY7E+Ld17ptvYZCqmoDRIfNu0G0e2DCtKBtQALDaoHq+EoJ0ANoJ9oyOl+GUPdVOKyZjzOhXawuga9fiANjeDfHh0N4y3l2OQ6P1JwSTqRLYS5GrVQQgUYnkMGIncyHFM1TvJvmHCMJpFdygoh3BbZh2Xq6Xo+7rZeItxX5cSLatHdAmYkTvl1xeK1WEAEREAEREAEREAERqI6ABLvV8dPRfZAAK0jtL2NotYcwtNpM1IDCGPqKFRr0oG/aBsOqbQORLoYRaRotw0IffHyMHUJtCQy6aEjglHoDler38ByFYezi8GloSGg7EMLdHVQp7ovPj+5JBERABERABERABERABERABERABERABESgm0Aaw0u3vwQ77f2wscHjadX2NQi+EhzimYKT7WCjHS/BpJ610gQyHfDuvACixtdg630Vwip43TXwhlhVQFtBYjvYeA+Wjbcqjjq4IgLsBNH1Dt6rL+CZngGh7ts4TRhtF0FSgzxAUW5sBMS4I9B2ZieIcrkOwW6CE97XsVaIAqULDEJWcRudAD4x6XYKePG9gWg3RQHvckzLMFHAuxy6XSxnsK/X8y08WMcxUiw7nbTugjLUBJWhGv1xU/pFQAREQAREQATqS0CC3fry19UjRiAJoWX7MynT+R8YKt5B4jDcWlUBAkvb8xAGuGZMLVtgWDX0BpaRoSqqjXEwKtasTHdiOKkuN5zUu9hYrQCco6WhUtxyQMwM2D1hEiNlsWqMB0KpFAEREAEREAEREAEREAEREAEREAEREAER8EuAHudop6VYN/0WjqrGTjsYAsmtYJ/dETa1HbNCXY1e5fenUDwQsCJHCnfh6KPrpazH3bVVoKGNdwuIng6JmzaMqpbQMONVwNShfgmkIPxjJ4jO6dlOENU8w34vyngD0CQ2Es88nNjEOR8FQa6bINJNULzbpnaOIEgVt38RsCJetDemINhNQbCbWgoBr51QPMI8sww86IW3NwLLVJPx/doLnU5QpqLAXkEEREAEREAEREAERCA4AQl2gzPTEX2RAHSUXe9haLWHYXB7PG0yS6q8SQp1N4VH3SkwAk+Jm5ZJMEQMUaWlSqoNe3hqNTzuQrjbieHTkq+gIj0fDxy8M1QTYhvj2doXnhimoZFhE1h49XhVg1PHioAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIRIRAEuKT9icg1sUIaJkFVSSK3uA2M6Z5V4hK6A0OQzpbj41VnFKH9m8CmQ44aHgb04tZ76R0+lGpd1LYc2PjjWmFp90Be8kxQ/9+smp89yk8pvCq2/4Entun8V59H9dDE0XNAtrHrEAXbRjx0RD3YW4nCHatUHcwHv5Eza6uE4tA3yeAPJ36AMJdinfhiCq1qHueXgwB7+KsgLfKNsiSEPn9wqiyzXvAsdDeLF+hjVJ5uiQy7RQBERABERABERCBfAIS7OYT0Xr/IwDvDJ1vQaz7QArGClgpVlWBAJ4Z4hOMaUKvwpadMKGXfJzGBwURAIH0Ghhz50C4Cy8MSXhjSL+HjV1VoMGwUM3oxToQRt3mTemWoYpz6VAREAEREAEREAEREAEREAEREAEREAEREAERqDMBjoC2/hGMgPZgFU4VKCQZCxvtzvAANxU22q3iJj5INto6/7R96vLWzjs7bTqeS5vkixBHQSRVqQAyNgaOGQ6C6GlawjRpNLU+9ZxE4WboHbpjFsS696dMEu0SphZeddkusRGaJ8ZCkIt3b/ccDm2w3kSvunz/qu0iCo+D0tBXCaCdO4X2R4p3k+9jnp3SEOensWw+wI1XM1JBMW6DUNbaBd+vQxKmZeu4iaGjlIIIiIAIiIAIiIAIiIA/AhLs+uOkWH2VAMW6c9Nm3b9hrHi+CmMFjcAbo2KyE4xr8NjQso2Gseqrj0wY98Uh/TphJOucAYPuyzDo0qMzHr+KAirEzR9BhfhwVIgnSbRbEUMdJAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiUHcCVqz7MMS69Kxb6QhosJUltoWNds+4aYNDhQSGW+fIVKtXrzZLliwx7e3tFd8nTmOGjxhhRo0aZZqb4blBoX8TgD03Ce+GHS/Bzjs9bVJvYMO6ypCwbaHlIHjapWh3FJ+02od7XsiY32PEwbkUc1UYYkjqp+FQ4kSMAjdmWIUn0WE1I0CP0O14Ptv/hedzFn7napyH5KcS3jRjI6DDhZfNODxFN2HeNA7v3E0g2h0Kb+ZN+QdoXQREoLcIZJIQ8K7MincxumxyAUb+xIgF6ffQHrkCqYCH3tACRLqJ7TAa6OEod+0A0W5r73zDQku/TiQCIiACIiACIiACdSIgwW6dwOuyESDgxLp3Z8W6ldpqh2SNwLtjaDVURqxBTfWRCPzAEU8CDbro7doBT7udz2YNZmsqTPMAGMQg2h14hES7FRLUYSIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAnUkkFqeMev+A7Hu/RDrLq0gIbDHxsahYztstG0Q63I0qlhWU0ux7vz5802M6sIQwsCBA83mm28ewpl0ir5AIAMRZBdG8Gt/Km26nsHzC4+GlThnoGi39dBu0W5ieDjPajG+FOteeGt4iq1d4Ejiqi/Bk3Vtk13sdrS9AAF61m1/KWXW/yNl0rMQIQzvmvAXEoMwOz4RolxMTZtimoAJnnStF139/gV+CW0SgToTQFskvcJ3oXNG8l20Rb4DD7zzodmdjx0ccTaMdwME/InJcCz0CbaTJ3LlrzrfuS4vAiIgAiIgAiIgApEmoD6Okf55lLiaEUA9pPNNeNb9ZxViXVRA4hPp3RQVEBiCmyfCCKwcVbOfrM+dGMarpo1h2ILXhObNY6YDol0adNnLNXDv1vUQ/z6LRo0YjKxHwBsDPe3KONbnHhndkAiIgAiIgAiIgAiIgAiIgAiIgAiIgAj0RQKp1RmzfnrWs24lYt02CEW2iZnW/WCn3RkeHof1NIzRs25YYl3yX7t2rVm1apUZOnRoX/w5dE8BCVAYzqHAE/CM2z4+ZjofhSBqDhogAjoIySw2puNBKKdajRm4T8LEh/R8jgMmq2R0etYNM7wwL23ufi5mjoZTCYUIEGD71xyIyO9Be8MbSE+1P/dAtIXh2U5MQpsG2jKaNkN7GD3rDvL/e3d0dJgnnnjCTJ8+3b5Dg1LiO3zLLbc0++67r9lqq62CHq74ItB/CSCb8nvSygnfKivehafd5NvwvPsWxLvz8IqAB16DdsaKA5omUzNRlmtNm9hAXAej0KqNsmKaOlAEREAEREAERKCfEJC8sJ/80LrNngS65qfN+nurEOsOhmFiCiod+8IIvD16jtfQeNYz5VrrawSsQXdLGHRHw5iLHukdj6OS/Boqx0G97WZFu+ubUyZ2JITkE1AhVhABERABERABERABERABERABERABERABERCBCBNIr8cIVM+nTccD8Ey6qIKEDocdbCqGYkan+JYt4FABQzPnh/b29ooFuxtttJE9Hb30ukDh2Pr16yXYdUA0twToFZdC26YxEO4+nDJdM2DjXRkMTmYhhJbICxRCDoCTkFibf0FkkCvNhafFSsOXD+t2XX3NvXAt7AlvQXCsEA0CyUUZ0/5oygroAjsHcbeAR896090CAt2tY6Z5S0wQ6iaGcoeL5G++bt0686c//tG8OW+eGTJkiBk/fry/Az2xUqmUmTNnjp2OPfZYM3XqVM9eLYqACPglEB9MQS2mreImtQqedyHc7ZoLAe9sinczJoPvVqySTwRFu6/i3TM6ZTtO8VuoIAIiIAIiIAIiIAIiUJyABLvF2WhPHyWQhDFq/YMwmD2HGkfQHoO0RYxFj/m9INTdO25a4FXXwNOugghUSyCxERoW9kiYBA26m6CCjCHUbCNFkIrxOgy/9gye74HoxfoxGNLgwVdBBERABERABERABERABERABERABERABERABCJJAOKOzpnwAvlAymTeDZhC2mnHwU4LhwptH4W3x7Hhe3NrbW01TrDL1HlFuwFTu0H0TAY2PIh+/Ya2tjYTj6uDvl9e9YpHwXjrdnDwAefL7SPTphPOGTLvIzUBbLzpd+Bp936IdqEVb8PQ4lFqf5gKxxNnHPZhs6JXtBvgFn39PCtWrDAvv/yymTVrlpk4caLZeeedzSabbFL02GQyaTo7O+3+AQMGlBXpu/gU4DM+Q1dXl5247OccjMdr8lzMn8yn9Q4Z6Kg7XoSzmhfwi3RUkBq+W0fisdsWAl0Mcd+8Ld6vm0A8Ds/PlYZHH33UinX3228/c9RRR1V6GjNz5kxz8803mzvvvNNMnjzZDBo0qOJz6UAR6PcE2LyNjiaJ4QnzwuqnzI3X3mxa5w00u4/ay+w0YjczZsAYE48FLHfgnZNE23vHZhDtjmgydFikIAIiIAIiIAIiIAIiUJjAhzXrwvu1VQT6FAEOr9b+JMS602Gs+CDgrSG3xLeAwe0AGF6mQliJikzYoWvGkyb12vMm9dZsk17yvsl0Bhw3K+wE6Xx1IZBJjzDxzj1MOjHNZJKbIw0BXtVw9tF59xKTeuDfJt72oInFgz7odbnlul00fUZxI2/dEqULi4AIiIAIiECdCBx910N1urIuKwIiIAL1JzBt/afqnwilQAT6OYGWlhYzfPhwK8zaZpttzJQpU/o5kb5/+53w6tb+IIZsn4t7DaL2g34kPgl22kOydtph4dtpSd8r1uUyh3TnFEZYsGCB2WILGJt9htdff90OB+8zuqLVkwAex+bxEO0eGjPxYdBMwmNu+m0kKO0zUcgLqTlox3gobb2ZNm8eUDDl8zKVRHPedXkshbvPzk2Z5+b6vTF/V3z88cfNGWecYWbPnr3BARtvvLG5/PLLDT2s5oc77rjDnHjiiXYz43z5y1/Oj9Jj/bbbbjOnnHKK3bZkyRLrMfuGG24wZ511lt320EMPmX322afHMYVWDj/8cPPYY49Zr7Hz4EG23iG5EELxl+Elc1kFKcHz2kSR7k7wvomRJRNjINQNwWENBbujR4+uSqzLu6FI94ADDjD33XefFe/Wy8suvwPs0KHQNwj0599zxowZ5rzzzjMPP/yw/TFjcJ/94MLtzbSxB5l9xxxgdhkx1YxsGxXIqXZmKTo/4B2UnJzWSKB9I4voLkRABERABERABGpEIIAKrEYp0GlFoJcIZNC5uuNFDK/2GHq1Lw94UXSMTkxBT+tD4VWXPeQHhGsE7nzoLtN5/50mPmJjEx+3qWnacXdYRpA94WmBV/Laq6Oy3nrk5wJCVPQgBOxwgK+i0eK+NETceAKCtAWkR5rM6BNN87H/ZQZ8JFFwKMAgaemrcVf/9zH21trGHgm3AchZyG+NPG8bg/tQEAEREAEREIEKCax84av2yFOnnZUrf7pyZyPOeR8KIiACIuCXwH6XTLZRDz74YFQLUA9H/aCR57wPBRFoRAL0Urh06VLzzjvvmKefftrcfffdZt999zUf/ehHG/F2lOYyBFLLIUh8Im2Sr8AekywT2bsbJtMEhnJu+xhGQNsZdtqBLK2GHyjQ9Qqy6F03LLFu+KnVGaNIgA4/BuwD2+wgjKh2T8qk5yCVfp91eElNvgQvzGPgaXcoPSDW5jkPwo1iXXrYdeHae5Ohi3WvvfbanGDWXWfIkCHmgw+6nVIsXrzYHH/88ebcc881F198sUkkCqtJzz77bHPggQcadv7wE1IpuPtGOOaYY3LX//Of/1xWsDt//nwr1uWxX/jCFzire+icw04Q3hYlH0mCZ2g6q2nZHe/VXeKmiR51Q2o9dr/dqFGjfCSkfJTx48fbSKtXrSofuUQM5xm5RJTcLq+H85/+9KfmggsuMEcccYShSNx5Pv/a175mnnrqKStK5v7+EC699FLz17/+1ey+++7miiuuaLhbTqfT5rjjjjP//Oc/7fvku9/9bsPdQzUJ5u/3wx/+sMcpIPU3MzteNbPeft08ufgxc9j4I8zB4z5mJg/d3rQl/HsQT83OmK43MxDs9ji9VkRABERABERABERABDwEPqxdezZqUQT6IoHONyHWfQRi3QUB724wtLN7xMzAYxKmdadEqGLd9KIFZt0vzzddLz5lmvc4AMMM7WhiG6EbM4c3S8OogoZCa1qhkJALUVoPiFHRgxGgKLxtl4R97po+AoNswNGd0vMx7MzDadMFTyUKpQm4PNbo89J3qb0iIAIiIAIi4JOALW+y7MlyaAPPfd6uoomACIiAlwBFugyNPvfek5ZFoJEI0MPuuHHjzF577WVOPfVUc8IJJ5i5c+ea6667zgp5G+lelNbSBOhYoR2OFbqegd1qbem4PfZSrLsdRJCfgGfd3Wor1vV612UaKNitVbjwwgsNvYoWm5588slA3nhrlU6dNziBOMS6Az4SNwOOxoh928LGG0QEuQZeCp+GjfcVtGlAwFvPQLEuPep6wzX3hpuoN998MyeWpUj35ptvNosWLTLLli0zK1asMH//+9+tF1um4ec//7n5z3/+403OBsv0ntvVFSyN9OB79NFH23PddNNNhqLOUuEvf/lLbvfnPld/Byd0ApJagJr8ilyyyi8MhUfoj6L96zNoizgkYZonhifWLX/x+sU455xzzLBhw3xNfBZd+NnPfmYXKfJ87bXX3GbzyiuvGHorffvtt3Pb+vrCzJkz7T0z7zZi4O/H35GB75T+FAqJdb33P23//cwx3z7KDPvsAPPuLnPNjK5nzPIO/267+Q5K4l3Ed5KCCIiACIiACIiACIhAYQIS7Bbmoq19jEByGQxb0+GpdBYqB0H0i0NgrNgrZgYdBbHu1uEaKlJvzjRrL/ueiY+dYJq2ntLtUZdePhk452KU121C9aeWBNiLvXXbuBkIg24zROMG4nHfAc95aiY8lTyF5x4eSxSKE8jmuuywPhz0pzt0zxtnvfgdao8IiIAIiIAIBCCAD6D9+tkPoV3BwT3nDbE/wC0rqgiIgAg4AvSsy9D9l1Xy7Hr+PHtAVPdnk6eZCDQ8gQkTJpiTTjrJbL/99uaaa67pVyKYhv/xytxA1zsYsh222sz7ZSJ6d8ORZgJDtQ84Ch4gd4DX0lb3tvZGqn6ZQt18se6SJUuqP3GJM2y99dbWQyG9FBaaOOy7++aUOI12RZQAn9W2nROm7ci49Q5tCjuFLZj6zHto13gS4vb5QRo1Cp6q4o2FxLpn/F+Q4eD8Xfrf//53LuIDDzxgPvvZz5rhw4fbbYMGDTKHH364mT59ei6OV0SZ2+hZePbZZw1FaUHDySefbA+hZ9gHH3yw5OEU9TLsuuuuZocddigZtzd2pldCIEdNnc/HJTYSXnUPQPvDJ/F84r0a9siSvXHPvXENr3f10047zV5yxx13NJMnT/Z1eY4UwE5JX/7yl33FV6TeIcDfj3mXgR3F6hnYKYjPCKc//OEPNU3K9ddfv4FnXXdBeodmR4X77rvPepL+zv+cZ075+RfM4VceaOaPnWcWrV/kopaew2l5Gu3y6eocYZe+hvaKgAiIgAiIgAiIQIMT6NkltsFvRskXgUIE2Pu84yUYtZ6HlaK9UIwi27Ji3YEf6+5VnGuxKxI9yGbrWffqH5vm3fc3sYFwnUo9JW3MjTQPcsOKWzkBdKtomQSx+BHGrIunTNdTeEjgXcFXWA8vDM/Cs/SmaMzYGw0Zzb6O6neRXLbrvvFur9YfNvk02nq/+/l0wyIgAiIgAmETwIcxE+MfnLjIvCH2h81F5xMBEegXBHKedbN3m1vP97wb8f394sfSTfYrAvvss48ZPHiw9bZIsUtYw2r3K4gRutn0GggQYadNvZEtc/pJGww18a2MaTsCYt0psHFh+PZqQ2trq/GKsHg+bssX61JEkx+v2mtXc/wtt9xiZs2aZU9xxhlnGArbvSGVShl6gFy/fr3dxzjeQG+9HMZ9zpw5Zt26dWbixIlm3333NZ/+9KdtPvPG5TI9iL744otWmEYPohRV/geeTblt2223NZ/5zGfMfvvtZw/jNX/3u9/ZYeEXL15shVDMvxw6vlig51QKL5955hmzcOFCM3r0aLPbbruZ//qv/zL0dloocBjzf/3rX+a5554zzz//vGlubjY777yz4bUOPPDAQofUfRuf2TaM3mcgYlrflTLpOUgS61zlAuLQKUPHC2nTNCZm6LG3VmHqlnHz3NyeSk9uy/ese+29yQ3ihZEmJ9jl777LLrsUPOWYMWOsCPeRRx6xv3vBSJ6NP/rRj8xhhx1m9txzT8/W0ouMTw+/FOzeeuutVihc6IjXX3/dvPzyy3YXvfnmBz7TTCef0zVr1lhBL+/ruOOOM01NtWmaTcNjecav1/KBcFazJzzrHpowTRvX7rnK5xK1dT5vf/3rX0sma9NNN83tpyfW8847z4wcORIDZQbzicVnSiE6BJgP+U2kF29+e6IS1q71m4krS/FXv/rVggdSrMtpg4DXAz1v73/RXmbONe+aNS+sMYOby3sX4rsovc4aFzc4pTaIgAiIgAiIgAiIgAgEG4BHvESgIQl0LYDHhufgscFnxz97kwNQAfkIjBU1EOvy/O1/uMo077q3FevSFJJx9pAC80jvt7D0p+YE8BA0b4qh0z6GZyWZMkkMhWYgxvUTMguNff6bN4/Zc/g5pr/FsdmOHrPYCI95LDtvuPX+9sPpfkVABERABGpDgN9CnJmiXC6xnNqI67WBo7OKgAj0dQL0XkiRbqPP+/rvpPvrnwQoxqPgiaKaL33pS/0TQh+56845cKzwAmwwAfQoMWil2g6jB0h0ag9BrOu86FKMy8mFQmJd734Xr55zeuN1ngApFswXmtFz3oUXXmiTeOONN/ZI6te+9jVz9dVX99jGld///vfmoosuMk888YTZZJNNeuynMJb7KerlsOcXX3xxbv9DDz1kz0cB26c+9Skr3F2wYEFu//3332+HGf/BD35Q0JvfG2+8Yfbee28rjMwdhIXbb7/dfP/737dCR3qx9IaVK1eaL37xi+Yf//iHd7P529/+Zte577LLLjMDB0KNGLHAZ7d1p7jJwK67vj1lMvN9JhDOG7pmZEzn1hnTtiNrZ+EH50WXYtxr7oUHlGzgdm/I3+/dV+2yE59T7E0ht/Oum3/ec845x3AqFR599NGckJzib4pm2fHDT2hra7Pe3a+66ipzCwTyV1xxhaGH3/xAMbsLFOG6QNH8T37ykx55hfucIJneVpk3N9tsM3dIaPNMEqfi5CPEcEtNEGT3Z7EuMfG3pWfzICFK4s4g6VbcDQlQdN2ffs9f/OIXG0LAlkJiXXY4mDZtWi5+09i42XTaeLPy7XX+nArhU2LfSbkzaEEEREAEREAEREAERMBLoDbdOL1X0LII1JFAph2GLHjXTc2CERj/fQXYoJp2gkdS9Cxmr0GrUPB1oL9InQ/dBeEDjMsbYTgnJssqILBgBYM4R956ZPf7u13FCosAnouWzWDQPRSedtelTIoNGx/aTotfBdFSr8MLwyvdXhhqNVxg8QREfw8QWZFuo8+jT1opFAEREAERaAgCEKp1lz9ZTsXXkf9t+bTB1hsCthIpAiIQNQJFPeqyUx9Co+yPGlelRwTCIkDvmRQMUlRIwZNC4xFIraKtFkMkv9P9XvVzBxyyvXX/uGnbBfbUNhZMqwtOrMuzOIEuRblcpoddF/LFvG57ved77LGHFdbQc+jdd99tPeBSLMvw3nvvmbPOOssuc9vnP/95u8w/P/7xj3NiXXqUPOaYYwy9lT7wwAM2T1Foe9RRR9n1YcOG5Y5zC4899pjh9JGPfMTGW7Jkibnyyivt7nPPPddce+21huc4/PDDrbiX3nIp9KVHyUsuucQcfPDB1gOuOx+PP/roo3Ni3SOPPNKK5mbMmJET33784x+3nne9ImKKjp1Yl0JMnnfRokWWxcMPP2woWN5qq63Mt771LXepSM3jA2KmdWrcpFYiL/wbTkaW+0te+i3EfzltmjeLmcRG1ecD71WdWJfbnDddina5nR52XailWJfX4Dv+//2//2cvx+fopptuMttss427fKD5pEmTzG9/+1tz2mmnmXnz5pnvfve7Vnjr9yQnnHCCoWCXgUJbl8fc8SwTMn0MfI6Zl1ygl2knbOe36uSTT7a7+HxyqHt+w84++2zz97//3R0S2jzGFl+frb4ZaO6S8zImOTljmkaH+0yFdkMRPNFdd91lnn76abPFFluYQp6VvUmmt3N2NnrnnXfsZgogf/jDH9plen3me88bgnocZ2cJvmP5rqTHc6aNHSneffdd+971epYOem6Xrrlz51rP6a+88orp6uoy22+/vaG3dXpCDxquu+46M3/+fJtfzjzzzIKH833uvi304O7ttEHv7o8//rhhWnjf7MzFie8L9z13J12+fLn51a9+ZVfpUZZea//5z38a5kP+Jn/+85/tMb/5zW/M0qVL7bdk//33d4fn5kGuSY/vd955p6Hon++cmTNnGnZcYQeCZDJppkyZYo4//vge90Tvvr/+9a9Ne/uHw8OyowA5MTA+j3OBZRP+zvxWvvTSS9aT/g477GCfgcmTJ7toRefshFMo8Nn0hkMPPdSy4jZyue+++0xyMd4Xb6VNS/rDspL3mA2W8S6y76QNdmiDCIiACIiACIiACIgACfisugmWCDQmga53M6brFRiAV/lMP+wSdni1Q+KmZVL4Yl2movP+OzHU0AHdCXJ2EIp1GRptvTvV+ttbBPB8tMJImjkkY9atxtBpb+DCPto3MiuN+eDpdWbFcLgumeCzi31v3VPedTgMUUtLi/WCMWAAXF33QshlO3st60swu8RZ46zbROuPCIiACIiAJcChWDm8a2dnpzWKNyqWenwXWR7t9qzLQgZXnKfdxlpv1N9c6RaBMAjoHVg5xUb3rOvSXzkBHVkvAsq3/slzuHsKnsIU7L76+izz4iuvmXlvzTcr4L2zUcNwiCwnbT7R7LzD9mbKdttG8ja65qVNcibKlZ0+kwfTUNPuMdO2e8LEhzgLjs9jfUZzQl2vWJeH9qZnXYpmm5t7ejL1Jn/s2LHWE63bRiGQE+ycccYZhvlixIgR5pvf/KaNQkHu5Zdf7qKbBx980PzP//yPXaen3HvuuSd3PXrVo7D29NNPN/TYS0Es1wuFz372s3afGwaeokaKLBlmz55tPfuef/75uUMZ3wmgKHBzcRmBXoIppGTg/e+33352mX84RDmPo6dVetTmvTKwjvenP/3JLlOQSy+mLlCoTPEbxWpMf1QFu0xvAs/ygL3iJr0EbRePIj98qNNyt7PhvAPiytcQf8eMSdTIy667KEW7FOp6xbrc5/W86+KGOafwlQLDp556yorRKELbddddzbHHHmu3T506dQNRXqnrn3jiiVbcTS/UFJQfccQRdip1jNtHj6uTJk2yzyi/OfmC3WeffTb3/J500knuMDt3nncpbqdYj/V6Bj7zFJNTzEsRMEXrYXv2jMOxND3n+gpoKuianjbr8OoZgE4RzZvUpj3MV1oaKBLfp/S6TCFpOcEuxaB8j7nA5UsvvdSufv3rX+8h2K3E4ziFnjxnOp025513ns073mu55UrOzWMp0swXFd97771WCEsv6EGD9/4//elP9xC6u3NRyO4YuU4nq1atsl61nUjexWVaGPhb8Dvh7dxBb+zuPPRmTT4U+bpAeyHDNddcY79fFCO77xW3V3LN1157LXfNvfbaywqJeS4X2MmGIm4+P/x2M3jT6eJR1M+Jge8RJ9ilAJjvIn5v88N3vvMdK/ylOLlUoGC5UOB2tsuxTEDxrjcel5+88xkzZe2upoujf0Ls7yfwXRQfWJuym5/rK44IiIAIiIAIiIAIRJ2ABLtR/4WUvooJZOB9tHMWvOu+iQqEzxAbS48NCdO6LYwTCZ8HBYiWnPGkiY3YGLWUbNZj0lhfacR5gPtW1BAJ4Lls2Q5eGKZlTPtKeGFY5O/czQvazEarB5qBmzRFulcrexqzNzGNJ5zo0YOGgloGl/26rwGvglj40IxQen3l+piZubjVLPqgyXQku4/qefyH2dvdQ6H9bU0ZM3ZI0my7cacZOoDCKBdKX59nzz+fO1JzERABEeiPBGhwp7GbgUOgDh06NNc41og86vFdJCf7dbEfGPzh/6ynXYp3G2a/Tan+iED/IqB3YPW/d6N40C2XzupJ6Ay9RUD5NjhpDplOgdOrr76aEzAEP0v3Ee++t9D8/e57TQpCl6m77GQOPXB/M3LkiEpPV/fjli1bbmbPnWcefOQJ8/Bj080njjzMTBi3Sd3T5RKQXg+xIUZBS8PBgt+Q2Bpi3Y8mQh2y3Qlxvd748sW6FNL1ZrjhhhsMp2KBgsW99947t5vi3htvvNHstNNO1n72ve99z3r5pDCRgeLEUaNG5eLTux/DkCFDzB//+MecWNdFoKdaCnQodKWgqFig+MyJdRmHokYKKunpj+emYMgbmObx48dbz7svvvhibheFt05oRQ+RXrEuI/E4inF5X4zHehFFj++//37uHHwXeAP3M+20K3rT6I0TpeWmMfAaDa1z+j2MpPaqvzyRno88NBveDbeKGXrqDSs4Ia7zrsvz5ot1z/g/KIZrHJgnKbqjSI9CVwY+W5xcoMiPz+shhxziq65PT530yEmhIIW1r7/+uqGgvVxgJyiK2ClAp0dneuukKN6F22+/3S7yuad3T2+g10sGigedWNft5/m+8Y1v2NXBgwe7zaHN48PwbDCZaN4y6fKnzSxFG9pDaGNYnTFt+2ZMC4TaYXgyL3/l/hGDvzfbOJxwd7vttjNf+MIX7M2z84QLlXocd8dTBMrA9+0BBxxg3//jxo2z2yo995w5c6wnXXsS/GHHEApa6d2Wzz+9tvP5DxLomfeiiy6yhzCvf/GLX9zg8Ntuu81uo1CVvBh4LSfW5faDDjrIvuf5zmdHE070Gk+xf6HgRKwU9rLjCOtS+d/9/OOqvSbfC+TDdxa9ErPc+jt432ZgB5PDDjvMbL755va9Qi/0HR0dhp7zGSiSdt9858k4lUrZ9x7fZQx8tnhevtNuvfVWK+KlCJzxvZ6VbeTsn6uvvtq7WnDZpcHtbI21mV9/7Uqz/cJdTOfzeKn4dY6Fd1B8FN5HQ92ZNBcBERABERABERABEcgnIMFuPhGt9xkCycXw2PAGjF2rfd7SAGOaPxLe8GqFrpp87XmTGLfph2pA2NVoWrPDDPOARlsvdJPaVnMCcQz/17pbwnS8nTTph+EDr4NPUZmAinQX8kNqJwxxtYmP+GVOV6vdNGLSWMmJQxNxCCQ2MNTS266lASMsLDXIg+CZnZdan7e8xdz1ykDzwgKfw/8EALbLhA5z1JR1ZtII9PL2mR7GUxABERCB/k6Anuk4jN3w4cPtd6Qv8KjHd5EFUn5VWD4tOMe+gttd/Ijs7wu/v+5BBIIQ0DswCK3icZ2H2kafF79D7YkSAeXbyn8NDvlLb3HO41glZ6JX3RtuvtUc94kjzV57TK3kFJE7hmJjTryf6U8/Z3515XXm1BMxlHJEvO0m38NQynNhe/HjTRR0Y9CbtuwZwyho2YJmiMQLiXbd6bmPwpneDBT1lLJ9eYWCLl3MBxzum+I/in2dgJAiQ3oS9YZnnnnGrlIYO2bMGO+u3PInPvEJK9iluIzDt2+6KWzYnrD11lv3ECy6XZtvvrkVVFIglC9OZBwKpBYsWNCDKQVfLlC0RkFufthjjz1ym2bNmmXz+6RJk6wAimmkN2EO8U4vvttss42N6+pPuQOjvIDHumXLmOnaI2bS76PL5DIfiV0PL7tzkI8Q3+YLH4f4jVJItOuOvfbepHlurg/1pzugijk73XLYenqSpGdbivT4/LjA55wTvUxStF7O0QPtytdff70VtPO5oXDPecB15yw251D0FMUxULR78skn22U+rxyynoEi4La2Nrvs/kybNs2mkcfQqy49aVLczueTZcxaCHXdtenNMjEedfpheKaWu61l5ujz3PUYOlO8n8bzCCHjTnHTNBbCXTgOUaiOgBOK3nnnnVY0znILPb3mh0o8juefgyJQdoBIJHr+cJWem8Ji5hkGer31CtMvu+wy+0w78Wh+Woqtb7nllnaEBHqPpTA3X7C7cOHCnGdX572YnfJ/+ctf2lN+8pOftF7W3T3SWzW9wlOISmE/25KKfeNuvvlm+70oljbv9jCuyY4B7CxA774uHHrooTnBNt9j3/72t60Nk88Eyx5OLMtvOL3LewPFyI43O+d4PR/zHUMhM/fzvMUEu87Bgfe8xZZpGTzxgFPM14/8lpmwfJLpehzlt26nxMUO6bE9ho4DTePC7VzS4wJaEQEREAEREAEREIE+QID9LBVEoE8S6HoL4kRMfkMCPdNbd4+bxAhYy2oUUm/NNmYQep1SEMikYW5T2IjrNWKk0/ojkB7SZdZuudLEJvmLz1j0Nt31du8YV/2nqnhMGi/pNYfiKzdEUfHYle9xedDv/IHZA80l9wyviViXd/H/2TsPOLmq8u+fmd1NDwRIIKHuQuidIChIwNAUqUEB/aMUQSQgKk2KCCLlpYM0QZAiiEiTIlWQFnqUEumBJAQIhAQI6dmdfZ/vmX1mz569d+bemTu7s5v7fHb23HLqc099nt95zsvT+tr4ScftI0rlr3wOpCFTDqQcSDnQ8znAOMF4wbhRTeVXd3Kqq8bF/Ngjo46dp/Zgtzs/Vpp2yoEu5kDaBybH8FKWa3vK++Q4ksZULQ6k7bYyzgJ+mDZtWtmRYFkXsO4hB/5frwHr+swAtEv5KCfl7XaSaeXiyTmDhdBIJFqL+g1EVrthncn0rY6sFnCMAnc1T0HP9F013SuvvNLWaep10I/3QfSzn/3MWtzjHcAqrCuec845Hbwydj377LP22QYbbNDhnXsDAFjJtYarz8JAUPo+zFphkLXbl19+WYNZK4KcjuL/tt9++4IfzQ9gR9cSMWAtyoS1XYBfDz30UCD4txBRjV1Qt/ttLPqI9aSOR9TU5UTf0TyVdVryhQG0CzjXJe4VzOs+r/Y19ZHv+/7775spU6ZYC5JqmZS0Ad0qGLJUXnbeeWdzxBFHWG+AaNXKZalwgNaxKgoBHlbCYrUC57AG7NNpp51WsOILsBerpNRvLIACGvT7HT98pfcNYiU3K2DwWCR7FLD0vPAfOTP3jhYz/6kWCww3LbFi6ZGeqWPLLbdc6C/oGydZ0KgWx0lTLY776bOZ4cILL+wE1i03bjZDqGVrLKe7YF3Sxno0dbkcAkAM/fvf/7YAWzcO18L72LFj7Sssb2Phmh8W4hWsq+E0Pu5feuklfdzBPeywwyKDdQmYRJqAp12wLvFibZdxGnrnHdEVx6D33nuv4Fvj0AfDhw83WESGR2eeeaY+7uQuXry40zP/AUDdtfqsY246/DZzwS6XmRX/12hyb8iAEwOsS5zo2xvYcJVSyoGUAykHUg6kHEg5kHIg5UAoByKKAULDpy9SDtQkBzhiDcEVR/pEoqXEuu6mYrGhsboLiNyM6bIzWQxbi3BR1j15lwz2xPtIjE09VYsD7IYdsuEga2nXRDx5qFVO82sWxUjrgipIdKtUUCwUYCkxzu7fuFnRVp9381YFiSPo/jEB0f71pYgMj5sRzz/pkF7U/HnB09uUAykHUg4sURxgnGC88C3b9DYmdMW4yMBjR0M7ANkbYWNHt0e8720fPy1PyoEiHEj7wCLMifkKIBCU/y+u3vtuW7y1+r4te6lTwxxI221lHweLiYASyqV7/vmwtay71sg1yo2iR4SjfFgQprzdTbm5YlgBmZRYcoxCGTmxvmFDsfK4vPbIUULF9+MCdN3r+DF1TwjAsIABlZqamjptYMRasFpJHDJkiHrt5LpHm1fSvjpFHPBAwY4BrwIfffll+xncWAJ+4YUXzLhx4wrHsRMfllh32203a4l38uTJgfHU4sP6FbK2rmeGRctd6+ci350m1lClTVWDXNBud4F1/XIBDtxrr70sWHvChAmF7843j2rkARAbVqIhrFG64Dc/PfdereoCLvzoo4/sKyyDQrQ31xK0fSj/sPiMVWuAuy6wDsvBBx98sD1NDnB5tahhRalTsuEBC5dxydYvsaQ5//YWAe42m7mPt5jFU3OmNSZYL2663e2fPjLsx4kI1aQgi+NYcXZ/bj3D4rhP3/nOd/xH9r7cuN1w9LlBxNjjjhtBfoKeYSVXyQXo8uy2226zr2jvzPUgxjk2sPNzrbjDH4DF7maRuXPn2jD+PwXe+8/D7pNIE6vaPrF2XHfdde3juOPgZpttVohu1113NX/+8587AJ6RF8KjYlbHGxoaCnH4F/WmwWzcXzZ7rTrOHLvhb8xWX33LND8r40zEeZsbX2a5tjnciBSC4vIlvU45kHIg5UDKgZQDKQdSDvgcEORgSikHeh8HWmaI0OpDWUyU3jBoC89uvz7ryTE//aorBG5dxJlvki/kaSQV053wznvmivseMg9PeMV8MXeezXs5/4YMHGB2GrWxGbfrzmbUmqvHzkc1dvCXU44lNYwKiQYPHWQWbZAzzbL7veUVKlMJEsFaiwh0mz9rNQ0rV7eul8hJrNcIGtgNTrmLHQ8YK1LHszbD/KO81et27rTfvz+rwdzsgXVHLN1sBja0mg9n15n5i8oXQPTvkzMrLSWC0MUZ8/GX7UMz6TUtt9g0LaudWXt+gvPrFCy9TDmQciDlwBLCAR0XGS+WBKr2uGinqhkZHe0AGey2Bryf8uYH5tE7nzQTX3jDzJtTvkJrwKD+ZoMt1jXbjx1tVltnlcB8BKXv5te+XxIqQ1rGlAPCgbQPTLYaFCzotkVbuOdUHKHCvfcexTlH3QLOmDNnTtvb8hz6eRS8WGHTI74L6Wo+vPT99+WlnIbqKg6k7bZyTgNGiArS8lP73xtvmZZcrtda1vXLi6XdCS+/aij3+uuu7b/usvvmT0V2haHfiNYa69YWWe1aImcpX9QSuWxq7VLdyAFrwOPbb79tTjnllEJOnn76aXPttdeaQw45pPAMEM+GG25oXnvtNfPmm28WnvsXWOdTKmaJV/1U4pIfpQ8++CD0CHP147ubbLKJufjii835559vywWY8tJLLzUffvihtciKRdUgUJsfT03cy+n1fdbOmsUjRcb7SX6+UTRf0oZaPsrLd/sMapdgFg0T86Va1FU3ZvCqeqfuAL496qijbDrU61GjRpVMEwu3AHy//vWvW78AZ13LnGERAAJX4hh6LDkrYJc4dPOW+lEXkPFJJ51kf5MmTTLjx4+3ALtnnnnGeiFeLGz6Fjg1fCVupo+xlps5fTLuMfY2XcQAn4pqTXRszRMFsCu6s/o1c6ZhddlEsUrG1C0l9a46Va+SYpcddvnll7f9ZlgEChwNe1/pc9/ieKn4sDi+/vrrd/AWlsdy43YBu2uvHTx3oO4DIn3iiSc65KXUDRtH9ttvP/O3v/3N3H777RbETpjp06dbq7tc//jHP8bpQI899pi55557zCOPPBLbOi0WlMuhStJcZRWRZwVQOSBnogHoe8wxx5gLLrjAWvjGwj7ERgQAvFiCZmwsRjvssIP57W9/28HLsLoVzMZLb2Y2WXaU/a2/zEZmWL9hJjunvAlYc7bZ9N+sQU5IEH17OD64Qx7Sm5QDKQdSDqQcSDmQciDlwJLKgXZU0JLKgbTcvZIDCLdyn0QsWn85Yk2EwA0rdaGUQZMSl8tW597m2rnX93959Elz4nV/NSftu5c55yf7m6FL5a182vdFihr0/rPZX5m/P/GM2eN355qzD/qh+dH2o/MxOOlqPjR9ve/g5kOl/7uYA4BXETJCAG/r18qYlrdEkgYevATlpotQV4RtDSuX8Fhjrykv5a4GYNdWeyxmofwWN9Pm+vf3/W9gB66sOWyR+VDAtduuMdcc2bTQDO4jEnMnHg0fxf1qUZ157v2+5p6JAw3xvjNDJKttdJ88+/m2YkmkRP7Uf+qmHEg5kHJgSeOAOy4uKWWv5rhox0JhJKBXLOkyT83PB8Pvn3nweXPbH+8xu/14Z/P9cXuapYYMEvxsm3+Jy4aPeD/7iznmxcf+Yy4+4Srz/Z/tbrb69pYl0w/K35JSF9JyphxI+8Bk6wCKZ8CvcVwspF199dVm//33NyhOOUI1Tng/PSyvAjwC5IEFuJ122il2fMlyJY0taQ6k7TZpjsaL75WJr5tRm2wUL1AP9015KXd3AnZbBLCbE3lUJFpKZLVynHvdcszykqFSMZUD1vWP5U4mp9FjwbrggQceaANsueWWBlDVjTfeaC3Pjhkzxqy++uqFyHgPsJGNJWHEeyUfDKbPk3I33njjQlSvvvqq2XHHHQv3cS6wtrjpppvaHwBOjj0HlMkR91hQdXkQJ96u9ls/TOS7UuebX5U28lXp1FtF94F81zSW9hvmQ0WYYe/LAev2rQCcBahVrYheeOGFRi3bBuWv3LYHuPD3v/+9BbkDnFXwbFAa+gxw3Y9+9CNbr2hfAGzVYjWgwyi0xhprGH6AELGKqUC7p556qiqAXfJUL5Yt+21jTO6zFtPyutQVEVvHJgnWOlMsOs8EuNtqFq3cmu+bG0WXJsDd+uEZkx1YqneNnWqXBxg4cKAB5N9dFNfSqmtxvFSey4175kz58G2keii9d91iVttdf/41bQrA7r/+9S8LPgU0rdZ2aXPumMBYx8YUgKpBhH9tk0Hvy3mWRJqsB5Mk4jv77LPNN7/5TXP99debu+++20YP8P+iiy6yv+9+97vm5ptvLugO/fQVLDwoM9isO3ADs/6QjQwA3Q3kt/rgkWZwQ17n7IeLer84t9j026SP6ffNOlO/QrLlj5qH1F/KgVriAHNr5qP0xch5aINssGAuwZyk3PlMLZUxKC/Tpk0zk2Uujo6BPqu3Edbc33jjDVssNtkjA1zSidNPFsmpLqtK3V511VUjs4M1G3MO5mJqgT5y4NRjyoFewoHytkj1ksKnxei9HLAWdkWQEIWyI0TAIEKGTN8uXEAAvIMQeli3/T7/vP2eKyzrAta9//QTzbjddjZDB7dZkJOX1qcTX1B4C/Sz6di3NjzxEB/xEn8+XN5x/ReNv8176nQtB7Aio0d+YxW6vknqrwjIolBOrOu2fBbFZ3w/TMjuuOMO8+ijj8YPXCIE5S3Xek6JqAttSOt6kPvFvIx5eVrfQlRY1gWse9yYz80Oa8+zYN2gcLSlKM8B+xIP8REv8Su9/GFfQ/ql4lH/qZtyIOVAyoEljQPuuFgrZUfYwJjID0F/0lTNcTE/dsmoY//4x1gWfj/lzakWrHv0eYebMWO3MYOXHljUf6n4CE88xAcImPiLpR8aX9JMT+NLOVCjHKjFPhALs9oHfvwx5hSTpWr2gb6l2lL3WO8DrHvOOedYi7gqqC8Vrtj7pZde2sZFnMSN9cRi/uGu/z5ZjqexJc2BWmy3lBGgBG3XBe0lVfZqttu4eXx/8gdmzTWa4gZL3H8Qv6s1h6S8lLvbSKaSLSKPap0VLQdZMarQsKpYZkvQ1Mgyy5ZxNnyR7NLvoljsTsK67EsvvWSzcOWVV9qxSME4WNhtaWlH6KkFUpTL+PVp6tSpBUu9AGBps9UkgAqa19/85jeB1umffPJJw7Ho/KZMmWKz8+CDD1rQI5ZRP/roow5ZBLzLEe1KOjbqfS271PX61bImu2JU+a60KXQf8lcufe/ryasGR7VjxGNnC0BrY2OjBd6de+65Juxoe6zUA5xVigvKxkLlVlttpcEjuf/3f/9n/f33v/81p556qr2mrgVZx6UfB5jLjw1YPn3rW98qPMqJtfeqkVQlrJT321nq1UhJpdLPPVfAv2IwZNGDObPg73JK3G0tZs49LWbe0y1m0Xs5k5sTXh+z2XziSZVXZSzZOjFP3QvItzjOPLHY7/DDD49c6nLjdq2sa/8blCgbLsoh2gEgXUiBuq7lak5SUAJYr2BdLMQC8sUyOxvg4NPkyZPVa2Jud6QZNfOAcm+77Tbz+eefW2vD9Gk6nsLLX//6152jkuZJG22qX9Ocuv1Z5vh1f2uO3fBkc9T6x5p9m/Y3Gy+7acVg3eZci3l11n9tn9NnDWnz0YazznlNn6Qc6AUc+OSTT8wll1xiNyagN6efArBL38VcgtOZzjjjjKIb6XoyG7AE/4DM2R944IGeXIzQvHOiB6cu8MM6fHcR86EZM2bYn86NouSl3HDF4r733nvtN3/99deLeev07uGHH7Z87K11pVOB0wcpBwI4UOkyLSDK9FHKge7lQOsiWXx8LnkQIUIUyq4iArGVurgpyK4iS7poKXF/xX0PWcu6G62+Wlu4eOHZxRSUHvFhsZf4g94XFlUh4fOB0v9dzQEmUwihlRpWzpq6lfSuhCuns+Y+FyXJ4hL+ynh93nnn2aN3sFSQNFHeOBPOOOkXmqENhC3BPOXd/P1bn7YLiXg7sKHV7L7BXLPqMi2B/vHjho96T3zES/wukX6p+Fz/PeF6oey2SynlQMqB0hxYvHhxAYRT2veS6cMfF2uBCyiSOY6OHwqEpKma4yIDjh397MBjb4reP3rnk9ay7iprMhkp7T9q/MS3q1jsJf44+SnEnzTT0/hSDtQoB2qxD8SCiPaBcYW1UdhczT5QLSFFdVG0YFl35EiQENILtq2dk3ABrhA3acSNLwofUz/dx4FabLdwA1AcbRfFU9JUzXYbN6+fi7J0ueWSBW/GzQP+9x67l/m/H/7A/MPh90MPPWif8TzJOSTlpdzdRcigciQ/L1oOkHFhuTFJwpoVVgKTAHESx7Bhw0It2JWTbyzfAu4r9gOcpMQx53qsNRbZAVdx5Pell15qvTz99NPm8ssvV+/me9/7nlHg1i9+8QtriQ/w7meffWbbPO1fLRSefvrphXDVumBcUwAWwIW9997bYLEeMAObfa699lrbJwE+Yi6x0kqsNYx1scqIBUHKBB/oUwECjh8/3px55pnWH2VlHO1J1CBg3boREXM8X7C6HMYlbatc2n901mzSlJxO5EfbZc3mYiW4EuJkAQiLkdTpO++80wCK4PtifYvj6bHU9vzzz1t/gGKXWWYZex31H+MRVm7j0OjRowvgQt3UEmYBeMUVV7QAROopdRTDFtp2AewcccQRhaR32WWXwnU1LjIizu63Udb0/26dya4j36YCC8iF/AnGuFWMgbT8R8C79+bM/L8JeFcAvHP+Ie7jLWbhGznTMkt0D86+ZTY38J3effddM2tWxJ0bhQQ7X+g3GD58eOeXNf4kaAzyLY4nWYRy49bxgryEgXIBjAJQL4doh2ohHvAp4DYFuDMXdQmArtJVV11laI8rrLBCQT8Wlj8NU47bHWm6+XQ33LjP3WvaFRsHsLpL39LU1GRf6zyeNsjGjoWv58zcf0sbvSvfVn+43IHmgJGHmG1W2M4M7z/C1GcrB74vbFloJsx8wcz++kzTbwPZcNVRleZmO71OOdDrOcDGIjZeq4Vz+rtVVlnFrLPOOnYuoZtYWOsh65k4cWKv50lawOpwgDXSxRdfbH9xgMPlhqtOKdJYUw6kHGhHfKW8SDnQSziQmysCga8E7NYR7xZcOmkBdSIArlumMmFScORFnpI3kozoPjzhFXPOT/aP7D9qvPjbZ9utzNm3tiljIuYnEm+LFL8ar5jcqtLL3YFbjbSIU9OLEj9WKXQSHsV/XD91y0odliNmmuvlAzrCsMB4xEtO2gftpG5IF9f7wAx1/0Ot9vmcSP8hF+2cyd9P/6rjcPnh7DpzZOMC8Rns3w8f535LiffuiR2ttLSnXyq9ZPlJm1IlYUOiOgVgAABAAElEQVRDg+EXldywtEkWphBC9rFjx5r777/foAQ64YQTokaZ+kuQA4AJsQrCsTtPPPFE0W+r3xKFWv/+/RPMhbHKFo7xmjBhguFIt+OOO86gPEwpz4FHHnnEcMQiSgh2m6qSstr8+X//7/9Zpe3XvvY1c9lll1Wc3A033GCuuOIKK1C/5557Ko4vjaD6HLCjjR0Q5R9/mbxrcIXc9xNfeMN8f9we1l/Q+1Lhi73fYsym5p83PtQhPT/90PA2p9X7hwAWAReC1UGDBhkUYVh3quacr3qlSWNOOZByQDmgSvSoLgArjt+O6j+uP6xPAUqKG07LU02Xef3//vc/a/UQMA3WhVdeeWXDMerLVmjJkvknyniU1QDSsCSVUsqBlAM9lwNYdbOy2ihFELFD3fJyzPrS7ZKUKMFK+UGe0djYaNedzOPKJdbQgGSKHRFeTtxY4eJXjADYAkJdsGCBUbAgAJ3jjz++EAygE+svgE/HHnus2WmnnSxAAOvt9913n7UsSp+NBb4gK3zXXHNNlx0ND1gLS2Ns/FegcqEgbRdYDbz11lsLMiVAZFg7ZWzEuvAWW2zhB7GWBs8666xOz2v9AXU+O0zqPZv4SwFxxUtBvtunvLaywhBjrjg0a/45IWMmf5pfzpXDo77SZrccaQT8W14+3DSpE5xegByCeoo8JIwAw5ZqM2FhWbdR17FEHYWQaeLXrVe77757YNC+ffsajFr87Gc/syD473znO9YfdVlB8Tz43e9+Z4/FDowkwYecztdv0zqTEaPZC/6VM80TpfK0Y/8rS0n0EK1Sd1o+lRP9XpZ4l5b+W4DnWQGe18mplugr6M/rh2bMtqO3Nf+4+x/muuuuM2PGjCkAoONkABk1c28s9zHfXHvtteME71a/euz5c8891ykfanGc+oHF8W984xtWxuB6xOL4RRddZB8Bzgmy7uz61+ty42a8VDr66KPNdttt1wEcz9rEBZ+r3zgu7Rtr2oBjaY/QmmuuaY+Jd+MBGKzky6VZj8QF4GtcxdzuSFPrCPliI4tPfH+eY5kYua3qWvBH/7LtNtuaz6d8YTYZuLmZ/3yLaflE2uV00QN+JG30I2mfs8WjAO4HGzm6Pbm9Gmb2otnm+RnjzU2TrjP3PSQbTVOwrv/p0vsljANsNmOuDm222WZmt912My5mAVkH+hH0YdAtt9xi9XSuH/si/VezHBgxYkRhvaTW4ms2s2nGUg6kHKh5DnREINV8dtMMphwozQEL2I1oXTczSNYmAtbNRMeglc5ACR+IrlpVfiVulPsv5s4zQ5cabGOO4j8o/l1OOdtss8E61qKu+36YxEv8Gnms+POhauI/ggN2ra277rpWaFPtTGl6UdJB4MCO/2oRi+CsGIfJCMYT6wqlqBUruylgt8Am6ry1Qi2CJtxMmyta8cL9wmbrqxBm/qKsGdxXJBwh/kvFV+z9UhIv8btUSL9Yem6AhK6xZIFVL2iPPfawxy1Fjfrggw+2R77g320DgJsA60IIsFPArmVFl/8DsIuQb/PNNy8K1iVjbj1AmYYVgyQIMKoPvnAFokmk0dPjQOiMwJ4fylUAQV1Bb775pq0fWGhKgvS4p1SAkQQ3uyIOa8/Wzld1XtjBlSy49/PmzDdLDRkMbjb/XF3x5Ppj/mnvY7wnXuInR3HDSzJVIRTHjHFq/cVNBCUJmw6wcuYqT1w/6XXKgZQDtc0BNiihgI7qzpkzxwCCiuo/rr8hQ4bYY8Ljhqsml1UxjtIYC3hB9JOf/MQCyJqamoJel3xGGnrMezo/LMmu1EPKgZrnADKomVNnCUSktCXMzxfMMq8/87LZY89vV6Vc9Nn8aoHibvTSTdT0v4B3ITZGuuBhxgss66633nr2/bhx4+xGWW5QLrMOP+WUU8wdd9xh3+s/QFIAxXzLhrx3QUTq33UBKJZLpMn3ALSllsg0roMOOsiceOKJprGxUR9ZF8AfG1rOOeecDuMQc3E2e1x44YWm3PGnQ0JdfJMRTV1WQLRWvotF6hJk5bvzxCBDBYZIsrJA221zVmm1QdQlvt/WW29t11RB1jvZyAxQkGPgqe8uuffaXtz37jWyeo7vvfvuu93Hodf77rtvAbALaJyNSmHEehFAKaB5tQasYN0tt9zSnHbaaWb77bcPC57484w00X4bipXdQRmzYFjOLH4pZ4G2dhGfVGoiJjeCq2yRk/1a/ieY834ynx4qdRpjOQLcXWf5TczuTfXm2dfHm7tvutcsrJtvchkCxSeAugqEjh+6e0LQJ1EXkCcgD8eYBRvcsDxMvcXiOBam1eI4uifqChtMkKOzMYM6RDxxNvOXGzf9PmPNr371K5vnffbZx15vtdVWFlT/l7/8xdx+++0VMRMr2ptuuqktMwB26NBDD+0UJ20Fy7oQhh7gDXUAHQP9Bdask6buSJPNQArsh7djBNiORXHqCOM8RoG0rIABj/7l0Wa1YU1mwSeLzFN3jjeDnxpqTlzvdPONDbY2826VNv6ZSOnymMGk2WPjy7XmzAdzp5p/f/ywuWPyrebQ8w5OwbpV4XQaaU/jABuPIOa39PXu3ITnyIt5PmPGDDN16lQe2dMlom7EsAHSf93KAV1zdGsm0sRTDqQc6DUcSAG7veZTpgVRDrTKIqR1od6VcAfLjl8B7XYlAWawQEABHsiFgA/EBRhY7N6Gyf+L5N+Lb5dTzjJPTXxTfnlh7on77NkxvbLiJ1Dtke5cq6WcLVwYtUKWn+uM1GVDXY4C2KWNJH86d/mZ7+aQtvVJmynmBmWxmH9Av5W8Lye9oDBJPkOAjVAxilCQ4zdUgOTngaNfVBiH8iWl7uEASjoI4V8cYgdwUoRiUen888+3u1Irtcam8fUWF4sxKFIRAuywww69pVhpOWqdA4xh+WlqNFfKY0c9GfhihYvqv9z4q8DnZ5991nz/+9/vACZQhQrJoUA744wzzAsvvGBuvPHGii1MVqEIaZQpB1IOlOBAuZZs44artv8SxSz7NUc6A8b1gV5uX0jkHGX+97//3VqOAXTjE5vH1DIdCi3ABymlHEg50Ds5APjomhNuMDst2NVsvcLokoX8bM4Mc9Nf/2K+f/HuFtyE/KC30oorrlg41ShOGQGx8gujkSNHhsbLOyx50Z9PmzbNWgADyDt06NBOgAKNH6CUgqX0mesy7+UXRhz3G0aAGI466ihz5JFH2iPRORadjaPwphhQGLAlP8rx3nvvGTa4YOW9pxOASivfjQLYFXFzb5Xv7r333oYf6ytAeQBaOHmolBV/1mr8otJtt90W1as1EqKnkEUJBNjyqaeesicCTpkyxZ4agGXfbtvYKafe9xmZtcZrFq6UMYuez5mWd2RRnpS1XZ8p6B+mCYB3mgB4kZIPMGaNoeuZVZceab7MzDJz+84xiwYuMIsHLjSLB8jJif0XmVxDix9Lh3t4RztHttzTaNdddy3Iyk8++WTDj82+gHchrEvHtTgelQflxg0wHmvGnIzG6Wz8XGKjB1bfmdeXS+SNeYISwHif2KCBkR42qmBdnZ9L9BX+2sR9X851d6RJPtk0g1Ei+r4f/ehHNuuAd1k37Tf2B+af1zxo5k9dYGbfu8Bc/eiNpnGZ1c3QumFmpYErmyPXO8YMqh9kMgtkA6q0u2rSl4u+NK/Oetk8+OG95oGP7jXjTj3MHHzIQdVMMo075UCP4ABYAOamEHNZH6zrFgJDOgrYRe8aBNidPHmyeffddwv+Ghsbbb+76qqrhsZNHl4UufT7EnbWrFnWWA/gYdYArKt8S77kgY3YbBr4+te/btN6+umnbVh0U8y/lPDLuIDLhhLiJd/bbLNN0ZM5P/roI7vZ4+2337Yn05F/5nRR5+6vvvqqBTiTD/JIXn1Cb6njFLxn3FBiEzgW7j/++GMze/ZsuwmCtQMndzCn8L8TZfzss8+sRXP8/Oc//zEvv/yyLTNjIyeFctoUBE996+9x02OzOnFqvuGTfnfKCr822mijgqV7vjHfCL4qcT9s2DDDiZlhm8rihItbBs0H7ty5c+0GfDbeEQ9rTeouJ6PE3bBKPXvttdfsupXNnaxZmXswB+HEw5RSDvQGDqSA3d7wFdMydOBAK1iiiHiiDDt9yzcC0CHdWDcijMxTm1vqHs/Wa0T/TnxY1gWsq3TW3+4yJ+67V9ut5qPc+DXWJdfFWuBdd91VlAFMpqpNWanH1OUoS/GJz79m/vfZf80WP9i00xFD5BMBBxM9rHGwmxjhIpM9Jo1MhlhIYBWTyWMxQvHKEe4QRzkRLogQ6jMZBaiHhYKuJm0Fedfa8LNZ8O/9fPnvk76Pm57vvxr3WDHB+kkpYpd9GCFcBfA0c+ZMu4AI85c+rx4HUICpNTR26ncXcZwbRLtHQZdSZw4gdEY4gGUYf+Hf2Xf6JAoHEN4AIGJMY7EPbbLJJtYSE0dU+YQFFMCXjHl8D8a2Rx991Ap5Fi9ebIUgAN+Joxj1lDHRlkEGNDsaZphVcNPZ7fC+EKjdX4f3pcKXel9m/DZYgv8A1/3whz8sgHU5oh3QGkIn6gKCOiy9MGdi/nPppZfaI80SzEIaVcqBRDhAH4YQG4Ez1mGxTsQ8HYUgVntcQmmApX3mb9R35uwPPPCA7Rd5h2Aageu3v13aCqLO+YmLI4J9gTjpIpR+6KGHbBawmoVCoauJfNHemdvbXk3vfbftveavEE79RQzfKVxAeNLo5K9E/ISpBmH5SxXibOTjGPM999zTCspbWlpsX4iVRMZaFM0ou/UI97D8VHI0fVicve05PMQKIIAGQD+NjY127kG7ZRxyCevEWNeEdtppJwM4iGM5OUKauQ9tnqM5OUHFtcrpxqHXPaXdan672+X73CGginfeeVsUU1/I/HGEKJNGmr1F0anWTrs7j12dPkc2M2f65tLfMt/dQGWRxXMxv2W+md+cV3QDeDv99NPTk3mKs6yst7T/tdZaq6yw1QiE8pb+zO/TSqVFObDS2FsI2S4nqTEHKUmi/7B6kJIee64HNgTRD/DrqcTcF2BBrVD9cmLxdps6U7+qgHb/K9Z25ZcTYK2ptnEP6dZbxZBgveljljPDzXLU9WXEAu9QMagjhztlyJecIMhpmHVD5NnS4oox9EwfK6CoCvuYX5dDYZsJill1BkgOgOWPf/xjB4Cqm345FseLpVlp3IT/wx/+YDdEXHzxxW50du3IZmXdPOIDwDp4LnKDnA3gMsSaMmgMABCGDPnnP/95AfSsUTLHwJK8rk9ob0ph30nfq+uG0WflphkFiKRW6YPSPfPMMy047qY/3WxyYiF3RL+VzDJTh5u5DzWbulmDzA0/vtVMfWmqyX5Zb5atX870q++fVxtrxqvsLmhZYN6Z/ZZ5evrj5uEP7zcT5jxvfnv6b9N5WpX5nkbfczjg9smAFZF1hOl0Nt54Y7tJjdL5pxIiW2H9zxrepUmTJlm9BHN4dGp+PwKgEXk0MgGXkN/pyaeHH354BxAucT722GMWSElekeUoKfgYGRmyRP+0OcCYgIrBEHAyZBAAF8NBjz/+uEZpXcLQryPPiLIJh/wjb4foQ4N0mpSPckDoahSwiw4oCMOBXAVgLhsFAeFq30x4wL2AkpGloEedMGECjwuEfhUZCwRmwl1TlZMeZcPiMnmmHMhrXeI0THhIPkkPw3VaVvWnei7d4KbPXTdquHLKoOlgzAvr965xPfSb5A++slEHsHQUYiMTm4bcuPQkT8ZbNm8C3E0p5UBP50D77LWnlyTNf8oB5YDMQ+Q0jmiUlUV/XTSvifpqwzPkNYASc6l7TbyUP+99Hqybt6qrUZy0nwjIPX/6rtNz3597Xwi0ZF+wu4kdS91O1OOIdXn257PNrdfdao665HB7jBBH4bnKeQRHKPFRyv/rX/+yRzK55QO0edZZZ9nJOYrCMGKCy/Fk0C677GKCLGqwo+2QQw6xfvbaa69uAexqtbaZkEYQdJ9/F/Q/2H+7uK+890EpFctfe3rhIZN4Q9349a9/bcGDYfEBhnMtpwb5YzLNbr+UuocDgA+VukvpQT0BxAEFLbA1f6lrQnfEpryJzwGEOwiCVMCjMTDmXXLJJXasuvLKKzsoCBBGnXbaaaapqckKu/yNJaoUuOeee4oC1nrKmGh5IgNhKyBaOyAGux3eFwLJRVu4Du8D4on1vtz4bbjk/iHEow5BHA2JkEwJgSygbYSQWBVAsPWnP/3JbnKJqiTSuFI35UC1OICigHkc8zmXnnnmGcP8HiUsLgBcJSzW6XweqxnUe20H+FEBNcpllKXFaPz48TZ+/LB+CtrMxxxS55Hbbbddseiq9q5g+bYthcK9KCigwn3be3UKz9Vf24tOz7voveYrSZfv7SpvAG+7yhX6O6x+sHmP+b6euAFAm40M5YIikixDT4wLa8Uo1VxinMHaFsfBM4fZf//9C69Rzmm75R0b41zFmlpBA8yLZcEwBSIR9pR2Wyh8N14cI5t2Lr/8ssAcnHnmGfIdfmHObbOiF+ipFz5UsC5FqxPhazaiALZFEIjNDgpRx5cTTjihF3IpLVLKAY8DMeS7YrbUmKg6EC+Z9HbJ5gDA8L5rZU3DCAHtrim/V3Om+Y1Wk2O5W23grrIeC9HTxQLvdCzwCiFLEIN5FsS7rAB2BcybEfBuVjAdWQHw1gmAN7uUXHNi5kDZIpyAZhtQE7+4hFVctYzrhtWNh+4zvWYezElz/LDuBxjLt4CHn7gWxwGERaFy4iZedEZsxmPzDFbZsc4HgEnnjwBp+JVLgNSiWK4GQMtGMtargKxYY2EkR8FxQXGwfg167ucVWU8QlZMmm7z5FSPKwQ9iupObK21vtrSD2cY0fDnI/HLUCebnTb82Cz9dZDJfilXs9+vM/AnS2bOXqTVrVjGN8mEI3XU0v3m+eX/OJPPSjOfNE9MfNeNnPWE2Hb2JGX/ueLuJt+tykqaUcqC2OYAcZIUVVrCnRgAyRH687bbb2s2byywjA5tDgG3D9Os33HCDAUgL0Q8D5ESfhoyOzR9sdmf8YsxQuTPv6Y8VrEsYALRY2SUc4Fr1E7SuIhwyBiXyp3EjT1CZAmVkI9Lw4StIvO9bGSFh6Z+ROfobOBSsS1nRCSNTVMuwyI6QJ+qYomn7LjJE1edg6TZIn+iCXBWzAVCUk2Mh8j1q1CgLFiUP8IRvhGEpwNFYE/YJPy75ZXPfcV1pemzUVgIEjD6JegBwmvEMHh933HF27GNDMN8UjAXEmAdepRgYljGzVLhKy6CGoqi3bBaHMGQDrwElM/5FMd7EnMOVXzeJbo45A+UFTE2do75jmR85ZEopB3oyBxJY1vTk4qd575UcAIQrv0gk65zW7hBsKbpOXC7t8cFk2Hlu89/23l4XeR8UPgys225dN5+eJqnp+W7R/FnPyf1D0AGAC2tKWGQFJMNEiUkKkwyAEAcffLCd8CaXaj4mLH5iPYpjBpj8rLLKKnbSxyStR5jVpx5HrMvDVlzeDJ0x1BgRAsBzJmmAkXxCMajETiWsGqBoZcLFhBbhAwr+MGICzsSZifT9999vJ4/+ooTvq+QqGfVZV7i2DWTkP8pzcTNtbof7sIyE+S8VX6n35aQXFibB5+zQ5FtijSmMqCMukCPIH7vzmfhjwZkFq0tMtFH+s2uRRRb1k92mHKWLha4gQlCHUJQwk2V3JgsBLKywS58dkmFE/UVJzaKHdJtk0o+FMHZg+oACFrZqTUAV5pSV3aMsMAErUB6sTwdROeUiHhZdN910k91Ny2KERS0WsWiT/s7boHSDnpFniONc/DYZ5D/sWTk8gVcstOCHEotmviG0zz77FKzjuGMC3/S+++6zggEWa1hxcMHGLPoAH7BTk12WCCSoN3yTICvnbtwcvwMAhTGHHaCEO/TQQ01jY6PNE3ynP8RiFvkm3Z133rmoQDJO3bKJhPybOHGiBcYgDHDB8rQNNkGw8EXAwi5bNljwbRG8UCYWq3zjMKLes6glDayDsljfb7/9Ai2v+3FUo176aSR9z9jOmKT9ExtJuMe6JG0Z4Av9G8fP6a5yNw8oQRSsS1iEJoRRYRHH07G7OqxNUZ96wphoyyxjVEYuANVyxTyz2D1h8v6i+S8Vn/++3PgJlyS5O9eZqwYRgszf/e53BcuGjHUIagHv0jdxmsAvf/nLoKC2v0eQC40dO9bOe5No67RXQHNs1qD/ZE5N/cXCT5Bl6HL6dvpPPXbs2GOP7aR41AKff/75VjmJVVY2akHV7o/p6/76179aCwX6PRDkMe8MOi2C/p4xiSPWaOs+AXo9++yz7WN3zFJ/KF8ZrxgzOLIN4ThzEuqMC2xU/13pshlPhe+MD8znEIozZjEOMscnn1iLdo+80zwyR4IIy1wJAalaqWBcZv224447qvdOLv0rwmWIMcwH7DJ+6SkNjOPFxrBOkSf4gDkgCuCoriYd1X85/kgjbjjNV5LuBRdcUIiObx9Wp8kr8z3mZ7QFteiMcoT5OnM/FeITIaC+RhknmfcHKXjwQ1+mc2+UJtQfnZP5VmXwDzG/oa4xR2KNwLqePNP+qWM++X0u1j/o35AVAEy47LJgMKYfT5L3jB+6QYS5P3M1FDHklc1CyE9URhLU/tRiGWGRbTAfYrMR4eAn/RkAjDDqKe02LP9d9Zw6rWBdLLzsLxvEsPo5QeaK9K/w+w9/uMSMlrUvR2IvCcR4qkBbytvS2mJyEQWwdYLCqveQWMRVag22JPA1LeMSwIEY8l1ruIGFWkopB8rkAODXfpvVmYbVs2bRBmJt93UB7r4lKoZpIiOfX2ak5QaTJM0ckUPIr+UDB8QrwMSMAHYzFrgr1wB3xfIuFnizg8UFwIs7qA3IKxaqa518oK6fX0BF5Vgc9+MJui83buSP3XH6iV8GZC7My7uSkkizVYDwFpj7lYBzxX5Fbg4gXXEFqJv7Ugy+fCHXX0r9F7f1C2kMC6ROtzbYYtI0uou+WvyVmTT7HfPyrJfMM58+ZZ6d+ZTZYJv1zb2n32PX/92VrzTdlAO1zAF0BKzjIcC16Bv4sT5samqyfRj9WJhuDzAuskwIYCzyCwWKstlDwbzIOJHfKVgR/RCnYkHoKLfaait7rf+QDaM7Y22KXID8+ITuS2V7anEWvy5YF7Cla4yJTd1sfACQSb59WQvjDic1gblQQiaBTAOivOjjihFWZUkTwCe6OvQ5Lm4DMKvKmEhHx1n0NqqHPPLIIztgS5BBYgkWnvnAXDcv5B95KDJ0BTC7793rJNIDdHvggQcWZNZslEFfwHf74osvrFyd8qGnQu5866232iwgww6ycOzmj29eKlwSZUAmAn5E6y2ydIzkAI5Gf4Y+stQJKWokh/zDD9eSLt8L+TLfFp2wtgG3rOl1yoGexIEUsNuTvlaa10gcsDLdiDW7VRY+/LqcCoBASd8iIGTZJUqtgmlP/71mULzt8tuzzVMT3zBYyj1xn70Cw+9yylni500NZV31b2+KxJ9HZLTnJzR/HWJP5gbrLwACUdQh3Nedpho7SsFTTz3VsIMqyeMEmWC6O3U0PdIHdIESO+g4HvVXC26curzmRmuaK4693Ly+/yt2Eou1XJR3QRMkJte33HJLgd8sMEaPHm0n9gCV+F5hCwv44loyBITp7zAGLAkBBg5SMNqXVf6H0AOQbjE3KAvF/JeKr9T7ctILCpPkMyb8LCyvuuqqooDdq6++2iYLEJdFFwsJn4iDBRRKdBewy4IDMIkL5Cas7oLkHUABdzGJXybsbjq6gET5fPPNN3dIQ/MCYEotuOkzXPIGGJZFNWADJfIGmAACDAlIUoF/PAMIgBKfOs5iwQ8bt1zEyWKVBbJbNp4DtjnppJMs4KEcMAvAAwgwayVUDk9YkCkfNW0WVWrpCwCC9kU6JrDwOv74483zzz+vQQpH0vOOBasewaYetA7QtwCcgo8uadwsuDnKme+nBHgBABlxIJDwQdgo3VEYh1lUjVu3NN0gl00kyi/AGgoG5WgcfQ6ozD+KnDxiAYM6iUDEJ6yA+uWi3IDSqVvFqFr1sliaSbxDKKBtlm8OIEgJS/NYOUFwxZhPG6bN+kR9QijmAi04eu+aa66xXjnKqdiGhp4wJtqCMCba+SnzVBnt+Ct2L4HsKGr9RfBfKj7/fbnx28Ik94+j35VoB2FKIuYz/pyG+ZK2WYCyblwaJ+1W/eiu/krbOsJaNrz44whp0s8xz/X7z3L6dubumnfGJcroE8I07V/og5Wq2R8jNA7aEMZzLDiw2cffDEQ/ALiQuUAQYBcBtJYV4LGOWZQHfrNZSIXEWkZc+hzKqpt+3Hddcc0YqmBd+n/m9yhdlRQUSF1hzPXXYeqPuRWgbAVIIhimb4Pgp1/3NRwumxZYN9AemJ/RF7vzJfpfrav0yd1FoRZxWUML+e81n/7z7r7XfCXlohTRTSqARv15hJ8O83XmIfRBEHN2ALsolLQNaRjanRLWeF3lD8+pF6TH+lNJ5260VdYfzOlcYt1KfXTD8J580P8xdhPWJbfPRRmgG3Xw4yud3HDVumYDgFq+QbHHHN5VwFA2VcIx34O37jpJ80W/dPnll9tNIzzD+q6uIVhnFQPs9pR2q2XtLve8c8+xSVNvnn/hxQ7fgc0yI9fIA0uelX7OnUd2V367Il1/vFvUstAszglaJQKtOXIt0//NzgpkxifWMSmlHOjNHGjF6qj8IpHoPzxse6RgqaeUAz4H6gQQ23/zOtNnzaxZPFmAu++0muZ3BUj4vqz0wf6077n3g1b3num3AIdb50s+PtZsyMM6eS7DREYs7maWygN4uQa8mymAd+WdBfG2gXkHyDvwj+2i3urmPY19yeIAcqzFUkexlmt/cg8od648E7cVkK6Ac1stSFfetV1bq7ktNcYqWVbNq59rZvT51HzS70PzyciPTP1SLeak7xwnmybzwLAay3GanZQDNcUBNiOzqRYDYchYldAzsWbnB2FQYbvttrNGYVTGxnPkwxDP2ECroEeeARhFzok8F2JzsYIVkVcC9kQ2guzFJzZdANiFkJcEyQ6QcfrrVU7dUdArchlfXrPDDjtYwC7xosvyZSdjZMO/C9bFH3lWwC6bs6MQG7bR80GUw9X3AQBVcp8DcIYnGGzAoIZL8Jd8AdiFH2GErieIn0H+k0gP/bJrYALgNGBhNcKEnEgByUF5qPRZpWWArxgScustxiIweoF+DgLj48rT/Tyz+R/ZMYTc3gXr8gydDLIwZJR8P4yk+HUMfymlHOgpHIgIa+wpxUnzmXJA1tx9879IvJCFUmv4OBwpirI8KQhMBQSl7tsSOfvvd1mwLrdn/e0u+9RazHXC5y3rBoB1992rLRZxHP/tD3nedue/D7vvEDi5G5S3EMAYlEsMtIA9VdmGRS6s3ShwqZKUAasqWBcFMjulmLihDENpDLiHCSr3xY4SqCQPSYTNye5cdqFHIdrIkOWXNn/+85/tsc2EYbdS0AQJPy44msktClqd2AO+KwbYZTJ72GGH2WyRhgvYRemr1th47oIGopQjKT+Fam8jFOGdhe5qc2i/99PLh2t/n/R93PR8/0nfowQGsItyHKAS1vl8wgIlfiBAbIcccojvpeg94HkF66JAB1BKHWORSl0ByM/CUo94VWAMkVIPAcPg0j8ANGFSj5VCLMBRd5Ww8Ktg3SZRfrPrlYUEeceiN2HZveqDmDS8AlPoi1gosjOQfgQwAVb8WLi6YKW45SIddovutttuBeAKcXKUC1bKFMCMFbwXX3wx1oYCLCdq++XY9qQoKk+w2IcVPhZ+bMCAWNQjnICwHuwTwFMIgBv+2FGri1bGBv1OvKfPIQ3Atvz4JoDCWbT5i3LiZAyACMcR4OwG1qOeWViy+xMCsEEfyXsFlVBvWBC6R/ZUWrdsYjH/AdZlvATgR3+N0Od6sZwLsXOY444bGxvtPf8QnNCPK7HohUcINgBzwRPiC6Jq1cugtJJ8xo5p/dYAVFywLukg7AKspt8W/gUBdgFq+0Ir6r4CdvU4p7C894Qx0eZdBjQ7ugHWZXJYsLQbcm99YYk35H2p8KXelxm/LUuC/+gjlOgTqC+MUz5ITP24Lm1OhamA/X0QC34VTIlgU4Wtbhxx2zr1kXFCAZDa1gHOsgmO5/TD9J0+aE3Tjdq3A+giHubN9KHuGKhx6djFPWOzT9pGk+qPGQMUrEufRl+IxQaAqyr8xlIf1j4VuObnKc494xrjmQoUTz75ZNsnM68A1AqIF0uXjHPMHbqaXIAkcxZ/3o1leb4Rmzeo2/jxhffMRRQ8qPnnW2o9YawtRsy12GTFuE5dQUAL6FlJrfVyH1SH1F+1XUDEgG2jupqfqP7L8UcaccNpvpJy3Y1TvnXksDTc9aUeg4iigbkgAni1qMxcWdeU7pxd41UrsYDoAQAzPwOkS3tj3o7VXIDkSrRH6prKDxDk018DnKT9E451Cqcw6BxUw6qrYF3mSOS5mgoRTdN3mY9oH86mRResi1++A30nmyFoUyjr/A158JU5jmt9hpMr6BNp7+6Ri3763PeUdhuU96581iCnYTB+HyaAc7/v5Ls1yZqTtfKnM/JKp67MW3ekhaUin+Y1zzUj1hgRSW41QFBYJ8vGmruPym/w1riQCRD30UcfrY9SN+VAr+MAwC4TUVcRSwfS6ziVFqgaHKgTq7V1G9eZvmu3msUfyW+SWNwV0G7LFAEbfih1c141Ui0jTgCO6PX4Sb4KeGJkEugGB7X92gC83GOBNyOi4YwAd7PWlWeAePvn3Wx/ucY6r4AVU0o50IkDUsnYd9Q6T+obAHIRF+e4lv66VdqFfc51GzgXS9Gtoq+zdRSdHXuWEJnVMkm7yK4kfcBqxizXNNgMX2Nps9mKa4m+XeSCKaUcSDkQiwPoFo877jh7miNrbvSpyCMU+EpkgHk5EQi5BTp05MvIorCcCwFSdPVO9qH8AwipMljiVEI+4Mtq0ImQDjJiNtmXoiB9IXp8pSBL65wmR/4xzsS1T0EnM7l6OtW/+eH8e+QyCthFR+oCc5HtQvAQY0tK4Et8wmItOlLktGzaLkZh4OewMJWmh6zW1eNpOqoL5T4qvzRsXLfSMiAXCaq3yEWQzyPj0joeljdXvuzKjV3/1Cs1KoCuIwXsutxJr3saB1LAbk/7Yml+S3IgOzC/+C7pUTzYI3ZmycJJdj/aHbZRAiXhh8UZ65yobluavtXcAmhXLO0SXx6s+0aHHHa0rBuSroaImh/8VZkY1AHMqsIO63cojlHmoehAaRdm+Slq1jja+LTTTrPeARowYcVCGER6xI8iD3Ab6Vaano24Cv/s7l3ZhBYVfG6FVNJOXHACwEefmkSpFLRzzJ3wqgLRD6v3TNABNaKAB4jJ4kCBzzq5xi/AuO4irfb59KU/kIt2MUT+Pjxvwf798HHvy08vPGSlb1Dq6kIQ65N6HLQbL5YnIRTDPqjN9Rd0zQ5TBcsByHXjB3QISATwDe1QAbsu+Icdmaropz4B+CHP1FHaOuBXCEW2KvRR+gOQQhENAR5gpx91FRDTGNn9GQaq8a2r0leg/IbggwJNyikXcWBZjr4OYsOAu1imDIAHABsAsHHbkg1Q5B9HoiuFlU3fx3Wj8ARr5VhGQliggF2+1wEHHFA0OeoA1tD8Y2d4BtFfsUDTHb4AitgkwC5kCJ7pUd72gfPv0ksvLWws4DHAaAQqCroCFOyCH+g7AWJBgFzxDyVVt2xkMf5R79npjHViJSzKKdCEOq51nvc8177b/2aAQSiPll3jU7da9VLjr5bLIl/LTJsJInYrA+hh7hEmrNF27YZ3hU7scC5GPWFM1Pzb0c0OiPKPPxRfei+eOrwvdV8qfKn35cavhUnIpZ9hMwobPqhPjEuMi4BxERLSdsJOZEBQq/UL8KYP2EVQqBtegsDiFCFuW2czFAAuCICaa/kUy+RY50Y4yfiFQNfdoGUDtf3z+4mg8Q4BJtYj2HTH2By021/HePjmCmbdtJLqj+EnYFwIYSBHhPGtlOi7dYMIoObJkycX5gLqJ64LmFH7Tua9lFOJTU/wmPf0yUmPv5pOMZcNPhCC1zD+ky8AfBDCThdsyTN34x33EGsnhKcIy7Wfzb8J/s+YrxtxqFsqeGVeoCdvAC53v1dwTNV7GtcyruYkbrhq+9d8JeVOnz69EJW7jiw8DLhASM+6HgXVlClTrA82JPDjdAUF7NJ/hPVBGi2yAPwpMTckHtoV63h3rgOgT9cJZ555pp3XaTj8U9fpH2kPzBF8gCV+6XOZA0Ytq8afpKtWYujH/NMUNB13vc6mLHfOih/W4i5YV8PBO23vKA3px8OoJ7TbsLx31fN///vxTkkxFqFAvU02sui6rpOnXvqAtuPTzMUzzYczPjSr91/Tf9XpHnDLyOFrilyG7dRMQNuJuFPAbjs/0qvexYFWASHmOA49qkGGgQJMEMBhSikHkuZApl/G9Fldfo1Z0/KlAHenCnB3sljdnSoARcEG5aZL3yyAxZojhowF0ob4WbyTO4bINWqfASL3l7aTadMh5kG8cg94V95leQ94Vw4j4WeBvPZagL64Al5MLfXW3JcvP0NSLdCttS6Uum3rjsw8pG7nFojLPddtAF0F5qKDs9diSddey70Fs0s8PY6o88Olbq8sG/VWzZh6afMNqwhoV8D7KXi9x33NNMM1yAEMy/BDzoUMCHkE8hF0mRgBglgzsjZnze9am8UfOIUgUuAvLvI01W9yj/4PGR1pLVoU7YQTTQNDOT654MrlllvOf23v2QwdRkFh0MMoqWxM78Nc5EuAmJFlI89B74osBwArcl0IQw3KC/tA/vGeDeRgIFijK+/0fTGXjdtsno9DlaSneBg/Pddarf+uGveVlCGoDmkeqQvIjt16ru9cFyNeSpwIWoqiyKNLxZG+TznQnRwIl8h2Z67StFMOVMABu1tWds+KXLc0NRvT8onsEv7cXbyXDlaJD5stzZtms9R9W4L3//5Es80G63ZIHtAulnfDwLonYVk3Yvz4s15L+df3HXKS7A3HRfiTE47H1N09ahmrklT1CAEUYBwLq2BdjRNrY2pxLCw9FC9MMsJ+ChbTOKvhtgjonHpspD6XJPl2WY6KEiEUk1kUqFAQOCkMdOIqNKNMphU0RjqqFORaLcmhkAfE0l2Ur/NtlVom34H3YZkL86+T+HLfl5teWLgEnrPQOeKII2xMWLRiQeQSR64AtIFoq357cv0GXbsgAN96FGnTBllQAYpUYnEG0YaXXnppfWzd7bbbzk78CeMeaa3H56KEByTgLuAAgwIIJj5Id+jZG+cfYCpfad7U1FToL/RYG4KUUy54q20FQKoL1iVOgGEKaMYfi/KopGVicQsPkqI4PImbJrwFeOGDdYlH6wUbKxSsq/EDltJv6dYbfY8LCEOtgOtz7fe5B+jtAx9c67QKpMBvEnWLeOISdcQF6xIea7sKdtJ2wnMWwwhsIKzM+vWYfv+mm26y7/1/1ayXflpJ37sgCR33gtJQ8DWLfIRaPgUJmHrbmJgvM2OhjIZ2DAtw7VvnuQTq4N9/78cT93258fsfMIF7gK6/+tWvCjEhIAToTpujHY6RjR6MI0GCIgDvkJ7gUIhELlxgSxAwHL9x2vqcOXNsOoT7jVioc8G6PKNvdK2ZkqcgitO3A+hS8q020Aa173H7WPWPm2R/rGkRLxZ/tT/kHgKIqpaxaetuP5n3Ef8/AFclPz2E8wDpmJMAHuxqov/WPs3deOfnA2sVSpMmTdLLghsmdNU1G1ZNSxHfWfthTiVQ4nQEzaO7dtD3XemqMD6qq3mL6r+r/Gm+knKxsq9UrB6pH3V1bNUNBPo8jov1ahesS1g2wbDmgLC66dY/XbszF2QTlkvMf3UDAX21njzh+uGa/r47wbrkASs8UJDSyb6Qf24eg/qysPl+nPlLT2i3yo/udFESHiOWX7/x9S1Nv759zIojhpstvra5jN/ndme2uiVtPUnJTXxGyydm0rR3rKLafR54LYCXlhmtZq/tOltEIm4sdKeUcqA3ciAnwMic1H0TEfSVEf0HhktSSjlQNQ6I9rhumYzpJ1Z3B+5SbwbtU2/675M1fffKmvrRUv9GSv1DhNpTqiFt60sBW34kIMx3RJ/ysoCQxwsg+ZFWs+junFl4a87MvyX/m/e3FjPv7y1mLr/b5Hd7s5lzR4uZc2ez+eofcv1As5n7mPgZ32LmT2gxC15rMQvfzplFk3NinVgAVJ9Je56dB32a0sukqn3CJS5i4TVAW3jPN+BbLHpfvu1bOfuN5r+U/2Z8uzn357/lV/JN59ye/8b2W8v3nner/KQOzP+bhJMfdYM6Ql1pfiZfd3KilqAuUaei9tvd/j1oq0uJfnBNAedum7FtmTZN26aN99soa9t8Ctbt9i+VZqCHcgD5G/oXDFf5hCyIDfToLdmAqAaG8Kf6eU5zdAlwadDP98M96XK6FsaIAAG7YF1OKw7Sa7jxcB20kVfzhCEulWf54YrdJwk2dU+/U1mOuuQB4xQuAXrmVCJ0ogCPFaxLObFa658+5oblOkgf6ftx7ytNr1R+3LSqdV1pGYLqkOY1KmbAbT9B9V+fabxRcCrqN3VTDtQiB/Im5WoxZ2meUg6UyQGOrskuI4Fld2CUI6RycnJnsyzc6pevKzPFeMEsNFh2UuUlGbJol0maSKyL35NEPqABtLvLKWcZ19quWtrFmxJA3RP32bMtWPT4I+XH5ldTSt7FAhWWyIIICzFYTUS5xjEO7lEAQf6LPVNLU4DhwixNceS5Ai+mTp1qj8304wwCZagfdiJVmxZPk0l73oBa6aSwvjBEap+0EwgFHhOwIMCfWsLN+yz/PyARgCHwCctZWDAFxIi1VIjjSotN4spPOVpI2zqkThdzg2Iq5j9TIr5S78tJLyhM0s848pgjV/mWAH1cQAULQW0LvkI9Sj6ampoK9QQwFAtM6spaa61lgwOs9a1DYR1L2yeKfACI7FZV4Ii6bvra7gEPsCvTr/vs7sTqHopAPUrFDc+1v/DT95pXF5BQTrlcECjWv/08kp67OEVhGRU4gbVhyAdv2YcV/IvDk7jJYP0wjIK+MQu2uXPn2vqodZL7IMJSsU8IMJSCrCC6gHKO0FFKom5pXHFcBcK4YRCeAIiiLioAivdu3WJ8CyJ4on22+94Nm3S9dNOpxrV7dFMx4ZT77YMW+u7u73LzWetjoi0XYxhCfBnoIrnWK54j+o/rr9z4bWGS/UfbOuecc6xglfkoFtVdcCgCQH74AcTvbn7C8rtaeMayqGtlVy2LAqJ3w7i5j9PWXWFlWB/KmEU/wTyQ8Q7LuT7F6duJjz4TS7OAZF3gsVq6pG/xNwpomkn2x1iqUEIQHkQuX15++eUCiDTIb5RnarEXv5wycPrpp1sru7rG6E6hq7t5yN/c4pbNnfsH9YH+PMwNG+eauT9zNoDcWD/G0ifHACqFjU/6vtqulj2qq/mJ6r+r/Gm+quGqkiNK3C6QNor/ID/uUYfue5178wzlC+2NebNuQqD9B82jXZDrK6+8Emj1mrlOdxNAf0j7kaD8uO026LssKe02iDdd+eyiiy40J8oJHz4NG7a82WHHHcwtzgYF309vu8cadhA1C5JlxoJPzdzmuWZQg5xNXoKQcX32XzlKKoB0jRfwKtIjNu+zobPU6RhhkbFJi37Jt0Qf5j99nnIgKgcWfyQgsI8j+sYqIvJdLIZWQA8JYPHGJ3JmEhZTyyRUG9/7etbsPzprVpA8pdQ7OUBdq19BQH4r1JnWjWVjhRjAaf5QgKkfU28FnCp1NycGRVpnSfnjGfSrHYblJCtin6J1Xr49uP/zmWxrJwJkNuhW2izvZvrJ877SHsX6Ls8z9jrvGnkG76wuhnf2Wp6hmeean73OP+PeyH2mXu5RV9prua+TuOXePiP93kTCdyyMA2xubZE6hF0K+eFyb68FbG2fL86/xyouIFlrHZdnUufsM1zq3yJ5JiJb/RmueW4t54oFf3F5Zusq3z2U2r556Pse8IJ6J6fUW0u6I6QajZB2PEJA9yuJJd0hbXWxBxQjzWLKgZ7AAeS9KpNF7oWF1jBC5sq6BHAi1naRFblGiZCV6elhYXGgV1dALEbJVAe2yiqr2JO+wFko0BbZL4bS4hIyBfK4eDEdb/cSJx2iJ0bOgzweObTqcJE5+3o7DAWonISN0MiCsAasOkU2c+v3qrRk6Am7Mr1K8xsUPokyuLpIPw0Ff7s6ON8P99RZ8D8Q4PZSwGl3Q7oNlP5LOdDDOMBSIKWUA72OA3XDZIfv0IzJybEkpSgnwrDFk1tN33Vb84vqUgGSeI8ky1KbW+oev9Zr3v/9vz8p0KJuPk5jTtpPwLpY1lWKGT8W1fJUKn+aQLKuq/D2Y3aPnkbBWy5gl8mvWlwsJmR300OZ5x/tAOD12muv9bNZuA+zRFXwUOEFx/U0vy9CsYiC1ay0jTppG0qAkCG3nPouKRdQJJaGscoKYAPlilo9Ig0X0JFUmnHiUW7kXRFSWOhuW5OTO7334yzlv9L3cdPz/VfjHmUxIN2bb77ZXH755R0Au1deeaVNEivY5bRLQFDXXXedtVJIRABd+NHGWJgC3sXSqWsRl3oFKAhwLQChsWPH2jxgsRmwDO9dK3e0+/Hjx1s/1MFSE3ntI2wA559vzVRfsSj0qZxyARxSCgMo6Htc+qYogF0Uki+99JIN6lvtdeMr5zoOT+LGX6ofBbCMQOLRRx+1u5HjKHCxzlaMggCafFOfkqpbfrxR7hHABFFQfXRBt2uvvXZQMLtTWkHrrodq1Us3jWpdu9bl3GOc/PRcYK8bxvdXyX2tj4m2bFLF7eiXYR7NTWe3w/tCoHZ/Hd6XCl/qfZnx22BV+sd4OG7cOPtD2IRwECu5f/7zn+08ByuHAMXonxWoibAJC90IA2+99dYCYJcNKvfff7/N6QEHHBCa4zhtnXSVwto67zfddFML2FVwm4ZRN27fjhVhxmPme4w5KphmrIaYQ9AGgiip/pi4FbDLJsAwoR7AVfpJxgz8V3oqBuDnY445xlpcRjiplj+xaK9zEteCbRAPqvXM7c9cK6l+ei6wN+zb+2HKucciNYoLiLrCHEYtnvLOBSCWE3+lYRjnGdcZ7W2vpve+2/Ze0yuEU38Rw3cKFxCeNDr5KxE/YZIkFzSK9RcsR0chjmOE3Dl5lHCun6amJve2cK1KlsIDuXAtb2Jd17ew6/rlWpU7/vNiG3x8v9W6R/ZAf+IeB+in5SpDwvjkhynnvtbbbTllSirMi1LHFazLuHK89G9jxmxvZSxaR1968cVErLknledqxuNaw/HTmTZ3qvlkwXQB7I70X3W6/+qtuWa9ZTY0T335mPTF9MbtxKYoxvhyCFkGm5WQBZTTLzE+TJkyxf7YhFQL4P5y+JCGqT0OAERrniIGGURPEYUyciJx3XJ24RbFe6AfwLqnihXJSkmahbnt2Zx5Z7oxVxyaNVkmUSn1ag4AMK0XHUP9MEGQCng3N0f0EwLWteBdcVs+FV3FpzKXFrf1S2FF92N8kv0eADwX5H+MUe2ttv2qPcG2Z4BvAeNa0K40kj7yHLBug1w3yNrDutyLH7T28iuAegW8a++Jo+2XAbTLtbiFa0C9zn3hOX4heW1FPG2uvXee20v/Hdlv+9mSOPf45x19gPqxz+AP4Ns214ibvxaP+tz3w3O6I0C6AsBVwC5u/pm4FqAr79qAurZeLZK1G/4tSFf8EG9JEv+9mahXsnkiu7zoyIfl3brh0l75CeieE2oL37438yEtW8qBbuAAchIFgLLmCDIG42aLtQUE8BaZjysPA3zr3rvhOOHzcznFq2+/fgYDDwAt1YgQshtOlCQ+lxQA6T6Lco38lDUe+eGUVZVza1iMlmHZFxAtazSMrlWL4BOybAznfPDBBwYZJ9aEId/oBEYCFKy79dZbdziFVfNXLk80vOt2dXpu2kldJ1EGV7bs5guLz6qXKyVvQ46tVqcJF2TchNMFX247SXQt0XO6sm833fQ65UBP4ABT/5RSDvQ6DtiFh5z0nZsSoWhiALX5bQHtbtxq+jR1nMBECF2eF+ZgJBXV1VQc/3lLu2eLpd039K11LVh3n7YJkeO/aHoaQ1T/+KsiubvI/GTcdwAcyiUmsArqCpv0ErcLeOIYW59QwJTa5eaHSfJ+8TQRhkn9NVKPo1BW9KoA2iEmtEzAoDCLxvZlAv84JhnALvTwww9bkCXX7Ghbb731uOw20mqfz0BeyNbeE7hCt6AsBvv3w8e9D0qpWP7a4w8PmdSbn/70pxawC/CTXYwskAABYlEQ4n25hEU1lPrXX3+9+ctf/mLbKEporvk1NTUZjtlubGy0SbC7E6AHIA+OtlYwKsAjflgD/u1vf2uPAyeA2+5tBCX+hVnc8Re7JaIxccvlKt5Lxc173ZlYyq9agMVfFCBwqfjc93F54oat5BoQ2P777x8YBYu0uLwMjCjCw6TqVoSkOnmJw3t2aysVA6wHjYtxeRm1Xmp+qumuvvrqhegBHgOk9AkBGeMTxNgUh69+XKXua3lMtHmXgbEVEK0dIIPdDu8LgeSiLVyH9wHxxHpfbvw2XPX/MTcdM2aM/QFCBNTEGMQci3HS7W+xVM+YxXsEqQBFAPoquVZf9Zm6ceqkCsAIq2Ahjcd1ta27fYP7Pk6ahEMoq0BVxms2ZXFEuI7PlYJi3bwVu1ZwWykQMIJ01gMuv4rFW+wdvDr77LMtcId5DPMTCPD2RRddZH/f/e537RyqWP9bLI1y3zFf0jFRv0VQXHr6Be/8TYpB/st9Bvgci8psuKI90H50jHFPbyg3/krDqcKErgsq3LcpUgr3+deF/4Xn6q/tTafnXfS+kLGELlyALnN/vlsp4jhI2gBUTRC4m49ioHTXn16Hzff1fXe6bASgzbIJAyVY0IYHLPIosVaqFtV6u61WuaPE+9DDDxW83X33PWYrUQa6hCJJ24H7vLdeF7O8NHnOe+bjeR+aNQaPLFn8fs39zUbLbGpW+3B1M3nxpA7+i6XRwaN3Q3tBcT5q1Ci7Idh7HfkWwO5NN91kHnzwQWuhv5TCMXLEqcclmgMtcnR78ySZfcyOxoasgK5cgwzRQnX0hWXdJOllOXb+nxMyZrfNu1JKmWQJ0rjK4oB87uzgjOnDT7p3wOctX+RBuwB37U/q93+eazVDBFQ5VKodWziXuFoCGJWfAH1dbUP7ta4+gr6C9w7m8WsD7CqI14J120C7pk7CuIBeDSPrViu3sff8E3+Fd8Sbf6+yALuWIXn7k3esZbgmkF53eC+Pc/KgDZBrWsSfXFtLuZRfn7su0ZVNNjNlh+4VAbH0vIy0QzHSkx2ad9H/1WERG9Du0rKlPkWC9IpPnRai9jngGjLCcBA6hjAZIGBR1Z/oaVgAUllbIKedNGmSBcqq7FZLDy4CS64Qpw4B2HVP+sV4gvbhGgbdlQKJ9VlUt6mpqbCexUiDf0Lak08+WTjZqNo4A/JMeVXPyZpMiTWeS6zDlVxciT5DDhkmD1c/cdyuTi9O3qL6TaIMyOTee+894+rjSJ9vpgBqrEcXI9doCWt4jHn5hPETNVgCYDellAM9mQO6t64nlyHNe8qBThywFnblSA+7a7XT284PWt5pNYtez8kRKV20wJOsWRLXXjr3+lxdfaX3rgtod5sN1rWP+FewrKuBxLWXzr0bnpf6yn+u90XDW0/J/2N3WBi9/fbbhVdRLEoWPHsX7AJjsgy9+eab3tv2Wz2GkifFLPG2h+i6K47uWTQxZ1rei1hvZXdtHUfdtFnYvfHGGwuZDTueuOChwgssnuoOp4svvtgoIOAnP/lJhTFXHjxfx9taggilAu/Dkgnzj3ALKvd9PnTn/6Xi6xwi8ScAj7Tton5HVAAAQABJREFUqHVpLApCWJELAsLFyQTW56gjgG1YALI7Uy3fAH7yAfIseA8++GALfgKEzhHl7gQeK7161DjtHsU3dOSRRxp25xX7Jblgi1Mu5S/5pEzF8sg791h1woQRADGoqampLCvIYfF213NAIArWpY5gsZI+G/AVfGF3bbmWl+KWqTvrVpy8uuMYiuYwwoK9T9Wql3461bhnka8bcC655JKCEMlNi8W/bmLZc8893VeJX9fymGgLa8caGcfsH/8YG4vcS6Ci70uFL/W+3PgT/3KlI8Qi+KWXXlrwqJYl9cG3vvWtwnyIY9IgHaMOOeSQUCGuho/qupZcAcyGkc65EXYmQRxpBWAZUqu6Clxl7PGtHSSRZlAc2ve7lsF9fwirFUDl8kv9tbSgTexMWJMoRoBy+aYI0B955BFrdVf7H6z7q2XZYnFU450CxxFouusbTYv8Mo5C9FHVBh8BXof4BieffLK9hk877rijve7Of6rYiOpqXqP67yp/mq+kXLf9Pvfcc5GiZdOC0g477KCXVXXdzaD0Q6Xm0ZzSUKukCifmtjpWuHlF2aHrMJ7TdqtJtdxuq1nuUnHP/Kx9Q9zKASdf3Ns23peKp7e8b2gQwVMIvT3vTTNlzvumORc8xrrBsjI/3GS5zcw3hn0zP890XhZLw/HW6ZITdJAfuPKCTp4iPGADgo5XrowyQtDUS8qBYA4IcG3RWyLffTeifFdAeHUrYt1U1mgV0KSIp7UFJXHYTg2Gn0+TxapqSks2BwAG1ovuoe96WTNguzozaK96s3iXenOFgAavHZIxNyyVMXcKYvdJsTT7htTlGVKNBcebUhwO0FUAeBVLs2ah/ObJ7yv5fSnA2FkCARaAdOsncv2R/KaJ16nymyK/yfJ7X6wfvyfuJPlJn5N7V1zZX5cTlVvuLfm9Kc/EPlDL6wK2lh/XOVGd2XdiNMb6JQxhiYO43pcfcU+RZ1MlbUnTpk0eyIvkibwZsFPklTyTd8qQUjwOSLebkcPisiNFx/e1jGnYOWP67Z01/fepMwPkN3CfetvmaHt9181aS+xRwLoAs8vdEBWvAL3PN7KllFIOKAfQj6688sr2Ft3iBRdcYPEH6LEg2hryN/TjnGCq5OpU99hjD31sjRThXwmdKYaLlPS0DwCpgH0h5GxqdRaZAafsog/BQq4ScpKohHXaPn1k0BbC2Al6I7uZQ+4BFashJ9IvdsqbjSCBf+gBFYCrBhgaGxsLuh9Nwt10DnhaN2tTdsDLrvwePmmZNHxct6vTI3+utWPAq1g5jkJh4ZIqww033GBYJ5Mf5OucbKenC2JYpBRglxOh1aou5QIUrt+Hb4W8XcG6+FPsSZSyp35SDtQiB1LAbi1+lTRPFXMg21+EVqsKdECOh4pEsmBc/F8BP8qisktIJmWWxLFXzn3+ufdeM9X2WEamQnhAuyftu5e5//cnmRMLlnXb39srx3/c+IuG13wl7GJFJmxioYMwSVY6+dPjKHQ3VlAx3F1nlQCEg+Ku9Nmi92UhLceXRbW+kJEdtrQL07fVgih+97vf2Syg2BsTwUJSJfnlOGKAKBAgTKW9995bL7vN1Tpe1A3IXVH/0uYqel9GegFBqvboiCOOsHFjNZnF32WXXWbvAcEqCKHSxOvr66313qOPPtouMlVBDKCOHXpBxHEvu+22m7V244J0sPKnpGCDqEADDZeUG6VcG2+8cSG5IOBk4WXMCwXKb7/99jFD1qZ3FtpK5557rrXsiEXAvn372scIQopZEtSwSbndXbeilMMF3YbVLYRAClx146xWvXTTqNY1QqUzzjjDRo9VU6x+qpV9HjLWu8c1jRs3rlpZsfHW8phoM2jHMBnF7B//GNOK3Eugou9LhS/1vtz4bWGS+8e8ExCjzmfCYub7hhFjwAEHHGBfA46kvQHihH74wx9aN4l/bnvFEmYQMddGYAYpwDXIX9xnP/7xj20QgLoIQxUEeuCBByY2RyiVJwW5YS1BLbf6YbCwp8RpAUpqkTjsWLRiAGiNA5d4sDqB1V3CAFiG7rrrLut29b9TTjmlkCSgYhe0yyki9IHaL7p+C4ESvmC+pqQbitiApUoAfdcdrgqBo7qax6j+u8qf5ispFwG4WlK5/fbbzZ133lk0ao5MdE/e4MSJMAoDyIf5L/acfOpmP+3jivmv5Xf0m6p4YOxRBQd5RjHMBgCd3//mN78xpayKV1rWWm63lZatkvBrr7N2Ifh5551rVBlL33rZZZeagw46sPB+nqMkLTzsZRfFwPlf5j43b335hpm5cEakUq88YBXzzRW2NY0Na3Tw71uZ6vCyyA3zEjYXJUHaz6hVrCTi1DioQ8jNAOr/6U9/MmwsdBX16i91ew8HmsUK6aLXBCgQEezKUef1q4jVxIEi4+0GGrVG1vx0p3r780G7smqsiFij0AZcS3EVRRgxMIp/9AT8dK4WMWigN+QtGp8eUR3ocQl4mLEAw4yZNihjJiyTMf8UMO9fls+aa8X907J5EO9fBhlzj4B4x7eBeD+Vqg0ErtL6tASwNy1ib+aAtAf029k1BJw7SsC52wsQfs+s6fd9AcPvkzWD9q0zg/cVgO5u9WbA1gLQXTtr6pcTnXh9PKbQR2HgAaAWfWFK0TgAaAyDD2w8xuBMSikH4AC6UU6OQv4Lsemfk0PPPPNMc+qpp9qTQM8//3zzr3/9y77nH7p5F8CIlVrV4bAGwD/hf//735s//OEP1uou4bbYYovCybmk68p2r7zySuufNK+66ipryVet+BIWvW7U9k5ZXD0+cl5ONSVuNhDTFiD0LaqXsw+q+M83PKF6OTdJrB2rMQLWbOecc44566yzDLgITvsi3/qea3RHb731lhtFrOuuTo/MAVRWQrbKN4lShrBwSZWB+TygXfJDXcHIlhKGPqJgCPCnIHT0+8SDDhiZ9eOPP26j470adNL4UzflQE/kQArY7YlfLc1zJA40NMpCRn5RCSu7C1+U3eyfd4EoQCZPljR7pe61ECH+T9xvL7G0u47MBts8lorPf18ifhk98z78+DVcwi6KdQXWuFGzWwulN8SEtdLJnyrzUdgzgfWJnWeqsEaR7+468v129X2L7Ey29TWidV2Ejf984R6zz+F7GybmOolhQckuv64gJuwusVMQgGV3U6Fa24xYG4FtVzjt9/ah8y8frv190vdOUvayVPy+/2reu9/S3fG53377lZ0sx0kC8jnooIOMD5JhUagAARKgPvP71a9+ZcMEtV8s6aqC282UtnuAnGr9z33PYuKoo46ywBUWAJVS3HKRHhZ71Bofynf3KBLND7sKAdfwK2YpVf2j8NDdrttss40+7tEuQBAl5Zfe4ypIzH1WzeuurlvllKWxsbEQDDC8r/ilXSkgv+Cx7aIa9dJPo5r3WHRXUB4ASQQyADCbmprssbgKVEOIpcKaaubH7UdJp1bGRFtmGXDs6GYHHntT/F4CxfLfVfHbwiT3b+zYsRbQyAkFxYRf1113XSHRoM1lOlYiSNPxC9BHkpYRXavSxx9/fKe2TgYRmmm917ZRyHgFF2wK0T4ZaxG6cULLXUHUkYO6gOWf//znnZTuWFP45S9/WYjPFYwrLwDB+X0kAegjguiiiy6yc5Jjjz2208ZD+NHdYy9l/MUvfmGzzqYMrJDS/1FHUcwpaJYNUl2xsce1xqz8RKlRC6SC46iu5jmq/67yp/lK0qU/UaJNq5VufaYuAFzm9GrFepdddumggMKfu7nB3SSrcVTiqkVp+qCg03SmT59ulUnMo10QbCVpViMslkBdqy8ohLHWw0Zi+hWs5EDIRtg4WW2q5XZb7bIXi3+PPfKKevz8SRSfyy4zxGy4wfpmxRHDzbHHHGO/lSpcUVR9bfNRgeNLsTR60judA4Tl+bVZ/zXvzhYzfhGoPltvthi6ldlhxM6m3jlC7Wc/+1mE0D3PC4BiFPHM45g3MC6yNmNcRnbGmO2elNXzSpjmOIgDnPa38JUWa8kyqrXJbKMYKhHArizCuoVckC7AXQC8SREbggDlYzkNK3JdRRgj4GQAfshFKiXWERpfan3R46bU22bBMc0Ugztvi7XdpwW0e5cAeK8fljXXCNjwmjYQ7w2DjblzgDF9tqoz9etnTXZlkToMzpiWbqr3XinS25QDyXGAPd9S37MryRppPenfv5Exfb4t4Nyx7eDcgWI9dxDgXLFYPXD7etNvkzrTsKq0CwHCmwq74Pvuu8+u25APuad3JFfA3hnT66+/XlhLnnfeeb2zkGmpyuLAUkstZU488UQLqHUjQO/oEvoHZBJBMjjkLchRlNDt6Ylf6EoJ42+KZnO+e+IQ/gGiAmrkxDdkggAyIfKi1mlVRsVz95p7JUD9hx12WAEjQbxaHuQW++67bweZjwItNXzSrurhiBd+uOXWtMgDcikXqKxWhsF6gJE49NBDC4YD4JfKyTX/6mqc6gbxKYn0XDmZplXMxejB6NGjO3jR79LhoXcTFq6SMmgS3/nOdwKN7ZEmJ8WutdZa6tW6ymN19SWyavSXquOnzrmbZTHcxFq53M28mk7qphyoBQ7E3GtVC1lO85ByIBoH6leQHYVryaRBjm6JZIF0vljZfSlnFqyUMQO+kTWZvlVc/SP3IfqorhY5qv+4/sqJX8NUyWWnE5MTlFJMIp944gm780knTBxzH0QooNXKTNB7nqHEx/oMO3SuuOIKa1mPySoTsm9/+9t2AocFC0DDpdILS6OqzxdmzIK3W8ziCbJzTeptFJq1cJb5z8yXzCOTHxT5b37HGzvu2EnGxMYlPV5QXfddsWsmxlBYOBSLKKpUIctkuRZIm0s+L9ZGoCPzzt+H5zPYf3vvUd778tMLD5nkG5RwKIWxrKvfEwtalVirAaxEfYQAQtE2WQgyUefoSnaRQtQhdplCAHuxVEc4+gsWuCzAmLjTh6hVPZ4rcfwlynuA+izKUABgBQgwIunSt6i1Qx9Qp3HEccspFws+gPTwFAE/O1hZnGCdAysjAAsATNA/AbZR6z7F8jVhwoTCawUxFB700AsX/ATwDOEAPELRguU33XDRVcXr6rpVTrloJ4DKALtjQYE8cw1QkPrPjm94F0TVqJdB6VTrGUKBx2X3LQIz+hfItSRMv4Zy0N0cgJ+4ghLCQK7gxhc48L5Wx0TyBtnRyw6Q8o+/TN41uP77Uvelwpd6X278ZDRBAjShm5wYi9jUseuuu9rxg6MDaUMI6bUN0TcHWZkjrM6HTjvtNJtDAOVB9aSS7NPWschIPde2Tv/PJo975HhuVSggtA0SDpebNvNAxi94Rd8McUQb41VXEYCWA8Uy5fXXX2/nCQjwAKLS7hgPsSShAFU2xrjCPRe8y1z1wgsvtHPlDz74wPaff/3rXwOLgbBX5zGsKRi3V199dQM4mOcKsOlOUCrfnA1NCnTyrX1RZ/xNG24fGDbHD2KI2weGhYMX2l4ASm+yySZBUXX5MwVpRHU1g1H9d5U/zVeSLkfA05aYL0A777yztXLBmh0hOvNU5q70jy4Q1gWdan4ApCmxpgCMRt+ownh9V47LPJm6xVwZZQGWVJg30k6xWnmMgCgVTEwbr2ViHYMsBCvstFnfcjgbHrGywzw4iOJucmY+BN/ctu/GW6vt1s1jV19zBOPdd98jbWN/+41IX+vX6NHbmqvFQupH8u3GjPmWzRrrZ8YJqMDvepAaeRI4kl4mPjcoRFzFC8Zg5tPU2yB6be4r5n9fvGo2XW5zM6A+uN664UYMWMnstNJ3zcTPXzPPfvWk+6pXXdO+UbqrfEULp3WEe+Z0zO04aYYxO6zda9jU7QEckHUQp6ctekFWX59FzG8/ASaItcX6Ee19RcSQiXgDrOsCdK9+uNlMmJSXMyeSgBNJFMCB4z297MkckOq8SCzxzmjIGGywW/t2rWKB98A+ZrBY5c19IUZ2vmw1Tz3TYv77as4MaWk1Q6TarTckY4b3kfbzlcgsOOl7cU9mQpr33s6BhS0LzezFX5q+Q/uaZVYdYjJLiexQgLoZAa7XLS2426XFcrr9iSwSd4A0jArBuFF4itxK5xvoS1OKxgGObEeOwfq3u/WcbPoaOlSOVxVCDpbkCV7RuJH68jmA7AHjHLQv9I8zZswwnMDC/B1sAnKPUsastt56a2s1l3UC68k60b+jf11zzTUD1+vo51mvIzNAhsk8CuMtpKUyOuSBbGBGVqmyl+22287wK0XgCNC3UQ70b8ix0cXy82XayGH4hRFyCtX3hvkp9hxQdJTw8AvsB7Jwys13QT7NxjClE044wfKMjeeqZwXIW4z4BkHpl5seBqSKEf1NUHqEQS6H3J3NYpQvqiGasHDlloGNp0rkB10Jp7sBNkf3rn2U+lG3GK/JC7yZOXOmrdPo/fn21MWw+DTe1E050JM4kAJ2e9LXSvMaiwMct9NHjgFZ3NRqWl4RCVgEav1IhANP5OToEGP6ricC83aZeYTQ0bwgThN5Q57EjXI/ZOAA89nsr8zQpQZH8h8n/pkSL/FbipifQvxtxUjaYfcYgDkGeHeQ13QA1LFbLIyYaBQjwHpMApiUsYMUoBKTXo6U5OfTNddcYyc9/vNuuW+RVN9vMAuflqPSpkfPweCNB5jdx+5i1p6/mhk5cqQFQqJYCiJXuRr0nsk7E3qfbrnlFv9Rp3uACyggWAzstNNOnd53xwPbHAUgKSYUpEGKeqzNde/71nfsQ/qLMPCrhVkzuF9roH/iccPHuZ+zIGOIf/4imzPLkkL6Ifmz8Xcx81AUoVxX4r4SQkHPgvLmm2+21vg40sUnhEcAcZVQxnN8DEplAMT8VMCkfugrXCASoBz6F45JYfGKwj6IELQkYQ2wnHKRH4BGLKzpA9mEELQRgbJiRVbB8kHl0GdqXZdFZ2Njoz7u0S67LAEpAIJCOOYD4wAlIZhQhXm1C9vVdavc8gDGeuWVV6wAD0W6r0xH4AAoHuvQPiVdL/34q33fv39/c/HFF1vQLlYB3377bcNYCIgPYU0QOCVsbqB5RcAUNCYiAFNQnPr13VocE/N5zENFmO8xEnVy5Zn7fMCg/mbOF3PNoCGD8s+996XCF3s/54s5hvjJUcFfxPjFW6LEJhLmhDreMQbxCyL6n3/84x+h/TNxqLVTwv/gBz8IiqaiZ/SPbGzhyKigtk7kjFFYm48L6CqVMcqj4Gb80nd0JSGIZr6PMJa+jO/Gzyfy5W/uQKDMeML8gjmxPy9mc19Q/0iZ//jHP9oNQQCC+flzEvrXUv2Cn8ek7w8++GBrWRRrMPSDCHOxRAHAij7SJ9ZJQX2c6++xxx5zb+01datUOBWEEwBLHbVC1B9AtVHdQYMG2c1izAPihIsaP8o30ojqX/1Vi5+0LSxbMGdnLq1z8KD0WPPde++91lqk/565Bm2CeRqWuFWZQ7tFgVUJAf5+4IEHbJys8/V0GT9O+gXG/1onNluwKYR5C+2WtQ/tlrEGBYZPrA1KtT82K/DzCUVIMarVdlssz13xbitRpr428X/mP7Ip5L333jOrNa5mNttslB0HSJ/11xQ5wWnatA/tmKvK2Rmfdeb3YTKH5NeTifbsrzG0PPMEUfXCjOfM1stva9ZfZkN9HOpmpU/eeNnNzF6rfd98+OYHZtwZtTNehGY65gs2OyAf03UrCk6OaqXt056xzskJOyhp2XDEaQ4oym+77baYKaXea40DzZ+2mgXjxbruux1ljsXymRXLug1riVVFsU7a1QRYF4u6Ll31cIqQdPmRXifHgcN2bjCbbdimnFs5a4HhJ3/SbDLLC2BdzOwObG41h26aNU1rG5Ob3WpygHbll5sjAN45ci0A3la5NlzPk3xRVaM3teQKksa05HCAbrmPyMtQtQ4UV6zfZgbhStXrnzNXXXCpmbVgptn90N3MtruPNlkB62YFrAswN9Oxa+1SnrEuA3wHuAogVErROMAcDSMzrJ9cC57RQlfPl1oQrV4KacxxODBw4EC7mR8dQDmEvJawccIjh1Ewrp8mug93Te+/j3KPvKZSmU2UdJLyg4ysUdbj/IIIHrsbyoP8xHnW1emRN+oZv7gUFi6JMmC8AZlVpUReAOemAN1KOZmGr2UOZGs5c2neUg5UyoEGhFgbyEop6jpDFu0t7whQ7tGcWTRFtulWYRFvowQQyIW4Ue53GrWx+fsTz0T2b4GCEeO/VeIl/jj5sfFX+nGKhMckPhYjUIi7hNIb8FoQqJZBuxwCqPPII49YC5Z+eBSIN9xwgwWE+e/KTc+PJ9a9fNNF7wug/Ln+JveuhLSVJ0IMsllsqa8PNN/abxt75ACWkcLAuhFiK9vLxx9/LJZn7rbhOZ44Csiw7MRiBNQ2WMwdPrjjcSUrLdVsnpssZi2cNlwsfBx/xEv8LpF+qfhd/0ldF6vnKInVEiVWTYtZRfOBQGHfHsUTinPanku0fYC3KPNdQD5Hn0ycONG2X/xAKLAhFgNYncFiJmAUlzhK9tFHH7W7XN3nXJP29bITGQuc7s7QICCfHxYLnkEUt1waB0r0888/P3CBDaAYK4HF+K7x4GI1HHLBy/ZBhH9h9aASnrhxhlneC3vuZhlAFGA0XwgB+BtAlQoP/DpXKm6tT25aUa7LqVvF4g3jsVs3w8Jru/PLjn8ssbnHwWscWM0DpKZ8C6rT5dTLsHJoul3tApIYM2aMBc6x252NLF2dx1odE+23sGObjDoR3Q22WNc8/9iEyP6jxos/4iV+O2OOmJ9C/FWoWIBg6XsZ94KIvgMQKPPYYiCwsWPHFoIDiFTL8YWHbReVtnWsBHBkepAwlrKwcc21KkCyUdpCUN/g5h0rte5Y7h/T5vqtVn9MvFjDBRjt9+ncA9QFeOiOR+SLOQMWcbFK7Iej37zqqqsK2Xf5AFjzueeeM8cdd1xhTNI5CfwfN26ctexZrF4UIq7yBeWiDgPexfIKc4kgsG6Vs2EBn5oG/K4VimsB92tf+5rdWBU3XFT/AKJJI6p/9VctftJGOCqV+TLA7DCivbz44ovW6lCQH9ooa0MscrsEGBjy26brR6/dOY7flzDnxZo4p8r4BCgfq/v0gy5F6XNd/115TX/DxhGOmmTTB2NHEFi32nkCqK1US+1W89SdLtaSvimWnH98wAGyTt6u0xiywgrDDetXPUWmO/Na7bSxMq+ygqC0Xpj1jPmw71QLagl67z8b1DDIjFlxJ7Pnqt8zv/zpr/zXPf4eK+AK1sVSG/MzrIJrH8e6DjneQw89ZHbbbTdbXvpPTg1KqedyIPeVgHWfbzHNL8m6K+LpaQDB6uW49IbVypOBV8KtILDuT69cWEmUadiUA6EcKFbf2Mi7WMCNX/TLmM9Xypr+W9aZgTvWm8F7yW/fejNIfgP3rTMD9sn/+u2TNf2+lzV9ds+ahh0F7LuVWDPdWECSa4o13+yn5pP50828FkH0ppry0O+RvmjjgNSR+bn5ts58mplu61DdJvk6Rd3qs0e+rvX7frZQ/wZKPRy0T70ZsHfG/L+3TzNXT73UfLrSNNN3XamPAkSvE8Bud4J19dsiD0jBusqN6C7rx1oC60bPeeoz5UDKgZQDKQdSDqQcqBUOZMTqgsUA1UqG0nykHEiaAwAc594mu9UnADyIGLvsgmyQxfuAXepMgyz8xahXIjR73J6m73f3ix3XhHffM3ucJsdK/v5Es1HTarHDhwV49f0pZpdTzjZ3n3a8GTVy9TBvgc/LKUdgRM5DgE5Y6QF8pdZe5syZY499Q7HMexVYO8ESu8Q0P7tJOZ4QQCs7dqIoChPLQLGIpO4u/iBn5v2zxSx6NmcyYoU1Eom3+i2kLn+vzvRZreslT3p8GeAPjl3XY8ixHKpWZaKUg3g4NiRJoDHtMffTEabfcNdaM3x1O4r8/Rfzs+aYfwwrZHXE0s3mS3l23JgvzKrLuNYkgsMXAtrOpHP8+n7q5w3mvMeGmKVl1/XHX7Zvrb5gzxlmiDzLd0bB4fsN31Wj6RUu7RHrRABgaPulCHACADh2NQN8ino8JLuOsbyF1RqO5qi2cCpuuSg3wAWOzuHHkSYrrrhiJECV8oyyKegIQLQPTlB/Pd2l/4a/1Jeo37+aZe7qulVOWRjv4Bt1hJ3acQBbldbLcvIbFoa2j4C0mnOEsLTjPE9qTCTNaoyLX7x8uNntvn+bg0YfIaOVWJi046GOax1d9/2Ut6aai399lTn6/MPNKiPpr8UyZYzw+XG3c/xT351mLjz2SvPLcw4zq629qo1Xx8Eo8R80+khYVTXCeiHWDjneCQAbAJwo4xUZwlKiAt0AgFb7+Dy3vTIeACD1N7MkySh4Q5/CvB5AKMC+7iQsxdBPYI2OvoI5aJT+grkFx6bR3rC2oBshopSFo8gIyzFdugkiSrhy/PSUPpCyMe5Q9yZNmlSwtHDsscd2OEEhCg+q0Qduc8Y6ZvT8ve3mJr59oVeSjaH23nclo8zKAVmdeOKJ5txzz7X1nvVjnPAF/xpOXYn7XeETm1U5YQIryB3y0ZZ+WPhyNmlJlLGJbzlVLIeyVmMeQT/IBtw41j1oo6z7aWPVmMPR9pnvEz/z/ThtOTZDIgZI221ERpXwdvLJJ4ceDVks6Jnn/cH89KD9ZW1VmTXnYmnU2ruZM2eZq6+7yZx8XPHjNpPKNyeghG1wIo1dhu5p/nzAzSY3yZVvhKeOry8GzTIr/WSY6T8qazIC1IpLyBmZA5U6cjRKvPQpbILEGq5vbCBKePXD+hWZB8QcDb4xdwgj+lrdiMX80d1IpGGYg9x0001204T2NZtttpmVA/gbXQlzxx132BNYOGqVeRun+3BCz/P/v70vgZOrKrP/qqqX7AlZIHvSCQkQIAsJWwaiifzREWRkVxQVEAcUFEQcZQYdfzCC6OCCgMMijIyAQliCyL5MBglhiSGBhCQQyEYSspC900tV/c+53bfyurqqu6q6qruq+3zJ6/vW++497753X33v3PPNn+/KxmgkHCjQEiGFfQEHSlHBf/Pmze5diwO4qHBO/0WyBc/JyEYcbMuBFIwEw/7uzDPPdKRlHkcFYr5Lsjx8tySpmSGCP/vZzyZnm1jOFgMeyPdlqrNzcN6CBQuMA0EmTZrkzjVz5sxE3vmYie+FWMirMat+LGrxVZnnSIJhDxARu03Mj3/3mB82FQtgSaaObVAyDZaK6267uDK4ym57ut5Sqeuec3zEvv3Z7O9Pnzmjf3hl/Pfffz/R3tmXM1oO7eKLL3bp008/7QanM6oI2y/ffVqKyMd2QWGOV1991fkOOcCQZHgOLmQb8xEf+L6aahAPFa6pbM32zvZSVVVln//8591g4OTvB1QZZ960d955p5kyHiMokJzPe37RokXu/Ym/J6lQzrp0Ntu8w+zk6/Lf3r5+QsS+/qnM2lsc7vtYNbwcu6HGC/VdTnf/+m57838X2cjBo+ySb19qcZDn49yH6V7O40rgfq3fGbUdG3dYZaibVUQqLRLKzz3Y2a5zqdYnFo9BgLnO6sP1Vt67zLrtV2lbdm6xHdXbrQzLow8dZSGomlM5926IjSxZ/rZNmH6Inf/d8+21Ra/a64tftTeWvG5Dxw2xo6Yf6VTzkwfW0UfCqCk0Dn6k0ASjMlFQhL8n6e9nNMCvYuBX8qDqIK7Z9rfsz3ge/gbngG4OMuY5qQxLnw2jJ/J3GH/L0i644AL3e4nz+XjuMg8+dzmQk+8wfO5y8OSpp57qhFkYjYXGyALJz1G3ofEPf/8z6iqN75fpnvV8vvPZymc4v0UGseR7BgfXU/yFg6vZx3Pie1zy96DkutPHz+hLfLYTL/YFPIbvgnzvYf+TarBaNudMvlbsOyiqQREW1p/vYxy86X2JxILfwThIn/2W7yM5sHPGjBnc7Pbncd7oo+IAML7rfPDBB+6di33P6aef7tqg30+pEBACQkAICAEh0H4IiLDbfljrTB2EAH+M75kbtb2PwAm2MYtCQJW3fDpGQ56I0Y55Iu0mCLv0NNOXkEV6z/Nz7Yd33WtXnX2qnTVjug3sCzXJLI4Pnm/z9p3257kv20//9LBdd945du4svMBnWZ7Kk7MnHreGPj/uJRN2WzumS2zHdXZk3aeiVvcKFhoERDOqemiwWffTI9Z9esRC5RkdktedGLKTznR+ePAqY1Q6+/nPf57VefhDmIQyOi/yZZ6wWwnCbtC1528rfx6/fNPcfrZw3T4H9bhBtbYOpNpTDtttx4zea70rY+4Qv3/y8S0t76oJO8XeOW/1tGEgA6/YBNmMRps8vMYuPX6bX0zc9n6FP19nI+z6+iltOwL8sEWFHhqdUiRRyIRAZ0KADkqSb7zzu1jrlq8+kfUrRL+YIOwe/62s3wtffupVe+B3c+zkr5xoR806wnr3Y8w/FDTL90vuv3PbLnv1+QX2lz88bWdedIpN//RROeVz/oxLi7UpuI8Tjz/+uHs/4iCmQhDUOrLy/GhBEiaNH4RIEpEVDoFSeQYSARKBGNabv/m88UNRKjKP354qLcQz0BN2qcCerZEwQtV/EkxI6gl+GMw2L+6/fft2R5gi4YoKtAyVnq21F2E323Jp/wYEdN/mpyXkSti994GHbTRIkcccNTU/BSmBXF55FR/mQa4/58xT2620119/vRuQn+qEfcP97H/Oe9Am7TzSymobQ56n2jG4Du+JEfDYuv9TxCoPg49rn9skuFfa+WIk7FIJ/IwzznBlvvXWWx1RJm0FGjeQ7EpiLEUGPHHRH8PBZMcee2zC/+bX+5QEjSDRg+tJxv3DH/7gyMdU8E0V1Yw+PRJVUpGwgwRLf55gSmJIMuE1eE72u56EEzyOfkMSSOjLIEE62Xg9KfiQbLlgsG3bNoe9Jw4l50kC03/+53/m5Z09Xgv+35tRq54TtdhynIm/mTKxngYRkLD1/EzEwgizng9LJux6VdNkMi7JuiTtekve7tczLRRhlwP6PZH1nnvuccSzVO2C75qMcpVM/OK+bL/Bd1BfbpLHTj755EQUolSEXUYO8CIU/jifclD87bff3uScLRF2ScJi2/bK2j4fn5J8lXxv+22lmqYi7KZrb2/8onuTarbU3rIh7DbJtHHhkksuce/wVVVVtmzpMotBNDq+B4RekHRJ1oWYKoi7DfNrlq+1u2+823qW9bRJE6fYjE/McKRewzGvPfeaxZEOGzLMBu8/BCEScRzzqolbdG/McNc2aR+pyqJ1eUSArxV4R9i282PbXbvHuvWptEEjB2IdSLf4tMPpw43r7H9ffNH21O9xCst76nfb7jpM9Ttt5mkzbfWG1fbXJ/5iE449xG695xZH2A3juFknzHKDWnjf0weZ6rnAfpZEyyBpN0jYvemmm+zBBx905M/kWnNgDcmtqURrculv+XuSkXVojJRBorA3RifkQBkKhPhw8A8//HCCDNvW5y6PZ6QY/03Qn5cpf9+SdMvvhLRUz123IfCHgyD4bPXlDmxKzLIPILbEkX4GDtTmb2tGf2Dfkcp4vUjyDWIerDvfz77//e83qQcHivA9jGRYPsuvuOIKu+666xLZ53LO4LXiQKZ0A8JIsvZRakjgbinkPLHwEa/4jsOIYKmuB/Fi+0hFOk5USjNCQAgIASEgBIRAQRDYJ99XkOyVqRDoeARIUqycFLb6VXGrexGEOoyOzcgw8rfuZSiawnHWAwOiGaIkb6FxvF8NKWcZysdZitRvP/dTM2zCqOF2y2NP2XUg2m7bjVA9OVq/nj3sxKmTnLLuNCjrJnyDLZw/ZflyPL8OywIBNNm61WiHT4OsOx9XKguyrnVDu50WxocMOIU6gKwbrKX/IcgffVdddVVwU0bzVB3hB4JCmGv2ULKCZBVuSDhuGtPk5ZNBzA0SdkmqpdLu66u72aNv9bLqWn8DZV/K7hVxG9an3inrBsm6zOnkQzHkP4PyZX9WHdFVEKA6Do3Ol2CI8q5Sf9Wz8yNAsiP7mWIn7Por0dY+kfkUsl90fSHOEQ81KuWie2MP19LyP3zmaBtWNcSemz3XHgfRds8uStHkZj16dbfDjjrEKeuOhrIu31NbO3+q7bmdvXBHUVWXhAU63vnxhnb11Vfn5cN/4Uqdec5UiaKyGgm6TGlUPhNZN3MMc92z1J6BQaIEP0plS9YlToV8BqZTrG1pPT84VuFDPz9w8kMXCcVtMfZn/LiZUlnXK/C2krbl/Dq28Ajovi08xi2dYdJhE+x5DGLvSoTdNxYuslkY/N+eRiU3WipS5fbYNvvpn39i3z3sh3bM/sdlplaIl8IoCI7VT8BRFgm5cNLZknbbs/6ZnIuDWLxl+s5Eoh+nZKNyHQm3/rcGiTDsS6jg+eijj7rdqd6ZjghEvwEn+gxI7qCiLt9ZOTFPkkNI+A1GKaAar1dDpc+O/SHV6jhomIPTaJ/+9KfdcclEYW7z55w2bZojTLION998MzfZlVde6Yh0JFqStEI1YyoGk1zM8lx77bVO0Y5qu95yxYCqy56sSxIWB72QwMQ6kBx055132oEHHugIOf5cuaQk69a8DVERtOHYu8iBP3QyMfzYiRyMNj8lnDeybvJpPXmS679xYsMnQyrocn2mZN3kPAu1fO6557qsqdBIEi4Hwfzud79z7YK/Q9j2qdLsje+Np512WoKsy7bI3ykkVD300EOujbPNpjMORvSkPL7v8f7gfUAS+9KlS12bZKQvRlxozaLRqFOX9O/DHHgyYcIEl8+f/vQnR/wigY3Pg1QE+dbyL5Xt6dpbpkrOBaknP/2RKwwlVQwJaXaK8cePtNv+5TeunVVtG2NLbl7aQMoFofeu+/7Ltq7ZYqdPONO+fNaXE2TdB+560N5f+r5VRrrZoRMOtRHDR1rt7lrbsnaL7dq62yrCFTYWz5bhw0fgAxCIwRAhXvXOKgvHwlYWLrMyfNCpiFRYZXkl5ssshPX8TdK8dM2KW1Ir+CiMQ+W2PlYPGOqse+/uFq7AVeCjqAykZ/wj+XLP3l1WG6u1Hv16Wve+3W3b9o9t3fp1VhvdCxJu2D77hc9ar/4Y3YDvYHw/uPdnd9uaD9fYP/zjdPvCWWdjH2CH9ctWgjT/49NsR3SH7YztsP/3mRPs6GOPtnVL33PP/XtuujOBX594D4v0b444+yIahWrYt/Ldnkq2r7/+ui1evNj4nCIJNJVdemnD4HL66Tloh7/92AfxecJnw1lnnWXPPPNMk6hIbe1vWQ5P1uX3ucmTJzdTlU1VVr8u2+cuFc2D7yTs5/k+wPV8J+GAV07ZGMvAPpk4ceCDH8Th86AKLgmqNBJT/fsKf1N7si7LwYFCVOBlH89rxYlq6en6AT+Agn0H3zkY9aa1iC1tPSffe/hexfbBPoICEFSCp3GgAd+3Ro8e7aI58X2IQkfXXHON284+kIO3aP7d0g8U4Tq2Ow7OYkr/Hdsy2x37SUbOySZSDvOTCQEhIASEgBAQAm1DQITdtuGno0sEgbKBIet2bNhi6/AD7y38BMzUIUbS7t9AlqyNW3eSdqvwoziSe6VDFWBQ0hKEQMzy915g2f3iDiwHt08dO8buvPziFvdPPr61ZQdF4HzJ+wfPv48xkSmArrb6kyMCdNLUrkSINJB16xcA82y++8LJFDkIztyjwxYZ0NypkGORsj6Mjkw6HBm2hc5t/rD1P5Yzzcx/8M4mVHumeXM/tmaSdFtLR/evsy9N22l/fH0fcXg9FHbzYST7vru5Oaua5+N5WbjWypePciiPzokAQyfR6JBKVhnpnDVWrboaAuwf+NGW/UUxk3bz0Sfy2ha6X+R7acP7H/oekHZdH+TeV1teHjV+hJ1/FdQ6XIeFguYhdb0zi5DB+RvKGShvkd0IJDt41VkWjR9hfNjVIitqTsWhAkkwxDRVPqgcIys8AqXyDCQSVKahghlJQAy9GFSxyRSpQj8D+QGOlm1KghPrl2/Lthx+/3yXQ/nlFwHdt23Hk0plFRVgXeRghx5ykP3vS68YVWe7AmmX9YyAmMB6t7eRtEuCRiri2as759kTax+zET1H2cheozIrWhSk3bfjVg3yDt9ZKydkr7Sb2YnaZ6+gQuhBB7Xt+px33nn2/vvvu4Iz3LWPssMVDH1Ngg4JGRdeeGFaEhGVcEmkYehsGhXp+H5HMiQJMiTs+mvJcNG8tjSSShYtWmTDhg1zy/zD6D6eKEIyk1e6S+zQOMN3YpJFSJ6hUR3Pk3CpWvfjH//YSGr0xv29AhxDX/t9uT0XDEjmvP/++132yQp5JMWQ+EKiFMvI7bkalTZr3oaP9/GYRd/BuwaacKbG6GmVxyAC4IgGjDI9ri37kbRLom6QrMv8SOItBqM6dVB9kKrN/h5i+PUgYZdkJhLXaSQpeVITl2+88UZHtiMJLJXx3vG/4UjyJRnM+7WZL9sjiXZsp/S5+fsjVV5cR0KYJ+sGlSy5jaR4ksm4nedpLS8e01ksVXt7471Y0bQ34kx/KklwJBm+v2GlWa+YlfXlh8KQvbn3dVuxbYVN6X24dT+24eMhny3f+NxX3CW68rtX2ik/OdHi+MZI4n5ddb19++vftsXzEQWt5iC77YrbLE7CLm6v3194q1Vvr7ZykHmHDRtu37r8W460y+9EfG78/ZW/28vPvuwIvWPHH2izPv0pSAE3PFOe/8tztm3Tdhs5epRNPXoq1uMYTK/932u2a9tO6w5S6ZRjjrCK8sb3Jxy2af0mW7FkBWoRsnETxtuAgQNcmZcuXGK7d+22nr162iFTJrh1gIDVtbr6Ops/dz5cMHEbfeBoGzFmhFvPbZxCfFT5iXBg/n34C+a/NN+isahVdC+3fzzzs9ZnP4Q4xXbuv3TJMnts9hyri9XZ8f94vH3yM580w2cXknZ/9fPf2MK/L7QaEHOv/s+r7fCphztxGgrULFy80M7AIJY98d321z6zbfYcEDZ5btiffvAHW7F2hQ3s09u6HXFOw0r8PeXEk2xd3Tq3zHvfkxu5gvcf771g/+x2TPGHBFgO6PD9F/tMEixJ5mc7oWqs70+TDyfhkgTf4POEIje/+MUv3HOChE+vPp+v/pYEzSeeeKKZ0n5y2dItZ/PcZV28ijgjLhAbb1Ss989Ovy6T9J/+6Z9cVADuS+z8wDB/LEnO3r74xS+6WSro//KXv3TzPJ79fSTScI8SX/+Owz6CA3UOOOAAn0WTlAONWeZMLB/n5LX629/+ZqNGjUqckgrDnnTNPoJ9E1Wc6YPgAHrft7Gv4rte0PzALa5bsGCBI+ty/uyzzzbiwvZIv/rzzz/viNbcJhMCQkAICAEhIATaB4H8MI3ap6w6ixBoEwIVY8IWnQGn7sdRi6/NIiuQJOvm4efn7qh1OwHO4INB2u3GX5/ZW3gQXvjxo9TCDT8K3I9YZuN+7TJtzLNUlhuLm6+E4c6qq6udGkO+8izVfGLVcatdCkfus3DkkmSeqTJ0Y4VDw+DM/UTYKkAy70jj6GIqHuRqdGxw5G26H8u55hs8LnHbuZV0TwFvWMP6psuzxjUoBgZJu27nAvwhWdefL115WEpf3gIUQVl2EgT4wYsKIP5jWSeplqohBJog0K9fP+dcpbM7nUO8yQEdsNDWPpFFbo9+kR2g611I1nULXmm3tJY74BK3eEqGyiOJYCRCcFN5jB//O9MgCoYDvOCCC5zCBxVA2O8UKjpCi0B30Y2l8AzkpaGSD6dcrT2egbwvSXot9TRXjHVc+yGg+7ZtWG/evLlJqOFsczvlpBPtVzffjn6rn40/cGy2h5fM/svffc8emvO4Xfatph/u27MCVJ8jwZrEOBJFPDEOQ9PtyQ//YuP6HGSnjT7LepeDuJOJgbTkSLuxmCM+VR4ehiqi9+xkkkHx7EPFWBoHOvG3QrJR6YzE11RGdTcqwdJIDPNKdfStBsm63E4yEkM1UwGU+3FQvScJcbu322+/vdlvqcsuu8wRdrkPQy97EqEnQHI9w3QHybpcx4E5d9xxhyPXkABF4k6qsM1UL/VkJx5HVWC2GebPd0kSLIPGuvBcJFMxlLe3XDHYsGGDzwIql8MT85whRlTg4/tHsIxNdspgIYbwfTWLYrb3Sfh4l+M3FYl3mRrFIiHGwHZeSEVpT8T16rosXjJZ9xu31mRa6oLux98cQbIuT1ZVVWVURyaxmkqEQWOUE9r06dMdsTa4jdeYRCz+fiFZKdl8ZBSSp6jQGLxvSPri+fj7jsdSMdrfH8n5+GUOcvSWfM8MHjzY3n33XXd/5jogxedd7GkptbcglvSr+mctlZ3ZLtJZ8NkydMRQqLvCu1LZ0FdFIAF7y19uSjxbKnvs+37zyOY/J0jdS59eav3HNu0bZpxxpN12zq9t9uyH7IB1g23l79GmQNiN45Pj/Q/8t72x6A07/ZAzbcbZRzUMDABh9+iTJlkcaVl5mZWXgeWKx5Abo4h0SE1/u/LzN9muHbvs1MNOtQvPxvsC1m/tvd5u/vlv3X6//+7vbeD+Axuqiio88/Tz9ovFP8duMbv/+vut56DAt076k1gdTljt5qGK/+4jb9k1t1xldXgA3nzbb23YWf2b7HOkHWZn33OKe7Z/vPs8O+nEff3bvz91lTs3+7dp50xqKEfj3yNHHGHfvvYS17899vQcp9JLdeJ0tnbt2gQZ94YbbmhC1uUxvKYk4WfiQ+dglmDfwOfD5Zdf7gi7zIvPoqlTQZxOMp6DaqnB5wl3IYH0xRdfdCq9fPZ4wm6++lsSV1Op3ScVL+Vits9dEj9p9HsRk6Dx2cmBFSxLJsRof2zfvn0dYZURbe67775mhF2vrst3CPrfaH369DESnmn0FXuyrluBP/TR8TrSqI7M8iYbB9pnStblsfk4J9t6kKzLfEkGJzmXmHkyNNdnYn5/vlcRx6B98pOfdN9f6QMplGBS8HyaFwJCQAgIASEgBJoikP7Ntel+WhICJY8AfxRXTgZpdzOIkE/DqftxFlUCWZIKp9U7EbJqZkM+kX4NP7CzyMUio8dbfNdOC/Xuh1+rOBI/fks2zabiGe7rRz5muHun3S36cdz2/j1mNS9ELfYeqpmNIxe7h+DvqJwBZ+7EwjpzC30BqJ5Fsi4JIIV0VPrbsKE+HBvecFumW545bo9VDaizv7zd0xauxYMlzzZ5WI2dfNhuq6KyLqy18jTdnufCKLtOgUA2TqVOUWFVoksiwH6C/QXDoFJhoJiVdnO9QO3VL7J8rjd0HQz+8H+j0i4k6V3xS2J7rkAX6Dg6wTl1ViPB4dZbb+2s1Sv6eukZmL9L5BVqSz3NHyLKqVAI6L5tG7IkMo4YAUW3HG340CF23pfPtjvu/qOddspJnVJpl8q6JOuynqxvRxvV1bzCGskRVCCrq6uzXrsrbQve4Xvt6W2haIa+Vk/a3Qv/7h74wKYgulTfDI/taCAC5/chldMNOCRh56KLLgoc0XSWhDGSL4Kk3uOOO84R/pruaXbUUSBvNdqyZcsSZBa/jgQiEg+TLbiOv7W8UaHNW7p3XCrrelu4cGEzwi7V6fv3hxMzyUaPHu0IuyQ/JhOauKsn+TAEtLdcMagC0ZMYknBJUhH9kPShjB8/3mXN87flt2V0G3y8b8DH+xx8vCuRJUhzGRu+3JUd3hA1MFVI9ozzyXDHVCRKf+htT9cbFU+LwY455piUxfDXLEgAYxthuHMaVQRTtSeKRJxwwglGxdtke+2119wqEslJZCLZPWi8h0ns40AEKlC3ZkESIKNOkJBHgpgXqkj3LGgt31Lc3lp7K/Y6tTb4tq3PFqotjx07tjkM6OpOOfUU+/PsP9uH29bahu0f2tChQ91+m2IbbXX9B7YlstHKh5Ix22Dl1tPPJtIYBr3s3r3bPfueWDPHrT8kfiCU6xuOO37YMXb6Vae49XOWzLaLP4Gon412xw9utXk75zri/NBj0pOW/f5Ma7pX26bYR27VlOmTIYgU3MpvMSHXR/D+9SrU3CPXZ3vT3PctBQd6eJX4fVsb5jjgxA8MSd7mlzkAwPfhfh3T4DVbs2ZNSsLurFmznP8yeBzn+Xzic4rEUU5+cE0++lvmz/eDXC2b5y7fTbzif7qB6uxXOfCCCsXZ2Je//GUjYZeq/3yX8crqbMscPET72te+5lL+IaE6VR9ObNnvB68h80hljD6QjeXjnOxzko3PHA58Sr5HkvdLtcz3KQ4wYZ3Z33EwFOvVs2fDs8GnqY7VOiEgBISAEBACQqCwCIiwW1h8lXuRIUDnbffpYYtvB2n3JRAOoJ6bsdEZjJBV1TuiFv0obt0YimoYfsBipGimFplwhNU9/5hFDgZhl4Yf2PjfEOa3FJdZZln+EMBI6Lo1UFx4JWa1mOIU2mjgxWR+jl5QXjg2jPYZsUgftq7SMv5YpnIFlTFodFgWkqzLcziUqGrNoeVIQ41pS8tVA+rt0uO32ba9EVu2sdw27Cyzmvrc8a4si9vg3vV20AF11q8bGoIrDwuXWXnc/qyMTAgIASHQhRHgBzT2G/z4z36EKlX84JXqo1ypwNQR/SJ7RvZocfxJmWJbyvV+/yLZXirXWOUUAvlCQM/A/CBZ6sq6vvz5QUO5FBoB3be5I8wP9UECYi45HXrIQU55ds7jT9sbCxfZ1MkTbdzYKhswoDmBMJf8O+KYLVu22or33nf1iYCkQGXdYiDrJmMRJKFSFbBmYdT2PARC47vYM1M/GLiDsXfhp90LPy1Ikd3hBys7oPGFNPmERbpMoskLL7zgyKmpisjfMskKjoxMRsJF0EiG9RYM7e3XJackLHn1Ob/Nkx39sk/T/ZbyBCKSypIV6/yxgwYNSpBhuX+yUIMnKfr9k9MgkSa4Laho6NfnigH7zbvuussp1zEvEig5EXeSuUjepZJxOhz8+ZulaMd1G+DjnQcf7//Bx/sh9si0bTMzNOUwuHrdZiJ62sh9xLtm58nzilQkSpJ1/fo8ny6n7JJVB30mJF4nGwld3lIpPPttEydObEbY5eAthiSnUWk5lQq2P57pvHnzgosp51mGK664whgSnqRE/ywkeZ0EXt4jbYlGkfKkRbzSt6ugsnOxtbcgfCRRehswYICfTZm29dkSJHcnn+Dggw9OrFq0aFGCsJtYmWKG9wJVSZ977jlbsmRJs34k+RBGgvjCF75g999/v/3pT3+yiy9uIOxyUMNf//pXt/tXv/rV5MMyWk434CrVPZzrsz1dQYJqtbzv0hkJqrNnz0632Q488MCU2zIhPpIQnM6C25YvX+7Uv/PR3/J8rbXZdGXi+myeu0GSNdXL01kuz7pPfOITrn/m8/ORRx5JqPB7NXSe6/TTT292Sir+zpkzx5555pms1Wlzxa0t58zmHmlW2RQr2LeQ0MzBJfPnz7fTTjvN7UXiue97klXfU2SjVUJACAgBISAEhEABEBBhtwCgKsviRqBsCMiMs+DU3YPga6/CUwb13IwNu8fXwZH8FEJYrY9b5T9gOgih13qTttC6lU851moeuNPCICWGIrz9oE/miHn02DGPUlpuvb7aI3MEYjsRHm0pFBf+FrP6xWgP2ZDJG08T7xa3+gk1Vn3IbtteC4b5mszPXyx70gFOgi4dNPx42R7m7j44YXNJSa49alS08e71d3Ee0hzK0x5Y6RxCQAgIgWJHgH0IP67yQzZJu/yYTdJrqVpH9IscsEKyLjvGkk5L9aKr3EKgDQjoGdgG8BoPLXVlXV/+tiOhHNoLAd232SPNcMb8UJ9MOMw+J3Nk1m9e+FV7e+kye/OtJfbC3L/Zxxj8Vaq2Hwg2VaNH2KwZ042E5FKwEIQQKqDoF90OFy18tfS7Zmx8X4Xvq/ZJRETbAnGF4yNWMbp0ok0FyUKbN29uprh3zjnnGKegXX311fazn/3MrfJ+s6AaYXDfdPPbtwPsNtrGjRtdDqkUcoNZDx482P0mY/0KaW3B4JRTTrFXX33VhSi/5557XLbUe5EAACv3SURBVHmZH+c5VVVV2VNPPWWjR4/OqArxWrTJlSDr/l/U6l5HI80myl/jGULDQdY9Ae354OzEQjIqYCs7BUmUxUieJBEyUyO50FtL6rWp2jHVeZPJ8T6vVOmOHTtSrW6yjmW/7rrrnNLl3XffbY8++qjbznDlDFfPiYqUVJBsjSDcJOMSXij29uah5Ts2iW40EttSDRzw+/q0Lc+W5LD1Pk+mwW3BNh7cJzhPoh6VSVMZ/Wfpnp/nnnuuI+y+/PLLTtWT9X7iiScS2QRV1BMrM5jJ5h5OV7Z0p2mtf2NkLm+8vxmhK5UVss9Kd06Wo0+fPoniUIyAVgz9bTbXLPg8bal95oIxfaTnn3++XX/99XbvvfcmCLueXE2F4uBAJ/qD+d7EQRKpzCvsp9qW67p8nDMbvDMpJ1WG2d+QgH/HHXc4BWcex3ub01VXXWU/+tGP7N/+7d8yyU77CAEhIASEgBAQAnlEQITdPIKprEoHAY5Mj59oVl0L0u5COM6yIe2ymhAyqH8lbrH1IFceBdLu1Aa13VAGd1TF//u81b35qpWN4+jCRgdTwtFUasulc82LtaRxcIioqluD8Gh1r+IDA0m2UBfJ2hBGqBztsO9JvaxiLH7YZ+67zPpUne0AD1VDSlVBPBNgpbbc2a6L6iMEhIAQaAsC/HjtP2C3JZ8ueSw6QNcbhtgfcqF5Wgrbu+S1U6WFQCMCegbm3hT4cYwf5PlbwD39/HJyWuTbc0dAR3YUArpvM0eeiqTHH3985gdksCfJraVCcM2gOiW3S7h7yLrBpxXfjcHsEEmIN0TNzrge8a3wrc2Fn/YjqO0eDz/t4WFjlLVky9eghmi0wXGXCWEruQzB5WDobBKqvvnNbwY3p5ynOiKNyrZe9fXwww9P7MsQ3K0p1yZ2bsMMz//SSy9ZUP0wOTsSHklEpOWipJecX0vLbcWA5fvVr35lv/jFL2zx4sVO+fimm25yJDWG9qbablCtNV1ZqPZcswh+XqjqRpfhTSLbbw7IOHSAWeWnwlY5BSIhuDc6wjyJ0qcdUYZ8nDOoRLp69eq0Wb799tvNtpHgS0VcKrpfcsklduONNzbbJ9cVJOVyYgh2qrY++eSTdttttzmCMNV8Ga6c7a+rmG9nPi3GelOZ1RO4SQrM1HJ9tqRqk/6cVF711trgJQ5k92RdEm7ZjqdOner6Ca9iTpXNoHqwz3vmzJkJNVOqk1Jl94EHHnCbv/71r7cLqbytz3ZfF59OmTLFz7pn/YwZMxLLfoaESyqRFsr4TElnwWvrn1/F1t+mK7tf78vNZb4j8FmXylK1uVT7Ja+jWiwJu8SReA0fPtwRUbkfSeZBIxnVk3VPOOEE+8EPfmCMbkDVXL5D8Z7OVUE3eJ7gfEecM3j+dPMcBEKyMyeSwDlQierZfP+kMboAsTnzzDPTZaH1QkAICAEhIASEQAEQaL94OgUovLIUAjkjgJZfOTZs3T+LcGmT4fgC2TFrg282thJOuMdjtmd21KpfhlMYag6NXL+02VXMPNlCMShGbMcIb34BpJVq2lB6/c0FAVzz+k0I3fe3qO1G+6l5AmTdD5BRg88/uxzRfsuOCFkPtOeKMWjcHePLza7MRbS3v/0aitSgtLuveKW2vK/kmhMCQkAICAEhkBMC6Bhd7+c6SLeAbJqmJbE9p8rrICEgBLo6Ap7M5R6BACOxDBIvLbHslgLLRba9sXhKhECnQ4Dhyffu3Wskl8g6FwIk2HabDr/WJ6GQ23Kk8dQVr4ZLDaIM1Q/DT/tU1GrfB/G3bt+uDOf84Ycf2qpVq/atzHHOh9QeOnRojjk0HOZDO3Pppz/9qYsQ0lKG69evT5CqggTYSZMmJQ5jePT2MBK+aFQ/TKeAGCQlBUlShShfvjAggYdl/e53v+vIxp78Q9LuypUr0xadba32PbS9J/GN4GF8I1iE94ZcyLoQf6yYiW8Wx0Qs0qewDt6EfkiaWuVCnqwsT5NZB60med2rLfr7NlVRXnvttVSr7ZhjjnHrX3nllZTb27qyZ8+exucAVXc/+OADp+bMPB9++OG2Zl1Ux2fSLnJqb2X+jb2w1V23bp1RLdfb6aef7mczTrN9tpDMmC5a1Jtvvpk4Lwl2LRnfm7zdcMMNduqpp9rIkSPNk3VJ6E1HnGSZv/rVr7rDSeyjWioJ5bRk9Xe3sgB/8vVs90U77LDD/Kxdc801VlsLSfQkIxm0kJbuecNz+ucUydVeqbbY+tvWsCkvL0+8p996663GyBjJ9vzzzyfaUvK21pY5kIIkZtojjzxizz77rJunWi4H1wTNb+O6//qv/zIStNkvsG3TCvHO1BHndJVp/OMHlQXXJc8Tg8997nP2P//zP00GXjGagEwICAEhIASEgBBoXwRE2G1fvHW2YkIAIdcqx8EBdhJIuyA7Wo8cC7cTxMv5cAjPjtmuOfW2d2HUYjtadhZ0+9I3re7v8yy+d3cDt9L731KkblWK9a60WN+h23OErKsfFkX72LsARN05cOI+CCfua2gvaEc5WU+QdY8EWRftuAIkdNNTPWsYG+6hxpsM3uqSXs669jpACAgBISAEhEASAq4vRG/o/vMP+8YSXE6qlhaFgBAQApkg4MNPlnqaSV21jxAoNQRIUHnxxRcd2aTUyq7yZoZA2YCQdZ/RBtIu3GvxNRBXeDJmu+Fvq34FPretXGl27LHHukKQnEBVMRKwsp2o0MmQwlRPY1jtIPEnsxo23YtkPRJ1aSS9nnfeeQkFx6Z7mpGsS3U4b0GyFMnIJKrQGM54165dfrdEOnfuXHfvkKyVD9JykER16aWXJga0+BOSBHXZZZf5RZs4cWJivhAzuWJAZdOvfOUrDnsSuoNGQg/JlN78oB2/7FK0LQp4UMiDbc4pRK/DFqzP1kKDQNadhW8Vx4Os2x+/vwpsZxyTfyfy1DEFLnQO2R999NHuqJtvvtlSEeVI4vIkueTsPVGOhEavQhjch4TKb3/72+7eIhmyNfvlL3/p2tv3vve9ZmRM3sP5Vo9vrTzttb13d7MpVflv01PH5D/PZEw2b95sJ554YmJgAklumVyntj5b2Cdce+21ycWx9957zxG8uYHqs55422zHxhXbtm1LbPL9RGIFZkjEbcm+8IUvuM3s90i+pJFM2l4Dp3J9truCpvjDfpd9LY0qulQK5v1PkiOx/dnPfuaURlMcmrdV8+fPt7vuuqtZfvPmzXNK29zgBwtwvtj6W5apNfvGN77hdmE7piI1CbrV1dW2adMm9yxtq4prkEj+4IMPunNxXfL9QJK5t+QobLFYzH7/+9/7zXlLO+KckQjIDo2W3J/x3eXyyy93fY+/h/2+TEmA9gNbgus1LwSEgBAQAkJACLQPAg3DiNrnXDqLECg+BBpJuyGk1d2iVvc6vGk7cigmDouvj1vdFjjplsesblLcyhF+rWJ0yMI9mzsOwgcMsx4XXWV7fvdTK59yrIX69HNkCOfM4+5U5wE5womZJS2TL1EU23OAqasfEtsVh8IHpsUxq38TirprgUhA7SNrfPqalYOs2/0EfMwYLbJu1vg1HoC7DdG+G5R0SznNtf46TggIASEgBIRAEwTYJ7r3TbxyhviSyxR7uLSElptUSgtCQAgIgcwQ8GScUk8zq632EgKlgwAV4kjWZVhnEiVlnReBsv3h55oJRy3eP2tfhEru5hzqCr5qFD7e6nVRq4cfrgJCDRPGHGqf+cxnXOj52bNn55DpvkPYBs8666x9K9owR+Ltr3/9axeam6qab731liNjHXHEETZ48GAX8pkEY5IBqfJKu+SSS+y4445LnJWDTBjymQQZEjWo/kiFWBIVSZD561//at///vcdGbiqqsqRrRIH5zhDVcevfe1rdvfddzs10G9961suFDXDs7/xxhuOdESCF40k4n794PsuoOWKAYln999/vyvZsmXL7JZbbnFE7HA4bCRP/cd//IfbRmLc2LFjm9Qgths+Xqjq1i7AN4G/o61uwGb8XMrFEmRdENbLBvHHV+HtyzPCtgJlXgg16nzYuVDHnja2fcqeTXkZAv2xxx5zh5Cwzntp5syZ7n7w90a6/Hifk+hLtWj2PwwjTuI8SYRsLwwh7hVHMyGgdevWLdHeqBbP+3TMmDFO5ZPt8A9/+IMrype+9KV0RSrZ9d85OWzfuTNm2/fkeJMk1fzCE8I2YUR+2htJuS+88ELiDBxwsGHDBkfwvu222xLr+bzgMyITa8uzxefPAR0k4n3+8583KrqTYPqTn/wkMbCD7a81C5KLf/zjH1uPHj1c30DVUxIdr7766haz4LOP0+LFi+3f//3f3b4XXHCB8RnZHpbrs72lsv3mN79xCuovvfSSux99H+CPIXmRJEbiXSj753/+Zze4hoqwJFKz/bGf9hacL7b+1pexpZTvNnxukgDNtsP3r2Tju0qu72M8loOFmDcnWnAgkz/Xpz71Kaesy+Urr7zSPXOJ55IlS+zGG29MPI/9/vlIO+KcJKKzHe3cudPd17NmzXLvifvtt5+75zkgie+YbOt8prAvHDRokG3fvt0NHPORErheJgSEgBAQAkJACLQvAiLsti/eOlsxIoDfllQmDVWCtNsTZNt5OTqDWTdEUIm9ByWHdXDULcE0MWTlh4C4OwrE3V5NHQiRsQdbz+9dZ3vvvcXiH623sjEIX4OXZWck69L8IcW63FBK/W0FgdhOOHA/QHtYiva1OG6xVXBM5RAWLXgaOnLLp0N14RNhKx+GRuzbSnAnzWeEgIeuIaWGYIPjsNSWM6qsdhICQkAICAEh0BoC6ABdb0iyLl8wkJbicmvV1HYhIASEQCoE+FGYZN1ST1PVTeuEQCkiQEIJSQwkNpHcILJuKV7F7MucIO2WN5J212efhxtsBsHU2i0YNP8uxssfEbejJk638ReNt/fWvOdICtnmSoISSVNtVdYNnpcqrgxBfOGFFzri34oVK+yMM84I7tJk/qSTTkqp/kfy7Jo1a1yIb94zQfKZz4BkDqop+lDQfn0uKftJEhlJaqOS5B133OGm5LxYrtYIYcnH5LqcCwYkopEc+cc//tGFhT/qqKOanZ64eSVkbozXwMdLJedF8PMugCDDB1jZPKo6d83IQkMRBRDKut2mg6w7sMEbmdGBbdzpAHCob7kwbI+/EbIPPsqZa2yVuE+PPtBsclX7lT2bqh955JFORfH88893KqlsJ8lGsnuQmOm3k2hOQi7VLklouuKKK/ymJikVO70SaZMNSQtf/OIX7Xe/+50jsvF8nDzJyu86btw4u+iii/xip0kPHhqyB74XsWcWxW0dRG9ytR74hsf2dji+t+XLSHIjcbIlo8ouByhk+h6Sy7MleH62J7a9a665xk3BbZwnWZf9QWvGgR9UEScZnAM6gkrtPJbEVCpFs+9JZ1Sh/c53vpPYzHbcnpbLs72l8lGF9YEHHnCDX5IJo8SHRE5OhSLs8n2Wyt58nqR6ptxzzz02ZcqURBWKsb9NFK6FGZLLaSTtBq2qqsoNVNqyZUvOhF3ehySXkoRK43Nz2rRpwdO4eQ7OYBsneZj9PKegtYU0HMwnON8R5+T5eV+yT+Hz7Nxzz3VFIin/lFNOcWTwZ5991m3joC9OyX0PnyckG8uEgBAQAkJACAiB9kVAhN32xVtnK1YE8Pu+fARIu58O2V6oltbMBWl3LQqb6wBzkDFj78BxB2JmHZwQdYeCuHsQJqigRvriZI3+BKe0e/l/WO0Lf7HaZx+xUP/9LTJ0pFnP3haKRPah1ciXcDxCHlsMy/tKp7lUCOAaRbfj2kOloBZtof5tEHVXY2UbiboGbm4YTaQCRN1uR7Wf6kKqKnaWdf52aqhPo9JuonKltpwouGaEgBAQAkJACOSMgOv9XAeJP/xP8q5fRq4lsT3n2utAISAEujICpa6s68vfla+h6l7aCFDVjkp3q1evdh/XGTqXSqLtFXq5tNHrXKWnymgPKO2Ge8CV9iz8tGtQv1z8tDX00cLXuw7E3WVx6zGxv007dKCVTYMfGETDYjASTx566CH77W9/60hC69ata1YsqjVSiZfEi3RGJdu+ffs6BVGvlub3JaHwhz/8oY0ePdqvcmkwjHKTDYEFT+qoqKgIrIUvvbzc7r33Xqf4yLDSJIl44zGXXXaZUd2UZKOgtXbO5JDWwWNbm88FA4YmJ8GGpKIgaY11mDFjhrsmJBjFESWt7kP4ed8GUffNuEVX4AcS1JxzNvp4R4GsC6VQ+njdN4OcM8vtwDAuzeemNb0+ueWUn6OS24rPtbU2w/2S26c/linVcWtqaowKo8F7g30LiXlvvvlmYvfkMgwfPtyee+45pxT96KOPJvbjDEli//qv/2pnn312E8XRdOUlAfiVV16xa6+91v77v//blcXfN7zHGTqe23r16tXkPJ1loU93s9OPLv72Rrypsjpy5EhHAmT7SUXm99cl3SCITJ8tPp9gOn78eLvqqqsc0Y6DIrzxuUT11aACq9/GZ3IqI4mPz36SxYPtn4MVOBjBK8anq8dpp52WIOzynklWG091zuR1mSjy+md/qnLk8mxPlY8v14ABA+y+++5zeFC9nu+frBcH5dA4UCyVpcPY7xu899PtS/V7kqipsD1//nx/qGtzJGjzeZJsufa3meCebp9gXZLL45dbeu4yX9bnX/7lX+zdd9+1rVu3OnVyKpTz2tx5550+m2bvCYkNLcwQQ0/Y5YCMVMZ3orlz5zo13mQl5R/96Ef2zW9+M0EaDraXTOrO8wWP8efP9ZzproPPl2lL9wgjApCgH7zPfX82depUF8GBbe7pp59272u+7yGhmYNWiIXfP3hOzQsBISAEhIAQEAKFRSCEF1F+epUJASHQiADVUPe+GbOaF2IWXY7bA47dNhtG/oaHh6zsYJB2x2EaA+IuRsyHkijzdX+fZ9ElCyz6wQqLbdpg8drUPwzbXB5lUDgE4mUWjw2yeP2BcOSOs1j9QRaP4od+HI2grRbaizaz3CLdXrRQxUL8gGqLV7ithekcx8e+MaRzVES1EAJCQAgIASGQBwQ+95cX8pCLshACQkAIlCYCM6pPL82Cq9RCoBMhwA//DN86YsQII1nl0EMP7US1U1VyQSC2C37ahTHb+xyUTEmQbIOSKc8fgqpohP7Zw0JWcVDYyoaAuNuUh5pLMfN6zMcff+xCPG/cuNFIFuS9QHJRphaLxYzHcuJxJCBlSj7J9BzJ+9XV1dn69esdIYdhlg844ICURJbk4wq1nCsGe/bssZUrVxqJlcSeFkebc0Tdd0DUfQtEXRC/bXsbS442F4G4R+WnQNadFLZwz+IhMbaxZkV9eDQaNYYGp7IjSWPsb7Kx3bt3Gwn1JP/y+D59+mRzeLN9ea+vWrXKEUP79+/fbLtWdB4EUj1bUtWOzx2SakkmJEmVtmvXLiOplGRdbk9FEkyVV6p1jFzAsjCfHj0wIiYDW7ZsmVExmEZlWA4A6SjL9dkeLK8nybKvGjNmTHCTm+c5SGIk5ldeeaWRCFko888ADtwZMmRIRn11sfW3qbBZsmSJI4WSYDp58uRUuziCKNX5qSbsr0nKHfO0ks9/Pm85wJVk/HSE6jydzmXTEedk+92+fbsbRELycLKx/nxfYz9Iknqmz4HkfDrDckuE885QP9VBCAgBIVAIBDjISZZfBETYzS+eyq2TIEBHXO27cAa/FLX6BdAQ25qnikE0N3QAHHIg7ZaNbSDulg8LySmXJ3g7Mht+QKhbh2klFDvea1BZiCOcmEXzU6rQQIzYPCJk3Y6LWAUI38X2MSE/tVQuQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYB8CcYgp1CyP2t7n4XODsmmbVE19tuDplY2Hf3YCiLvjw0b/bKibSJMeHqVQZq5GxDT4emuXg6iLyGlOUXdbHpDpjbY3OWTdZ8HHO04+3jwgqiyEQKdBIBVht6Mrd+qpp9rjjz/uCMNr1qwpeYIfVYJff/11B+vChQttwoQJCYjr6+udyjWVh2lPPfWUU2BP7KCZjBAg4fz66693+9599912zjnnNDmO7Yntinb11Ve7qckOWhACQkAICAEhIASEQBdBQITdLnKhVc0cEID/t34jVBxei1rtPKg4rEIeCH2VN8MA7MjokEXGgLiLtGwkpv3lpMsbvu2QUbwGjtuPQNJdjfQDOG5XYkJq+yLAtb0UiGQUrkJYr2OhuDAtgjaCjwf6ftB2XJWDEBACQkAICAEhIASEgBAQAkJACAgBISAEhEBpIBCDuMJqiCv8DeTJ+TGLb0Sx4YJrs0F8LAJRhTKonZaDPFkB/2y4t3xvbca1VDNAm4oi+l7dKkwrQBCHmm4Uwgy2Iw8VYrNCoK+Ko+Hj/QeQxIeH4fTNQ77KQggIgU6DQLEQdqmqS7Lqk08+ac8++6zD94YbbrDLLrus5LGeM2eOnXHGGYl6HH300XbkkUcaFeOeeeYZp6zLjdOmTbPnn3/eunXrlthXM5khQAVXqpB7GzdunB133HGO9E2y9Msvv+w2UTV63rx5LpKA31epEBACQkAICAEhIAS6EgIi7Halq6265oRAbDdG0i+LWc0rcNItyqPari9NGZx1gxrJuyTujoCDeFjYIgOg7FBkIdl8kbtySvXl+s1UWEB7WNNA0CVJN74ZqNTnF5kQot2VTUJ4tGPwwQBqH+EeYurmF2HlJgSEgBAQAkJACAgBISAEhIAQEAJCQAgIASFQKghEt0Jc4e/w074EcYV3QaSE+m5erDu4kxRTIHGX4gpV8M0OhG8WA+llnR+BOEQ66jeBDP4+fL4g6NYvj1sMfl+rzlPdK+H7h6JzxXEg605G29pPPt48IatshECnQqBYCLs333yzXX755QlszzrrLLv99tute3d0lp3A/vznP9vFF19sO3emVt4599xz7be//W2nqW9HXLIFCxbYeeedZ0uXLk15+ilTptjs2bONbV4mBISAEBACQkAICIGuioAIu131yqve2SEAFYf6jVBxWBCz2tfgsIOSqu3NLouM9sZgzfABUHIYCSceiLtlCMdWNgQTHcQKy5YRhIXYiSHQoiTpridRF/Mk6q6GkAcUmPP2YSBYcPg9qO5RfmTIuk2Bqi7ahFR1gwBpXggIASEgBISAEBACQkAICAEhIASEgBAQAkKgKyIQB0m3Fuqne1+OWv1CDKLfAhTgosuLeWGFA+GPpW9uVNj5ZyN94JuTGmpeIC6aTODvj+6Ar3ctFXUhzACibhQk8LyKMtClC6GOssnw8U6PWMVYRNcDeVcmBISAEEiFwH333WfV1dU2adIkmzp1aqpd2mXdiy++aCzLyJEjXVlOOukkC4U610CDTZs2OXXXd955x1avXm0DBw60iRMn2mGHHSbF1zy1spqaGps7d66tWLHCTWVlZXb44Ye76dBDD7WKCilW5QlqZSMEhIAQEAJCQAiUKAIi7JbohVOxOwaB2F448EDWrQFxt34hlBw+RDnyrKqaqBnIu055d3jIIkMbJxA3ywZhnk7iSGJPzeQbgWijw/YjOGpByo1+2DiBrBv/CCfLl3pHcrnxUSA8jE7csFUeAVXdKhG1kyHSshAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRAF0cABN16+O1qFkatZj7EFai2m29xhV7w08EvWwb/XARR0coprjAYQgs9ycLs4viXavXRTBhNr46iDBBkqEfUtChUdWMg7druPFeKggwgflccjehpkyHIAJ++2k2eMVZ2QkAICAEhIASEgBAQAkJACAgBISAEShQBEXZL9MKp2B2LQHRb3GrfgdouQrDVLwGJcxPKg1H5BTOqO+wHJzHUdsOD4eyDcziyPyYo77qJjmIReHOHnwTdXXDQbsG0CROJuhvgrN2AywoHbvxjZI19CmZQ6AjtD6LuoXDigqxbcTBCo/WV579geCtjISAEhIAQEAJCQAgIASEgBISAEBACQkAICIGSR8Cp7b4Xs5rXYlYHcYU4fHl59+HRb9cPftmR8MOOAmmXJF5GRRN5tzTaD0m68PvWw9dbx8hpIOdGV8Hvuxo+3+2oQr59+vDRh4aYlU+BIMORFGSQqm5pNBSVUggIASEgBISAEBACQkAICAEhIASEQPshIMJu+2GtM3U2BODMqwe5s2YJHMKLYxZdCiffVlQSTsCCG9V3B8JRDMXdCEbnhxFaK0zybn9M+2EC2VPhtdJfhXgNHLNwyEa3IsUUayTqxqCeGwNZ14U+y7cqR6riUFgB1zFyMIi6E+HAPQRh9nAdFWIvFVhaJwSEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBJojQP8efbS1iIoWpbgCfbSFMJIxKarAiGhQ3GVUtDIILDjyLv2xElQoBOpZ5xlHRLzodvh9QdKthxiDi54GRV0q6ca3IbsCCTOEBsDPS0EGRE6rhJ+XfnqZEBACQkAICAEhIASEgBAQAkJACAgBISAEkhEQYTcZES0LgWwRgIOvbiMcwm+DuAuHcHQ5HH9bkEm+R+e3VC4SeOksBtkzDMdgeABSLvdDCmdxhGkfLHcjQ7SljDrZNiooVMMZu6PBSRuDozYGdeQYnPYk6cZwnZg6J35NO9adyhwk6o5HOD04cSsngKi7P1bKqd+OF0GnEgJCQAgIASEgBISAEBACQkAICAEhIASEgBDoNAjQR7sBaruLqLYLH+27cAzuLGDt6N+jv5UR0YbBrYfURURDVLQyCiwwIlpX8sMWEOpWs/Yquptx3TdiYvQ0EnXXwfeL1LWDQvrq0Q4i40DUnQQ/L0QZygajceC/TAgIASEgBISAEBACQkAICAEhIASEgBAQAqkQEGE3FSpaJwRyQIAj9+tJ3F0WIO5uQkYFGrHfYhHpEOwBn3AjadelGNHvSLu9kfaGzxBpqBdSOI/DPTBf3mKORb0xXgvn6x4QcDHFd2F+J+bhkI/tQAqyLkm68Y+RfoyUKgp7MBXSSZsOrTJcE6ghRw6CAxcqCxVQ1o2AqCv1jXSAab0QEAJCQAgIASEgBISAEBACQkAICAEhIASEgBDIHIE4BuXXrQVxFxHR6heDtLkShM3dmR+f854+Itpg+PswhfeHD5AR0TCVUVyBBF6ROHOGt8mB8OtGd+HaMmpaInIa5jfA5QtVXRc9rT3EGXriGo+FIMPh8PUeHrby4fDzVjQpqRaEgBAQAkJACAgBISAEhIAQEAJCQAgIASHQDAERdptBohVCoI0IgKBbj9H8tctB3MUUXQFH4YdwDO9tY775OBwOQ5J0qf4Q6gOSLoi7oV5wGHMdybsk7nbHvJuwno7mSqyrbEjDdDi2pwossIyBjBuvgaMVTlamsWqke7GMNAbibZwkXTjdY3DSkqwbB1E3DqJuHERdzltdPoBrYx7AMTwMzvlxZmXjQdTFVAZnvZz0bcRVhwsBISAEhIAQEAJCQAgIASEgBISAEBACQkAICIEUCNB/WLsKE4m7jIr2Afyz8B22i1FZl2IKiLAVhuJuBAP4XWS0/pjnBGGFSN8Gf6tUeFu5Irhs9AlHIcIQhRhDdCv8w5yopguxjBjUdB1BF75iw77tYvCjR6rg62XktMNA1B0J/zkj28mEgBAQAkJACAgBISAEhIAQEAJCQAgIASGQAQIi7GYAknYRAjkhAAdhFMqude+DuLsibvXvwYkIJ7FR4bW9nIeZFpz+RCjshuBIds5kOpQ9eRdkU/PEXaZU4uW+5TjIpZgnibdxcvPMD1OIqhGc98Z6QwEh7lOq3FKBGBMVijlZHZysJNlionKuI+rCwW4k7IL0TKKuI+lSJdcRdpFiv6Iz1D3UD874UXDeUmkBYdHKq8JwxhOYoiutCiQEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIAQ6HQKxavhnV4O4uwTE3aXwz1JxFwP9293oV6WvEGq7oQENaZjL/UjehXACCLxhCCw4QYX2FExodyDSn5C+YRdFjVHTtsNlvJ0pJkZOA0k3vgUpFHUZSY2+4nY3XCOnqHsI/LyInlY+Ateruxy97X4ddEIhIASEgBAQAkJACAgBISAEhIAQEAIljoAIuyV+AVX80kCAjuH6dXAOr4RjGE7h6PtwLq6Hc7gYVHezhZAO4yakXdTDk3XLsI3znqhLbmooZHGm2C1Opi7+u6mRrBuvx8YoVpKsi/k4CLuOgMtlknlLzaBOHB4CR3sV1HTHwHk7Bmq6Q+W8LbXLqPIKASEgBISAEBACQkAICAEhIASEgBAQAkJACHQeBKjSWrcWxN134J9dDv8sxBVIAO0w/yNcok4kAaTdEEi74f0aU0RGCyMqWrg3iL2NkdFI4g33xDqquJY6mZcR1SDOEGPEtN2N6U5cC0wxREuLMXLaNqSY4lDUjYO463zo9Cl3hAFvKiWTqFs2PmQVB4Ooi0hqjEonEwJCQAgIASEgBISAEBACQkAICAEhIASEQC4IiLCbC2o6RgjkigBIqgzdVfsBHMMfwDEMdQdOcYTvKkqV2Fzr2dWOq4Djdn84bhH+jFP5aE5Q04Wz3ZGXuxoeqq8QEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAoQgQY2av+o4aIaHUrYiDughz6IdigxSKsQEIuBAFCUNsNgbwb6gX3IiaSdznPCGlUdQ1xHwoHcL4S8yCQMg1XIIWvst2JvSTiumhp8HX7FJiSnOsipiFqGkUt4iDqumkXCLq7MM+JJF2qHjOiGkUeisGILUQYImOhXTEOJF1ETyvbP2whClbIhIAQEAJCQAgIASEgBISAEBACQkAICAEh0AYERNhtA3g6VAi0BQE6Lus3QdlhFRzDa0DchcJDdC0clJvhIO6IkF5tqUxXPJaO8EFw2g6D/3s4HLYg6paNgpou1oWoQCwTAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQqA4EYALlsIKdRBWqIPabv37IJBCYCG+FcUtFtJoMnKMakafJBR3rUfckXdDJOx2wzpMRtIu57lPkwhpWAbRNEQycOPk5hujpCFAGjY2Tkh8hDQGS/OR0uKMhNY4uXlER2OkNBKgDZMj6ZKoS+IzSbrwb3M+DhIuJwNhN76n0e9drPgCm1B/QAQxhkgVBBmgqpsQZSA+MiEgBISAEBACQkAICAEhIASEgBAQAkJACOQBARF28wCishACbUWAIdnqP8IE0i6nKFQdYh/CN4x1TlmAzlFZxyJApyxDz+2PEHRD4LhF6LMyEnU5kaSrMGgde310diEgBISAEBACQkAICAEhIASEgBAQAkJACAgBIZADAs43ux5R0VY2RkVbBb/sOjhkdyKzUvXLkoxL0i4jg5XDsVmGipCsWwY/ZqRx3hF2sS2EZSZh/MFsnExd/Ld4w7IjMEdBwiVhtx7rMM80Xou0kbBbtCRnFLlFQ3WsN3y+8PFGRgEeRE2rGAN/72D5e1vETRuFgBAQAkJACAgBISAEhIAQEAJCQAgIgZwREGE3Z+h0oBAoDAJOeXcLSbtwEMNRHMUU2wgn8UY4RT/GOaFUIGsnBOjQhqqCI+kOhk97CJy1nIaGLdIfTluGl5MJASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBIVD6CIB/Gt3RIKjgoqKthm92Dfyy8M/ablQPiawTIECSbi/4fOHnjYyAzxeR08oZOY2k3d7YyO0yISAEhIAQEAJCQAgIASEgBISAEBACQkAIFAgBEXYLBKyyFQJ5QQDhwaI74RgGWbceE1Oq7sY2wVG8GQTebTgLwovJ8oQAw8X1g7MWirnhQQ1E3QgUdamoEDkAUy94a6k8IRMCQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCoPMiQL/sx3GrWwtRBUZE47SukbxL5V1sl5UQAvTp9mkk6Q5rjJ42AkTd4RBm6CefbwldSRVVCAgBISAEhIAQEAJCQAgIASEgBIRAySMgwm7JX0JVoCshEK+HL3g7yLsg7UY3YQJpNwY13tgWCDxsBYF3K1KqPTA8maxlBBACLgQlhdB+mAaAoDsADlukkYGYQNgtA1E33AcqumUtZ6OtQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAh0YgRI3qVPdh0nEHg/hD92A/y0G+CPhV9WEdGK9NpTnIE+X4gxhBE9rWwofL7DoKQ7DP5f+H0lzFCk103FEgJCQAgIASEgBISAEBACQkAICAEh0MkREGG3k19gVa+TIwBibnQ3iLsg7TryLki7sY/hLN6GFOq7VOCNI4xbnKoPXVmJl87Z3phIwKWCLqf94KgFWTfcHw5aEnU5SUG3k98wqp4QEAJCQAgIASEgBISAEBACQkAICAEhIASEgBBoAwJx+F7hj6WgQv16+GQ5uaho8MNiXXw78obogqwDECiH/7cvJkZP2x8quoyYNgQkXU6YD/cASRf/ZUJACAgBISAEhIAQEAJCQAgIASEgBISAEOhIBETY7Uj0dW4hUAAE4nVwGu+Coxgh20jcjcJJHIcCRGwH1jeSd+O7sA6OZWO6B4XoDE5kKOGGeqAuVM0F8dap54KkG+4NZywctVTLdURdhDhjmDOul3puARqgshQCQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJdBAH6YqP0wZLAiymGifOxzfC7QmTBEXi7spBCIdsBRRoozAAhhtBAEHRB1I0gapoj6jKFD1j+30JeAOUtBISAEBACQkAICAEhIASEgBAQAkJACOSCgAi7uaCmY4RAqSFA5Qc4hmM74SzmBKIu0ziIvZwnaTe+B8tMqxsm24vlvagojovTqVyLCYq+7W4RnLECzlc4YK2bT+Fs7Y55EHQbUixjPkyyLgm6IOxynqTcCIm5PFbqCe1+6XRCISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEh0JUQiMOH6gi8m0Hc5QTSbgxpbCt8rIiMFofIgouG1hkEFNrzwlI9lxHUSMJl1LQBDURdFzVtIHzAJOtyG/aTCQEhIASEgBAQAkJACAgBISAEhIAQEAJCoJgR+P+yGoNALwam9QAAAABJRU5ErkJggg=="
+    }
+   },
    "cell_type": "markdown",
    "id": "35bc2c92-d90e-4a26-a04b-5bd7dc4e7d23",
    "metadata": {},
    "source": [
-    "### Filter to `lof` variants passing filters"
+    "### Filter to `lof` variants passing filters\n",
+    "\n",
+    "![Screenshot 2024-12-20 at 11.12.35 AM.png](attachment:7348e149-4f8f-4eca-aa55-6234293c1f3d.png)"
    ]
   },
   {
@@ -2654,17 +2667,24 @@
     }
    ],
    "source": [
-    "var_ht = filter_by_csqs(['lof'], ht=drd2_interval_ht)\n",
+    "var_ht = filter_by_consequence_category(plof=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
     "print(\"The total number of lof variants passing filters in DRD2 is: \", var_ht.count())"
    ]
   },
   {
+   "attachments": {
+    "440e8e62-f912-4c99-9687-12d655742ab5.png": {
+     "image/png": 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e1m8SdUE0qHx37tzZNtlkE389I0aMiAZrtP3FF1/Yn//8Zx+/fqsdd9zRn5NGKF1XV2eTJk3Kchw6dKi/PlmAljVnXPsgoHt08MBesRebWfyVL5srr7xybNg0AT788EMn5C0uZB08oJc1V/rK64MPPmj/fejfNmbkGvbZ1Cdt+pTn/CXoGXPkkUdaua85ykfXr+uLcysOW94/p5szL3F54DgEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIHWSKDJQl5Z2ZVIVRZ5//GPf1iukHfOnDl2yy23eDbf+973GjG65pprvABYQtuokHfKlCm28cYbeyu+4QSJ2eTOOOMMO/300/22hKe33nqrFwMfeuihjcSxV1xxhT+2wgormI4F8e/ixYu9GFYWgk888UQfz1tvvWWXXnqp7b777lkhr8Sy8tP5s2bNsr/+9a8+rP498cQTduWVV9oll1xiRxxxRNZfGxIUh/yFA7fddpv985//tPnz59ujjz7qRa1xQt58eVJ8ui6Jn8X9N7/5TUjC/ve//9nf//53L3y+/vrrs/75Nl566SV/beGYztWiOIOQV4Lko48+2q6++uoQzK/vueceL3AWj4MOOqjRMe1MmzbN1llnHZ/HcFCiYbnHH3/cigmAQ3itx48f71lG/ZQX/R5PP/20DRo0KHqI7TZKQB8IjFwvXrg9fOjyXmhfqpBU6chJqB+c4pL/Pj8oLuRVeKWvDw5KTT+kmbuWWPeJh2+xVQcuscG9P7e9d17TPpz+uQ9287+edB8ZTPPPszQi+dw0yrGfRGxdjnSIAwIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQFsjUFuOC/r+97/vo7nhhhtMQtmoCyJOCVdlHTfOzZ4923bbbTcv4pUl13vvvddbZr3gggv8qWeddVZWGCxxrsLKSYganPJw//33+12JXmVNNjhtS8S72mqrmay8xjmdL9GqRLsSwEoku9VWW/nTfv7zn5uszwb3r3/9KyviPe6447xwdeLEid6i73333edFvCFsU9cS8SoNiVoV9/777++jlGhagtliTsLpN954wwuKFe6UU07x+88//3z2NFlCDiJeXfvLL7/sLYNKFC0ny8Xz5s3Lhg8bl19+uRcsB14SMMuKrtx3vvMdmz59eghacK10JYiWu+iii7zlYd1Ho0eP9gLhPffc02SNGdf2CUgcG2cRVxQURmHTOgl1r7rqKrv6r3+0R+6/0aa+9ZA9/pArQ26Rn6xwDxnYOzbaIYN6edFvbMAUAR5++GGfj5EbLWt777KJz4fyssXGK/vlxB/uYJ2WTDZ9ENFcbviQPomjDmLoxCcQEAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAASsyRZ5xXDUqFHeUqpEr7JWu+2222bR3nzzzX77kEMOyfoV27jpppvs9ddft4022shbmJXVXTlZedX2McccY7/+9a9t33339f7bb7+9F9rK0u1RRx3l/Z555hkv1h08eLB99NFHJquWwQJuEPzK+m5SJ6HfAQcc4IOvueaa3lrw2muv7dOQIDZYmf3DH/7gwygfQYgqD11Lz5497YQTTvDHy/FP1nCjaUgsK6vF+g0k7t1mm20KJtO7d2/TIgu8crKGnGtJtGPHjnb++ed77mIst8Yaa9hmm23mLR1LDC3OEtfmuhtvvNG+9a1veW/x2nrrrW3zzTf3Qsdrr73WC4dzzwn7dXV1dtJJJ/ld3QsS/8qJt9IeOXKkF3brNw0ibh+Af22GwOTJk/21SBiqJbNkU5s5sy57fXMXZDdtyoez6sNOmWEff1lrjzzyiN+XhdoVV1wxGzAapzzD/uuvPG3z57xvy3RaYl9162PL91rGlnxdH+eC+Qts6tSpNuGpF7zoP6Q15cOZPt7hQ5b3a+1P+WCWTZv9uA0fPtxy0/aBGv6FdIPoVfvRfEbPnTBhgvXpPttWX3GIu36XxkczrVvXbvbZrPnZdEdsvJKd/+frfTmMnhtNM7qdJn2dN3RQvZB3qks7uMAhiHyHDa7nUIqQOsTJGgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQHslUBYhr0SfsvJ63nnneWu5QcgrUek999zj2e69996JGD/77LM+3JFHHumFu9GT9ttvPy/kldBXFlm7d++eFZLKAm8mkzFZ6ZXIU07WXJWurMsef/zx3i8I/bbbbju/n+Tfzjvv3CjYqquu6sV6EuNJKCwnK8BBJCxLublO4t5yCnlllTbqdN177LGHySJuOQR14pOPUdeuXW3s2LF2xx13ZK89mg+JmoOIN/hLxHziiSfaj3/8Y29dN/jnW4upRMISGQcRbwjXq1cv++53v2vnnHOOjwchbyDTdtYS5N9z1y3WbZnOVrdolhfSfjV7uhPXdrWnXphuz7/yqc1dUGNDB/bwZT1Tt8j69elqC+bNsmU7zLd777jC5i9YZGs5AfmMOZ1MgvcpU6bYfx+6y0PKLPnShg3u4/2emPiO1Sz+xFZfuZ/V1Xa1JQu/silTP7PM4hofdnD/rn59/iX325abrWkfTPvc709tEA+P3GyYT3vqR7OsV/c6mzztZbv3zqttrbXWsg8+XuTTjop0dW0hH0MHdPJxKW+Z+cNNcchNdkJgnavzFP7Y/fvbvK8+swnPfmjPv/yhqfytumIvH1b5GNi/pw3uu6houj6w+xfSH+as7GYWf+m9C6UvbnIS8N54+wT74KPPnai3Xvgf8vrBhw3Wems6mliO/dbm/hz+QQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEByAmUR8io5iWwl5JUF3t/97ne2zDLLeLGnjkkIO3DgQG3GOk0nL/fDH/7QW4QtdMIrr7ziLeH26dPHttpqKy+ilXVcieiCeHinnXby1mBlqfarr77yIrgQ/5Zbblko6kb+EpQqjVy37rrr1lsLdeJhubfeeisbRJYxc53EziGfucdK2V9ttdWWOk3XLiertuVwEilLsPvUU0/Zm2++6a39zpkzxwttC8W/4YYb5j0ki7pyiquYe+GFF/xhiXllATjXBUumQfCde5z91k3gscces/vuutXWWGV5W32lekuvy3WXsHaBPTPpfbv61tf9BW618QAnLF3Ohjlx6fLLdbFePXq6+/Jr++zzqU58OsMmv/uKvf7OLC/0V5yPPny3DR3c2yRiHbx8N7f0t8cnTDLFfP+j73phcKfaAVZbW+sFwkrks8/m+bQef2a6E+pnnAB4OXvy+ek2ZEB3l3YPZyl4rg3s282JeHva3PmLrGOH2Tb383ft0YfesyeeftunHRXyRvMxqNdqXlCsfOjalixw1n+daPbRh16pP3fsLqbwZx69j2XqFtjzL01x1zPT53OVIZ1sUP/u1t9Z1V60aIntMnZlJyKuT/fGfz69VLr+Ity/kP7IEau4tJcvmH6IQ+dNdQLe8/90v2216RAn/q0X8ob4pk6dZh9M/9KmTpvjhcZj641wh8OsIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQCABgbIJeSUi3WijjWzSpEneIu63v/1tu+GGG3wWDjzwwARZqQ8ioWhwEgPnc0pr4cKF2UOyACtruFr69u1rEnnKEm+nTp1sl112sSeeeMIvgwcP9iJUCYu7deuWPb/Yhixg5nMS/EWdrMUGt8CJ8mQtONd99tlnuV4l7+emr4hklbdcTix33XXXrGhXguYBAwbYcGcxNFgezpeWrO/mcz169PDewYJxvjDymzlzZvZQod9fAUJ82cBstAkCW2+9tfVerpNNfe9F69C1s7umibaorv45sNF6w806dLN5ziKvBLxmi5wQt8Zmzqm1JU68/s7UjLOUvYb1WzDAhvXoZ3vuu6a/XxXn4AHL2dT3X/OMPppZH9/wldex5595zMaNWsO6d+1k/VfoYTNmL/BxKmDf3vXhdt9+JVumqxMND17WPvhksU+7Pn2zWS7teQtq7d3JX1qmdjnr0XuI9R26go0at49P2yfY8C+aD6c1tg6R51uHZQZZhy5dbPjqq/tzV1p1fW9Bd8bsxda1W61ttN4wGzKor0trsU2bkbHajhl3zfqIoNamf/a1zfyyk62x9qZ26qnbLZVuyIPSX7xwttUs+cqKpR+NY+ig3rbP7pvaioPry+8H0+otBw8dVP9xQ6bDHBsyZKBlavRb4SAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgLYGyCXmV8GGHHWbHHnus3XTTTSaLtRJ8SgAqy7hJ3YgRI0wWdG+99VbbbbfdEp02btw4++Uvf+mFb0FQK7Gu3Pbbb28///nPfZzBiq2Ev+V2gwYNshVWWMFbrZWYWaK5qPv000/t7bffjnpV9fYpp5ziRby77767jR8/3v+eQTy855572t133503/7KUnM8Fi8WjR4/Odzjrt9566/ltibVffPHFrD8b7YPAmDFjTEuwnH3DrRNs2V711rx3GjfQdhpn9tX8esH6lA+/EX0/+dy7tqBmJdtk6+/bJg6VrGIHy9iyihuNM5Bccc1xNm/eXFumy5e20vD6NGo7dnBWb/uY4lu4uIMPetzho23O/Hrh/8gR64fTG61feP0L69hjJdt71wMapR0NlHtt0WPaXnHNxvk+4IAD7I33n7MNN1jHdhgzyAd/9e16IW0498Npn9u/H3rGTj39XH+N4ZrD8eha6YtFsGodPabt3PTlN3zI8nbacd+2wDqs5R91tR3K+iqJRs02BCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBNk2grOqrvfbaywt5b7nlFlt11VU9uP322y+x9VudsPnmm3vRrYR8+YS8b7zxhi1ZssRWWWUVCxZbZQlYgmGd07lzvWXI7bbbzqcvQbEEtv/+979tk00k8TMLx/xOGf9ts802dtttt9lRRx3lLQD37t3bxy7rwWmsEpcxS4mi+uKLLxqFmzt3rk2cONH7/eIXv7D11/9GvLho0SJ77LHHGoWP7jz++ONezCzmUXfXXXf53Y033jjqvdS2fi+5119/3aZNm2YSSEedLPZ+/PHHJsu/Q4YMiR5iuw0RGDt2rL8aCVPnL+xgQwbWlyV5BglpVEwq4a3ChvPyoch3LJPJ+OdGt+W72gdTpzqruD2tU/flbchK9RZnhw5911Z3VnK7dS1uwVvpd+5VPP2Qp3z5CMei64MPPtiuuWKavT1ljm2x8cr+0Kjl+2aDfDj9c/vnPZNsy6138B9RZA8U2RCjYmLf3FOVhlxgHda54bQ/dOjQfN74QQACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEARArVFjqU+1K9fP5MFV7lzzz3Xr/fff3+/TvovWMu99NJL7aqrrrK6ujp/qsS7v//9772odNSoUV7MG+Ls2LGj7brrrl5Aeu2115qsug4cWG9hs6amxmRBVlYoZeV38ODBXpgXzi3n+oILLvCiYVneVR4OP/xwO+aYY2yNNdawRx55xIuNy5leU+MK1ovvu+8+mz9/fja67t27++uQxz333JP1//rrr+2II47wlnqznjkbX375pRctB3GwfrfLLrvM/v73v/uQ4ffNOS27K0H2Djvs4PfDbxoOTp482fTbS7h98803B2/WbZxAsALbHJcpUf+vf/1rG7PD/rb12H39MnS17ezQw4/1y8orr2yff7EwUdLlFpZLcKs8PfH8HCfYfd6envS+ffTxbL9o//eXPWDzbaj96le/SpS/UgJN/uAbq8dx56cRCMfFxXEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALthUBZhbyCdtBBB2XZSTS75ZZbZveTbCi8RLxyRx99tEkcvPPOO9vw4cPtlFNO8f5//OMfTWLTqAviT/ntscce0UO24447ZveD0DjrUcYNXa8s/6611lpZUfFf//pXmzNnjv3tb3+zkSNHljG1pkc1YsQIH8kTTzzhLdxGuR166KH+2JlnnukteO6yyy7eOu6NN95Y1KLnpptu6kXL+t1koViWeY899lgf1/jx44taTA1XdN1119lqq61mL7/8sre6q3wqLllGDSJpCYpxbZ/AFlts4QWscVf60fTZprClOgl2x40bl120r0VxTnj6ndhon5k02UaPHh0bLm0A5WnkmH3sjQ+72WPPzbYnX/zSbr3ndb/fc8DmdtZZZ/l8po03afik4mTxx0EAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAegJlF/JKUCurqnISW8oiblonS7aXX365F9XJwuuDDz7ohbGyxCqhbD4rv2PGjMkmI/Fb1EkEGtz2228fNrPrDh06+O1OnTpl/eLyLSvAcuHccKLy+OKLL3oLwDfddJPde++99s477/g8y6JtUhfijeYpem44ns8v5C16LN/2AQccYBLXSmwr9/jjj2eDScCrY/otP/roI/vPf/5jffv2tbvvvts22WQTHy5fOhL8BsvHEydO9NZ7ZZ1YgsPTTjstG782amvrb7/ca+zdu7fnduSRR3oLypMmTTLFJXfCCSf4+2G55Zbz+/xr2wRUlh954s3Yi5zw9LuWr2zHnhgTQOlPeCZeyPvW5LnNJqjVdR12xHG21bb72KBVxlifwVv5/fPPP7/Z0gxYVlllFZv2SbxId/LUmV50H85jDQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALJCNQsXLgwkyxoZULNnz/fpk2bZgMGDFjKCm9lclQ4VVm2nTFjhq266qq27rrrNgqo65B1SwmTJewdNmxYo+OV3FmyZInNmzfPJKhdZpllGmUlk8l4Ia+Ew/oN4gTO4WSd9/HHH3uhcxAKh2Np19OnTzfFp/SD+DdtHIRvnQRUVo46fD/7y//taQP798p7Ec+9NMV+8dsnbMKECXmPN8VT6e+y0yh79v7TC0aj9K/658d2zTXXFAzTWg8cfPDBtv/Oy9vIzVYtegmbfusce+PtaUXDcBACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEliZQdou8SyfRNJ+uXbuarEJ27969aRG1wNnPPPOM7bvvvjZ69Gh75JFHvDhWAt6HHnrIW+SViHe11VarKhGvsEikK8u7uSJeHZNwVwLkgQMHJhbxhvN0TlNFvIpL8QwaNAgRr2C0MydR/OZbyiruuwWv/Lpbn/TWowsGaMIBpd+n34o2/dMvCsZy/iX32X777VfweGs+MGrUKJv+SeFr17WJzYJFnVvzZZJ3CEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACFSNQ9ULeipEpIeFDDz3U1lprLW9191vf+pb16tXLevbsaTvttJPdfffdXtR61VVXlRAzp0Cg/RI47LDD7Na7nssL4IVXptqCJb1sxx13zHu8HJ5K/7Yi6c/6qluzpl+Oayg1DgmZJzz9TtHTdVyCXxwEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIpCeAkDc9s4JnSLj79NNP2znnnGNjxozxVm5l6VYWek888USbOHGijRgxouD5HIAABJYmICvWM+YsY/c98spSB3/jrOFuu91uS/mX00Pl95V3vrYXXv1gqWiV/g9+8IOl/NuKhyxhxwl5353yqbc03laumeuAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCDQkgQ6tmRi7SGtLl262Mknn+yX9nC9XCMEWoLA6aefbjddf5kN7N/TJzegX0970QlrP53dpdmtwUpI/L0DjrLrbrvUamrMNlh7qH3y2ZwWS78l+BZKQ9c+9+uO/nr791sub7AXX/3QjvzxUXmP4QkBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACxQkg5C3Oh6MQgEAVENhll10sk8nYfyb80+dm2a7TbdrMjiaB7+qrr97sOVT6b7zxhkv/NevYpZ89/uQbLZp+s19gkQT23Xdfu/2e5+2HB2+7VKiXXv/QPp7VycQHBwEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALpCdQsXLgwk/40zoAABCDQ8gTuvPNOn+ibb75pu+22m62xxhotmolKp9+iF9uQmFjvtft2NvGeXyyV/G8uudc699rcxo8fv9QxPCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAgngAWeeMZEQICEKgSAhLvVtJVOv1KXLvE0l17DLCHJ7xhY0eumc3CK29+ZLWd+9r666+f9WMDAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABNIRqE0XnNAQgAAEINDeCJx22ml20V8n2GtvTfOX/tnML+23l9xvG26+m+2+++7tDQfXCwEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoGwEahYuXJgpW2xEBAEIQAACbZLAueeea1/PectWHtzBPv8yY/c++pHdf//9bfJauSgIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAItRQAhb0uRJh0IQAACrZzAbbfdZrfffrutvfbattdee9laa63Vyq+I7EMAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKgsAYS8leVP6hCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALtlEBtO71uLhsCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhUlgJC3ovhJHAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKC9EkDI215/ea4bAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoKIEEPJWFD+JQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCLRXAgh52+svz3VDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIVJQAQt6K4idxCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAG0erpgAABAAElEQVQIQAACEIAABCAAgfZKACFve/3luW4IQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBihJAyFtR/CQOAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0F4JIORtr7881w0BCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQUQIIeSuKn8QhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAE2isBhLzt9ZfnuiEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQqSgAhb0XxkzgEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAeyWAkLe9/vJcNwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBFCSDkrSh+EocABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhBorwQQ8rbXX57rhgAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKgoAYS8FcVP4hCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALtlQBC3vb6y3PdEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhUlgJC3ovhJHAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKC9EkDI215/ea4bAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoKIEEPJWFD+JQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCLRXAgh52+svz3VDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIVJQAQt6K4idxCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgfZKACFve/3luW4IQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBihJAyFtR/CQOAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg0F4JIORtr7881w0BCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQUQIIeSuKn8QhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAE2isBhLzt9ZfnuiEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQqSgAhb0XxkzgEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAeyWAkLe9/vJcNwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBFCSDkrSh+EocABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhBorwQQ8rbXX57rhgAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKgoAYS8FcVP4hCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAALtlQBC3vb6y3PdEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhUlgJC3ovhJHAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKC9EkDI215/ea4bAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoKIEOowfP/5XFc0BiUMAAmUj8OGHH9rcuXNt2WWXLVucpUY0e/Zse+mll+yzzz6z5ZZbzjp16pQqqs8//9xeffVVmzFjhvXq1cs6duyY6nwCQyAfgfnz59v7779v3bt3r/g9lclk7N1337XXX3/dampqrGfPnvmyXNBP57/11lumcp+kjKUtkwsWLLD33nuvKlgVhMABCCQgUE3lXtmdMmWKL7u9e/dO/W6sq6vz5VLPDb1Xq+F9n+AnIAgEKkKgmurF8+bNs0mTJtnixYt9vTYtkDlz5tgLL7zgT1O5V72hkGtq/aJQvPhDoFoJVNt7vhycmuv59fXXX/u2x+TJk61bt27WtWvXcmSXOCDQ4gTUXzRt2jTfhq6tbV82KtK269viM7LFbzgSrAoCbelepl1fFbcUmWhFBNrCe3/69On28ssvm/oG1B/Y1PrLzJkz7cUXX/R1etXrcRBoKwSaqy1cKh+1Ob788suq6YNvS/WhUn8TzmsbBFry3d7UfvlqIV5tz8dq4dJW89GSz/tqLSNpxtJDXXvhwoV+/K3Y+FlruWfaV29na/lVyGe7IvDvf//bll9+eevcubO98sorS127BvzPPfdcf1zhct2SJUvszDPP9HGsvPLKNnz4cL+tc9QxmNbtueeePi3l55NPPlnq9Lj86ISLL77YVlhhBdt6661t8803t6FDh9pdd921VFz5PCQ+OO2006x///621VZb2aabbuobSbpGpR1177zzTjavym/uMnXq1GhwGzJkyFJhwjk33XRTo7DsVA+BWbNm2X777ed/O63zuddee8022WQTH+ayyy5bKohE4bqfJJZdd911vfB1m2228WK6pQLHeDzzzDPZ++inP/1p3tBx+VGFe+TIkbb22mvbmDFjbJVVVrHvfOc7vjMvb4Q5nr/+9a+tb9++tt566/kyJrH7OeecY4sWLcoJWb+bpkyqU1FsJA6OspI/DgItRaDayv1FF12ULff/+te/8mKIK/c6T++h1VZbzb8fVW5/8IMfmETzSZw+jtloo41srbXW8s8Nve832GCDvHWHaHwa7A/vP4WPuokTJ2avK7wPc9d33nln9BS2IdCsBKqpXlys7AQIcfViCXD1fld5V71Y5V/l8dFHHw1RFF3rgx+9k/XO11p1/Q033NAPAOY7MW39Iu65lS8N/CBQDgJxZUdpxN2fLV2/T1I3CWySPD9C2LBuSrs+SfvkkUce8W0OtZn0PBkwYICdffbZIXnWEGh2AknKUFw94Pbbb7c11ljD9xepTiwx+kEHHWR636Z1Ser3xfJzzTXXFKxHr7TSSmmzY3F9cYowTbu+nM/I1BfDCRBoIJCk3Ffb+75YftL2Q+e7EdK060M7PreNrv1oP/Zuu+1W8HmksLn9APnyhR8Eyk2gWFlSWknaA+V87yd5zxZ67zeljKmv/tBDD/Vjdttuu63vy994440LjknEPTffeOMNPwYycOBAGz16tGmtMQ/1BeAgUCkChcpOyE/cfd2UtnBII6yTtMXjnj86/qtf/cqPs6+44oqmRWPz8tOxJC7uGRiNI0meqdtHibFdKQJxZVn5inselPPdHtemL7VfPkmZjPsNCtU70o7HlfP5GJdnjjedQDnKSDmf981VRkSq0D2ej2K+9mzU72c/+1mj09KMpeujAOVF4+Wqa6uvsF+/fqZnTWt3mLhs7b8g+W+1BL766is7+eST7a9//Wv2GiRijTpZyjnkkEPsf//7n/fWl3+57qijjrJrr73WN9glFNBL/brrrvONCn1BIXFfUnfjjTfa3XffnQ1eSn4ee+wxO+mkk2zw4MF29NFH+wGVv/zlL7bXXnvZ22+/7R+k2QTybFxxxRX229/+1jTwceCBB/rOiMsvv9wkXPziiy/sd7/7XfasTz/91G/vvPPOvjGVPdCw0aNHj6yXGlgKL+GjBi5z3aqrrprrxX4VEPjvf/9rBxxwgP/tlJ1ccbruUd1fxx13XDa3uWFU6ZH4TU73pn5riVIvueQS23LLLb0ITp1eSZy+gFKZDC63jCTJj/J35JFH2rPPPuvFPRLy/uMf//ANnDPOOMMuuOCCEH3e9amnnmoXXnihFwOdfvrp/sv766+/3s466yz76KOP7M9//nOj89KUSQ0oSDwvd/zxx/sKjzodVNnToP9zzz3ny1CjBNiBQJkJVFu5l+XraEOilHJ/xx132L777uvfbSpPEsrr3a33teoDN998c1GK6nTYZ599vEVxNUr03nvzzTf980IDABL76aOZfO6UU07JPkNzOxv10c0xxxyT7zR74IEH/Hsbi/h58eBZZgLVWC8uVnZ0+XH1dJW37bff3lvilWh/7Nixvkyp41/+Tz31lKn8FnISCqjzQfVXnS8hsD76Ux1A72RZ5taHQMGlqV8kqa+EeFlDoNwE4spOkvuzJev3uv64ukkuo7jnR2547Zfaro9rnyhuzZKz9957e0tCP/nJT3xbXW0oCXnXX39923333RUMB4FmIxBXhpLUA9TmPeyww/z9q7Zvnz597N5777W///3vpndm6DdLchFx9fsk+fn44499UnpHd+nSpVGy+T7CbxQgZyeuL07B07Try/mMzMkquxBITCCu3Ffb+z5JftL0Q+cDlaZdn6Yfe6eddvIf/OWmqWeZ+h2WWWaZ3EPsQ6DZCCQpS3HtAWWunO/9uPds3Hu/KWVM41w33HCD72/Xx0cS8Ghf415PPPFEo5m64p6bmjlPhkE0RvjLX/7SVl99dXv44YftyiuvtC222ML3E2oWQhwEWopAXNlRPuLua4UptS2sc3NdXFs8yfPn8MMPNz03NE72ve99zyehNoeMZ8k64NVXX52bbHY/yTMwG7hhIy7P1O1zibFfCQJxZTnJ86Cc7/a4Nn1T+uXjymQc/2L1jrTjceV8Psblm+NNI1COMlLO531zlpFi93g+ij/60Y9M78dcpzw++OCD/qPUcCztWLrilrZNH7Z997vf9YazpI3T+/uWW26xPfbYI0Td+tbOvHCGBQbcAy1/D7gvAvTEyrgGfMZ9leu3nUguWx7dQIT3UxjX4M+E8NHfylmc9WHGjRuXcaLd7Lnui4+Mm3Y34yoEGWfpL+sfPTd32zVA/DnOUljGPdx8vIo/hEuSH/eVUsYJeP25TlyQPddZ8/N+TrCY9QvxRtdOROjDORFvxnVIZMO6CmD2+pXPcI4TP/rwjz/+eNYvHMtdu6+Sfdj/+7//iw2bey77LV8+xPzWW2/1v5nuYyd289tOwNbo93MCXu/vGtUZ/bYqL04klzfMQw891MjfNbx9eCcUb+Rf7Pf+xS9+4c8ZP368X7sKQqNzk+Tn0ksv9eeq3Ie03DS3GV2D8p+bzxBGazc1gA+jcuqm9cme7wYDMipfOv/pp5/O+qctk06s6ONwHRPZOJSus/Th/d3HB438o3ljuzLlpK1xr7Zy78QxGdcAyN7/KmNOdNuoHCQp9zvuuKOPw3UWZs/Ve05lWXE6C/hZ/3y/qeoKCnfVVVc1Cuc6P7y/8pDvPNeA8cd1vt7PSi9fuFw/PTv07NUSfR/nhmOfcl+ueyDUc6ulXhxXdpLUi1U/Vbl1orlG5c5Z8PP+ce/UE044wYfLrdeEvDnhXaN409Qvkjy3yvXbEg/Pieg9kKTsJLk/Q5jcenNz1O+T1E2i1xjKaJp3b1Pa9XHtE+XNfUzknyd/+9vfss+NDz74wPup30BtjOg1sE25Lec9kKQMxdUDlB+F0f3qBLTZ+1Xt6FDPdh+7ZP2L5T9J/T5JfvR+13s+aZ9boTzF9cXpvLTt+nI+IwvlG3+eE8XugSTlPtynrak/L00/dD4+adr15ejHVjtCzyn3wX+i52O+PONHWU97D8SV7STtAaVZrvd+kvdskvd+Pg5xZUxjfSqDGuuaO3duthyqL0D+bgbKrF+S52YY+9CzKJof91GRj09jcFF/tim/zX0PxJWdJPd1U9rCudcX1xZP8vxRW0Pl0xmEatQnrv5x+elYsX78uGdg2jwrfIizHP0fuemzz3MiyT2QpCzHPQ+UTrne7Una9KX2y8c9R+J4Jal35Isj33hcOZ+P+dLEr3zlv1xlpFzP++YsI6Xe4/nuN41v6b36wgsvZOuwoY8vyVi6xst1vtrZ0fjdDBbeX8+cqH9r2651F4eDAAQqQGDmzJn+y2JZ5O3Zs+dSOZD1WVml1Ve67sFt+SziuQ4A/xWgrNVGj8sSrab7kKWAXOt7SyXU4PHTn/7Uf82r/OT7cjdJfmQlTBZBZd0vaiHMPXT9VD+azlMmzgu5MH236wRpZNFE5tUnTJhgbrDRm0MP57sGk9/s379/8Cq4lhUiuaSWVwtGxIEWI6B7SVYnn3/+eXNi9bzpatooWbbWl06aYjOfkxXm3//+90tZYnYvcB88WNTId27UT9Zq9dWt7mdXKYgeym4nyU+4z/XlfHA1NTXmOuP8rqwKFXLBytDBBx/sp9kO4WRd44c//KHf1ZdQwaUtk2IuJ0t/URf2w/HoMbYhUE4C1Vbu9U5UuVPZX2eddfJealy5l5XM2tpab6V+0KBB2ThkuUvT4MnJom4hp/e4rOjICsD+++/fKJis9OrdeNpppzXy147qCLL+7cS43tL9UgGKeDihj69DyHJoroWxIqdxCAIlE6imenGSspOkXqznmazu60vgqJNlXTm9o4u5++67zx/WNJxRJ2u+TpRv+jpZ3IJLU7+Ie26FOFlDoNwEkpSdJPdnS9bvk9RNAqckz48QNroutV2fpH2iGXtkgUBtn+jzSG1ozQbiBiR9n0M0P2xDoJwEkpShuHqAE+x6y3Wqo8oSb3BqR4c6evSdGI7nWyep38flR/G6D2u9dWDV85vi4vriFHfadn25npFNuS7Obd8EkpT7anvfJ8lPmn7o3Dsgbbu+qf3YmqVP4wXqD9BMZzgItBSBuLKUpD1Qzvd+kvdskvd+Lr8kZUwzXcmpz79Tp07ZKE488US/rWmDg0vy3JS1Q42RqE8g6jSTj5xm7cFBoCUJxJWdJPd1qW3h3OtM0hZP8vyZMWOGj1r98NE+cW3LTy68o/1Ozr+4Z2A0eJI8Kzx1+yg1titBIElZjnselPPdnqRNrzyn7ZdPWiaL/QZJ6h35zs83Hleu52O+9PArL4FylBHlqFzP++YqI8pjqfe4zo06J97141uayXbttdf2h9KOpQc9jNq9UbfyyiubZtEKdfHosda03bE1ZZa8QqAtEdAU9cUEqBLCKoxEuYWcwpx00klLHX7yySft5Zdf9iJIiWDjnIQCt912mxfgbrnlll4wlHtOkvy4Lxr9aTJfnuu22WYbe/TRR01TAAVRYG4YmVCXU+eD8uS+mDL31YgXOknslMsrCDDViLrsssv89EEaoFRYTS0UdaFxpUGf22+/3U9n3K9fPx9W+enQoUM0ONtVQEDTvx599NGmwTk1AvK5P/zhD0vdF7nh8olu3Vc35r7U8UFVmY9zGgBXXuQuvvhin6d85yTJj8qmRPpRQZ/i2myzzXyUxTrdnLVtH6ZXr15+Hf2na5J7++23s95py+R3vvMdP23QJZdcYuedd54XH6ripH25vfbaKxs3GxBoDgLVVO6ddTo79thjfeNJH9S4ryrzXnJcudfgfrRzPkSiqUT0npeLfvwSjoe1+8LRb+rDBr3LnEVgc18U+vec3pcbbrhhCNpoLfGxGpDOorb17t270bFiOxqM0LkM+BWjxLFyE6imenGSspOkXqx3Zr73puoBcqGDohBL1X2c5cFGAwchbHjX6/kQpu9OU7+Ie26FdFhDoNwEkpSdJPdnS9bvk9RNAqckz48QNroWl7Tt+qTtEw0kyqk9ntvmVdtfLjxT/A7/IFBmAknKUFw9QH0++cqI6uua5lbvy0J14ujlJK3fx+VHcWpQYPjw4TZ58mSfB723N998c9/HpHp0EpekL07xpG3Xl+MZmST/hIFAIQJJyn21ve+T5CdNP3Qum7Tt+qb2Y1955ZX+41z150WFSLn5Yh8C5SYQV5aStAfK9d5P+p5N8t7P5ZSkjL355pv+NNUPok59dBo/k+GAIFhI8tyMGgWJxqeP++QKGTmJhmUbAuUkEFd2ktzXpbSF811DkrZ4kuePxpU1xqxxAInuwzizyrP8dCz45ctH3DMwek6SPCs8dfsoNbYrQSBJWY57HpTr3Z60TV9Kv3zSMlnoN0ha78g9v9B4XLmej7npsV9+AuUoI8pVOZ73zVlGSr3H8xE/++yzvfepp56aPZx2LF11YBnNkWZGxrAmTZrkt6UTU107iUYum3gVbiDkrcIfhSy1DwK5otTcq+7WrVuuV9F9iRI1SKdBfFnoGjFihF1xxRVFz9FBVRBkyVMDDbK8V8glyU+wKDhkyJClohk6dKj3U5hCQt7XXnvNCxx/9KMf2XXXXZeN409/+pP3l0XfqPgxWELQl8i5g4+XX365HXLIIdk4QgeoGl+5YfViVPimWlLJJsZGWQhIaB3n4spR9Hzde/o6R1ah77//fn8f/OUvf8kKaKNhc7f1Ndyzzz7rxa2ydu2mtMgN4vfj8qPyJmFdPgvDKmMagFQ5KOS22GILf+iuu+6yww8/vNE9+8wzz/hjQRCvnbRlcqeddjJVnsaPH++fI+uuu663/vP+++97fx3HQaA5CVRTuT/llFP8pbrp6htZzsi9/rhynxs+7F9zzTW+YeGmwCsqtA3vLFkNkPBPlvOi7pxzzvGWyaN+r776qrfCKwviEuincfpaUwOUGvCTtW8cBFqCQFw5SlIPjeaz1Hpx0rKTNj8hbyq/P/vZz/yuyn4xN3bsWC8MeuqppyyI7RRenTHBSRCw8cYb+/p8mvpFHO8QP2sIlJtAkrKT5v5sifp9krqJOCV9fsQxTfr8Sto+Ce2B0B6Pph/8Ql0jeoxtCJSLQJIylKbcuyllvfBF78B//vOf1rVrV9OsNlrHuXLW7/XelYWc3IF8temVxzhhcdK+OF1TKMdp+9qa8oyMY8lxCBQjUO5y35R7Oen7MslzKE0/dC6f8K5N2q5vSj+2nk3q41df/4EHHpibFfYh0KwE4spSkvZANIOlvvfTvGfj8hzNj7aTlrEg5B0wYEBuFKbxBQl51b4fPHhwo1kolwpcxCO0QTRrT77xhiKncggCTSYQV3aS1AeimUjaFo6eo+1QDuL6wZM+fzSGeNhhh5nGxoIRII1Nq5xpPLGYi2MSzk2a5xBe66bUh6LxsA2BtASSlOWk977SLvXdrnOTtukVNtcV65cvpUxG409T74iep+0k43GlPh9z02K/eQiUu4w05XnfXGWkKfd4LnUJ///973+bBNBhlq3cMNH9fGPpc+bM8ePYo0aN8h/VR43kyULvt7/9bW8UqzWLeRHyRu8CtiHQignoRS+hYXASpyYR4Fx44YVeWHj99df7rxTC+aWsg7WfYBksGkd4iWn6wUJOnRuqSEk0KAGRHuB6MVx00UWmLxklInzxxRezp4e41l9/fdN0wqooPvHEE/b973/fTyeuhtXIkSN9+GA1QUxkGXiDDTbwFg1//OMf+680FDa83LIJsNGmCMiKTfjCRxe2++67xw6uKZw6z08++WT/ta2sczbFhfuwb9++eaNRJ16w1JcvgAYJNbWCvnqS4F2LKiGy0KlyIqdBgeBKKZOy7iWncqgluOAf9llDoDUQKLXcq3NO1r2OPPJI/2FMua91woQJPm511p911llFo5eVLzmVcX1F+Mc//tFWXXVVk3h///33t9NPP91b3dAzTU6WfsOzKjwX/IEE/zQYceaZZzLgl4AVQaqbQCn14qaUnSQ0NJ2u6ucaxNfUmvqqvpjTtEJ6Du2yyy4mAYKm8NPHPj/5yU+ypwVhf1PrF9kI2YBAKyNQ6nu+nPV7ISvn8yPJ8ytN/mU1VC6fhdDQbg9tBh+QfxCocgL//e9/7Te/+U02l/owPfrBd/ZAzka56/ehrSzreGoz6Dmgma40k8d2223nregWmxUjTV9cKKOhzEYvrVhfW6nPyGj8bEOgGgiUei+neV8muc40/dC58aVt14f6fSn92DLsoXaC2hxJxgZy88o+BKqJQKnv/TTv2bTXm7SM6aMjfeDTsePSw/Chbh6EvGnzoPD6uED9BXLKU2sWKviL4F+7J5CkLZwLqZxt8RD3ggULsh8Jqg0RnMafy2HlvtQ8l1ofCvlnDYFqIVDqu70pbfpi/fKllskoz1LrHUnH40p5Pkbzx3brIlDq8765yojolXqP5yMftDq/+MUv8h1u5FdoLD3MeCNr+apv6wN/GcNRu/uEE06wu+++2xT/BRdc0Ci+1rRT25oyS14hAIHCBCROlYBPa1n6ksBPA/6qBBRy+qJDD0t9VbjPPvsUCpbYP1j0CZ2N0RPDAGIIEz0Wtpdbbjm/qTwdccQR3kKhRE7nn3++6WtKfU2hPAenqRHUKSkRssQQPXr08OFuueUWH+See+4JQb1wVwMssmSqB7m+vpQFM+3L3X777dmwbLRNAttvv73pCx293PX1mqzqysKtKsDFnO4bdYDLMnSnTp2KBY09Fr7Aj1rTi56kCoZE5YVcTU2Nr4wojKbw0j2sL4NVZk477TQ/QB/9eimUt6RlUlMYyGrHzjvvbE8//bTNmDHDr7Uv/ySVqkJ5xx8ClSBQSrlfuHCh6SMPVf7jRLalXJPeZbvttpuPX++pMPheKK6oAEAfrUjML4tj+tJQYgE5+Qend6A+alGZXWmllYJ3orWs0+t5J6vcDPglQkagKiVQSr24KWUnDoM6BEMHgsQ+miEizmlATmJ8lUlZ1tb03fqoTR+5henFgxi4qfWLuLxwHALVSqCU97yupZz1e8VXzudHkudXmvyrPS2Xr/0R2gj6mBAHgdZCQPe/2vX6APa3v/2t/fnPf7aVV1656Aex5a7faxps1bVvuOEG3w5XfV6CHImK1S7Xu3vixIkFkabti0vbrg8Jl/qMDOezhkC1ECj1Xk7zvkxyrWn6oXPjS9uulwEK5T9tP/ZXX33l+wj1TMo3NWtuvtiHQLUTKOW9n/Y9m4ZBmjKmPjnVCRYtWrRUEuHDgFBXXypAjIfiVd+iZgjQzJb68B8HgdZOIElbOPcay9kWV9z6WG+HHXbwFrM1hqixxDCeqP521UnCB325eUm6X2qeS60PJc0X4SDQUgRKebc3pU0f1y9fapkMvJpS70g6HlfK8zHkj3XrI1DK8745y0hT7vFc+jJQpXFxjXdJ31LMFRtL79WrV/ZUWffVB/XSfmlWW42ba3xfRnJas+vYmjNP3iEAgW8ISGCoRSJVLZMmTbIHH3zQ/vOf/9iee+75TcDIlqz4yX3rW9+yhx9+OHvkgw8+8Nv6ckOdCRILJXFhSsFgLSR6jkSTckFwED0WtiVAVOfD5ptvHryya+VRVkglLgxxyPJuPrf11lt7b32lEZz8gn/w07pPnz5eyKxrlei5e/fu0cNstyECtbW1Xpg2cOBAO+CAA/x9NHr0aPv9739vhx9+eN4r1eCghL5rrbWWqRKkKT/kwkC4Gu3333+/F9YlsQIkYZw68fQ1Va5TR6A64bbddtvcQ4321Rkv69sqC48//rivjGyzzTY+Xk0XEBXlpymTwfq18qdKTocOHXy6mhJU+7IAKsvYstaZZNrSRplmBwIVIlBKub/66qu9xUxZu9W7NLiXXnrJb2raDzUIVO6STskV4tD7VY0wlXU9T/RsiXPDhg3LBgkfvASPzTbbzG+Gd7ieU0EgKMFveGYpkAQPGjzQM0vv9txGkvJ0zjnneBHCwQcfHJJgDYFWSSBtvbgpZScJIJWtyy67zHbddVf/Lk1yjsJIECTx7mOPPWbvvfeet76twTrFJxfqxOWoX/gI+QeBVkaglPd8uev35X5+xD2/0uY/PCdCGz/6E4d2e7EPCaPh2YZANRCQVTsta6yxhl++/vprP0OFBtrVts/nyl2/17On0EeuGjxQu1ztdX2Qns+l7YtL066PplfKMzJ6PtsQqBYCpdzLad+XSa41TT90bnxp2vU6t9R+bLU51LY/99xz+Tg390dgv1USKOW9n/Y9mwZMmjImIYGEfxpHiD4DlJ7a93Lho1y/k/Cf6j7f/e53fZ+ljNxoGweBtkAgri2ce43lbosr/htvvNG/RzUzlsYGgtN4oozsHHrooXbTTTcVbAuE8IXWTclzKfWhQvnAHwKVJFDKu70pbfpi/fJNKZOBYan1jjTjcWmfjyFvrFsngVKe981VRkSw1Hs8H/2k1njjxtKl9QluxIgRYdOvZfhRWhsJfKVPy62HNwpcxTsIeav4xyFrEIgjcMcdd5gEqLLKNWTIkEbBJRSQkFfTiBVyocPg5z//ed4gEvJINBgG+PIGinjKCoqcpkXYb7/9skf0tZPyIhfCZA9GNiQUlPBYgsJcFyz6Rh/Mp5xyimk6k5/+9KeNgqvyIxcNKzHmG2+84a0S5IoQFbe+zMj1bxQpO62SwPz5801WZiVYyxXryhqvfvdwb+W7wDDgra9+JKTJdfpqSIvur6SWLiTc0zkSAUetZQbheTFhn8qGvgDu27evSYSsJTh1IMiFQT5th/KWpEyGcq5OxiDiVRxy2pc1EAnttais4iBQrQSaWu5VxuTUcacl18lKvBY9F4I4JjdMvv2ZM2f6wXxZv1MDQkLgJC5YyZMwN9dJnCsXniX6ICVY19MXjblO70fVDw488EBv1Tt6PAxGSHiANd4oGbZbC4Gm1IubUnbi+KhsqYNi3Lhx3nKfOiuTOHUyKF9rrrlmozqGBPmywq06THTQryn1iyT5IQwEqoVAU9/z5a7fl+P5keb5lTb/4WNDffSjqQSjzyB9JCCXpj5TLfcB+WhfBF599VU/ZfQee+zhO+OjV692ugYVNL10IVfu+r3KoUTDY8eOzU5pHdL+/PPP/ab60gq5tH1xadr1TX1GFsoz/hBoaQJNvZfTvi+TXF+afujc+NK063VuKf3Y6h+QgBdrvLn02W9tBJr63k/7nk3KJ20ZC/3nEvNGBYEar5ORDs2mKWFgGrdkyRI/k6XG2lT/CR/zp4mDsBCoJgJp2sK5+S5HWzw3zmCARx8N5rrgJ8uEpbq0eW5qfajUfHIeBMpNoKnv9lLb9HH98mnLZD4updY74sbjmvJ8zJdP/KqbQFOf981VRkSt1Hs8l7hmrpLRRo1d5xvvDuGTjKWrf1uGKd5++20/Y70MN0ZdMMgnPU1rdclGEVvr1ZFvCLRxAvoC59JLLzV9mXHhhRdmG/76gkgiIbmoUEiC2mjngKYh/OKLL5aidPHFF5vM9Mukf5rpfSSW1ENTX31IXBsaNnfffbfvnNh5550tak0wNz8SQup6ZPVTAyJBWCuBoeKUi1rrfeutt/zgiawbRh/4qvzIRUWOqozpupS/o446yh/XP12nxFh6aYgjrm0R0D305JNP+vtKAprQca6rlLhVorZ999230UVH78uNN944O3V9NJAqAD/+8Y/9NDu6n2S1NqmTxVwJec8666zsfa2pOYNFH1nai7pofmbNmuUFtRLryMJPly5dfFBdx89+9jMv6pFIL7g0ZVJlQ539Kq8SvUs4FJz2lWcdZ6A/UGFdrQSaWu71hb0s2+c6CWAuueQSO+GEE7x1nOjHIrlhc/f1DpLwQI0KTdUjK/PFXLTcKx2Va73X1ciJWvb64x//6KPRO1NOwj5Z0M7nDjnkEP9e1fTDuR//aDBCVjxUxrHGm48efq2BQFPqxaWWnTgut99+ux177LF+mst//OMfRUXy0XKveH/3u9/5+sv111/fqK6iMqxnSe6AXdr6RVzeOQ6BaiXQ1Pd8uev3pTw/cst7mudX2vyrjas6gCwK6XmibTm1Z37zm9/4d78+cMRBoJoJ6MMV9RWpDSzhSnRWjH/9618+66E+HK4jWs7KXb9Xnfmaa67xbWf1ufXs2dMnq7640B81cuTIkBWL5kWeafvi0rTrS3lGZjPKBgSqiEAp93K0rKV9Xya59DT90Iovmp807XqdW0o/dlQMIH44CLRWAmnf+9GypmtO+55NyimujOXmQ2NhJ598sp122ml+7Cn04yt/crljEknyof5/TRN83HHH2RlnnJHkFMJAoKoJpGkL60Ki5ayUtngcjDFjxviP8K+88ko/5hcM3khErxlA5KLjztH8xMWt42nzXEp9KEk+CAOBliaQ9t2u/EXLVylt+iT98mnLZG6+tF9KvSPJeFza56Pygmu9BEp53rdEGRHRtPd4NF/RXySJNd40Y+lHH320HyPTOFqYwVLpaaZafTSnWW6i/YfRvLSGbYS8reFXIo8QKEBAwkQJWP/0pz954Z0ErbLSpYEMPaDUWRCse0o4pIqOrO9KQCg3atSovDHfddddXuAqsVDU0lfewBFPWfCTxQA1ZDSdoB6gqoxImKvKkARQwUmcK6si+vL4f//7n/dWJ6umKbvhhhu8QFJfKktorE4SWRg888wzGz1wjznmGD9wsu2223phlUyjy0Kxzpcoca+99grJ+Y4RXbfEFJpeTYOVEvCGjpPjjz8+G5aNtkVAHWayEL3JJpvYkUce6cW8+u01ECin+zQ4ic9lKXfGjBledK77PyqMDeEmT57sN3Wf5TsewuVb676++eab/X06e/Zsb6FPHXC6L2VdOypWl1hYgnqJa1W+Jb7TNchP5fewww4zWRnRwLzKiAbnowK9NGVSIn91LKojcP3117cf/ehHvozqS6tQdnU8+jFAvuvDDwLVQKAp5V5WqbXkumDtXe+PtOVeH6roa0O5e++91y/R+Pfee29fxuWX+xySn6zkSsgrob/K6DrrrGMSFuvZoQ9ugihHXyEWyps+pFGjJd9xCQN1fWrsMOAn4rjWSKAp9eJSy04xThIbfe973/NBll9++aVmkFBnnOrwcvnq6Srrqquo/v7cc895a/j6El+zV6h+/8tf/tKfG/6lqV+Ec1hDoLUSSPOez61Pl7t+n/b5ka+8p3l+lZL/8847z3+Up3bE888/7+sOf/nLX/zPr3ZFr169WuutQL7bCQG9R9U+Vbt0s8028+373r17+49z9W7U7BTROm5ufbrc9XsJcfSeVt15q6228n0Keq+rfMvqnj4UV31dLl/fV9q+uDTteqWZ5hmp8DgIVCuBNPdybrkv5X0ZxyFNP3RufhR30na9wkrgl6YfW/3net/zca7o4Vo7gTTv/XK8Z5Pwiitj+er4mjVPVrJlvEP1BX04/9RTT9mtt97qx8TUTknj9DF/6EPQDABRYzWKJ/Tnp4mTsBCoNIE0beHccpa2LZ7kWr/97W/7sWWNc7/22mtZgxoyrqFxbLU7FEYu3/MnLo1S8pymPhSXPschUCkCad7tymNuXTptmz5pv3zaMpmv3Kdt3+v6kozHpXk+Kk5c6yeQ5nnfUmVEVNPc4/nKiOLQO1TjWnvuuWcj44w6FnVpxtKPOOIIXzfWDLrSskhHI8N0mkFLTrNXtGaHkLc1/3rkvc0QCF/1xQnkJIaNus6dO5umw5T4T40YWSaRU6edvs6V8DU4WQ2Qi34tGI4VWqfNj+KRFUNZB5aVsCAYVr5lrSRq3TdMUZJrlVCiXQkRZR0oiJ50vjojZeU36vRAlsDx0EMPzaal4/pqUsJGcQhOlg8kbpZQWIOVWuQkxLziiitsxIgRISjrKiQQrCWHdb4shvs1N4wG0SSc0/2jezM4/ebqAAvWdBcsWOBFvPKPWo4O4aPrkFZYR4+F7XAsNz/ylzBHnfK6f7XIaeAxt1KhzgK56P0pYbws/kiELmF6cOPHj8/7JX/SMql4fvjDH/ryp3wE8a78JRjSIGV0cFT+OAg0J4FQdsI6X1qFyllzlPuQfkgz7EfX4VhunmUBPLhgYT7sa62Bfr3TCj2HZCFb70QJ+i666KLsqRoIuO666xoJ+LMHE27MmzfP1NDROzMIghOeSjAINAuBaq0Xx11sbj39pZdeyp4SZsrIejRshEG4fPV0DQ6o01F119D5oNP0EZ8+3JGoJ+rS1C9yz9N+7nMrGoZtCDQngdyyE02r0Hs16XteceWrT0fTCNshrbAO/tF1KCdhHT2WdDtfeU/Tri+UTsh3WEfDaXoxPTdUjwgCXh2XFS99/IuDQEsRCGUnrPOlW6geIAsbw4cP93Xh0Nek8zUIoLZrmC6vUH06X1rBL1+5CccK5UflRx/A6WPXqJV8Ceb13g7XWKjvK8Sfb50vP2na9WmekfnSxw8C5SQQykJY54s73PO5YZLey2nKfUgrrNPkJ2k/dKH8pGnXp+3HluCIj3Pz/Zr4VZJAKGe5ZTs3T/naA0nf++V6z4Y8FXrvx5WxfHV8xSnjMZpOXIZnNIYnp352jV1JPJTPBV5hHcKEsTLtX3vttcE7u5a4UB8+4SBQCQKFyk7IS7ifwzr4p2kLFypnIa5S1rnPH4kNJ0yY4A1H3XnnnVkjVAoX+ufCh7Bxz5+kz8C4fCetD8XFw3EIlINAKMNhnS/OQs+DpO/2QnXpfGkFv1Dewn6afvlwTpJ1XLnPF0du3pKOx6V5PuZLF7/KEAhlI6zz5aJQGUn6vK+2MhK9xwuVEem85NSHVswlHUtXHBofk05OBvv0oZwWOWm/NEt77sxd/mAr+lfjpv3KtKL8klUIQKAAAZkpnzZtmh8sUGdfrtOgncS+sgDaEmbE9VWwHtZ6GamjMjdNdX7IkoHEx/m+5JDpdH01oQaSHrjRl0DutWl/6tSpfnpQTTnYvXv3fEGyfrNmzbI333zTWxvt379/1p+Ntk9AFWR9DSTrzbkCGFnp3WCDDbyFu7iKRDlI1dXV+TLy2Wef2dChQ32ecuNVx4EsVT/wwAO5h7zgT8IETeujMhI6EJYK2OARVyZzzwusJKzPLb+5YdmHQDUTCPdyNZT7OE5xzyG966dMmWKffPKJtxqc29kYFz/HIdBeCFRbvTiOe1w9/aOPPvJfFauOrxktinUEJalfxOWH4xBoTQSKved1HcXq05W4zrjyHvf8akqe1dGrNvbXX3/trXyLDQ4CrZHAzJkz/exPqt+HAZBwHXH16RCuXGu1x1WutJaVoFxRTlzfV9p8lNquz9cWSps24SFQSQLF3vctXe7FoVg/dFx+0rbr6ceu5J1H2tVAoNh7v9zv2VKvN66Or3E7zfCnGQXUj59bXyg1Xc6DQFsiENcWjitnzcFC/XFyUSNVIZ1KPH+K1YdCvlhDoDUQKPZuj6tLV/L6KlHudb1xz8dKMiHt5iFQ7HlPGVmaubQ20qWtuOKKlk8nt/QZ1e+T/5O/6s83OYQABHIISOiarzERgumhvsMOO7SYIE+dEprysJALX09GLY1Gw0qMu8kmm0S9im5rUEJLEieLRLJmgmt/BCRI1bRW+dy7777rvVvqCx2JcJSXQvlRQ0bWNFRu8zkJkSXyTeriymRuPMVY5YZlHwLVTKDYvdzS5T6OU1x+9K5f0TVEtOAgAIHCBKqtXlw4p/VH4urpquMXq+dH44+rX0TDsg2BtkCg2Hs+rj5dieuPK+9xz6+m5FnthzAbSVPi4VwIVJqAROiFhOhx9ely511CYs2uUcjF9X0VOq+QP+36QmTwb+sEir3vW7rci3Wxfui4/KRt19OP3dbvbq4vjkCx936537NxeSl0PK6OP2jQINOCgwAEChOIawvHlbPCMZd+pFhfXCWeP8XqQ6VfJWdCoOUJFHu3x9WlWz6336RYiXKv1OOej9/kkK22QqDY854ysvSv3K9fP9PSllxtW7oYrgUCEMhPQJZBXn755YKCwPxnNa+vLJZoup8uXbo0b0LEDoGEBFQBl3XLYgL0hFGVJVioiG233XZliY9IIACBpQlUW7mvtvwsTQwfCLR+AtVWL662/LT+X5grgMA3BKqtPk15/+a3YQsCzUWg2urT9H011y9NvBD4hkC1lftqy883pNiCQNsjUA3vWer4be++4oqqj0A1lrNqeP5U3y9FjiDQdALVXJem3Df99yWGphOgjDSdYWuIoWbhwoWZ1pBR8ggBCJROQCb3J02aZKussor17Nmz9IjKeKZeMrK6W+yLxjImR1QQiCUwffp0++KLL2zNNdeMDdsSAWSN9+2337YNNthgqelCWyJ90oBAeyBQbeW+2vLTHu4BrrH9Eai2enG15af93RFccVsmUG31acp7W77buLZqIVBt9Wn6vqrlziAfbZlAtZX7astPW/7tuTYIVMN7ljo+9yEEmp9ANZazanj+ND95UoBAyxOo5ro05b7l7wdSXJoAZWRpJm3RByFvW/xVuaaqJ9C5c2cbPXq0PfDAA1WfVzIIAQg0jcDZZ59tWh588EEbNWpU0yLjbAhAoFUQ4D3fKn4mMgmBshHgXV82lEQEgVZB4LHHHrNx48bRpm8VvxaZhEB5CGy//fb26KOPmjOIUZ4IiQUCEKhqAuFdP378eNOCgwAE2j4B3vVt/zfmCiEQJRD68njXR6mwDYG2TYBxu7b9+3J1EIgSCO/51tqPVxu9GLYhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBliGAkLdlOJMKBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQaEUDI2wgHOxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQaBkCCHlbhjOpQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCECgEQGEvI1wsAMBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgZYhgJC3ZTiTCgQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEGhGoWbhwYaaRDzsQgECzE+jcubNPY/To0c2eFglAAAKVJ/Doo48a5b3yvwM5gEBLEVCZl6PctxRx0oFA5Qnwrq/8b0AOINCSBHjXtyRt0oJA5QlQ5iv/G5ADCLQ0Acp9SxMnPQhUlgBlvrL8SR0ClSBAua8EddKEQOUIUOYrx56UIVAJAirzTg9biaSbnCZC3iYjJAIIpCcQhLzpz+QMCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEBhAh2tk3Wt6Wpda7tZl5ou1qmms3WqdUtNJ7fuZB3d0qGm1mqs1mpraqzGbWtPrs4ylsnUWV3Grd3ekswSW1y32BbVLbJFmYXZ9deZBTavbr4tyMw3d7RwZjgCAQhAAAIQgAAEIAABCEAAAhCAAARakEBrFfJ2bEFGJAUBCDQQaK0PDH5ACEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQaGECdU5g+7VZZr6T1rrFaWcto/0Fbl9rv7jJF53BmYzT1Dq9bcO281vs9t1iS9yiba21uDidUrfx4na9nlea3rDUuu0ObtctphElbWvtlprOLlAnrd3i1qZ1FycM7mJWu8w3205TbLVdnb9bat0xpxrGQQACEIAABCAAAQhAAAIQgAAEIAABCEQIIOSNwGATAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCLQ0AYlt6+Y6Ya5bMnPd9ldu+6t6Py/cnec0t/Pqj/l9J+Z15nCdmFdrd8yJeSXk9SJdt2oZJyVwg5PQV0JeJ+A1L+LVWuJd59ctLG7fbTtDwVbT3Yl6u7vtHs6vYV3bzW0zatUAlBUEIAABCEAAAhCAAAQgAAEIQAAC7YlAjbMMGulpaU+XzrVCAAIQgAAEIACBFiKg2pazdONmpqxfe8s3brAtWMDRsYbtjJu60lvECZZxGtb+XMWjww1rn3tty4W1D9Cw76bHtBodkBmdb1ZhV2sF8fvOGk502/vpmPd3G8ECj9by0wBdrRtg82u33+CfjdsdxkEAAhCAAAQgAAEIQAACEIAABCCQQ8C1/+ucIHfJF06oO8dtz9H6m+3Ml66J7wS8fq1tJ+yVUNdb0M2JqlXuqv9AQt8eDcuyrm9hWbftltrlnLh3uYa189d2h55u7QS+sgSMgwAEIAABCEAAAhCAAAQgAAEIQAACbZUAQt62+styXRCAAAQgAAEIlEzAi2o1FeUiJ7ZtmIJS22E6Sk1TaYvdMYWRALdhikofNrtff64s4fgw8o8Iduv9XZy5/jkC3jDVZRABe8GutLla5Aqu3QGvzHVhJMiVy7eWnxaJczWY1rCdXUf8gmi3fkpNFzCIeBvW9f4uDk2vKb+O9WG07S3qeL/67fppON1gnabglLUdv663vFPv586Vn87BQQACEIAABCAAAQhAAAIQgAAEWiMB18aXhd3Fs5xQd7YWt6/151q07Zr1TtCbcYJecxZ324xYt9TfSn0QstbbU4sT8Lp1bW+37u3WvdzaLR3cdgf5OYu+/qPjUtPiPAhAAAIQgAAEIAABCEAAAhCAAAQgUEUEEPJW0Y9BViAAAQhAAAIQKC8BL6yV2PZrNyjmppfMuIkINNVkdluCXB3XMb+uDxf8/DoIeEPY7NoJZSXg1XlBvLu4xgt/bUnDseAvcW57cRp0kzC3QbQrMW+9qNcx8QJfCXQbtjs1iHWdYNeLdrNr56+wbkpOPy2nBL2dG0S/8tN+8NO0nV0ajvttF05rBMAOFA4CEIAABCAAAQhAAAIQgAAEWpKA+haWOKHukplukXjXretmOn3uDLee5foPnIDXi3bVt4BLTkB9AM46b00fJ+Dt4/S7fd2yvNt2Swfn10FrJ/xVXwEOAhCAAAQgAAEIQAACEIAABCAAAQi0RgIIeVvjr0aeIQABCEAAAu2VgNN/esGtE+bWSZC7oEGY66aY1Lb3k787bhLn+u36tUnM27BvwV8iXRfOFjoBrhP5+m0NprUn4W2130sSBmsgTuJcJ+atX7vfKuxLxNuwWBc3aBfZ90LgBr9aTdupba0btmsVdhnnJ+GxixoHAQhAAAIQgAAEIAABCEAAAhBIQ0AfEHvhrhPqLvnMLZ+6vgm3rvu0XrybcVZ31QeBawYCatvLMq9Evf3csoIT8/arXzq6da2EvWrv4yAAAQi0BQKuO9TP9qZ+a2c8on72NtffnTvbmz/mAiucFrepU/0/bYRFm2HbBwjHtOM6StVXGvpLG7bD5G/ZYwoWDaN+XLf4Wd86uAPalrGF7No9lxXG+fm1tsP5bhMHAQhAAAIQgAAEIACB9k4AIW97vwO4fghAAAIQgEC1EHCdjHUS40qUO991Qs6vjaNr7QAAQABJREFU39faLzqmbXfcJOD14t1wzPnpnAY/P0gmga46MnHtk4A6iZ1QV4sX97oBPuvaIOTt6vy8oLdhX9vyixyvbdj2a3es1omAvZXh9kmTq4YABCAAAQhAAAIQgAAEIAABEXD6prp5GVss0e7Hbu2Wuk/c8rHzdyLezGwXBmu7lblXZLW3l2u/93ci3v6uCT/AiXq1uH0v7O3m2vUIxirz25AqBNorASek9UYpnDGJ+rX267c105v3W/zNvveTv2aBczO++bXfdn4NAt6siDcr4HUvJm27JRomKuT1+F2wRsJd6XVzFwWUn5yel+GZGbZz1l7EGw0jYW6DUNeLdRtEvPXbLmAQ8Ub8syJfzc7mZnarn9FN2y6uhsXPzKZZ32TsQeH0vHczvdWvv9n28bsgOAhAAAIQgAAEIAABCLRWAgh5W+svR74hAAEIQAACrYmAOgrdQFadxLbz6td1bp1xg19+v2GtbS/i9Wsdr9+3ec5irjvXC3QR57amX7515LVB9Cshr3XLWI0b3PPC3m6uI1gCX+03bNeGbbeudX5+X0Jf+atzOXRet44rJ5cQgAAEIAABCEAAAhCAAAQgEEfAiaeWzHGi3elumeYEvG69xK3rprs+C2eJF4u7cQArdFwf9vZ1bfdBTtQ7yAl6BzpBr1t3dOsOyzYIyiqUNZKFAARaIYEgyg0zxYXZ3zQrnJvpzc8E57fr9/1sca4/PCNjEyGMtuWnDz4aFgl7s36L3bMpCHmdwFfbQaTrhblut825iPC3XrirPlZ37UHU28ltNwh3/axtfttR0Gxt2nZrc7O4ae2Fvg2zuPl9vQfCfth2xhr8zG06x52P+LfN3VFcEAQgAAEIQAACEGjVBBDytuqfj8xDAAIQgAAEqoiA68z0FnXnuvVcN6Dl1hm3zmj/q4a1hLkNfvLXtnk/dx3qyHS7OAhUJQEJdNXh68S71t2tu7vOXq0l8HXbtd7P9f02+EfXOla7DIOEVfm7kikIQAACEIAABCAAAQhAAAL5CEi8O7teuLv4ozpb/KHr2/jQCXg/ch0Xc9wJ7jiuFRFwQrGanq5tPtiJeIdocRqxwbVe2NuhJ+31VvRLklUIlJ+A+rS/dt3SfjY414cdZoGLzA6XkWhX/i6c+eORfZ3bIOrVhx0+TIN414twy59jYixEQEYWJM6VeNf142ZnatNMa+rX1dIwS5v5dfBz/bxu3wt8XR+uDyPDDW7bz9bm4kPw6xjgIAABCEAAAhCAAASanQBC3mZHTAIQgAAEIACBNkTAjVepY3LJV/Xi3Lov69eZLxuEu84/40S7dVq7Yxm3tgYRL1NLtqH7gEvJT0CdxE60az1ch28Pt+2WWr+u39d2rY45yz9hu4OOqzPZ/eEgAAEIQAACEIAABCAAAQhAoIIEXJ/HEte/Iau7iz9w4t0prv9jiuv3cPum/g23wrUBAmp/L+va57LSO9xZ6NUytMY6uX212Wmft4HfmEuAQIRARhZu3UxvMjpR52aF87PERWaNq58RzoXRjHHzFbZ+MYUJ20686/u2+YgjQrYNbMoasIS/EvVqVjY3W5vW5rfdvp+hzfn5tXtvaEa2hnB+pjYZdwhhJCLGQQACEIAABCAAAQhAoIkEEPI2ESCnQwACEIAABNokAVkiUAenmzpyibM0U+cGsiTMlWVdiXczzr/O+XuxrtvW2lzHJlYG2uTdwEWVg0AHF4k6epdzixPyal3bsF2rAUQv8HXTfS7nOoC1yM91HmPtoRzwiQMCEIAABCAAAQhAAAIQgEBhAvpgefEnGVs02a3dskTLB061K8u7rVW8G8RJEii56clrNDW52qUNS43WCiNRa3Rxu/6add1hcX1EmSVuPyx+2nd3kqaD17Tvmh6+tYrb3GV4S71OyNthRSfodUsnrfvX1k/X7i4NBwEIVDkB9yjKOAu4foY49V1HZ4eLzAyn4zI64YW7fpa4Gi/elRXdVvsMq/Kfps1kT+9LP1Obe5/KiIOfoc29PzQLm4w0hBnaJPSVIQcJfMNax2TRV+9aHAQgAAEIQAACEIAABGIIIOSNAcRhCEAAAhCAQJsm4Do66zRN2Bdu7UW7DQLdBqFu3Reug3O26+DUOljY1QANDgIQKB8BN7DqLfhK2Oum9PSDiG5dq2k/nai3xol6vcBXfhIAu2nd6PwtH35iggAEIAABCEAAAhCAAATaIQHXH7JkthPvTnXL+3W25F0n3n3P9X3MciwkWK1GF4RE+uizayZrFdBPHy5rgn7acHdMs8VokYhX7c3Orl0pS4FBxNuwXZNQyJuRSNcxkVXLIObNuL6hjIS8WjsBnfZN63zTz+vD73numNaabr5aRb+OT83yDtPKTsi7iltWqrVOw5zA17XFaYO73w0HgQoT8JZ1ndXcJQ2GJhoZnvCzw33Tf13fj+32najXP3fc4woHgWYj4F4T/t3rhL2+j1dGHFx/btj2ol75Sdzr1h3cIiMONRL+6t2MgwAEIAABCEAAAhCAQAMBhLzcChCAAAQgAIF2QkCdnd4ygRPlLpFw163r3KBVnRPqap0V7Lpjsk7graq0EzZcJgSqkoAbXPUdvk7QK4FvbW+37uXWvVynr/bddgeJfbUt6w50/Fblz0imIAABCEAAAhCAAAQgAIHqIaC+kcWf1dkiJ9xd9LYT8L7t+kQ+cgoviUyrwUlcG2Zz0cwt3tKfa/f5tduXFUAn/KmfytttO1FvrRfxumMN61pZ/mvh9qEs9npBr/tY3It5nYXLOjcVvZ/OvkHE66etl6XMiIVMbx2zYRYo/xtUi8jX/Qa1Q5yYd7X6pfMqtdahr2PM1OnVUErIQ1sn4J4DfqY49WFrprhgcEKzwjUYo8hopjjta61+bH1MgINAtRKIGnGQ0YZgvEEGGzQzW8N+/Uxtzo9Z2qr1lyRfEIAABCAAAQhAoNkJIORtdsQkAAEIQAACEKgAATeAIusEsi5T97kWt6+1BLtuW/sZbbvOTz99WAWy2C6T1ICcFg2oNdp2HXje3w0e6gt+bedZL2UtxwXLWoVR+OC0rUXWJqIWJ8J2dK1t10Ge0bphe6m1jmswrc5FWucCyTqR33fr6LbbxbUAAVlZch28Nb1dR28Q97p1h4b9DhL7uu0OTtzb0oO3LXD1JAEBCEAAAhCAAAQgAAEIQCA1AYlLF01zy1tOxPuW6y9xS2aGi0Zt2ko4tdklvNXMLK4NVz8zi/Zde85Z6fPCHlnsk/U+J+it1fTdEva0sEC37Ggcby/Qc2LeOie+qwuWNRuEehLm1UmYF2aHCv1W6q+ohHO8a/o57KvXWCe/OCu9g2rrp0mvRH5IEwJtjEAwPOH7sGVsooDhCRmikGXvij2z2xh3LqdKCOidrve7+nlDXUD9ujLi4Neuf1fbMuKgj3taex2gSrCTDQhAAAIQgAAEIFDNBBDyVvOvQ94gAAEIQAACSQi4wYw6TSvmhLpL3BSQ9cLdhu1ZbmBqphsA0fpLF5mmQcQlIyArK1r0xXxHN1jWyYHO7svfdZ417GsgLTtNpfPznWo61qEhjI6ro61BxOu3G/br/esH47xQNyridds1GtyL+Pl9+WmRK7YOA13F1u6YF+kqjMS52g/bDfte6KttDXAucfdS2HZrWd0JYt6wrU54hdP95v38vttuWPupN7XtpuH0frKaobANa29FQ9uVGlB1Sbc6p/tOU7It7zp2+7hbRmsn7q3to7Xr9JXQV9sa+A33TKu7SDIMAQhAAAIQgMD/s3ce8HIV1R8/u/taeiGNhIQEEkJPMAKhd6SqgAh/AQsgCGJDQVARQRSwgiAIihVUBBURQbr0GmoCBAIJ6ZAe0l7Z3f/5zu5s7ttsf7vv7b53zufdd9vM3Lm/vXfuzJnf/MYQMAQMAUPAECgOAUijbfPj0vKGEnhfV1/JW9pWhRza2caATCXjuAGZm2lbjYGYkHOSazcgE6IO02zjT+iB5gl9EPlSpL7AAPWY+rycf8uTezsTI9rRSrSKqEJv/fYhaZiohF5V7A01WQO7M38Gu1aNI6B+xJQPW0Um8GFH1WcdY9F9xCfiegyVXWmp8Xu17BsCHUFAlfXdQB/qCOrTDeHrVb+uE3Fwa/0eQfTVOoPrN+jItSyuIWAIGAKGgCFgCBgChkBVIWBE3qr6OSwzhoAhYAgYAoZAfgRcx4YqlODojC5LOjxVRSa2VJ2eunadGjg8bUqxBJh0gDGtZIN2sDRqhx2dZ+w36j4kXXdO17pNuBAE3eAxwgRIuy4OpEk9liD56jbX4JgSdxPr5LEkkTdF3NVg3dqUeOum03SE3QRJ1xN5422KPech7HrSbpLI6wi8+ryyduFY47BPruMaDgd+8Jg7p4pKcZ2u051Lrp2jn+ubJZ5P1J0g9Q5Rv65OAxrWDuOI7kPqZR1Whaee2klsj4ghYAgYAoaAIWAIGAKGgCFgCHRfBOIbVH13nhJ4ZyiBd4b6Tt7RdiUDnDvD4Hb2oi2WbIepoiuDLV1bzLXHdB9yjvolbKBlnh9Ef7aY/pbMMBXVgeob/WAJH1hsifoFlOAr63XRsJ1iOog2Ml4JvTsooXeHJKGX39LMEDAE2iGQ8mHjv04u+K5juh3zwhPmw26Hme0YAlkRoI/C+3mpX+DXxc+r/t6Un1dV/c3PmxVBO2EIGAKGgCFgCBgChkBNIGBE3pr4mSyThoAhYAgYAj0ZAciLUdRIlKgb1Q4KHJ+OtLskQd5Fcdd1WPQUkOgbcURcdV7pNJSOmMt0lKqC0m6fThSIuRB3HXlXz/ttt9Z9R9zV86wh+rp9XUPqRQXXrGsRQPHXkXm1Y86TelsC20kiL2TexJIg9jrCL8cIu0FvQRd3Xjv/gvscd4T3zurs61o09SHXDNCZjIrDUHX2Jsm9zuGr+26tClDu+e/qvNr1DQFDwBAwBAwBQ8AQMAQMAUPAECgBAdqDrQtiCQLvy+pDmaUNvrUlJFRsFPwMEGuGa1trmBJrhmkbi8W3tXRKbPMzFAtqlvDqK4h+EPCT4St7X31l76mf7D1t90PsZbBvpa2v/s4o9O6cJPSODDu/UqUva+kbAtWKgCfutvFOJhfI9jF82Lp27ya+OLOOIYDPmiUw+5vbD6tPzx3T754/52d5wyeYXFj57Xbr4HG28Zf6JX2f42puVjcfhrWWz25hlrVYKDFTW0xPcNwdC6w5ZtYxBIJ+XgYN+TqH1j/qEHQwYm/H8LXYhoAhYAgYAoaAIWAIdAECRuTtAtDtkoaAIWAIGAKGQC4ENiHu4vCkQ+J99XnRIcEUgkpq7JaGsi2dX+qEcoTDXuqAxCGVWpL7jrirx5PkXQi8bIcDhF6IuWEl5jrHZbcEy24qKwLqGI6h4OuJvqj3QOZNEnkh86a2VbXH7et0q3G2U4s6mderw1mPO8IvzubuaKg5MJ2rOnjDwxPrVEczig46TZsRe7vjD2/3ZAgYAoaAIWAIGAKGgCFgCHQzBJQQ1PZeTJqnK4n3JSWRzdS2HEqPlTLIUarM6oi7I9X1MELbT7rUKZG3jvYVxF3HlqpUBizdFAL620fXJMi8beo3iy5OLLFF6hvQbafErKuK2QD9/ScqmXdyWBp3DLvf30jbFUPbEq4mBPS9iq2NS5v6raP67vH+OR/2e0kf9grNbHf1YRfzO/C9QDQiKSAh9Qoc/jiO4QtnJrjktp/1zc345meAq9OPCcRcXUIBAm9qBrjUcQ2n5xNk3kB4vkXBRXfZDwWP+e3kOVbZSLwp8m4wDGWsLvEAYdfN0ubIu+pzDZB422378MzeFtUEkjO5sU5tI+7APjO2cZ5nyi3qt1URh8S2HiOtnm48V4P0MdD6CH5eN6BI6yUR6iaIOvThh+/pINn9GwKGgCFgCBgChoAhUN0IGJG3un8fy50hYAgYAoZAT0BAHVkoiTinJ45PpyKijk+cnrodX6kg4KzqDobDsrfeiC6sQ73VURnc7qPHlLQb5niSvBuGzKth3Fq3w0r0tQ6R7vAwVMk96PsXQ71XCbuxJJk3tV6XOB5n7RZ9H/22dlSwLewr+VeUONwtDIevJ/YO01ct6ezF8UtndITOaDoPzAwBQ8AQMAQMAUPAEDAEDAFDwBCoEgSiK+LSMlMJvC/EpO1Vbasxc1ElDLKUkjbDo9Q3MUr5V7qOqApr3ea6ZhAkpCuzLkcAwhfPRNsifR4Wqp9Nl9h8PbZACV8Mjq8E2QtulCoy1+2khN4PhaVhYtg9E10OhmXAECgzAu79Wqnv1SJ9xyDN69q9Y7p2ZW938Y9lw82TchGTwEeNIAVrZnpLbSeOITLhCLv4wxGbSBJ5/QxxnswbUqKuI/Lik9PvCPueyOu+K5HE9yVF0M2Wt1o8ruUxxF5H1FUiL2u33RbYhsjrlsQxiLvBmdvcPoIOPHtureH8NursftY2v40qNNuaTkW+B5ps1RjPJLMFUE8ZqYuu6xh45Ost5uOtmp/KMmIIGAKGgCFgCBgChoBHwIi8HglbGwKGgCFgCBgCnYgAaqBtS3VJKYWo30gdniiFxGtZrQBnJo5MnV4wBClXR3kHt8P+uBJ1w3reEXaTa7eNU1N9lWaGQFUhoH19OIBj6/QdVQJvDPJucK3TtMbdou/vmuS2qgEljumd4CCuREdhZ4BEJ8JgfVdRcsDJO0J5vF5lajN9v1UJ28wQMAQMAUPAEDAEDAFDwBAwBAyBrkAgrkScljmqwvt8TFqVxBtfqLlA9a+chp8DxdUttD00WtejlQQzOiz12j4y1d1yAl2htLQ97wbPQzqcp8TeeUlSr64dqVdXZTUlRTmS9xRV59WlYWw4QeAr60UsMUOgcxFw5N3l+g4pKb5tQUyi85W8q+R4fNkVV7zurFulrIf0qCIS0iuu/q7ktlvrNmtmhiOM29Z1I34x1slt1pB1OZ5ch9WvZgPiO+dHdITgAJE3NVMbx1Kztum2n7ENgi+L30eogVnckkIPiW09Bum3Vv26Qehx4fZPknpHJeszo3QwEgTfQfrM2mCkIFq2bQgYAoaAIWAIGAKGQJchYETeLoPeLmwIGAKGgCHQoxBIdhy4acZQK/CKIIvUD6Squ6h61ozh9MEZ2U8dPH3Vsclap5NkoRML4m5qrUReyLvsp4i6NXOjllFDoHAEHNEXci+LknljEHlZ675bq+p2/AN1BkP0dds6/ZuGcUq+uqoZQx2baWJHqsPXKzmg5qDKvdaJXTO/omXUEDAEDAFDwBAwBAwBQ8AQqG0EtA3VtiQuza+oCu/TSip7Sw9AwCmnMegYxd0tVRhxrLZ5tlTyrraBbFrqcoLcyWnpY0JbvRUy4rtK6p2j/rl39Zjuiw7OLavpLFORiarOO1UJvTsrUUoHwtrA9bIibIlVGgElLkZX67uipN1W3pe56u+aq+8L5N1a8mN7nCAp+lnhEJ8IzhKXnA3OzRrHNrPFuZnitMxnDYGXY6wh5uIbN6t9BPRRdv5cSL5ah3CztSWJvDG3nziWmp0Ngi/iDm7R7aSwg6t/1OJsitRzEG0Yo9+rMSGp13qOm2mgvz7gENvNDAFDwBAwBAwBQ8AQMAS6BAEj8nYJ7HZRQ8AQMAQMgR6BAA7PVerwRPVjoXYsLdB9dX7SQeBUd6vdwYNTEmeljtQOqQOHdZhtJe46ZV09Foa8q/uR5BqyrqkM9Iin226yGARUEcqp+SqBN6pk3piuY5B6tUPErdlfzX7iGGun4qsO5ao2pvtDrRdCL+pU2smNioObVtacvlX901nmDAFDwBAwBAwBQ8AQMAQMgVpFwKnwvhOTDU9Fpe0F9a8s0TspV9uJNs4QdWuM03bNVrqMU/KuKvBGBhippVafl6z5TvrsWucqQXG2+u3e0fa6ruNLNUa5VJ31sQkNE6lHnXcPVefV5wmVTjNDoJoRQLm0VX3ZrbwXs9WfPVv9WOrPLjvZvdwg4MdGeMLPBpcUm0jsq18b0iIEXkQn/JrB6hB6lbwbVjVdIy+W+0fpJunp9yKmqrz4duPM1pYk8sYg8urgELfmWFLMwYk4JAUeIPvWhIgDQizq263T+g91oAbqQTojG+rSZoaAIWAIGAKGgCFgCBgCnYuAEXk7F2+7miFgCBgChkB3R8CTd1H3mJ+Ysi86Tx09qHtAzitX51K5cWTqL0i62jkV0ikjw7pA3oW4G9ZjbtHtCMdwhFrHQ7l/AUuvhyLglB/UuYvKSUyJ/xB63XqVFhcc0/042yvVWazkXzedWzViRQclZQcKVW6qWVWt2iI5PZuReqvxF7M8GQKGgCFgCBgChoAhYAgYAjWHQHSFqvC+HJPmx5Rc9qY6WJjuuhyG0iIElvHajhmv5F1IvMOUdInyolm3RyDeqgrP78WkVcm8bbPUlzdL2+UQF1WZsSzWpP40Vedt3CehzhsZaMSosuBqiZQVAQadt7yrBF7eAVU5j+n74IQo1NddVRbR3HjhieQscQhNOOEJv/azwyE8wSxxSt41Jd2q+hW7V2b0c5GaqQ1ib3KmNt6pxCxtej4l6pDYdj5eJf9Ktb1fqsSLaEOEetAEVemlTqRqvQi5mBkChoAhYAgYAoaAIWAIdA4CRuTtHJztKoaAIWAIGALdGQF11qSm5kPJQ6caizLVGE5/ddxUFXmXaZGYRgzCnXYchAbqOrnNfngQ+zryGsIuzlDrtOrOT67dWxUjkCL4Qu5lWamiQJB5dWE7rgvHWbsp3KrJ8YtvVztLUOiNbKnLGO0MH52chlY7UGwKwip+8CxrhoAhYAgYAoaAIWAIGAKGQDUioO2dVh0sveHJmLQ8HZP4Is2kulw6bAxYRnVuGyWrsKhiagSyivFVOgxtTSagzxSDbFtVhbR1ppJ6lcyISq8wqLajxmOlA18b9ghJ0560j9VBh4/OzBDoYgSYTa5VVc5bXtdnXpfYPH3mIRh2tVEOKwkeH7YTnnBiE7qNzxpfNmu3aDmu5bYJT3T1D2bXz4ZAysfrZmbTdwwRByfooFUZfL5exAE/r4o5uEFK5ajjZMtQocdRrFa/bt22+u3aHpXesHvnCo1u4QwBQ8AQMAQMAUPAEDAESkPAiLyl4WaxDAFDwBAwBAwBiW9QB+f76uxUtYK2OQnnfhRnpzpfytKh1FGMcXhC2h2oTpdBSsrV0dSsIetGWCtxN8I5FHd1CjHrqOoo4BbfEKgwAlq8xNarg1cdu1El8ELsjakiVWyFHl+uzl6/9uTeanD6UrTQwaLqDZGx6vzVpR4nMOpWTFtoZggYAoaAIWAIGAKGgCFgCBgChkAOBCDAtLwVk/UPR6XtRW3klINUqb6QyNbaNoGYsq3OJKJqvM4vkiMfdqpnIUDbu1V9fK1vKKn3NW2Dv63PHgSrjpqSEes/pGTeAyLSsLW2i23Gq44iavFLRABRipa3lcD7qvq1Z8SUwKsJaXnbJZbuw8ZfzZL0XzsftifvMlNcXZfk0i5qCJQVgXib+nOZpQ0yr35fnJADfl4v4oCfF1EHXTsRh67y86r/1s2+tqPWmVj024XKtZkhYAgYAoaAIWAIGAKGQGUQMCJvZXC1VA0BQ8AQMAS6KwKqAgN5rvVddeSrKkdUpxmLKok3vkxvONrFN63Of6dSMEQ7oCDtukXJc4PZ17U6PyNK2jXyXBf/TnZ5Q6DMCMR1OlmcvlEl87JA6nULDl8tm+LLtIyC3KvThXap6fSHoSEJxSumaKt3pF5VvNJyyZSIuvSXsYsbAoaAIWAIGAKGgCFgCBgCVYlAbG1cNrwSk+YHYhJVhdQOk8yUCBYfF5V1o1bL+lEfSHSIstbqi2fGQF8ZNHiwDBkyROrrbSqjqnx4ypQp2tutC5Ts+Jr6AWdom1tVet0A/o6kr6SoyHZK5j0oLI07dj0h6t6X4vLHR2Ly9uLi3wUPQ0hfik9MDcvJ+4ZluBIwzaoXAQZHtOqMcs0v6TP9ohJ439W8djaBV98BhCdCzmed5sPGf+182Hq+UR8sClwzQ6CnIKDFcEzFY1IiDhB78fOqbzch5BDw83b2e6t9T2F8uToYpXGyKstvYYNRespjafdpCBgChoAhYAgYAp2LgBF5Oxdvu5ohYAgYAoZAjSKAk7NtsTo4lbjbOks7kGapU2WhelY2dNEN4cRkeqPN1J+5mRJ1dR3WdURJvG5hG4dok3k7u+gXsssaAl2KgHP6qrM3qkTe6FJd6+Kcvklib2y5Zo+pEkvvp+vY/fXSMkunFY1M0H5zVXKoV2Jv3XBzAHcMVIttCBgChoAhYAgYAoaAIWAIdB8EGKy44bmoI/HG5uh96cDqkq2vEifHhyQ6oVmWDl4ssaE6yrGu442h3r17y9ixY0vOlkWsHQQc+RFC76vqG3xF29go9K7tQP7D2ibWdnDjwWFp+rAOcO3fNf47SLwX31o+ZYLJOvX6dZ9XcnLX3E4HfpCeEZXB3wyOaHk6OTiiI89wMZCpDyiE73po0oft/desEZ8wH3YxaFrYHohAys+r/tyoknqdnxdf71KtHunaicys7yRgqFNtq98vHbzRuJN+v5R4b2YIGAKGgCFgCBgChoAhUD4EjMhbPiwtJUPAEDAEDIFuiADqL24qPZ3GsU1VN6Kz1DECAa4jHUil4IQ/pI86PFHbHYrjM+H8jKgDNOIJvDq1mKjipZkhYAgYApsgoP1y0Q+0DEs6e6NL1NmrS3SJcnl1wenbJcRe7bykM4dO9bptVNVhAooOWs71NifwJr+hHTAEDAFDwBAwBAwBQ8AQMAR6CAJt2j7Z8KSSeB+OSXxBB24a9bgtdfDgLko2UfW4ea1zpDlevhHZ8XhctthiCxkwQKV+zXoEAvHmuLS8q8vLSTXTuXrbpaoiarM3NEqkUZV5e02NOP9eZ4N40tXRDinxZsrvt4+LyNEftjZ9Jmy67Jj6hFpUhXfDk/rcPqvl6mLNScfHMmS/naYkcXdYwo8d0bVb1I8d7d8qz7zylDz17FOydm3xTOKQyj9vvfXWsvfee8v48eOz58HOGALdGYGgnxf/7nv4eNXX+776eN9PEntVUb5ixvdLBRrqdwtJrz3UlztaHbzWL1UxuC1hQ8AQMAQMAUPAEOhZCBiRt2f93na3hoAhYAgYAgUiEFPCW8tsJfHOVALv6+oImaPezTUFRi5XMFXcDaFMMFw7noYlibvq+KzD+YkKL0Q384uXC21LxxDoWQhokRZbp2WbdpC3qYM3qgvEXhy+UV3iui2dpeTgke+nPt9xWsapqkP9xLA06HRt4X5WyHl4bG0IGAKGgCFgCBgChoAhYAj0BATatC2y/tGotDykZDMddFiS4S4ZoaK7k1Qxboq2LcarQmifkMyYMUMggZVi/fv3d9FWr17dLvrgwYNlxAi9mFmPQiC2Rv2GOui/eZr6DV9W0pS2o0slRoaGh6ThQCVD7RuROvX3daZNvbCt5MudeWi9i3vDfapwHbCT9onIl47o3PsIXN420xBATbpZ/dsbHohKm6pJd0hJOi3t1K5y+ESLyPAI9VlrcZhYq39H9+tQ4dXyVzTMunXr5K9/+Yu8M3u29OvXT3y5mkqngI1oNCqLF8NEFjnmmGNkypQpBcSyIIZAN0dARWei+l2CzNu2WNfJJaavSky35QO9/0oI06jwTN1k/X4dHJEGFWcI6QAqM0PAEDAEDAFDwBAwBAyBjiFQ17HoFtsQMAQMAUPAEOheCERXK3n3HZ1i7DV1eugSm6uOjvKJteQGS/3focHq11QHfljJuxFdR3B44gRFibeXOj3ND54bQztrCBgChSFAH4p2pLDUb6lRtKhLEXtRcViki65j7+lxXcdXaJj2fXOFXaeYUOpUjjJF6ZtaDm+pyw6at+21013JvUboLQZIC2sIGAKGgCFgCBgChoAhYAjUJgKOxPuIknhR4i2VxKukkshEJUbuHpamnXXKZ522vaO+lMbGxnaEs3Qyb0fQRtV3/frCR1E2NTVJOAxrzqwrEQjrrFhNk5V4qyqEzVupH/HpmGvLupluisxYXNvcLQ9qo1z/HJlXfYDVblO2DssZh27sXgySefU2ymorVqyQV199VWbOnCmjR4+WSZMmyeabb571Gm1tbdLSkpBJ7tWrV17yvg8PyZ/wWGtrq1vYLiQNwnFN0uL95D2tBkNBesMrSuK9R5/PmfrLlNOvo+qbzo+tqpxhVZau03XdSC1zN1c/9oCQhDY+HikoHnvsMUfi3WeffeSoo45KHS9244033pCbb75Z7rjjDtl2222lTx8t+M0MgZ6MAIK4/fXd06Vha/2c6BiN6MokqXehDjhZoD5eneEgtjDp41VF37KYimq3PReXdWuiEjssLk07Kpm3sfq/YWW5d0vEEDAEDAFDwBAwBAyBCiGQoSlVoStZsoaAIVAwAq0vPiXR115QBdC3VB1vscRbOotFWHAWLaAh0K0QiJ2xucQ3NEls0SiJz5noFlk6UkKtlR9CHG9cJzJwmTo+VbojucQHLZHYoOUS77Ve2sKq7rFc4WYxMwQMAUOgMxCg6NPOyPhmvSU+UkcXLB/qlvhylQZfrqMMVm0moeZE51ZFsqPVnph2MG14Z4NseHqhhMbOdEt4hG43WZ2oIpinJXr0XQ+nHbFdQ8AQMAR6DgL7rj+u59ys3akhUKUINDQ0yKBBgxxha5tttpEddtihSnNq2SonAtHlSmh9LKnEu7SElOHrjhSp31VJdErirR+jZJKEYGgJibWPElSNZLu5udkt7UOVtrdgwQLZaqutCo78+uuvu2nlC45gASuHgD5zdcykpQq09aNDsuGZmLQ+pyR0xEKLZLOi6IsKdUjJkZB5I4Oqmwjl1XgBF0Lv829HZdrb5ZV7fOKJJ+SMM86Qt956a5PfcNiwYXLNNdc4Rdb0k//4xz/k5JNPdocJc+aZZ6YHabf/t7/9TT772c+6Y0uWLJEBAwbI7373OznnnHPcsYcfflj22muvdnEy7Rx22GHy+OOPy6hRo2S2Ks52taHEu+HVmKy/Swl2MzU35fh5lCwYGqgDw/V5j+hSN0aXLRIiFE51N89jC5F36NChHSLxgivk3f3331/uv/9+gdTbVaq8fAsY6GHWPRDoTr8nRHq+TyyyQ1hQkW9VZd62+UrqV+Ga6Dwl+s7TD9Uq/e06WjZoWROdrn7cNv2GaRnRuGOkbPWv7vFk2V0YAoaAIWAIGAKGgCFQHAJG5C0OLwttCFQUgZaH75KWB+6Q8OBhEh45Rup22lVbW/qaqjIDPpCg/69a9huPPLGimFjihkClEVh91gk6Gnm0hJd8TOT1ISIL+kuoRb3mlbKQvsl9WyQ0bK0u6ySs6/DwdRIZtl7f/QZVK1BFCXqe9L1XyYii103Dj6xUzi1dQ8AQ6MEIxJtVYWGZOnxVxSGqKg5OsXeh+npVuVeYWTZYSSkTTqFW7QxZOE7Jw+MktPxwN1Vb4yTtkFeSsU3VViaQMySz8qWz3NHP7XtOqv7p6521uOY+zAwBQ8AQKBSBfS7b1gU96KCDtDqu7XCtj9fymvswMwRqEQFUDZcuXSpz586VZ599Vv7zn//I3nvvLXvuuWct3o7luQAEmB1p/dNJJd5SSLwqfhnZJiSN+4SFNkNkIDXX8hjE3SBRCzVeyD5mhoBHAMI4U4ozm9aGUSFpeUzbzbO0kVzkOFTIvM1K5hVtCvfeK1K1M9NA4kWR19uN97WVncR74403poi0/jr9+vWTDz5gfniR999/X0444QQ577zz5NJLL5VIJLMv90tf+pIccMABwqCQQiwaTchUfvzjH09d/7bbbstL5J03b54j8XKNk046qZBLVTaMPn4ts1SJ996YxN7US3WUqNdbybv6bEfURVM3VsmBW6pvBiVeneWpUPO/3ZAh6n8vg0GYxlavgolYunkl5UJSCCqiX3nllXLRRRfJEUccIZDHvVL6l7/8ZXnmmWccWZnzPcGuuOIK+ec//ym77rqrXHvttTV3y7FYTI499li5++67XXlywQUX1Nw95MywvqbMdNbIot8qR+pVZd62d1Wpd05cFjy2WHp/0Ef61vfNmUzOk1p0Rt/Qulyjknl763W2gfWfM4adNAQMAUPAEDAEDAFDwBDIgoARebMAY4cNgc5EIPbeAtnw5+skrsMV63fbXz3P6njyBL4YzBid7sTvJykV1bPfmUjZtQyBMiKgDsy299SxvuFAiT22m5LRxoqsL5NUS3o2Ie/2g7y7RkIj1uoUY4l1nRJ5QxwP6zuucfBtuLWSBkpdp1/a9g0BQ8AQKAcCIe1IpJOmfqTWUXZR5+wqSL0JJYe2+Ursna/9QkrwFfrUOtpBlJ5h7fyMqTO4Wa/T9mZcGqbEpHEnncJ0GJ7o9MC2XzYE+BYlP0w1vS4bIJaQIWAI9CQEIO9itb7uSb+Z3Wv3QgBF3pEjR7pl6tSpMn/+fEERccaMGU59sVwkoO6FWu3eTWy91vVf0NmIHlQl0/dKuI9B2laZEpImVTFt2Kq8g/4g8QbVeMkdRN5K2cUXXyyHHnpo1uQhihWj3ps1ITtREQRQ0YWAWzdcCb2PRKX1Ra1PrCzuUvFFSsDUdwGCZC9Vlw41VRcTChIvCrxBu+G+1uBuh7ffeeedFIkW8u71118vhxxyiFNqX7t2raDqetZZZwlq1j/+8Y/lwAMPlFyDl1DbfeSRR6S+vnC/L4q/Rx99tPz73/+WP/3pT/KjH/1I+DZls7///e+pUyee2PXCJ23vqTqmKpxDrJMENzmVv4I39NFz6rtbKXF3gvqDttZFCbyRAZwoOJWqD3juuecKxPFC7De/+Y18+tOfdkF5JjDIn6+99prsuOOObn/69Ony4osvyk477eT2e8I/VJG5580226wmb5ffj98Ro0zpdkTetF8l3BeirS7jw/KLy66VB+//n0ze7MMyafAust3AHWSzxiESRlq3WIPMO0PLnqFRN6CKb6GZIWAIGAKGgCFgCBgChkDxCLRvcRcf32IYAoZABxGIvvOGrPvVD6V+lz0k1F89z0GDvOssua7a/WCmbdsQqH4EGHXcMlM7iZ5VIu/6j4q8UwEnE69tHyXpDl+jCtu6KHk3PGKNRFR9N9y3TR2eQaZbSP2fCbJA4m0vfb/60bccGgKGQM0joFzeyGBVYtGlcfuwRD9Qgq2SeNvmqZKDTsvG9GwxJd3KGr3TRNFWnlvW9KIvqENYp4BrezsujbuFpUEVHnBAm1UAAYXVfY0YjEIvXYZ1LZyvADKWpCFgCPQABFJKvHqvrhTUtnhGZd4qP98Dfiq7xR6CwBZbbCGnnHKKMM36DTfc4KZM33LLLXvI3Xfz21TSR8sbqhr5YFTiOjiwKKOKqhMaNewdlqY9VRxhRHnV3zKReJcsWVJUFosNPGHCBKdoWGw8C189CDB7TON22k4doIK8m8Wk5QklqC/W/BXRNo7NVWXeB5TM21+kSacoF/2rBstE4j3j+vKrU//3v/9N3e6DDz4okydPTu336dNHDjvsMHn66adl9OjR7vjNN9+ck8j7/PPPC4qhxaqjfuYzn3FEXpRkH3roIXfdVEbSNiD7YrvsskuK0JkWpNN248qrbn45Km0v6UNXys9D2aqu8shEJe5uq8tELV83V1K5DvDu6RZUYz/11FPl6quvdoTdbbdNzOqRDx9mFuB5/NznPufqM/nC2/nOQYDfj3cXMjK/TVcag4X8gLXf//738qlPfapi2bnpdzfJNy4716X/wLL/yk59JsvUoXvJrkOmyp5j95V+0X5FfbtcQszoNk0HaG2pZN7BdYJivZkhYAgYAoaAIWAIGAKGQHEIGJG3OLwstCFQVgRQ4nUk3l330+lG+iR7CPUSrqewRtZlRcQSMwQqjIByZ1t1WvgNz8ek9WmdWkwd4xIrM4m3qU1CQ9dqZ9IHOuXYBxJh2Vz3+7Q6oW1/h/41T+xvVOQtz76/iq0NAUPAEKgwAtpXjhoLC52VTImLQm/rHB0o8a6Set/VYnaRlniqqlsui+tUu62PatpztWNqqhJ6pyQ6lUydt1wIJ9PRny0Oedd9sDKva+J8mWGx5AwBQ6BnIJBS4k3ebmo/Xam3ys/3jF/L7rInIbDXXntJ3759BdLWmWeemSI69CQMutu9tui0zhseUv/M23pn1DsLNW2HhMcpYfJgJfFOiTjlt0KjpodrbGyUIDmL8xzLpMSbHi49rc7cv+WWW2TmzJnukmeccYZAeA9aNBp1KqLr16935wgTtKeeespNBz9r1ixZt26dI0Xuvffe8olPfMK9Z8GwbKM4+vLLLwuEJxRHIVv+73//c8cmTpwoxx9/vOyzzz4uGteEgMT08u+//74jSPH+MgV9NluxYoV7t5977jlZtGiRDB06VD70oQ859UvUUTMZ06Hfc889Mm3aNHnhhRec4uqkSZOEax1wwAGZonTOsZCqRI9SMu8hIQkPVC6lKuzGtG1c8Ow1+i5EZ+kg1odjrq1dP1Yf+E60KVuHZdrbQQECEY6lK/HeeF/bJuHKkU1P5OV3D5J4g2kPHz7ckXMfffTRgpR2v//97zu169133z2YTM5t1LFRBIbIe+utt2Yl8r7++uvy6quvurRQ/003nmnyyXO6Zs0aR/Tlvo499lipqyt/F23bIiWQv6q+5mXpOSlgX5/XOsi7OycGbkeGK4G3SojkBeS+Q0F43v75z3/mTGPMmDGp8yi3nn/++U6FFrX0Yoxnyqx6EOA95Ju4bNky9+2plpyhQF5JQ9ncW6u0yAtrn5UX1z4n4z9xlQw7YIC0vKK+V1S9V/lQha2d31bLoLZtY1K/RXHvRmFXsFCGgCFgCBgChoAhYAh0bwTK30rs3njZ3RkCZUVgw5+vSyjxKolX/XuJ6Yu5AjtYYF3V5xO5tf+GQFUjwFSNLW9qB9Fjqhj5cvEOiJw3F9b0Bq6X8BZK3h292q3rUOEd2KLvsTq+UdPWIIm1buh+CBKAO17m/ZwZtZOGgCFgCFQQAS3qUqTebcPStlwJvXO1zJ2tzltdYrrEl+v12/cHlpYhFTaPzVJ+8FIlDCtxuHEfXSZU37Sjpd1clcTiW6VZgaybUN7Vz1YN7lcJmpYNQ8AQqDEEUoq8WhZmVOKtkeM1Brtl1xAoCAFIehChINt8/vOfLyiOBapOBKLaXtjwpLYXpqtfROv3BZv2aER0Suimj4SlcZKSJXtTSy3NvOouCngs3jKReIPnfbiuXKPe65UDIRGmE9Buuukmufjii10W//CHP7TL6pe//GX51a9+1e4YO3/84x/lkksukSeffFI233zzduchzHIesu8777wjl156aer8ww8/7NKD2Hbcccc5Qu+CBQtS5x944AE3Xfl3vvMd+e53v5s67jfefPNN2WOPPRxh0h9jffvtt8u3vvUtR4BMn6Z+5cqVctpppznF1GCcf/3rX26Xcz/96U+ld+/ewdOduh0ZFJJee0V0cH9INtwbdW3Ygp91VVVtUxLV+uGqzMvgWU2rM8yr7kLSveE+zUTSOB609PPBcx3d9qR0SOAQvAcNSpvFMHmBc889V1hy2WOPPZYimH/60592zxIDQgqxpqYmpwZ/3XXXCcT5a6+9VlAETjdI7t4g53qDTH/55Ze3e1c454nKqLPybpZbYb5lFoMjtFwtxlRJOryVKpzvquXq5LDUocDbw3qP+W133XXXYlCrKtJnURm3wJsgABmbASQ9xX72s59lvNXvXPQdOeeisySu9bL6iToo4MWYLHtglfRZoeq82tVVqEXfUp/wO3El8hYaw8IZAoaAIWAIGAKGgCFgCHgEelhTzN+2rQ2Brkeg5eG7lBihDpH+6ohSv0rcMSN0wxH7NH9p+1V7vuuhtBwYAnkRiK7QziF1OjT/T53mqLxs9EPnjZszgKrvyvA1ElHybmjMKqkfo+uhSuit12nzkhFZQ9rtrHXO/NpJQ8AQMAQ6CwFVbKkbGtIlIvEdtQNysTp/1YHbpqo+US2HYwu0VCyHSu9KLdKfUJLw+9pRtb8qZ2mHU2Rg53RwdhaUXXYdvl2uPpog826sr9bYfpcBaBc2BAyBWkYgqwKvlo1YrZyv5d/A8m4I5EIAtU2IhJANIUKZ1R4CcSWDbHhZZ0t6Tkf5FSM4B4l3OyVHHqlksx3Ur9pYet3fk3hBzxN3IeuyjSKvt3SSrz/e1evddttNLrroIkFp9D//+Y9TzIVEiy1cuFDOOecct82x//u//3Pb/PvhD3+YIvGiQPnxj39cUDd98MEH3TsFAfeoo45y+wMHqjxnmj3++OPC8uEPf9iFW7Jkifzyl790oc477zy58cYbhTQOO+wwR/pFXRcCMAqUl112mRx00EFOMdcnS/yjjz46ReI98sgjHZmOKc49Kffwww8XVE2D5GLIyP/+979dMhA0Sfe9995zWDzyyCMCkXn8+PHy9a9/3V+qS9ZhJfH2+rA+q0qU3HC3todnal1C3YkF2Rpt7z6rU5RvEZNeU5UQ3J5LW1ASxQTyJF7iePVdyLwcR5HXWyVJvFyDMv43v/mNuxzP0Z/+9CfZZptt/OWLWo8bN05++9vfyqmnniqzZ8+WCy64wBFyC02Eae0h8mIQcP075uNTJyR/GM8x75I3VKk94Z1v1Wc+8xl3iufzz3/+s3vfvvSlL8mdd97po3R4jZBFVP0t8RVFJDVACXu7qALvPmFpGK+DI3qVXq4WcdWaD3rXXXfJs88+K1tttZVkUmIO3uCPfvQjNwhp7ty57jAKzX5QAyrRlHtBK1ahnEEUlLGUlSikkzcGWMyfP9+Vu0El6mLT9vl6++23ndL69OnTpbW1Vbbffnunzo5yerH261//WubNm+fely9+8YsZo1Oe+28Liu/BwRyowT/xxBNCXrhvBnmxUF7477lPdPny5XLVVVe5XRRoUbm9++67hfeQgWG33Xabi/OLX/xCli5d6r4l++23n4+eWhdzTRTi77jjDmEwAGXOG2+8IQxoYWBBW1ub7LDDDnLCCSe0uyfUgK+++mrZsGGjs5QBBOCEEZ543qib8DvzrXzllVec8v6OO+7ongGU8/MZg3MyGc8mBpG/YUxYjj/tWIlND8txY0+UfUcdKP3D/TNF2+QYZVCblkWUSVambAKPHTAEDAFDwBAwBAwBQyAnAkbkzQmPnTQEKodAywN3SP3u+ycu4H0jkHixWttP5Nr+VwECTF3HdHgtLS3OKVAFWSopC0xn1NDQ4FQzevXqVVIaPlLbYlWveCIqLarEG1/oj3Zw3bdZQluo8u64VVI3dpVERqn6br/mje+uvsSq3eUuknidO2+/g3dm0Q0BQ8AQKDsCIe0Dr98yrIMddHpQJdq2ztEOe1VmaHtTO5jmaFm5uoOX1MEZ0de1rF+lZF4duNG0Z0TqhidKX/sudgBbhTChxMv3jB2vzFtb+x1AwKIaAjWPgJWBpf+EpshbOnYWs2MI2HtbOH4HHHCAI0KVk8g74/WZ8vL012T2nHmyQtU+a9UGKfly3NjRMmnH7WWH7SZW5W0wc0fL0+qnWVxE9nSwYESnfO91FCReJTUqMbKc5gm8QRIv6XemEi9k2vr67GzNESNGOOVaf98QhDyR54wzzhDei8GDB8vXvvY1FwSi7jXXXOODy0MPPSTf+9733D7Kuvfee2/qepCCIdyefvrpgsIvRFn2M9knP/lJd85PJw/ZEfIl9tZbbzkl4G9/+9upqIT3xCiIbz4sAVAVhmCJcf/77LOP2+YfU50TD2VWFLi5Vwzf51//+le3DVEX1VNvEJghxUFiI/9dTeQlXxDOmybpA6zNqg3xJJk36nOcex1fqETep2JSNyokDVttJNPmjlWes5B5IfAGSbykHFTqLc+V2qcCIRbi4TPPPONIapDTdtllFznmmGPc8SlTpmxC1mufQvu9k08+2ZG+Ua2GaH7EEUe4pX2ozHsotI4bN849o5Bv04m8zz//fOr5PeWUU9ol4pV6Ib1D4sPfjfHMQzKH5As5GDJ7uZRAYyuVOLdML1LgTEihzdRfs3dYeu2vPpuR+nzpM2pWGAKUp6g0QzDNR+SFJEo55o3tK664wu1+5StfaUfkLUWhHAIoacZiMTn//PPduxO8lt8uJW3i3n///e3yyLH77rvPEWRRTS/Wgvf/iU98oh0B3qcFwd1j5AejrFq1yqlwe/K8D0teMH4LvhPBQR+ot/t0UL8GH8i/3uhHw2644Qb3/YKk7L9XHC/lmq+99lrqmlOnTnUEY9LyxuAbyN08P3y7sWA+fTgGrLFglCOeyAsxmLKI7226ffOb33SEYEjLuQwicybjOP1y1Akg9fpw89fOlWXNS+X/dv60NKwroAKm37jYMi2PVqnad8e69zJl044ZAoaAIWAIGAKGgCHQrREwIm+3/nnt5qoVgbYXn5LQ4GHagkm+gkk+REKyU3NdS/vVCnIPyxcOBxr7GFPGDRgwIOUcrEUoGJnM6GOcKiwogOBAKMr0PWqdF5P1D0Wl9akilQgyXUgJTDJgg4S3XKVTja2UyLiVOs3YWnXGR2Xl+rC8MbdJ3vugTprbEh5P/xr7pArZb6qLy4h+bTJxWIsM6AVhyltS0dfvaiHRPr30/VRA2zAEDAFDoHoQ0EKN6UAjgyLSsK0SeefFpeVNnUpXFYmYcs0pxlC4lWIaL65KD833qpNYVYvq9o7Kmt7qLdZr2nexFEATcdzXxX1wAFj/+Bb6fQ1SE+dLv32LaQjULALWNuj4T1crirv58tlxJCyFzkLA3tvikWbqdYhPM2bMSBEbik8lEWP+wkVy53/uk6gSYKZM3lkOOWA/2WyzwaUm1+Xxli1bLm+9PVseevRJeeTxp+WjRx4qW4zcvMvz5TMQW6P19heUzKgD+1zd0p/ItdZ6fXi8SNMR5SPxeoJuUL0vncQLwa4z7Xe/+52wZDOIjHvssUfqNKTfP/zhD7Lzzjs7/9mFF17oVEEhLGKQFocMGZIKjxog1q9fP/nLX/6SIvH6ACjbQtyBAAvRKJtBSvMkXsJAdoRoiTIgaUMkChp5HjVqlFPqffnll1OnIOR6AhaKkkESL4GIB0mX+yIc/kLIkIsXb2SAUxYEjfPkHb9iMI/BMF2xDfG8aWcl8yq5aX2rzhg2S3NB2yqfaZjoG/rOvKRkXh2wisJvpcwTdL0aL9dJJ/Gecb0KGVTYeCch40HegwCL8WyxeIP8x/N68MEHF+QDR9kTBU8IhBBuX3/9dYHons8Y3AW5HWI6CtCoe0KW93b77be7TZ571ECDhkomBqnQk3j9edL76le/6nb79u3rD3d4HVOF87guBVlvJfHuHpLeh+hg6GGVe64Kyks3D8TvTR+HJ/Rut912ctJJJ7m7ZlCFt1IVyn18yKEY5e3+++/vyv+RI0e6Y6WmPWvWLKe86xLRfwwYgeiKGi7PPyrvPP/F2IknniiXXHKJi8K7ftppp20S/W9/+5s7BoEVvDCu5Um8HD/wwANdOU+ZzwAUFlTmGQSQyTy5FcIvA0poS6V/99PjdfSalAvgQ5mFijH11t+rWjfGwJNDDz1Uxo4d68oVVOubm5ud0j7nGZTiv/le+Tgajbpyj7IM49kiXcq0W2+91ZF7IYcTPqjE7AIn//3qV78K7mbcRu0/aDM2vCJ1R8Skz7hGaX1UP0zrg2czb1MWxdbxobPyJTNCdtQQMAQMAUPAEDAEDIHMCBiRNzMudtQQqCgCba+9IJGRKkvn2y+6ZtNNX8yVa22/omhZ4vkQQLGHaX8GDRok5XT85btuJc/j3OReWJjiiKmU6HgoWJ1X/QMt7yqJ914l8eoUdLJxkHXx2Ya0NFCd/0rcjUxYIXVbrZDwsA0SikRl9vIGueuZfvLSgo1TLhZ/gcwxJm/RLEftsE7GDdZR4eo0DjGNr67Vw5R7P3NydtQQMAQMgapCgGnVGrYJSf24sLRNUjWu11Wld4YSeki9p+8AAEAASURBVLUzP75cs6pFXSlG3Jb/xWTN0vXS53Ad2DJRO8SoZNW4dfi7WNL9o8ebqJ9mXOu5jMf1oDteJedLunWLZAjUMALWNijPj2eKvOXB0VIpDAF7bwvDKVMopg5GXc4rlGUKk+8YKry/u/lWOfajR8rU3abkC14T5yEhs3A/Tz87Ta765a/lcyfrlMxVos7bMkvr/i9phb9QspmiHlI3atOhEWnaMVxWJd5MZF7/I3MOQk1nGmSfXL6vIIHQ54v3gGnDIQVCAvbEQsiHKI8G7bnnnnO7EGaHDx8ePJXa/uhHP+qIvJDOmAZ+zBgFP2ATJkxoR2T0pyAiQbSEOJROWiQMxKkFCxa0wxQimDfIbBB102233XZLHZo5c6Z738eNG+eIUeQR9WGmikf1d5tttnFhffspFbFKNiDzNu4clrgSoNZviEp8XoEZ08GqrS/qQNgJOgPNTrS2KmeZyLz+ajfe1ybT3i5Q6tVHKnGNSMXdd9/tpqRHCRfyHs+PN55zFlQpIbPnE4DAr3zTTTc5ojvPDYQ+r5jr08y2Zkp7yHIYZN7PfOYzbpvn9ZZbbnHbkIObmprctv+37777ujwSBxVelDchvfN8UteshB8/ziu06Wvks9RuHeqjg6BVcdlIvO1gqciOJ5DecccdjkxOvQVl2HQrRaE8PQ3IoQyMiER04EDASk0bwjHvDIZKbpCw/tOf/tQ9055UGrhczs2tt95amFEBtVkIu+lE3kWLFqWUYL3aMSI2P//5z126H/vYx5wqu79H1K1RkYegyneIvqRs37ibb77ZfS9yZjB5shzXZMAAgwhQA/Z2yCGHpIjclGPf+MY3XN8ezwR1D0+i5RuOGn3QICl7vBm0A9nXG2UMBGfOk242Iq8XBPLx8q0hbqPQS5m27uGotE3TGRXWF+C4bVWfYoHlUb482HlDwBAwBAwBQ8AQMAR6EgKdOxdPT0LW7tUQyIFAdI5OedJHR6lCyKO9o2vX7KnF/Rz3aacqjwCqPZB4UaGphPOv8neQ/wrcF/fHfXK/hZhT4oXE+7S+WR0h8aoCb2jyYqk/9B1pOPxtaZi6QMKo8CqJ98G3estl9w6qCImXe3xpfqNLn+sEywhfVmRbF4KPhTEEDAFDoFoQCNWrAszosPQ+sE76HK8d88eEJaKKMDKwAznUcr/hFS07H1OViAWd08nYgdwWHbWU72LRFyGCq5/q16bW1yXdvEUyBGoTAWsblO93y6d0Wyvny4eIpVQpBOy97RiykCLmz59fciIo8ULiPf2zJ3UbEm86GJB5uT/uk/vtaouuUjLiKzqLxlzn1SgoO0z93rhfWJomK4m3qfwkRkgzntDrM5TpmD9XyfX111/vnmme60wL5zPZF77wBafQxzkIV6gxXnnlle2C8u166qmn3LEdd9yx3bngDsRgb0H1XH8sGznKn8+mbphJHfell17y0ZzqILOppC8HHXRQKozPDyTIoHIxJC7uCXVeCGH33ntvRlJwKqEu3GBQa+OUsDTso8/zRmHXvDmKzdF351VVsl5d+LuTN9EsASDzQtoNGvue5Bs8Xultnkd+39mzZ8u7777rFCe9kinXhozrSZL58vKRj3xEvvjFL7pgkGu9Kma+eJDZUSHFIBV7Q+HaE+pQD063733veynVXwi/kOF4vlEMhUyYXu6kxy9lP4R0U4HyTfF1yvmdrbMlLan8M1XKvXR2HJ6xzTbbLOuS6TcuZx4LVSjnml6hPP36DHL42c9+tgmJt9S0GSThlbBRWg+SeLk2atM8y6UYxGLs4YcfdsTbYBpBRfhjjz3WnUKpG0VsFhTlPYnXx/Ppsf/888/7w+3WZ555ZsEkXiKW45qQqoMkXtJFnZfvNPbWW9pXXIS98847qdA+DX9gxIgRgoIyGP3gBz/whzdZt7Yqw7ZAg8B7//33OxJv2/taXsxREq9T2S0gAS2LXJlUQFALYggYAoaAIWAIGAKGgCGwEQEj8m7EwrYMgU5DILZksRIBacWo8xn/M2usFvcTObf/XYQAo2dR4k0f8d9F2anYZbk/7rOQ0cJti+Oy/qGkEm8R6i7tMt+7VULbLZG6g2dL4+FK4p26SKevW6fvbdy9sg8pufbPzxc3ZVS79IvY4TpcL1lKJNcJlUSSSRzfuF9E0hbUEDAEDIGqQQDHbv0WCUJvXwi9H1NC7yQt4VQhpiTT8p/BHOsfjknbe92vU6qY72JJ+BFJ4XdfF/ehcTv+YGpdE+e5FzNDoIcgYG2D8v3QEISwxH9d+/30dfKS1Xo+mT1bVTEC9t527MdBYRGyQql253/uc0q824zfutQkaiIe94fiMPfb1dY6W+vnb2j9vLBx2iK9lJO2a0iado1IuJ8vlct/F0HibnC7/FeqTIqQZCEJehs3btwmA/5RF/aqigMHZh85GZwivSPvl89LrrUnQeYKEzy3atWq1C7Kwc8++6ycffbZqWndSQ/l1qOPPtop986ZMycVvpo2Ivos95qqSqhT9JluL+KaPZsqDt32Wlxa3+2c9m2QzNtVJN50MCANHnPMMY7EPW3atNTvzm9eqPgD5DZUpTHUK4OkuPTrBfe9Ci+kw4ULF7pTKIlivG9B5Wh3UP+hEI0KNoTeIOEOpeFTTz3VzT4H6bycFtYxzSjtFmTObxKTdQ+oH32hDoLunEeroKx1VSDKyGwLMyhU0jIplKP6HFyCzxkK5el2+OGHpx9y+6WmHYxHmZvJ+PYEvxuZwmQ6hqqutyBxl2O33XabO8X7Tl0P4zvHwHaWoOo7+EA4Dg4iWbs2c6eQJ+S7BAv4V45rosKdbrQdt9tuO3e42O/ghz70oVRyRx11lPz2t79tR4TGXwhGuVTK6+tVVaFAc3ULLRsQSlh3f1TWPLpBRAcBFGKUReHelau7FZIHC2MIGAKGgCFgCBgChkAtIqBd5maGgCHQ2QjEW7Sxg2cE5wjtmCLX0956R6676165b9rLsnJtga2mDDc5sE9vOXTKJDn7qI/IlAlbFZ0Pc+5kALUTD3nnEQ3znmDcJ6PHue9s0wyi7LL+CXU+PqUvVSlKvJGYhDb/QMI7KIl3x6VSN3KNhOoTao7+NZ29vF5uSSPxbj6gTfrUx2XB6oisbyl9jEyvhpiM6h+Vta0hWbRq4yea643brFXGDfajpRMq3hvdIOn7PeGJsHs0BAyB7oiAI/SqQm/dsLC0TohJ8ws67a4usfl6t+0FgfLfvn4HWp+MyYb+2v9/QEQi/TeWmvkjV3+IQr6LHboL/fDFQ/zTVLKsM51/94158uA/HpXpz74u69aU3tHVu28v2XG37eSgY/eVLbcdnTEfma4fzK873yEQLLIhUDsIWNugvL9VSnE3mWxqX9UMsdR+2nk61JkyF9LGmjU6D3YHjHKejl9U2/xU4anr+nykXT/9fAcub1E7AQF7bzsOMiSFQslb6Veb8fpMicZi3VaJN/1+Uead9tIrwn3vsN3E9NOdsh/TaZhbZ6oa7/xEWVrIRSMTlMS7Z6RTpn736ph+XUj+qiXMm2++6aa99vl5/PHH5aabbpLTTz/dH3IiADvttJO8+uqr8sYbb6SOp2+g5uctl3KvD9ORNfnxNm/evKxTofsw6evJkyfLVVddJT/5yU/cfUGyvOaaa2TBggVOwRUF1kxkt/R0umK/briqTO8lElsYleiMwt6J2Dx9h96KScP4kKDsW2nzCrx+XenrFZM+zw6k3C9/+csuGs/1lClT8iaBIi7E36lTp7qwEGqDSp7ZEoAc7o3p7FF+9kRe0vCDunwYv4Z8/K1vfcstb7/9tjzxxBOOePfkk0+6IKSLIme6YqePX+w6PFCfDZSecU8XMEFRfKmOq9AB0HFVem7aOy4NW1dG+bzY++iK8MOGDXPlZrZre0JptvMdPZ6uUJ4vPRTKd9hhh3bBsuWx1LSDRN6JEzPXHXj2IZc+8sgj7fKSb4cBJSeeeKL89a9/ldtvv92R24mzePFip9LL9qc//WlW7eyhhx6SO++806nEFqtmi+JyKdaRa44erf6sDFYK+ZlkIAB//etfl5/+9KdOERxFfowBChB7UY7m25jLDj74YPnud7+bK0jq3N677iPNM6Ky4TH8s1pGrG1Mncu1EVM/YnhIWMIDcoWyc4aAIWAIGAKGgCFgCBgCmRDYyBLKdNaOGQKGQGUR8P42XbMZD+y7Cwf2/fk/PfioXPi7P8u3TjhGrjztZBnSP6EK6s7nyG2m80tXfyB/e+RJ+dglP5LLP/cpOeWgfRMpBK7r8+Gv7/fbrROx7H8nIwCpFedjTzLul/vOROSNq5pL88sxaXlCnY8rSkClX7OEJy6TusnvSd34VTpaWBNEccsRmFRzkM5y3b9rRntZgwlDW2SBkm7323qtnDOuWfo1RJPxEuGZkjyRTv71By0ReXp2o9w5vY+Q7ltLGlI3cpce+9J+qjySTM/nZ5P9VAzbMAQMAUOgdhEIqV+4YRtV5R0RkpZxIWl+WpW7pms5ulGAqaCbiy/TTqnH1Hk8NCa9VMUrVLjoREHpd3WgXN/FDudNv3mJ+h9q9CFXT823/+R/n5HbfnWnHP3pj8jxZ39c+g/sq5/RZHzNkItf4P7qlWvkuYdekKsuuEGO/8JHZc/Ddi86P1zPzBDoKQhY26C8vzQd0pBii1mjqHbjjTfKySefLHSoMhVrMfHTr4dSK4QkyB8oxh166KFFp1deVCy1ciNg7225ES0uvZenvyZTJu9cXKQaD839ct9dReRtW6hTMr+tdXr0DQqwkIrwNewekgZtD7iKYAFx8gXJVz8shcSbPr13vjyU+zxqhJ/97GddsrvvvrtAtvrjH//olGoPPPBA2WorFW9IGuchPDLgJJtx3ls6ScwfL9d60qRJqaReeeUVOeSQQ1L7xWygzrjLLru4BWIn06dD1pw9e7ZTXA1iUEy6FQ2rD2PD1iFp3S0kMZ1djLZrXtNxkm2z9D3S8O69yBshfwCt8jjXYraQpZB4GzvQ7obs6lVHf/azn4lXws2Uv1LfPUiH3//+9x35HUItSz6DdHfKKae454r3C+KtV7iGjFiIbb311sICOREVTU/Ae+yxx8pH5FX1y8gobcEP1GdqeSG50jArdRD04zrIYrES9XaLS+POOrh6hBJ6IwXG7ybB+vTpI5D/u8qKVWYNKpTny3OpaS9btrFgytUPlUvlPVfeeKcg8j7wwAOOlAqZ2qvz8s4Fvwl86y666CJHYM2UJuH9O5npfCnHynFN2oPlNNK7/PLLZe+995bf//738q9//cslD6n55z//uVuOPPJIueWWW7L2HRZCIsYPuF3TTnL+LhfJsdseL1vWbyWNOGsLtMhmIRXJ6ZxBJwVmyYIZAl2GAHVrZgCgLMbPwzvIwAvqEtRJSq3PdNkNFXjh+fPnyxyti9M/TpnV3Qz199dff93dFoPv8QH2dGO2lBadBWaMPttjxowpGA7abNQ5qIt5xfqCI1tAQ6CbIlC6bGA3BcRuyxDoVAQg5GG6cluB/cTx9udR4oXEe/elF8rZR39EhvRLKrEWGD/llUsmS3zSIT3SJf3EdROrYPic+UsGt1XnIoDqDFPlVJtRUfv73/8uDz74YNmzxv1mU9tpnavKjaq8GE/MblbEtTXOsPkS2fddaTx0ttSrEi8kXv/MB9cr14XkpfkbHRYo8ULiPe/AFXLwxHWOxBsMzztUzD4kYNIhPdIlfW8vLWgUrp8vPR/e1oaAIWAIdAcEUNGFgNvn2Ig0fkQ7khCyKLIFg+JXsw7yaJ0X63RIcELwTWShA6Dcluu72OFruW+YfnXcH//4pmXff/eNuY7Ee+6Pz5IDj91H+g3okzN8vvSITzqkBzmY9HNdP2t6HQbCEjAEagOBamwboEjry8BFixaVHchKloHpyrb59lH7g8R75ZVXOgVd78DPFy/X+QEDBri0SJO0UVvMFR6A08+XHXRLsKwIVON7yw1CoODdDZL5ynXjlXxvi83j7DnzZMLW44qNVvbwmfCuVB2S++W+u8S0Gtk6R2faUEXRgkzr/HU7hqRxJx2M11g+EsygwUhVls8od+lw7EpDjfb55593Wbj++uvdt8iTdFDkjUZ1wHnSvGIpnc6ETbe5c+emlH0hxvLOVtIgMPi8fuc738moZv/oo48K06uzvPvuuy47//3vfx0ZEiXVhQvbOwIh9bqpwJMZ99/GSt5HqWnzbDdN0gGs2+szXmA7NzZHibxzE+2yUq8bjPeJqQVeOBgpz/aUrfIEyHEaouvYsWMdIe9HP/qRQJLIZKjaQ6j1VixZG0XLPffc00cvaH3SSSe5cC+++KJcfPHFbptnLZOaLuU4hF0WBmal2wEHHJA6FFN1+HJavarqhpUkXpQ1i1OGbr4jJmv/HpX1j0UdYVw2Fh9FJZcpcDiceNbKdb/exxKOdA/GcbpCOfXEXMtZZ52VCeaMx0pNO6jK7svfTBdgIEYpxnsAeRfzBN6g0jUzL3iDcI8KLYaiLORflNwZGAdOc+bMcefK+a8rrllo/iHr3nbbbbJixQqnTkyZ5r+nYPnNb34za1IM+Al+J4MBIfBu07CtfHb0mfKNHb8lp23zBZnQsF1RJF6+uxFVjq8fV2Q5FMyIbRsC3QCB9957T66++mo3YIF+c8opiLyUXdQlmM3psssuyznArpZhQDn+Hq2z33PPPbV8G1nzzgwgzNLAgpp8Vxn1oSVLlrjF140KyUup8XKl/e9//9v95q+99lquYJucu++++xyO3fVZ2eSG7YAhUAAC5W+lF3BRC2IIGAJJBHQUkjPfnsmzf91d9zol3p232jIZr7j4jHrKdD3SQ+GX9DOdT6leZImfiGT/OxsBKlk4p6vNfvzjH7spfFA2KLdxv5kqorE1StKaFpOoTtFYyLRhqXypLyhUP0PCe9wnjXsvUOXHtRIKaxr60KdeSxc4sT/z/Y3OIw73qY/LR3dcK2MGRTOGJ0winczpZTtPeqRL+kHj+vnSC4avhe1mHZ1nZggYAvkRaG1tTZFz8ofuZiG0xVI/Oiy9D41Ir49HEp2c7Yvj3DesfWLR1/U78aISB/R70ZlGBzPT2rHQsVBuy/ZdLMt19IPjvl7uw+N2cu4/+I9HnRLv6Amj9PL5wyeCJMLlCk96R6nCL+kXk59U+mUBwxIxBKofgWpsG6A44svAYp24hSBeyTLQKycVuqYDBiXe8ePHu6wXGq+QcBBaSJtrFBKeDPhwheBoYboOgWp8b0EDshzvLh1S5bZKvrfF5nWFdqJutll5SZ3F5oHwxx17jJz0qf+TOwJ433vvf90xjpezDsn9ct9dYbG1cYkqiTde4OVDyuup30lVIYe5ymjZsoz6FaqC5SB3ksbQoUOzKt6VkmmUciH95VogLXljunQ/PTYK7pCumDr8mmuucUEef/xx+eUvf+mDyyc+8QnxhK6vfOUrTrkPUu/SpUvdO8/77xUNL7300lS8Sm3wvfLELAgNxx13nKBwD8mBQUA33XSTK5MgJVGXGDWKtoa4NSqOKA5yT+BAmQpB8IknnpAf/OAHLhz3yne0mq1ueNg966GhheWSWcjadLAq71Q57OR9wzJ5XPm6CU/ZPywfLpZEmnYjzESAoTDJM/2Pf/xDIEvw+6LWxTT3KLs988wzLhxk2UGDBrntQv/xPUIVtxjbd999U6RDP9glm2LwyJEjHTGR55RnFMEL/+5C5PniF7+YuvQRRxyR2i7HRv1IfaZ0IESohE+ce76eiMv626NK6G2Ttf+LCuIZzITXUWPQA7/TrFmzZPnyQuWCs1/V/wYjRozIHqhKz2T6BqUrlJcz66Wm7b8X5CUbWRciKcT1Uoz30CvKQ0qF9OaJ79RFgwZx19sNN9wgvI/Dhw9P9Y9ly5+PU8q6K64ZzGdwIE7weHCb9wpSLiq9lC3jxo1zp/PV4w8//PBgMlIn9TKp1xQ5fczZ8o2dviNf3O5rcuioI2Ro0zDca0XZuoa1iTrc5uX7thSVAQtsCFQBAgw4YkC2V0SnvBs9erRsu+22ri7hB7fQ1sPXM3369CrItWWhFhGgjXTVVVe5pRhCcanxahEjy7MhUIsIVB8DrBZRtDwbAqUigL+NVlCB6/umvSxXnnZyweELTZdwn9xvT7n81mQnTYH5cemXeu8Vikel13eGBUfsVuhyrkOF6xViqFj4ynkh4S1M4Qi06NSMra/og5tZpCFzQr1V3WWSvoCv6XM/cZWE+mwRCOc0/wJOisT+4g/afzYXrI7IOWOZEzJz+I1OjuLP767p/mt6e1WXjdfPl17gVsqwyTPuOw/r6+uFpVALxuWdpMGK4Xw/9thj5e677xY6hy644IJCk7RwZUQAkiEqIkzf88gjj+T8bf1vSUdbr169ypgLcZ0wTAc2bdo0YWq48847T+hUNEsgcP/99wtTNdI5wehU33lZaXyuuOIK15m76667yrXXXtvhy/3hD3+Q6667zjna77zzzqLTc+q8u0ckPDAkG+5TdRjK/fUFJrNGFcBejkvLxLg0aYeWWWEIuK+NqxfqP/5CibWwVguen/7s63L82R9z4TKdzxc/1/ndDtxF/vPHe9tdL/36WeO7nFbuH45ZHF84XPv27St0kKEGZXW+ymFuKRsCnYGA71wvdA3ximm8Cw1fbDjUqiArFRuvM7CiXj9jxgynkgjJBjXiLbbYQpiOfXAHlS+pf9JJTyc2RDWUp8wMAUOgNhFoe1+JvIs07wWqO0YmhqRhGyWAlJkDgj9j7Nixrt1JPa5Uow0NeSbXVOOlpI1qF0sug3gLOXXDhg3iSYQQd84///xUNAhQtL8gRH3jG9+QQw891BEHUHu/6667nBIpZTaKfZlU+37zm9902hTzkLhQJkMQwBOYUzeS3EBl8NZbb035lCCXoY7KtxE14t122y09ilMm/OEPf7jJ8ao7oGKiDRPD0jo+Jm3vJdpZOfOo71B0oaryLo1LQ9+Ot22HDxS57vNh+c+0kMx5P9Gcy3n9LCcb1VW4u45nmjyu43nimWC2A/wQPKf4Q7IZJNl870y2uLTbeNZRri7E8GkSNvhcffSjH80YtbGxURC7+MIXvuDI8Z40x7PsyfJEvOSSS9z02hkTKfFgSAc+o/QcnaP+ciXlSrEkXJr9+iy0LtHnbLoSeVVZs25CTOq30sEVo0OCbybgNC8qlxAOISz97ne/kwMPPDBFjC4mEXzU1L1R+qO+ibporZifPv3pp5/eJMteoZznA4XyPfbYw/kYggFRKP/5z3/uDkHayaQGHQzvt0tNm++lt3PPPVf233//dqR52iZBUroPW8ya9xv1bUizvI/YhAkT3HTzwXQgDHtL90vTHimWmO/TyrXuimv6Z4R8McAl3fj9OY6SMX5b39dCOMqXffbZpyBiNb/nvffcK9Mff00mDfiQTB48xS07DNpZybtDJRwqrQK2vm29DDykn86ooLOqFd6FlH6btm8I1DwCDEKjro596EMfkqOPPlqCnAV8HfSP0B+G/eUvf3H9dMEw7oT9q1oENt9881R7yavLV21mLWOGgCFQcwi0ZyTVXPYtw4ZA7SKASyvu/VpJ30e+/ZVr18mQ/v3cTZcSP1f6QzVd0veJF5V+IlZV/KcByii37bbbzjlzKp0pf71CroMjAoUAs/IiEFuvTsk3ipiekctD4v1wSHofHpENb70msbrhCf9jSJ98dUChXh1KroP7zW28GRttfUtY+jWq1GOW8BwPxs+2f+QJ35W9p+4gF56rjunkdftruqQftNT1c10vGKFM2yhfoAKGfexjH3PTNhWa9KmnnuqmjiF88B2A9ASJF8OxbUReB0Wn/4PIi/Pvwx/+cE4SLxkLPgd0sqF6UA6DpJpOygg6SstxjVpPA2c0jnwWOl0hCnWGvfHGG+75QNGpHOanjeqIY8N1SG2v00TqDK/r6pTM+4KW2QX2vzMFaevMmDSoOlC4V/vyvBz31/3ScPq3rr4KWtQj2631WHB/3Zr10n9gP/i0ieN+nR7P7xdxnnRJnxyl8lFgfA1WEaNDmW+cV4sJXoTOEwYjoIoW7FQJhrFtQ8AQqG4EGLhEx3Sh6zVr1gjkqELDFxtu4MCBbrrxYuNVEmXfYU5nMop5mey0005zxLJx48ZlOp33GNfw08Vb/TAvXBbAEKhqBKJK5I0pKawQW968TJUsVYV8Q4t89etfKSRK0WEos1mqwYodAOYHV1P+QurFGDAZJBXzvUCJd/vtt3fnzz77bDeAlh06nWmHX3TRRfL3v//dnff/IE9BIEtXQuR8kFzkwwfXEBdLNa7J7wGZyyuX+bQ+97nPyYUXXihjx471h9waIiADXa688sp23yHq4gwC+dnPfpZSJmwXsQp36oYqUVLbqW6w6gf5MxhXwm+U92ls/rCFhAhrG+1o9ZNWi/Es8fvttdderk2VSe2TAc4QCJlOnuc9aMF9/74Ezwe38dUzDfC//vWv4OGs2yeccEKKyAuZnAFM2Yz2IkRTyPRePdiTeHfffXf53ve+JwcddFC26B06XqdKmE37qJDC0qhEX9NnpcBBFO0uqtHiy1QBehmEXh0YvUXcPaeRsSGdNUmf2RHqW+nTHvt28TPs4H9cvXq1K49QK+6IQeD1BOmOpNOZcakT8yzgT8AfjsgFA99QKua5RaEcRWqvUE7fE88KA0/wozNgg2eIdIoZ5F9q2pT7fGu+9rWvuTx/8pOfdNt77rmnI9v/6U9/kttvv71DEKK6vcsuu7h7htiOff7zn98kTd4VlHgxBCDAhmeAPgbKi44+T5tcUA90xTUZJOQJ/2AL4R0Fcp4RvvOIBfl7hSQIDgxKgODO8T/+8Y/uViifshmK7m2L43L1Sb+Se5c/IDsqeXerfuOlX32i7zlbvHzHW2OtsnrYChm2dz+pG15c2ZAvbTtvCNQaAgxIwqjfUtYH6yYcx1/M8SVLlsjcuXM55GajKHSAhotg/7oUAd/m6NJM2MUNAUOg2yJgRN5u+9PajVU7As51DWFPaNConhkOp3z73FQiYmHh86WXfr6k9IlUfeZHulVTzpqbm6spO90mL22oUKgiryQGN+a/L1UlqJucIPE2qJLABlEirpp7G/WdyLXOlHiu8JCB850/Qkm8jz81XR7TBbvgayc4EjHxMlm+9DLFKecxHNs4GwtxFjKNh3cspeeBKWS8k45OGbOuQYDOOwynYDHGiOFyGR2O3n7yk5+4UawdVW/z6XWXNQozdLDiHDj44IO7y22Vfh8oFm0dlvgRyuFtUzLvi1oyFqIwo98Jvhc4qxvGmUM57w/ANyxRTS1srQm6r57+HEXFKzR8qennvdHiAzz11FNy/PHHtyMZ+I4WUqNj7bLLLpNnn33WdaJYmVY8xhbDEOhqBEpVvi02XqXDVwpHpoaGpJtOAAuWhVybKdH/9re/OaUZyDjpxqAyr2RHR1ephN/0dG3fEDAEqgwBre/NfXGB9JrbX3qHdWR1Hnvng7flnun/lv+pOt/5F57nSE/4D7qrjRw5MjULUjH3CLmVJZuNHz8+a7qcQ/mL8nz+/PlOMQyC75AhQzYhGvj0IVB5EpU/FlxDHvIEouBxv40KZzaD3PDlL39ZzjnnHDe1OtOrM6AUbHIRiCFhsnAf77zzjjDwBVX4WrOQ9tLVbakDVkcq4X2mvjB5LLZUeZlKrkw4HfMEruHTxx13nLDQvoKsB9GFmYryqf7TVmMp1G677bZCgzrxED9rWSGRIGE+9thjbgbBd999180yAOmu4gM+tS2PqnnsI3EViohKjDFXCRd4IdneNIzOgsez2fKWDnQbrC6YMarMu6WSeSH0jtRlWJLUW4CrBR8kgwxmzZrlBqpterHcR8CO9xzfcq3ZUUcdlfKVf/vb3xYWBgFD6sVKUSgvFINS04Ywj/oxM6kxmxtL0BgAgko89fpSjbwF1WchzKcbAzcQ72EAC2rsLEGjrEhvmwTPl7LdFdcknwymQayIsu+UU05xWYfUS7uJc7/61a8cDoRhSW+D8ZugBp4y/Vw48q4OAnF9afN0MMi7cRk6d5T839aniH59UkFL3WiLRWVp3/dl/OfHOJ+t6/YuNTGLZwjUOAJwAaibYtRl00m8wdtjgIsn8tLvmonIO2fOHPfN9OHGjh3ryt0xY8ZkTZs8PKd+6dkad/ny5U7EB1IxbQDaVenKv6TNAG0GE0ydOtXl6fHHH3dx6Zui/uWNsHwXWDPQhHTJN4rg6YrpPg7rhQsXukEgb775ppvJjvxTpyu07v7KK6844jNpkUfymm70W/rvFNjz3fDG4HAU8RctWuQGFTE4grYDM31Qp0j/nbjHpUuXOgV0wrzwwgvy0ksvuXvm28jMosxOhYFp+r0Xez0GsZOmzzc4UVcCZ+4VvHbeeeeUMj6/Mb8RuHpjf+jQocIMm9kGmxUTr9h78PlgvXbtWjcwnwF5pENbk2eXmVSKHcjKc/bqq6+6diuDPmmzUvfge8cMiWaGQHdEwIi83fFXtXuqHQTUSZmw5DrfPoFd0ALD50sv/XzJ6Sfuoif/R13wn//8Z04IqGR1pVHhxDGJUwSHBw7xyZMnOwcAU3ukG44PKoA41hh9jNORSiCVSSpJNDBQ0aRSmcvokGUqeIwpoYiXyXD2U0mF7JJrxHB63Na56uSen9/J7eLpqxOZEJJeB4eVxKUOiuSrxDm/mVg7zT8fxZ1VTS63n/4vX/hc51HihcTr7fKf3yoXKpGX3JR6PZ9WJdeonqCWks8YlZ/NcLpChFq2bJlrWGQLZ8crhwAdY149jZH9XWVMC4fx3tNxZ7YpAjijKcNRkkl3CGwauocc0SK8UQdjxA+Jy7rV2in1ht535mK6HSAxdVS36TcjPLrNEYv4puEEwPgmotzEVFfphmIKpEy+efwefNsefPBB5/xpbW11zhE6o0gjl1X6m5jr2kWf0w+Y+xqFAJadTdftzrsLtA/X7ny++PnOl5h+0fedJwKku0996lMpEi9TvUNmwxnFs4ADD0UU6kzUf6655ho3NVqeZO20IdDpCFCG4dzGEY2aLGpG1NPpKETlJ2h0JqDMT/2N5506+z333OPKRc7hsMYRe9hhhwWjZdz2dX7SonMx3VFOJJzV9957r4uPyhYdDZ1t5Iv3nbq8K/38fvo6ed7nLxXPhysw/ibxMsTnGpuEy5M+cSphKIX5jnIG+DEd+sc//nHnQI9Go64sRFUREi8d0HSC+6ngs+WnI1PcZ0uzux0HQ1QDadNDBho7dqyre/De8h0KGmrGqHFihx56qFPqYnpPpqKm7sM7jx+AGVeCKp7BNPx2rby3Pr9dveb3+buSLd56603tsFqp9cfNtZNpvBynHaBeHbWr89jZ1//J5T+V+G2N8tnxmyrspeeFMne2EnlnfpBQmuU8RLhLL73UZvJJB6sM+7z/22yzTRlSKk8SdOpSnqWXaflS5z5Qdaxlq1cyZESL8tjMAu5ivdZPVunSqnUDFSzo7gZJjXKApVaNui+Eg840no2mndV5ogTe9f/V2eyUhCv6zHTINK04RPKlSgR8RWc9UlJveAt9dllGJVR6UeIM91dvQI7eZwhBQVJQh/LUwcjUr0uxbIMMcqlAQzCH2AIRM0hcDV6/FIXyXNfsaNrE/8UvfuHITldddVUwOdd2ZBCzH1SSTgxrFzjHDn42CM0YbcpM3wCIYviQv/SlL6XI0D5J/DIoz/v2Ce+bt2y/kz/v18E4/lip1yyEoORV7DNd9wc/+IF7P3hOvEq9f04hnUFEA/c//OEP7jztLYx2GXV7zvVp6usGfLRB3lVBg+gCXdQfSj9afLkGTqp0l4PE2xxtlqX935dtzh4rTTuGe8R3yQFu/wyBLAgEy2RIjPg6svXpTJo0yfXVk1T6LIb4Vmj/04YP2ttvv+36JajD06eWXo5QbuCPxicQNPx3fqbUs846q913mDQfeughR7Akr/hyvHlSMj4yfInps9NB0oRsDIeAmSQzEXMRFPrf//7nk3Rr4lCu488oZHAO+cffjlGGZurT5P64D4y+Gk/kpQ8oE4cDvwqEXfgSkHN92Ux8ylpItAzAoh912rRpHE4Z/av4WDA4E8E2VSnX495QaCbP3Af+2qAxeyYYkk+uh6Cdv1cfzvdz+YFv/nhwXWi8Uu7BXweRL9Tyg6J79G+SP3BlAA/fs0KMWT4ZTBRMy8/8yfeWQZ0Qes0Mge6GwMbabHe7M7sfQ6AWEEjyIVKj5/Pt+3vKF67U86Wk7+P08DWjoRjhVK2WSTmOvDIl0w9/+EM3HRFT6gU77XEU0LlPZ/0DqoLC1E5Bg8xJXCrtdCBmMyq+THOGHXHEEZJJgYMRcKeffroLc8wxxxRM5I2rAmNMnRHxFS5q3n+hIapGsGdIGiaoE1P/guZfm8QxpynoOu6D+8Hw7bczh9/oBtz0vFfiDaaTIPFyJBE+eK799qbptc9/+9Dl3uPZ+OY3v+lIhdnSZuRjUGk1Uzgq2YwONOsaBCAleuuqzhCeE+9szNTw9vmztWQdQdujsYmo02ZiWNr2iEvz+zE35WM+PPherHxrlZx68Uny0OMPtgvON+/qq69236rrr7++XccBTqrvfe97Mm7cOOcESx9w4jsL7rzzzpxEtkp+E9vdTDl29MMSh1zrPjCZ1+3Ou2u2D9fufIZ0ijpfavrlwCKQBs49HIcYU0ziPPOGoxYyN85JVAhweP361792g18K7TzyadnaEKgUAnQgUI+jPhe0J598Uqjf0znLGmKuNxTufH0elQ2ee/8eEMY7rul0phM1lz3xxBMufcLQfso0yI86pK9H7r///rmSq9i5lFJu8gqpfe24wFL7yfN+lTruwyVPbHK8k877fJVzze8d7NSB1B3sdKG8QyWEQX3U9/0MHRC3GeDgO6HLmaeekBbqxnS2BY3vDOpcTCtPHebkk09OnabTzr+3nGPAXLDDzaumQfJlwG+2jkUSrJX3NnXzXbjxdR3M88tfXpsxBz/4wWX6O3xFfpRU3csYqBsevOKKK+S6798gF+18WUHvf5syE+evnSuLowvboeG/LxdccEG747ZjCHQXBMIDlPw4VD2J9VrXyEe21CCxD5SQpdOjRxo2eh+7CxZ2H+VDINQUkqZdIhLScXobHohJ23R9eBIigR2/SJvWid9XPuD7ShB8SdMdoAIaSkgPKyE9srkSe5XQG1Gl3roheqy3PqdpvviOZ6A8KUB2YinWUNH1SrrBuH5AYvCY36YezMx0LKtXr3YKzemKeYQpVqEcolghVkrapEufEYP0GFSDijtqfhCbfP0Rgg1LqQZ5rRCla4i1DDCjvQr5ijYW4jmeNJcpDdqvmY6n5xVfTyYr5ZoM/mbJZdwHSybjmrStv/Wtb7lBtLSpOOYN3CH7skCgA4sxW4yRgY2DpA2S/Yy4rH2vTaJK4I1pdSqqM1rKao3dntPnk+vQem1srcS3b5OJx4+Txm2NxNshMC1yt0GAd3b48OFulgnIh/iP99tvPzeoc9CgQe3uExJutv51yPoQbDHKYQie9Kfho2NQCIPg+X7xzfB+Z85THnsSL3Eg1qLKSzzKDB8mU7uKePgYvJE/nzb+BO9T4B4ZoDRixHBNd7bzERKX8hmfY/rADk/i5V7pE8an6JVk8R1R5vlvir92+hofoifyooybqT8xSH71nA0IpMw0i5HvKVOmOBIpeQATfiMEpyBNoz6cboQJWvq9Bc+x3dHrMYDbG+Rg+pN4DiBU8z0D4/POO899+xgozG8KxwLjmwdfJRdJlm9mvngdvQcvIMVzyyByDIEbsIaszPevEFEn6hxB/zV9c9QZuF9I1jxzPO8o+eOHNDMEuhMCRuTtTr+m3UvtIeD9bLpm001DzF0EjrubSp532znO54uf73zJ6acilmcDBwjELtSXUHCFPEMFisoLlQ8IEqeeeqqrCJfnihtTgfCK2hTTFVApGj16tKsMUnmrVXl+RjjRSPDG6CRG+VHxo/JKZRXMqbxBUko3Ogy9ERcVBDpgqYgRF6cEHf/ZjIo5FWoq2BCHqVSmN1b4fb0FOx/9sWzr6Gp1TCxXR0RyBHG2cO64vkeR7ULSuJM6Lxv9S7YxhjsS0v90qus6lFy3298YvP1WtvBZ0ktX4iUxSLwXnnti++u3v8rGvVzX2xiqYluM6OS3ZIR3NuMZ4fnIZYzmp0GA4nPwGSUOFXBIAYxypPHF88lzy5S8KHplMpxWOEuJM0dHc9JAQJGFUf2MqMxmPL90XvNOcF0aAyiKMWIznWhAg9erD/iOdO6V0aY0PCExcD+oVWeyUu6LdHhvbr75Zjf6lkYKjV0UtHgn00fqZrpupmPkGWNamPR3MlP4bMdKwQSsaICBhzfKI35D7JOf/GRKTSf4TeA3veuuu5zDgEYcCgNBEjKNQUgJlHuMysRRwXPDb5JJFT2YNtP4QEzhm8OIUeJ9/vOfl7Fjx7o8gTvlIYoZ5JvrfuQjH3EqhC5Ahn/FPFsZoqcOTZ8+3RFmcBIESfS8GwyOoEGM44VRuQy84LfFIcM90YjlN85mPPc0drkGaqI04k888UT3fGWL449X4rn0aRe6pjxvVIWZNp3isfWJhHJizrja0XT/Hx+U159BwjcxwIRvFGqUvMsQYijfmKrOj0IPpkfniCfxMjgFZwpxvBOJae4YjZ3tneJ5qtQ3MZjPsmzzrdGEINuyRT0y1z7XTIQrLHy+9NLPl5o+8cppwZHu1FUzGQ7OSy65JKWEyLcOBy6kXsomZh/46le/mimqK+9x8GLHHnusq/eW413nfYVMxyAOyk/q1Dy/KAJlUpIupWyn/PTTl33jG9/IOgDhJz/5ieu0RMWVAVxYpctjyro///nPTtHA/x44+Kh3ZppdgvKebxJTtfGupxtk2Msvv9wdDn6zfDg6Zfle8c1g6jec5tRJeGaChEcfvjPXDNLzTnm+D9TncJbzzeI7SB2ffKIunUklizoSRlzqSjhOvaoF32Xab4ccckjWW6J8xemM8Q1LJ/Ly/fKzOvAdz/UNy3qRMpygDkjHcKFrf8lCw5cSjmsUG8/nq5zr4ABPfvtszzR5pb5H/Yx3wStA02lCfZ26n3fukz/IfmP1O0m9P1PHD2Eoy3zdm84Unh9fJ0tXoSE8Rv2GZ406Em0E2vXkmfefZyzd0stc1EIo3/AVQFi49trMJM30dMq5z/fDDxyh7k9djQ4a8sogIvwn3keS6f3zCmfExbeBr4NBSMQDT8oziBnZrFbe22z576zjPNOexIsizMmqLIRK6DStK1K+gvcvfnG17Kv+GabW7gnG9xQC7g5NO0v/+v4F3XLDZjow6pBJEp+lvpk0I618bbC0KLZrCNQMAqiXhlWYKtRH22Ar82c7vkZ9Z+uUyDuIVpqZIZAdgVCjEoDUHx7uG5INQ2PS+rwOhFYCrhu0mz1acWdw761QF/0KSIRa/2rSevQQfaZHJAi94WHql1eiemSz5NJHn1sdnN2TLZ3Am44FZKNSFMrT08m0X2ra+B+7YraU9HvA50K9vDOtK67J75TNv0h/WHRNXPqsHCATW3eS6LNxWbMkKlEVvYkt1tdbCb2yoXIIxbSt3DqwWQbt20+a9tLZL7dUpn4Pf6crh7alXIsI0EdAOx6DdEt/Awvtw3HjxrkyjHIsW98eJF18mRiEWfwXnkCKUq8n+eLjxH/nSYz0DzGLFkYf5Z577um2/T98w/Sd0TbFL0B+0o2+L+/b8wq1hA2SeCFhBkWaGOzNgAiImuQ73ddCecbMTnAuvOGTwKeBcb/0x+UyVGi5JkRQ+urozwnyNiC5eh8T1/HfWfptfD/kOeec045bgg8S5VgwSyfsBvNC/vGH4kP3xObg+eB2Oa4HGfezn/1symfNABr6C/jdVq5c6fzq3B/9VPidb731VpcFfNiZFJGD+eM3zxevHPeATwT+iH9u8aUjngNpmv4z+iPzzajixXPIP3gElXf5vfAv89vSJ+zfgeC92rYhUMsIGJG3ln89y3vtI5AiCkKS0NsJ7CeYEglCIU4Vd97fMb7stPAFxa9E+j5PZVyjFgNRkA48nP7pI1PpLLz44ouFEVflnJaQimdwZI+/Ja4PGYPO7UzT+vhw1br+9re/ncoalRk63L2hwss04lRuUdelUy9TxYlK91/+8pcU3jQ8iEeFHwITv1e2BgfXYmoMT3KCnJk+IhkSJQZJOFPHozuZ4V9MnRXxDzKcyHRIHeH1W6sKAOoWGcy9VvqO5FpniNYu/FGfvEgee2q6I+VeoMRcyMDp6WVT4iU8ZUAwfL7rZUo/U5xyHqMhQIPzhhtuyEnkvfHGG91lIejSGKOBkW6kwbNH53qQyEtDBJJJkOBNXDpGMc5BIAg2MglLRT54Hd+wpFP6lltuaXcNl5D+g0jlFd/8MdbkDZIsjW1ICN7IGyQDDJIk5MkgYRmCAJ37POM0ItLjFntfXIdGLA3n4L1xHBIOI/MhQpRCcoGQgEFy7YiVggkNNY+jvzblEwsGMcGXRf6bQIPs/PPPl2eeecZHSU0vxjkasn4qNx/APwOULRCqwDFoPm0a4kwJze/nDVIDxDLSwFGRTs6mM56O5GwKrMU+W/66mdYMLvF4QeLwTlym2PHHIZulT2lOHlHM4JnEUZJuqIam3xf3DVmdZyuXVeq5zHXNbOfqh4elbquYtLwQldB6dRznsca2JhnSMEzO/tYXHFHIB+ebiCoKDi2++bzDvLPpxvOEsyxIwGAKv9/85jcuKFNC5RroUKlvYno+O7zPN8nVN6mH6teJv1z7ekH3FXPhCgifL73086Wm32Eg2ifAVIXeeA+ydR5Rn0mv01Bf8u8sBNpgWj5N3lsfxqsAdPRdx4nLQJj07wjXpJyjnptefpZStlN393nnu8Q9phtONl++UAZ7q2R5jDM500AxjqP4wCCg9EFClAOQDqkLZCLy4pj29woh2X+zuB/wZhCRdx77e2QNOZF79YOBguc6Y5tvqCfxUv5Tv6cz1psnC/Ks8M1Nb4f5cNStIGt74iQOY8o2DDzTn30fjzWDGWg38D5QP+P7G6wvUf76Z5Uyuassq4Kulo1Y+nmfz/TjXb3v81WuNZ0lvl0HmTS9HpF+Herr1EMogzDq7BB56Wjy75CPw3vnDfXeYKcQx3kuuB7tT2++7sa7SvuDOl3QaLfyPAbjcJ58UP7x7SZu0IJlLp0EfgAPYdI7o4LxKrXNwACvlEOHH3X4YMcM9+Y756jvgW2wneTzRbn0y1/+0g0m4Rhqvb4NQTsrF5G3Vt5bf69dtf7xj650l+a5eebZ59r9DgyiGb/1Vu78U1rOBeuRXZXfzriu/971qesrTZFehV2yr8hRJxwpDxz3gBx88MGbxOH7RDvGzBDojghAtBR9B6QQIm+z1kdauiMKdk8VQSAi0jA+LGElfjePCknLMzGJvqX12nKp86ZnWgmE8flKNJyvxF6cCcoTcsReVeiF0BuG5Aupd7AumqcIJPYMwhvpydq+IdBTEYhv0HdJuXmOLL9Mybq6RJfqgI4luq1LXLcr9j6ng04/2zZh6bN7H2maHHbvcXoQ2zcEejoCDFJmsC3CYfhYvdHPRJudBUNoYf/993diMd7HxnH8wxjHGFjryZAcg0iKnxN/LsagY09ixF8JCRTfCL6XdGMwBv36GP6STL4DfJzp7VVm6fFkWPwy6f4a2m1e2Zy+rHTfCbyEIImX65NnT+Rl0HYhxkBu+vkw7iPY3wcx1FvwOMRnMEHIAaGNoIEv+YLICx7ZjL6eTHhmCl+O69G/HBSegFANidiLM+En8kTlTHno6LGO3gO4IjAUfG4RkUAMAzIvBscn6E9PzzOiAPiOMfz2QRIvx+iTwReGj5LfD/GU9GeMcGaGQK0iYETeWv3lLN/dAwFPDlMfnbN8+/6uCw2fL73086Wm7+OVeU2nLgZhhk4nPsCQQH0nHApeqON4QlNHLg+J1ZN46VhmZBUVOjrJ6EyGqEfFlf1cUxJ0JA+ViEsl2BPjIJUFSbxcD5XS3/72t276Z/YZ3ZSp4kSYIGmaSi8dt77CDykvF5GXSu6ZZ57JJdw1gkReOoO9ehvHg2QCFyHHvzijitVxXYjhHHRT1KnjMpOlXit30mkKJrdYbdx3BwP/EvFCcsXP/+pIvJy6/OeJ0W+o7PrzquklWZV4v3aixkqQAoLhA5dJbaafT99PBazQBp3DEHnpNIfAhJpfuqFYSRgMctvpp5+eHiTnPqR6T+KlYx2iKc8YjVeelf9n7zwA5Kqq/39mZje7qSSEAKFmE3qHAFIjBAR+9A4iSpPyAywI+BMV0R8igggqf0VABCkKP6SL9CK99xoSSigJkED6brI7M//zubNncvftm5k3bUvyTvL2tdvemVvP/d7vBeDPgNO2ijXADIGSDwHJcKZ+AIBCZx9WQxjjyLsmMAIbiJdJcVbJMsAg7TCA45fVrkFwk/k3wAp1EQNIVhJSjwAygPWPAa0PYir3u4iH1aV77bVXHtBCmGwJA6uZAZthzXv22WfLWmgA06KVX7Z/r5VE1QkMf7D2MSBkYQbCYB+jBQLbcFAApCIA33DHClwbzNI22O/Ee+oc4gCEy8FvAlicwVxwsE6YtAEI/thKHKODbRnNgJPVoghADupI3hvYhHzDQNHf+qfavOUiK/MPIF7aS4B/1NcYg65Spl2ElcZsmzxmzBh3zx8MKtTjJgyG0REGD0Be6ITwwqRe+TIsrkjPtF5nu8bsMKWAiQDkHdQwWDZcf6MuIF7iwQgGiM1+W/QXBuQFwB00ZpH3Dchr20IVSnu92sRC8VX8XBsYY9jlSlenFL/XiMpyXyq84PsKw6/4+wt4pI4woU4gv9BOBcFj5sY/U+bMyMoiAAO3+G4MZInB04yw/vtyyzr5kXbCgJFW1gHUsjiO59TD1J1BMJvFG7VuB+hFOPSbqUP9NtDCsraLe9rmoNS6PqYNMBAvdRp1IQwPAFrNKA6zX6WLYoLpp12jPTNDIwvqqJPpVwB2BdwLMybtHH2HnhYfOEmfJdjvhome34hFHeRt3ASN+vRFDFRo6ee3tHxCW1tM6Gux+Ip2nbyC4RYwtImx+3IflofMXb3PgIsB4UY9W3qiuq/EHXGU68/SVauzv6AqyKZcKA5/fGnbKTIBQV8Qw7wxMNNXtjGl32e3cI1VFnA9wGD6Z4B3KW/022HZBWBuQnkkr5n9AAM/9TWASso//hinsGuD9UHNr50NxEsfiTTXc6LE4gye6Y9YHc5iRh/Ei1t+B+pOFklQppjECy7UQ6/0cXy2Gna6oE6kvPtbNwbj576/lNuwtPfks0bdPYP2+3gFogfrTn63lpYWYaz82ee5yaieTFtvxAWzkcmAVJM0JhvttugZ5sjEAJEJW0xwdocgmBebAGH/4Ac/KBpO/DLWQH/UgOX/nIWwxBfobjNZPWKJNVCOBhoAz26fkobVFMz7orLz6pFRwK3UGxSugOHsVAUhTu0E9lLXj1Aw73Jqrx+p1w7Uq/cO1KvPltF0LkN74AwT5Xxi7DbWQP/WgDYA2YVaTuYoSHeWnvXIwHT9hT7XnSkzM/W5su1mwbxFnBermUK0f5ZcTUG8Ct5t2iwpjaslXZ+tZuHHAcUaWMI0wNzi6aef7nZ/ZMzNfCr2CAPE8rmAfNlBCLsFc+jYl7FFwbSLAF70553cQ/0DQNJssIRpgn0gaKuBdZZ4sBGz+L6UhM0XMo9vEsbMzu5zpB/SJq6DEraTkz9PZ/NvQX/Be+wyBuRljtQH7GLbRdAhJEwm4EuCAsMtc6TYaVnMXUwKgaIL+ak2Pmy1/jyexWNzodxH1Zf5Lfdc7TdgFwnLt9hFsM9j47I8Xihtvn3Ztxv77slXRjbAXEcM5PW1E1/3dw3EQN7+/gvG6e/fGsAqp7YUrcwtAABAAElEQVQIh9+LcravLddfVPeVhG9+6nSmsQdIaxN5sOUxocwkHxMgTOYVYoqKmiS2SP75z3/unANAoCMLoxhCfITPBB+gN+KtNj4XcA/98SfkbHI9GLWx8PAcQGRQWnSyKWylmd8RtonFoF+7p+MO2JGJeQCaDBoMEG2dbtwCmCtLFLOVTUf0oVt6JdVIWEismOTeO05BVzz9+0J+KcSPPpVbQWlufDAv7wsx8QL25X1Y/BZW93O4e6qReguTvTZAhK3StpX244WpEmHCOAh2892FXbMi1UB0AHX98AEjAh4BlEM5NCCvDwoCvG4AAPITgDnSTB6lrAOKRZjgtol+wAAAp5igRgAVsDKQvAq4aaKuFi0EtgmysVJXMCmOoAcDoFTyXYQBEx11HcJCAn8QzTcAKgCEAPDGL0vOQ5E/bK1uUujb7H255yg6gd0cJiWMCAbk5fc64ogjikZHHoA9Lbh9Dc8Q6isGbrYiGKARCxRYtYygM9sS3D3w/lx88cX5BQc8BjCNocXAWICFfVAEdacxngN+xT1Sq7zlAivjD/meldGwGZvAQGcAFPK45Xne89zq7uBvBkiE77Fvt/DsXK98aeFXck4ooWR2gDYKESSVSMkee+0R6pLVzQB96HsUMuJYufYD8I1RrIguJnVrE4tFWuE719q4Bkr/8L+TmRdQL9Llfan7Uv5Lva80fBJaQ6GeYZEKC0EoQ7RLtIuAdDEeUnYK7eCAAdfyF6DOIJAXA6IthAkDkfMZ5ZZ1FkkB7EIArvlMqTCZw+aN0ZL2C0Ovv3DLeer8E6wnwto7DJuwTbAYj7Y5jB3A2nj05hts/bhqVR+jT0C6CEZCthrjtzKh7raFI4Cd33///XxfwNyUewbkaHUn/V6+04TFUOiY99TJtW5/LZ5iZxb+IBhkC+mfdAHsQzCC+iBMnvkL8rhHGDthVMWIbm1L7k34X9p8W6BD3jKDLP0C26kD0Ln/e4WHVL+n5TLpWkrK9Vdv95auWp2nT5+eD8ofR+YfhlxgvGdcz9j0gw8+cC5YqMDBolMD8lJ/FKqDLFhsAbgzoW9IOJQrxvF+Xwegn40TzjnnHNevM3+4J69TP1IemCAIAi9xS51LHzDqt1r4tTwbqwz1WHD3BYvHH6+zWMvvs+KGsbgP4jV/6M7KO5OJ1OOFpD+U20Jp76nnDz30cLeoaIuYWL1RF7jYuK6boyX0AWXHhP53Uo9IotnQnLITVJgQdgzkDdNM/Kzfa4BiErGosK26RBsC93u1xB9QWw0AGG9SNs3G0QrmXVOPVzLS8aYCBBk21hvQa5+iAMSsdivT0wH2qmBjGKwnwL3K0pvUc0JBvUll6k0OB9Sr18P0eqieB+sS4nhm2zQZn/uxBliMkZmvZW9OJ3B3tl5zKEg3C4BXwbuAeLN6dmy7OVNcz38xAN5VdHHfegkZsFEyx+4Ng3wssQZiDUTSAIQzHNi5sAFhj8A+wlwm5EAIY0bG5oz5fXZa3IFTCBMDBHPGnmbzm9wz/4eNjrgWLSqvcYdAJyg+6BKCsDBhkXQhCfPDPIyJ2cbsvtAZ+xLgZmzZ2HOYd8WWA7AVuy4CgYPpwj3QP7xnYTkYCMbopjt7X+zMgm4W1Zcj1cRneJhgfD67bfBdPe6r+YawPGRpJC9gO/bzub3zz5B7mbCDaCmJYo8uFUb8PtZAX9JAYQttX0plnJZYA0ugBlyTb+2+niPdmx6iuu+p8C1ddTiz7USw08I2m7YayJi0qonatiJgYoztZQ3Ea2HCTmYMZYXiY0KGzkehw0BkFmZPnZl8NwljTuUdnVwmVpEw0FIhMIo/0Rmlk21gMuKxyUKujXmOiXrALWWJN8FT0l+bsmoVGa/kymBnodFOeeh9oUjU/b9v+F/ZfusNurgAzMtRkIn3B51smBXEF5q+MgcTXRIb8YYB0EknneRcw4DFQMkXtm4BgINQVoPlyXcbdu2DA4JsU8RNGWSgBVjShEEbQhleZhmla/Bkhx12cAMC/PhbY9s2vEzOAx7wB3aARAEKEx5iK/q8YN0lIKvgZHqLArysvrDtcXBcyXehWysrAFV9EC9hAhgzoDPuGKxHFfsmBr3ooFZSjk7KjRPdAsgIgngJx/IFCy4MxGvhA6Ky39LPN/aeM+AMYw235/Y7cg8APAiI8NlsDWCB21rkLcIpV8gjPogX/7DzGgjKygnPGSRjyEFgsA7mY+r9a6+91r0P/qlnvgzGVc59tk37Uu2ddXgJj2ldAbJaS2HDkoGyGfxj7ApKmOGpT7SJwYRWfU9bqDp1bVTI2b31nmt8XdwH3wfDKfd9peFXrYfuAQCAPeWUU/IvMBwCgKfMUQ4n6gIQ2pEwAxJAeMR2fMgHohc+4CUMMI7bcsr6vHnzXDz4++lPf9oFxMsz6kaf/ZQ0hUk5dTtAL5MgywP9Zat7/DrW3HOuZX1scREuDMFWH3KPAFA1Jm3Kul9P5lyU/9fvewfjw2gPwI4+CaDCnhbqb6vT/AV5wXTAbmEyZcoUu8yfCxljbcwGC2op4Xe28Qe7GJiwm4Kl0R872PuePJuRPurZ0hbVfU+5s3TV6gwrv0mxfGRu7Gxtqy0ssOflnGHl9EG8+GVxDGMOBJZOP//Z2J2+IIuzfKH/awsLqKttpwrfDdfU970J4iUNsPYgYZNR7oX+8dMYVpcV6u+X03/pD+XW9NGbZyYPT1Wm2K23+oo0Nw2QlUavKFtusbm237ndRXozbT0dt+28RLz0vzPZaIjDuV/OFd8pi1eDQtgwescSa2CJ0wDFJFpRyQF+dTgWS6yBSjUAKLZ5s5QM3rdBBh6UkgG7KEhWgb0ysNIQq/AHQHGeghc/1Dbj5ay0P5yVRbdmpO36jLTqMf+GtMy/MS3zbtLjtg6Zf39aWp9Oy8I3lFX4w4ykATsWsftXkbLYa6yBqjVA3iSPtk/NyMLXNU9r3p1/Xy4vz7tJ87Pm7QX/p3n6hlyeX3Sb5mstA+lXNF9rmRB2e+8NEO8gBfCupeDdXRMy6GCtK/ZpkOZNdHFWDOKtOk/EASzZGsD+xvwLhFZBwRbEwnrmLVmYaMRDuLP5eXZ/9AXQadgRdMM98bIbFyRFgIN9EC+7G4fNa/jhcB22wNfSBEGX2bOC/ord1xKE6u+WZ7YcO5MGSCt8AQzNLkbMiQJINhAv3wnLbXC3Mt8v12HzkUE3/n218ZVKjx9Xva6r/YawPGRpjYoZ8MtPWP63ZxZuFJyKuY3PsQb6gwbidYv94VeK07hEasCNu3TlFZAHRmFZAHil7tFEzmM096XCC76vKHw81UdgrIK5LExglIFlkUk3toPwtxQIc1/smTFTAZIrxEzF1ukGyJg6darbfjMYZhhYw9ywcqk3xBiPAGr4q9uCaWFij45ZGBDQmHODfsq9hwGOdKAnmLZgPAXcCLsqwranxTp3YfHBwCiLF+2FOck/y+jWQ+nPtQCBLUjlH+cvXGnUMlHsnHfsXfju71Qwb5B515h5PS8CC++PYOKtMr5EiH8/nnpdw+7M1q38lgCAfKAFA0QrC8GJ9ijpaWlpyecTQFIMPMkrBkQHcBtkk4JNy8onE/wAE1ndaoASO/vxW7kHVMAqzmDep7zA0scEoW3J4vvnOjggtPeWVh+oUMl3+eBQ2MKDaSQ+f9DKRGZUQAXsxIjPzOgeVPmnHJ2UGxVsiYUk7DdmIDd//nyXHy1Pch8mYZPDGDZMwlgTfaA5W/GY1CJvWVjlnA0g4/vBqAJQirxowCje+3mL9i1M0InV2f5732+t86UfT1nXWq9TvyfmhFTuIQG1dsyXYaOUSqKA+L99mAGgWHtaIMhuj+vRJnaLpNoHtDG5bmq0s8bnWlFtGMvyF9V9peFXq4cQ/5St8847zxlc6Y/CwO6DRjEMcuAGcL+/KAqmeFjUEZhIfVZeYyIFXO/78ZNQTln3jZiF6lDaLGPKpL2DaTco5dTthEedCTMt4FkfkGzMmNQtwQUEFmct62OYLUwwkIeJr5eXXnopDy4NcxvlmTH84pZdCf73f//XsfLaGKM3jbH+oqLgohf/2/y+f1gdGOyH+X7LuabvT58NgDdsyTCDsp2gSaH2yd7X+2zfHvVs6YnqvqfcWbrqcbbJjyhh+wDbKO7D3PhbJvrvre/NMyZlKG/0m21xAuU/rB/tg19ffvnlUJZs+jq9LSwAQKweCUuPX27DfpelpdyG6aYnn1100YVyhu4IEpRRo5aXnb+2s/zDW7gQdLOk3cOe7cui9EJpz0RDWA1pHqpbSi/27QOCFz+VvN3Bf1bONYv6WehZajeNQmGyeIt6KchcX8h9/DzWQBQNkPf9/F/Uj87s1YqV9J6XsnL1fzIyRdlRKxWmNg7cKimHT0jKCsMrDSX21xsaSCnj7cDNFci7ZlLa31cA4TtZ6ZisbKDv6QgfLFFUcHmtE0921CmVbKumY5olQ9Omj+cxlaZA5BGjlSVUzXeJYQo80vuEcjIAMkzC7tt5dveD9F2jesTGEUusgVprALtWu+ZR2HXdoffzuNZnes7O1bOy7mZ1A6+MHtnOa8eyW3odbK1TWzy8pBYTrcOTLQlpWCMhjVovDBgDI3ZceIorLn4ba2CxBrD3mk0WuxeMroUEmyvjEkCLsPNiK/LJirCV2W5jhcJgXt2AspCV2RzYqquu6nYGA2dhAFxsvxColSvYFEhje7tWdr0s7IzIPDF2Huzx2KFtDhebc3DeDgIBs5OwQBpbEOzBNqfIIm/7var9NOYJezK+atMb5r8W3+DPRQbjMFC4PwcXdMM9eRb8DwLovRSg2l+o7jzFf2IN9HMNxEDefv4Dxsnv5xrAwuWk81zqHrfOaUT3pcILvq84fPcRNf/jT4QHA/e3sGbit1IgL51iY2gsZnz342OSL7hFBEDYK664IpjM/H0h5qq8gzpdmF4AscGUWgg8ADgZ8b+z1kkCLAkzMSyuADlIk7EkEZcP9IgaN4a4xJCIrnVVf8e7auybkZWGFazsLfZrT3JnOAVzxuvg/WIfuavge5h5YeB99MnXgk7dPSDeM045VK/Dww+GFwwk+D54H3Rfj3smkQHvXnfddfLHP/6xC5D3kksucVHCmm35r5w0AI668sorHash/gDAcFDGGLAC6oUZ1WfQJV8BFmKCD+DQ/vvv76KE4RkQDe99VjzK/eOPP+7ckAdLdfCtjnAevD9B9lN7xWAxKJV8F4Aik0LABXvPmbopCpCXicrnnnvOeQ2y/PrhVXJdjk7KDb9UPQqQGUPFAw884FYvG3g3SjywuRWTMOAmv2lQapW3guFGuccwEyZh+dEH46699tph3tzKagOz+w7qlS/9OMq97vhMt4/S+j2xQK3NEeTLRV/KzNYZ6nKtUNfTpukMUacUYrCz95We69EmVpqWgv40i7vWkC0u6YCGnLu8dwF1ddflfSn/pd5XGH7B76vBC9rDE0880R0YoTAawqr717/+1fVzYEUEQEb9bH0wjFAwemMkvOGGG/JAXhau/Pvf/3apOuKIIwqmrpyyTrwmhco67zfddFO3oMtAb+bHzuXW7bAO0x7T36PNMYM1bTVCH4IyECa1qo8J24C8LA4sZOwD0Eo9SZuB+2p30QAUfeqppzqGZoyWxhQKA771SXzG2zAd1OuZX5/5rKrB+HzAb6HfPuinknsYrJnQQMgr9GGMIZV3PjCxkvCr9UM7T7tOa+9qQbsPnjvfW3x5f+Yuov9u/kL8E0c3dyXCx08txQeTwhYD03QUYVtHxO+TR/Hnu2lpafFv89c2+ZJ/oBc+UydsvEFGXt8t1zbpE3weha0m6KfW99geqE/8bQWDcfiTJIX0FPRTyX1fL7eVfFOt/DyredxAvLQrP9T6beLEnZyNxfLoc88+WxP291qluZ7h+Ow5xLNAF9K1pduiRQkjY+f6y7PPPrugHxZL0cZXItgyWMyOLaCSeon2gYXzHCxO6gug/0r0EPvpexoA8OWYFyMkLaHEBhzVCiDes5TttFrRYiE3PpmRd6aL/OnYpCTpRMXSrzSQWiYhqY1T0rS2Mod+oscUtbW8p8QYHyj48GP9gbtuytZr38ak9nA66AqGFAVFAux1gk2BcqFzBe7oBPZy7eYQAPcqoNeBfGEaBdyrw1LOyYF6PUBDiWZW6owwPi01GlAwO+uRsgsUnAuwXAHmGa61v5LVcuGec90J2s3Sl5mbA/ByLaxlIs/2ZdHykVxZ64DVFRzfogDecUlpXEnLRVNcmfflny1OW9/UAHYSA4Yy5ggjifFTztgCAZCLzce3hwHK9e99f+wI+qXu+tXU3CwQPwDANHIhbDfsQEl4vhgw0n8W5Rr7KWM80hOGNYDMDCZgwLWM0SBjq5egJ2zZEOp8+OGHgo0T9mEkSEYBeYCBeLfddtsuu7Za+irVifn3zz0dnx93ra5r8Q2+bdlPFwzRLMBHStnbsGMbSzX+wkhP2I3wpc6dR9fSeU7f9u3HG1/HGuiPGoiBvP3xV4vTvORogL4ZfaioZ/vyqO7LdVdJ+OanDmd/1VkweP8dwIdKhY6tgb0KdYYJ2wdCsR1uUJiYKbUqLuinJ+7XWGONfDQAt8ImOejo0jFDCjEg5wOp8oLtlgHyIvfee68DX3LNCrj11luPy7IkpSuBk8tqIUppZi9lc1YnHW9kZeHaaUmNaMgZ57zYrLjkHqmhRS8WD3Fy957zwGVX92HMvHjIgXgP0auu7gvdByLxbkv595zW6fK4445zQF4Aoax6ZOBEHoOBEOF9pQIDG5P9V111lVxzzTWujDI5zTVHi07is133mDFjXBSsBgUAAviDLbINpAogiQP24J/97GduW3E8+OXeBVDiTyGGnuAguEQwUu53+RPypcLmva1kLOXWGGNxFwUgXCo8/325OvH9VnMNOOzwww8PDYLBW7m6DA0owsNa5a0IUXVzUo7uWd1tUgzIHtYulqvLqPnS0lPuObtQ6/VXdBvFt3Ngq1L+2zPt8mnrNGmfNEe2k226OcdwRvuE0DaVo9dugZV4UOs2sUR05b/WhjDLRJhrEMPPXd67GLq66/I+JJyy3lcafvlfXpEP+qYTJ050B+BEwE60QfSxaCf9+hZme9os3mNgBUACANjEZ4m1Z3YuJ0+aYQy/BiKycPyzlXW/bvDflxMn/jDWGoCV9prFWmw1bu1ztWBZP23Frg30VgocjIGd8YCvr2LhFnuHrs4991wH6KEfQ/8EAdR90UUXuWOPPfZwfahi9W+xOCp9R3/J2kT7LcLCst0yeBdcvBjmvtJngNJhYGYhFuWB8mNtjL/bQ6XhV+vPJlKoupD8fecES/4+9zr/N//c3HW+6fa8h97nE1ajCx+4S9+f362UsK0kZQCpJzjcT0cxsLrvzq4L9fftfW+eWSBAmWVxBpNjYQshYPAxYaxUL+nr5bZe3x0l3HvuvSfv7LbbbpdtdJLQFyaYrBz4z5fU6yBT08z2mTKnfXakz3WsdbOycs7/niNn/7IwkDcYR6TA1RHlhQn18ePHu4XCUf0F3QHivfbaa+Xuu+92jP6lJiKD/uP7WANBDWTVrpmZpX0OQF8RJAHoSgGI1QpMvLWUl97LyJ3PJ2SvzatPWy3TFYcVXQOJ5oQMGKvHmKSkZyugd6oCet9XcoypClxUnEwG5mYFMvY5oeOua0ayHA4jYj15UqrXMPIqgJeykxisAEV31mueAep14F69BtSruwByOICvu9Y5CM4KaoyZfdHnEiKaLWDSxbaYcXlH51wA6bbpmXuuO4G7BthlsZG7VuZdd83iI0DuvU9SWf6PQp5fUfP2KgreXU0ZeLXMN66qYF4F9UsMai9fn7GPWAOdGvAJjiAUYo6hkA0QEKnNn9juWQBVGVtgp50yZYoD0Jrt1pQMLgLmV4RdigDy+jsDQ6oQtOcyd2UAYwsn6rlF7Qw2noW8Ibij2iOPPJLfCaneOAPSzPfaPCdjMhPGeL4wDjfxcSX2DDtkIXu4uSnn3NPxlZO2qG5r8Q3Y5N59910ZO3Zsl2j5zQxYDdt0MfHJTBjDQ/IVFEhRjMgEIG8ssQaWJA3EXbEl6deMv6X/acDsWXp2l969+xjv3i6Dz+0+kn8LJORsjyy84Llo+M5x7f+wmqyQTJo0Kf8qCgNl3nHgAnY0OtHIW2+9FXi7+Na2s+RJMebexT76xpXfETKm1GDKrr766vyjQtsc5x1UeQFDqq2I+t3vficGFDjmmGMqCpmV8qkVtPxE3LIt+7kugH4iK4smYx3vGmUuj3eWBAVhhN539bL4LsQ9zLzbb71B3o0D8f7g0Nx9iPtq4+viPx9r/S4AJFnZMTZqGAgRWOd22GEHd13pH9jqyCOAcBgYsprTmHIARQWB8wyEjz76aAeKApzOVud+xx5WX9uynHLPhDhy8sknC6v5ih21HMiV812mX9LJNxVLI+/87dnxU0gAjiEMvn2jQiH3ff054BAD8ZJHYLikzgaUhV5YjRu2iKEe39Wbeauc7/HbMSagCwmM90GpV74MxhPpXuvxhe9kZNFTajzPLeQt6W3mohnywbz35MKLf5s3LvmeMArY4pZ9993Xf1Xz61q3iTVPoGurtHVx//lD21jkXhNQ9H0p/6XeVxp+zRVTOkAYxC+++OK8Q2OitAc77rhjvj/EdmuItVHf/va3Cxp3zX/Us8/8CpC2kFifGyNoLYStsQAyI8bCa4BW2p4gO0It4gwLw+p+n0k86A4jthmifX2Zu3Q6fKUY7BPFBLAuvymG9fvuu8+x9NrCQHYDMCbaYmHU450ByjF0+uMbi4v00o4i1FH1BiUBakf4DX7yk5+4a/T0ta99zV335h+b8Ih6trRGdd9T7ixdtTr75fepp56KFCyLGUx23nlnu6zr2V8kSj1Uqh/Nrg59VWwiir6ttRV+WpkEsXEYzym79ZS+XG7r+d2lwp45Y/FCuVVCdsq4o7O9LxXOkvK+sRG01GL5PP2pzF40K78oYvGbkCsFwtx1xT1y6bmXh7xc/CgYx+I3xa/YcQf7gW8vKO4j/C0LE6y98m2U4a7jp7EGSmsgo4DJzOeKKosIBkso22hSwYjVyhRAmRXK8bs0CkdQ3v8s+CS+75ca0Nnj1IiENCtL7+DdG2TIwQ0y8OCkNO2XlIYJmv/W0PzHpmTVZ8OeUQ9lS9eUZD/RaYF3lGlY2ag7Hleg8n06V3BbRhbekJHWf+SOBdenZcH/pWU+x416/LND5t2Ulnk3d8jcW/X6rg6Z/6C6eTwtrc+npe3VtCyclJH5kxYqm7ECn3VHwIwyBgMGLUk+0jNfv3TEosN3dI7u+Q34LRbp4oJ5r7W536j1udxvxm8379+533Ku/qbz/pn7jd1vrb/3AmUpJw+0Xp+RNj3IG+QR8kqHzi2RdzLv5PISeSpqvd3rPwJldZhidNdU0O5XE64sU6Yp25Txpg0TkhmiHNcxcqTsnwrbUiyxBkwDzI+usooi5FWYW/ztb3/r8AfMYyEs9Mb+xvw4O56a+HOq++yzjz125EW4N2HOFEIjE9sdBKAqIGAEO5ux1GIzYFfe3//+945R1/xhJ4kqsNkOGKAT8iqQoDBvZAvWARsbwRPxF9sVLmp8pdwxD2jAXCNmGDNmTBdSNsLwF6MDqrZF3Hw7oGbffo+e7JtKxV/ofU/HRzpsF0CuAbXCihxFCvmr1Tf87W9/E8bJpAf7Ojvh2W6EEI74+JWw9LKDtLHw8l2Axe334bfC3m4gXtwZ9iQsrPhZrIH+qIGG/pjoOM2xBpYYDcDCo6AFFgRnGUR5984A4t279/bh2NcC7iP598IL9V9J+OanDmdYZ2jgGxq6V1XWOBNttZ1CtrWgw2art8I+xV+lVg1wOCzsej4DkAAr2i233OKYTFn99KMf/chtL0xH55///Kf84he/cElgwm/ixIn1TI6LF4DKr371KwfOtMgOOOAAuyz73LhaUhatogaUmREMz+okPSkrrfdn3Op6mAVMXLHSMlLsbG79czH3MPOee9ENDtC77VbruzJezH0iEL8fj12X8m/ueuJ80kknOcY9WJbJV//v//0/Fy3gWAMnVJsOyj9svxzf/e533XYwsPICtAtb0Ud8bBuz1157uQM2XgPmGCsgbgAhwMITFYCAn1pKlO/aeOON81EyMLZJwvzDCi8MQL/TTjtVGELf8sYA3OT888/vtm0PBpJizIPmt1bn3s5bUb7DB+OSt8JWSWMcMkCrH2a98qUfR6RrrQwxxrfdp2y8OgHjKu8IHjOjO+TNR1+Xj+d/7FhCWcxi4Draen/bpxNPPDFCiJU7SaVSUus2sfLUhPikTXL9TfqpOR0XvdcgXCuK0yjunTvnKZr7SsMP+bRqHtHvxEBIXvGNpsEw+X0LCW3AEUccIb/5zW8caPLQQw8VwJ3IYYcdVshb2c/98gpzZlhZp6+NIQ0x4GvZEYV4+Na3vuX6mQB4MZIaOPTII4+sWR8hJNoujwz8BrsCR5hBj76ACX0NE2MwLrS9WjFgtIXBmXBgqeCgT4KOqVvpm/vGYt9PPa/PPPPMPEswYGPyne3gwa4j+++/f363EtzWW+ivmdhCIxZm2eSAveuNsxmHo54tjVHd95Q7S1etzpQjY1JmLAlon3xTSNh60d+pgx0qCkkh4Hwh98Wek04mdmA9p46r9+KcYmmp9h31JuNn6jH6DSwW2X333V2wTBj/9Kc/zS+Q5boUC3m16enL5bbab6vG/9rrLGZ/+c1vzpfzzjvfgUWpW//+9+vktFNPzQe/QLcjXdIF0D674ph0KMLl87bPZH7HfBnSOMQeFzwPmT9M1hqyrkyfrWirAhJkpSrgrNtj+iW2SLjbyzIfWDjGolWm96LOGccyPmHSnbqUiUwWZLJgKpYlUwPtn6htc1rEb4NFUUkNHDNoRC+1djZet10/bpfFNvtL712MQNahXlXCGAWABbbFMCb6qgIv4hlAAG0pgr2wWtsm9pZjjz3WhXfzzTfXrO5xAfbwH/JawwoK/lshJVk1V6a/VEDjxwpYnUa+VVCj5t3Mp2oR+EITFh0X1MNfUSI6SD8UY5VdkMvB/t+cz86czXQCWKZOpt5Esz7X+zfeeF2mfT5NVl1nVVl3/DrK3qtuOhl8ISPBDXpMDNAl0BQdrjncde6ZK9N6n2jQe0wK7lrvU2qc0Xv3bPF0hjroLtjPIcXYc889pSfGdN1TUOYT1TuM5ACes2nNQ+CP9ODMvbvW6sU9b8+9h0UX8Kxj0+WZ5jn3jDP5b5E+U1wlzLovPvKiLPhigbSsPlZGLz9agb4JPTRccJe45XcvKJ2/ecH3/eAF+W5ZbTNg3h2t2Wi0luPRCsZfWZl3h3fmRf0Mxk277LKLsB06O+bUah6iH2ioqiQyx8p4GHAaRDLMkcUSa4D+AztNAeClTwMZAHOaCDZhngWFuXkf2IgNlzkcxgPM01xwwQVufEme88kFttxyy7y9l3ixOxqhBORegDXpU+EPgfXXdjBiXhcQcJTyTrqZx7dF0Nh5WWgMcNf/HnZla2qiAay/QEhx//335yPyF57bQ4iMjN2YMdt5553nbLXzvTG5vUdHv/zlL92Cz0pxJz0dH985ZswY+1xHdIV9Fbt8qW8o5A8guumkGp2RLwDzhgm2xCj9bNwBdue3YX6fvjpzef74mzxoRE9hccXPYg30Vw2U6PL318+K0x1roJ9oQDtVTjpP2moVv7fPiuq+VHjB95WGb/5qfGaiik5TUFjdxZa1CB3ZajuFNsnPRH4Yay1GczN6MMHvr1IKpq0v3jPpZyAlQLsYQAEj02G3zg3vGVT0hNCR94WVhQAvK5WGldT4ME4LRdSxgRppOl7IyoK707LofQYvuaYwX6xcQtSI0Zmg3HnxfTCdwffBe5h4t9tqg4LhBd0H78uNL+i+nvf+b+mvEAWQVKmwLSWDjKOOOkqC4BkGi/6WLQAgOE455RTnJ6z8wrwbBtyxcg/A09gC/TQzyMDwCVALcGi1Uu53ER8MP1Z2mUjwtzSx9LAKkTRyFGNWNfdMBtrq2O23394e9+szk5ompi+752zgMf9ZPa97Om9V8i1jxozJe/vBD37gjEH5B3pBuQKoHyb1yJdh8RR9plX3onczsuCutHS8rEZ1DO9RRCdZVpu4igxaO9dgAGDDKIFRo6WlxW2vC9sdcumll9adiZJ4/HqU+2rbRMKomWiD5Fo/1zC5m+L3GnFZ7nsq/JopJBeQAR0Bgb/99tsFQ7/yyivz78IMZ9ZWYlyz9gswSC2ZFNmCyurFH/7wh93KOgk866yz8sBNH8iaT3yFFywWsbgxuNmCCvvuCoMty5sPZP7Od76TX7VvgWDI/v73v2+3XQzmpgsWv/isF+aYOiJMLrroItcnOe2007oYsnGLPnq77WVS4Hvf+55LOoBiWEup/8ijMC6QHxEYN3tiwY/P3uwi1j9MdvQFMYNy1LOlOar7nnJn6arlmfrEhDJtrN72zM4Ac+nTG+s14FN/Ygp3/qIHf/GshVHN2RioqYPCdt9hkpp2mH60sYJUE1+9/MIc6gP/ASXD7sOYnnoFVh0E2wgLKustfbnc1vvbi4W/zz775tu9y3VCdNkRw2XDDdaXlUav6EC8/Fa2mI7dY7bYfHxo+1Isjv70zvoAfpo/mj9VPm2b7j8qeD12yDhZb8SGub5lAVcnnHBCgTf9+zFA47PPPlvox9FvoF1kbEa7jO2MNtvfWat/f22cetMAALWODzKSUTBvFEmM1DZ0ZG6MFsV9Pdz4TLwAegH21koAvQLWh2kN1rmeEpjr2EmAwxZdVRM34wgLb0liawR42jBKmXo3ScmQ3Rpk6EHK5nlISgYelJKmfRUguIMCBtdT68Byqj0Fqi5xwnQCTLtqjswqo3XmfcWCThJZp2F92XH0zrLG7LWl/UFl+b1Lj1uVzfXGHKtrG2y/yu4K669jfIX1VZleYf117L8w/3Ye826EJVaPmzjS4lhjYQPmuEWf3abHv/RQVtn5d+txX44heP7DaVn+7VVlnekbyfBJy0vrM8oqqyy07lDm4NYXlD34RT1e0uPlzuMVPSujsGMVfk2ZhV/X443Og2uedb5vwy3+8M+hYREmrMT5eIjzSf0mZSte8IgemiYYcF0aNa2k2aVdv4Fv8b/NsR7z3Z2H6YOzMSQv1p3Ggz6NNVf1iq5hzkX3/AZpHd5u1LCpbLX8trJC62jJfKC/mQLOHYsuv2EO16YXS5AADh+lbcT6CWnUskiZHHiwsmrrMfRAZdbetUFZtrWcLqdlVMuyyb/+9S83bsMu6u/2Ye/jc7gG3njjjfxYksX6scQaMA0MGzZMzjjjDAFo64sPeuU5cxPYJMJscNhbbBEvbpnbMxAvc6X4CS6WZtG+v0MR7gFBAnZkhzhsgrZLJ2kxNluzURGPf829CYsKjz/++DxGgnDte7BbHHLIIV1sPsRZT7F5OOJAH/53W7ykAbsUeAgTA/GC9QAjwaIrIxRAXzY/ZOm3s/m3c5ieahGfbyezuIqdSfuECRO6OLHfpcvDwE0hf9V8g0XxX//1X6FAYuJkZ9m11lrLnLqz6djO9hJbNfOXNsdPnvNBvKuttpobK1e6yNfiic+xBvqiBrxuWl9MXpymWANLuAawzanNLUcBGuFs6ijXX1T3lYRvfup0BoRKp4XJKjqX//nPfxyDrHWkWOUYJkxMG+tk2HueMbkPWw0rev70pz+5lW10Yumo7bbbbq5jxxbbgIlLxVcojr7wnJV7TIrC/Ib+EJtM5ZoVeqyypcPji21TaGf/XbFrOsxIIX9MONpKQtzRia5GkoPUKLFOQtpf0a1/JpPZI4iuru94WsG87WlJtK+jN3PzxTDnWwGiekHxzEnu3u66n8PdB/2Xe989HntSKj5zV98zk3NMFsPEaxPvMG5VwxADiIn8iACQomwyQKQDzxaY55xzjntHHjJmQQC/MNvhj/qCgS8DMzr0xl6FJ56bsI0mk/oA+BmsMTEAaxAgReKlbjF2xCDQzsIo51zJdzEQBGCPTjH8s+KVQQss4q2trc5IBJCC+qmlpSUSs8fzzz+fT7aBG/IP+umFD4oCkIbRAB0xAQNTnC3E6KnP6+m8Vcl3UU4AmwGCh3GBNHMNgJD8zwpxdBcm9ciXYfEUegazxqLJaqi/V0G8upUdbClRJbmaTjSt1yD/eugOZ0ijfkF85mHqNSYN/UUDuCnXgIIfxDfoBA0RvK91m0iYtRTX2rgGUf/wv5NpVzirdHlf6r6U/1LvKw2fhNZQjFGBIGmLWOwB0w3tR3t7uytDGO+tDFH/h20lj1/rD/385z93KTzmmGPyW6C5BzX4Q1mHwZF8bmWd+p/FH7frNt820YAxN8xoXGkS6AfSftGOUTcjsDzQXvWUAHQ5Upksr7rqKtdPAAQDQJVyR3sI84QBV1kw4xv9fLAhfdULL7zQ9ZU//PBDV3/+/e9/D/0MjMDWj2FMQbvNjhiAhnluwJveBKvym7PQyQBQtAO+kGeCizn8OrBQH98Pw679OrCQP3Rh5QUAte2kYGH01tnAG1HPls6o7nvKnaWrlmfYWihLxiiz6667OuZNxuwY1+mn0nelfvQBsj4Y1dIDUM2EMQUgNepGM9Lbu0rO9JPJW/SVmUSAeYV+I+UUlrJTlSHVxsWU8b4sjGMYy8PaTpll0TOHCQsh//CHP7h+sD3zz+UufqY/hN78su+H11fLrZ/Gnr5mK8fbbrtdy8bh7jcifstfEyZ8VS67/HL5RH+7iRN3dElj/Ew7geT13QD1Xk4UWmGXNe8b5AOu4wVtsLF3WzTvz3tXpi34WMYNXcMeFTyPaBopG43YVFb/eKy83z6loLsl7QXlm8l4s6/Y91ke4Z4+HX07dqahzWb8G0v/10Bat4DvmKIDojnRviWpzKgpBWH1lgDi9YG7l93bIc9PqQ8iLgoQobf0EMerGtBsmByakAEcWr0DSk/PUgDlZ96h+Ts7QwlUtevSps8H5rwtXepTtlkYZwEA56z6uc9ffK3lv6AE3lH0Oeg2gJHi3HkNc++Bgw+T1g1apXlhsyy4Xuc+cGN+IPbBnuPu+WPX5ib33sZxbsxC9O7Qd7rw310TgF13ea+PM/qA6sB9s7rTa8esy70998/6uHIh8qVcmvXnVLL+pLYJSQXPc04p0D4Fg/by+myZroDdQtrCbmX9DeZLY4mmAXZMwI7B+Lfaec5oMRZ2xWIwdm9BsIPVcsevwrHGb4ppANsDpB2UL+YfYcJlxxb672ATsHuUIrnadttt3Rw+4wTGkymdf2f+FdbUsPE68/OM17EXYMOkHwWpC3FZ3Y49kIXN2CrN9rLDDjsIRykBR8B8G9/B/BvzHczFcgTnPrDDcBQS7BQ231vITbHngKWj+EdfYD+whfPd/C4tLS1uwZiFD5s2OmNBOrZ8xHZVMDfBM79BWPyVxgexVDGhvgmLDz/Y5bC7s4iM7wMgHkUK+av0G1iQakJ6mCuZPHmyA6Ezd2J1lLmxczFdkxZ0M3PmTJenmffntycvFgrPwo3PsQb6swZyaKv+/AVx2mMN9FMN6BBWwRCdiddzlPvhgwfJjDlzZblhQyO5Lyf8mRou4TuJmJ58+J2fUesTq80A0tHw+42/xQPQjtVlhYQOSDEBxEfngNX9rDgFwERn+H/+53/cEfTL9smlwgz66Sv3gKDZdoCOKoxJdODZRheAJBNOYeJPuoa9p1NPRz8otrVG8Ll/D6CBiQkGCWzZU60MUNaH9o2yshDmiqigLsC8z2YlkVFw5zvPSnZEoySGqLURI5QatRKdZ/++qaGrcWjggKzMXZiUobqFVph7wvH9l3M/T7d5IvzWRdQOOcnHXyB9Lnxz3ENnJpCYdDfhvhph4p6B5nXXXefY+4IrVgkboxIAXRMm6dlChclmgMUcZngyN9QVPkAJsA71C9utMEBjIj9MMMDUgj2wku8iPQCQKK/UgSxOCFugwLfCOmsg+rDvsGfGxstgdMyYMfa4X59ZlQl4AXAURrMgYA6wEgYLm0iv98f2dN6q9HsAab388svOsAcwxBZ6WHgYIgDLwyYdlFrny2D4he4zc7PKEKIMGw9lJP2m1sfdm6BCXnV/e11gsolOLLWoEVuZ6X/3u985MC9t4qRJk1xbCLgPI06YEaxQ38AixPAU1iZiGDOwnLkNnmvdJgbDr/xe20L1TH8v9Kzv/OeDhgyUebN0u+ThQ3LPA++7hVPG+3mz5gnhk6J8OBH9q7OaCn0n+oTW3tEGcYQJ9c+tt95asH4mDGNHxf/Xv/71sGCqekb9yIIXtrcOK+sEThsFO325QK9SCeN7/B0fqDt6UjBQ09/HSEtdxu/GERTSFVz0gaGZ9oT+BX3iYL+YRX9h9SPf/Oc//9ktFAIozBHsk1C/lqoXgmms9f3RRx/tmEhhj6EexMgLcwXAq7DtixknhdVxfroefPBB/9Zdk7dK+TMDOR5g9ugrQv5h4jrqeciQIW4RGf2AcvxFDZ9JOeKI6t7c1UuflC2YMOiz05e2PnhYfIz57rjjDscuGXxPX4MyQT8N5m6b5KHcMrFVjQAKv+uuu1yYjPNtN5pgmNQLtP99XViEwYIr+i2UW8Y+lFvaGiY2gsLYoFT5YxEDR1CYICkmfbXcFktzT7zbRidZX33tdXlBF4u8++67svqY1WWzzca7doD4x4wZIx/ojk8fffSxa3Nt0vbzGd31fbz2ITn6s1Ce/THGpAVvyQfz3pOtRm0nDclU0U9Lah28ycjNZGt1+8En73YBPeHx17/+dVH//fEliyCwj9m4lYlPdtWi7FOeYfNkRx4mb1mIxO4PTKCzpW0s/VwDCmhb9LaOcaOSE2jxSbE7mQK1ekMA8cLA68ul97LPfSyxBtRGoFkDpk8OWU/tCZo10nMU1Pt5VmZ9mJW/KQPt8u1ZGaWgTo7lNP8vo+adAbHyomuA6QkOwLABARi8XIOyDnYW0exHOaeLneHRxL8OPiv2Drel3gfDs/v4XJUGGrWMDdVjWbWOAd7V4RKLOhx4FwAvR0TgbjAdjMsA5QG6AiAVSzQN0EeDfIbxk8/4Gc13/VwZ42j9YohDLkcDgwcPdov8mQOoRLDX4rcc/9hhDKQbjJO5D39MH3wf5R57TbU2myjx1MoNNrIxOh7nCBN07C80D3NTzrOejo+0kc84ypVC/mrxDZA6YLOqVkgLoN0YuFutJmP//UkDyf6U2DitsQaWJA24oW5+1Wrn2tsS97uM31j+7z9P6Dg5mnsHICSiCO5v0HAJ343BI7jPp7+OPwrU+jBMMFHuC5PhgNoA1QSFxrwSAcx63333OcbLoH8mFv/2t785oFjwXaXxBcPpiXtWwzFBCWscWxfApFQIxFvP9EybNk2Zam5zUbDNcRTwYan0sPK/abOkpNbS37+cLLBQs3z7BpJ5YhdZ+ORKkp4+UFeNawBeGbC8znnFoQr09WTlYR3y1Pu67LmA+2qeEy7h+0L8fnrCwvfd1+q6WD5n8tiYK2FBLcaiFgQIFfrtmZBiQp2y5wtlH0Auk/w+qJ4tVF577TVXfnGDMLGNMEiApQaGTUAqvrAl7QMPPOBWxfrPuSbuq3TlMoyd/krSMIBf0K9twxJ8Xu53mX8m1y+44ILQgTdAY1gFi+ndwuEMyzjig5rdgwh/CuWDanTih1mIqa/Qcz/JAKUAqQWNE4DCAVqZUSGY50qFbfnJjyvKdSV5q1i4hXTs581C/q3cBb8d9zC3+dvKWxiw7AFeM72F5elK8mWh77B4C551cqL9Y91u8AHdtu+WtKRf1ZqwHBCvjnhSytzetGlSkkMWNxKAJyZOnOgAdayOZ4FLxWksmPjiL+rRJhaPsYy3rm1TXUc8b7DluvL0g89Hdh81XNwRLuG7HnDE9OTDL+OTozoFHEvdS7sXJtQdgEPpx9L3KiT7779//hVASWOazz/svKi2rMMqwNbrYUZavoUFbSxs8yVKWQirG/wwYLX12/Lgdm++23rVx4QLey6A6WCdzj0AXgCJfntEuugzwKALi3HQH/XmpZdemk++rwdAnE899ZScfvrp+TbJ+iTo/8QTT3RMoMXyRT7gOl/wXeRhQL0wtdCXCAPx1jkZDghqcaDvviLlMuZuscUWbsFVuf6iugcoTRxR3Zu7eumTMsKWq/SXAWwXEsrLs88+61iKwtxQRhkbwuDtCyBhJFg2fTd27fdxgnUJfV7Yx9mFJiiA9R9++OFu4/wodW4wrJ66p75hQQlbVrIYhLYjDMRb7/QA4DbpS+XW0tSbZ9iVtlPm52/pjkhf/eoO3dqQFVZYURi/2q4zvZnWescNK73ZCohrduZLeXv2mzJz4eeRol5l0Kqy3QpflTGN47q5J+wlTWANNxAvzG70z2ARtzqOcR12PBbp77XXXu7zqT/ZZSiW/q2BDmUoXaQLVrPKVhpFEsMVo7eqArcGLx7bRvFXCzdhIN7jLlHjaiyxBgpoINGo+XWk2mPWUbv9V1JypwINr1k+KVfo+XIFI14xXO+HiNyuNL2PK5r3TQWqf6ZZm1zlbOAFwo0fxxpY4jWg5SGhhIrJcQrOHa87Uu6k5WjfpDQflJRBBydlyCEpGXpIgwzZq0EGbZuSprWTrqwBpq9UsAfEIN7ytcf4sS+BeMv/gthHrIFYA7EGYg3EGog10Nc0kFB2hng81Nd+lTg9S7wG5py4rzTtcWjZ3/n85Hdln5/r9pRnnyEbtaxetv9CHl557wPZ/cxz5baf/1DGr1HeirBKvqNQOuw5AChYfQBlGTvMvHnz3PZxTDjz3gzZ5qeW5wULFrjVp2xzCNCVFT5RJhBrmYYoYQH+YYBYT11ESUcUN7YNGqAQtm+37cxhGjUWmqjhsP1IGACZ7dZbn0hL661pyX4SJbSAm2Ftklx3hjRu/Jk0jJ0jiUE+SgzjuDIHtCbl1Ft1VXunjF6mQ2brs9MnzpLVRvjsEzn35k7NLnrpN7fF76d+2Si/eXC4LDMwI9NmL7a+/Hbfz2W4PisWXvOKey6Odgm4ojzCZgQwhrJfSgAtUDZYBQ0gKuo2k6xShqkLlhu2+Ki30arc7+K7ATSwBQ8HW6PAtB0FaGU649sMjARQGvDWkiiwB6Bf8kvU37+eeujpvFXJt9DeoTfyCCu7ywFyVZsvS6UXFt6Fyk608KmMdLyi9eisUj66v09o1TFw/5QM3DIlCTWE97bUqk3kOwirULtY6XfOeum/Za9/PSRHTThJWy9lpHTtl7VbXc/++w/eniq/+59L5QcX/Lesugb1tTJZluE/1052D3/q5I/kwtMuke+fd7ysvvZqLlxrB6OEf9SEkytVRSR/sB3Cjsg2UQDbAOZEaa8IHGZFA8ABDK33Nnx+eaU9AFgaXOQS6aMjOkI31Cn06wGKAvjrTYFZhj4C7HX0oemDRulH07dgVwvKG+wMtkAiyrewpRl+2e7LFkdE8VeJm/40NqDdIe9NmTIlz8xw2mmnddlxIYoO6lEHbv/LdWRC6wFu0RO/fb5W0gWj7j541oTSywd8dcYZZ8j555/v8j3jx3L8592bPztr2JNVTyxiZUcKWJO7pKMz/kL+K1m8pUGWLfyWU5VplDaJfgT1IAtzy2EDoYwy7qeM1aMPR9mnv0/49PfLKctlKySih7jcRlRUCWc/+clPCm4xWczrOb/5gxx31OE6tqqO/blYHH3t3cyZX8hlV14rPzm9+LadtUo3O6b4C5++MmQbOW3Dn8q2K0yIFMXH8z+Sy96+WK6cepl0SM7mcskll8gxxxwTyX+YI+yM9IFKbV0a5jf4jDqFxZGw5wZJCIJui90zfsXmgdBHQ2/0HQoJda0t0KL/6C8wMj/0Qa699lq3mMLqms0228zZAYILYPFz0003uR1b2LKVfhu7AbGjz9NPP+3Sxu4lLCAoBlShLWABFYz/M2bMcH0tFnbBiI79Iih+nOyExCJcFliwcwzt3UEHHeTAzPiDsZi+JOmhbwnYma2Gd99992Cw+ftydYBH+suwubNo74UXXhAWiGy88cYurh133DEfdq0usguzsuBhXbB6mwJ5Z0QLFUDX4INSMmBsbTh6tjqjK4kAqRg/LinPT8l0SRDPLvvvpi7PLru3Q8LYeA/bPiXf3Z1eVGXCbiHGpP/ee+/l8zttObvrIJBUIPfee69btM4uJORf+j7FdvAjX0DY8cwzzzjbIQsPAcmz6JA8ZjtE0F8NW9wDIzZM2OR38ktLS4vsu+++bpFwcP4AVnLCRt56661uTHrsuABonzL/yiuvuP4T40kYzfmWJU1mzBHZ89zu+W2LlqS8PCkjyyhT77L6ejk9NldW0b3WS0pmVlYys3NHq64DSWmZWWwpX9I0FH/P0qiBTEJ3ZNQtJme3z5LW1AJpWDYlq22yqjSv0CRJ7RqkRujCDQW8u7OSEwDULbe9pT1jtyjG4Cz0ZvEx5CYwyWKzYbdFxmGMZRH6WYyXkFrUu4RBvcsCT/ow1Lssqtxvv/0cYQu7tyDsRBCsR92Lzj+M/9mlFaF/Waiup36nbqUOZy7SX7hOP4NF95DCsOiaNp6DflxwPij47dj42a2Juh190Rbgh74g/R7aH38RW2eyXd8mapzB34q2A7INyFn4fvpjLOo0WyJxMA/G4n3aLWsjWfA5YUKuv417/Jlgo2JhGH2d999/3/W5aHsOOOAAN+dk7uJzrIFYA7EGYg3EGog10HsaiIG8vaf7OOalWAN5IC8zfvmZQb2OcH/Ng4/IGVf+XX58yH5y8IRtZLlllH0ygr+weGbMniv/98gT8qsbbpFzjzpMvjlRO/Zlpqdpz/IByaV+eib9gkDeUn6WxvcM0Jh8ZIvVvi5s/YmRnQkJYyWDGe03v/lNWUlngAzQDBBjmKTVuLfg/rQsulcN4BWAvaQhI4mV5khqw88ltd4MaVhxviQac4ZrK2YXPzJcXvp4seF6zVGL5GMF2+69wXzZakybDG3q6t7Saf6L3c9bmHQMv7e/NlhWVpDwO58vRpxtsspC+c72iz+qUHhLGpDX9BWfq9cAE14w+iAYqwBXxBJroK9qgMUZ7R8qgPfFjLQ/l5HMVE1p9/mekslnq7kBuylbxcSU216upIcecFCrNpGklmoXK/mcPJB3+5PK7hc+cc8zcuOfb5c9v7WLbDlxMxk6XPso1mCVeZ47a5488+AL8q+r75WDTthbttl1y7LTQ7/26AnfqUQNPeKHSYs777zT9Y9Y3FQP4FqPfEiBSJjMAJyJMFEEeCSW+mmgP40NAAixPThjPhMmkMJAPvY+7FyPOtCAvDC2lysASdglAOAJYB9/wrDcsHA/e/ZsB6QCiAVjLVuulys9BeQtN12x+5wG4nJbm5xQKZD37zfeImMULLnVluNrk5B+EMpTz+iEvYLuDztovx5L7a9//Wu3UJ8IByUGyw/XOVMOH3e0DGoYVDINGV1Q8eRnj8qFr/1anpz7iANvAECpRvoikBfm8AMPPNB9VlSgMiBYALOQDxig0fTCIrOtt946b3+z53YGuOEDQHgOSPfqq692oGQYf8N2QcOmB4DFB2dbmD7w0p75ZwAjQSCsHyftroFzfH/YDQGWYMsAOB0Ufk+IIIJSiQ5mzZrlwEsGKAqGCbDpt7/9be367Do+YtHqghs7d50JRhh2rxtNNe2dlMF7pCQ5sHKgrB90EMhrrLtBkC4gXsC8JsH39pxzvYC8LPQ3gOs111zjAGlh+YK+JrtiBQFhuCX/+n1QSzegMnaxs12LwoC8vDNyCvNnZxbLX3755V3iLAbkBZxF3jYmbgvHzoCygmXb3vXXcxiQt1B+e/78gZKZn5UOQLyzMnLPf9Ly2LNpWTYtMjydleFqfl9Gr5fRcjS6URdv63Xneo/+qp443Uu6BjSfymBdTs7AgwAAQABJREFUADp1ksxa+KWMXndFeefDSfL2B2/JjLbP5fO2T+WzViXwaJsmMlLz/ON3y+iVR3fTSiXtLeNJduJB2FmDHexM2M2QBTQQh9i28rfcckseJFttvYt/dpaxOUGLlzPjW8C4zBMiYfWue+H9YXEEdaul23uVv6QN+Oc//+l2ScLOwAJuxtbs6EDbESb0iwD/+gRC/rfTP/vhD3/Y5TtYQEI/DJAsdfmpp54q5557bj74SuL0fysWOBVaKAb42na1AdhdbOt6dGE7ZNHHYQexsN+DhV7kjzAwcv6j4otYA7EGYg3EGog1EGugRzQQL17sETXHkcQaKKABs7fpmcusd+98ePf2/ps7TZD1Vl9F/nTHPXKuAnBnzV9QIPDSj4cPHiS7jN/YMfFurky84CucePHavcVv913O7ib+09MaAOzBgKs/AHlNNzZAZDD44x//2B5HPsNSwsRBIUnp6uSB2+hqfTXytT+uOXpeIZcFnnckJTt1uHTMHCTpD5aRtIJ5G9f8QlKj2iSRVAuhMmPtqYBdH8gL2BZm3uemNsttrw2R1kVWgArEUeTxwAFZWXlYh2Pi9UG8eNlz/fkufqXgcudE57nbfZHw41dLtwZg00EwyvhbnS/dWom/vq9pIKtg3Y7pGVn0uh4vZiU9qYK63D5Km4vGrbVd2LrvgHgtaZyrbRMJo1S7iJuKRdu8XP+vk1lXb0rdb7vbV2TlltHywE2PyJ0KwF0wr7Xi6AcNGSgbbLmuY+Ido0y89FNLxR/2vuIE1MkjLLwAGTDIw6qBnHnmmbUDBNQp3VGDhVUKJjaAu5wRmNJiEG9UDVburr+NDXwABZNV5YJ40VQ968BCDLfFnjMR2dLSIkx8MgEG0LgaYZzHpGcoE68x9pY4VxN/7Lf+GojLbf11XCyGjTdYTx7Uxe1LE5D3+ZdekYlKCtCTYsBbwJYLsvPlmc+fkm2X/6qsP2LDkslIah238bKbyX6rHyQH7XOAnPyjHANnSY/9zAGLW0yi9pkAAHIEBaY7gLg21gAgQ1sC4+dtt93mnMP2CSueD1axcLAbcGAzAPQBAy99Vg7CBDQCENjf1QD2XmNPxWZHewi7HYuJWbSG7Lrrrs5fEEDMO4tz8803d0BKvuGPf/wjr+T00093i2QAYAJmgf0YhmFAx6Tnl7/8pWPAg53XpFIdwNJsIF5AmSyGAdjENwAauuKKK2SNNdZwQB2Lq5pzx2dZaXtcQbyTGelEk+Squr36WsmagXiDsRqokufH7ZKbOoRxl+dRQbzBMOt1/81vftMFzeJIwLksjvnzn//s8gXjEPI+rM4m9Bv333//PIiXvMg4BaDVzTff7PI4ebaQsEjRQLz09ygflAPA7W+++abLk+wMxg4NpSSdTjs2SusPsyBlvfXWc+HccMMNDhAGsI36IAw4Xyr8/vK+UH5zzM+KGU8OTcgAPS57IyOXvqd2+eXURqHFpSGtrNQdWVETugzX48B1ErL1qjq3NldZtefpnkLaBdfmRs85e1KW6TNI3aMXtf6iwjidfUgD7GbVkeiQee1z5YsFM5Vhd7akhiVl/NfGSxImXda5D0rL7489Pwfcfe5Tmd4+TQaPGih7H7C3tAxZWV674wV5S+sT+UTk4EMPlvvuu6/LLkrVtreoy0C8zM9tsskm3Vhoi6m03HoXBnS/T0I7T3+A5/RJWAjLUY6QBtpk6l0WRNjiDgsD1lyAqwiAVeuvMKY2EC/pYAERjL208a+++qo7YFcv1A7YwgraDvoc7JJTaoeXauOk30O/isVetBEQQ8Acj5x88smuvzVmzBi3+xP9IQiQzj77bPeeNpBFXYj1LW0BCc+YH2LRFmfsd/SraJNoJ9lpp5yddQgvllgDsQZiDcQaiDUQa6C2GoiBvLXVZxxarIFIGkgM0OXzSB6Ip5cOeaDWBDVSL0YqLL73348fN1auOEWN157/HLJhsfty750do0h4fvxd0pf7kvhvL2iALUsxWjNB3NfBvBg4MUSy/QtGbwa8NoiOqjqbCC+15XvDSkkZuJOGujAt7c9ozq4E6z5/gGRfW146Phom6XeHS8NaMyU1drY0jGyVMcu2yzc2nyvXPbcYUDxNGXlrIYCAJ89geXZXIT7ipexRVgHxFjp39RnfxRpYrAG2YEIwVAVZSRa7iq9iDfSOBhyA9zMF776lCzFezUj6Ta3nvqgiLcpy0bilgniVibdhRTpZfUdq0SbyNVHbxYq/nLbG9U+17WG2jP8R7ldfa1U5+sfK7uEaKo29BmfX6kWMP5dOHHemt2IF1McjIAhjqSWGgw8+OL99a31i7NlQYSzxt6qGFeTiiy/u2UQspbH1p7EBTDYwngEOYgvHMCBRqZ+x3nUgE3NIuWeAT3xfraXcdJj7WqcjDq+2GojLbfX6XLRokQwYMKCigNZfd235z2NPCSy1SwOYl+9MKWCB7+5pAcwLcANA2jNfPCEvzHxWxg5dUwY2dNpHiyRoSOMQOWSzb8jQLZodQAswzJImPqPo2mtX9/scddRR8t577zkVsW227crDA7bQBrgDUOPYY491zHNhuoQ5F4ANW3AjMNjRvwMkCXAGIK+BC9l2mt8WAWzyyiuvyMorr+zu+cNuQAYgAUBszHh5B50X9IkBkQCqQWDTM3AuLHdnnXWWAHY0wb0xxrGFtrnlfSU6AOR5/fXXu+CDjHqAZQDEAKAijbyvVjJzFcT7dFo6ntP+RtS1j1rVNaynQN7Ve64MAOYFwOuDePl2wL19QWCz9tkKYXm2MsQ27j6QF5ATgHYE8JKBnbi/8MILhfwJOCxMKDs2hgP8C0jM7NqES34EBE4+xeZm5SMsLJ4BFDMQr898yTvA8oDMeE88pcLCz5IiYfnt+SkK4vXyGzaJdjXDz2pICPvWTdVjyw1SssvEhGQXiqQVvAuYNwOYt/MaYG8GYO8CtSxwdtd6btXytyAhbV+0SUO2QVKJ1JKiyvg76qEBmqcmnZdRNvSPPv1QZsz93IF2h644RDbbCbCuzkkNTsmQ5ka58ZLr5JEXH5aZC2fIw//vIRk0slkS6ndRe0Zu/jTX1pBEFr7ceuutXeoTyG8uuOACV08ABDW2+lq1twA377rrrm7M/FFVVk69y7cY6zg7NNCfMIHh3upOexblvM8++7hdBHCL7mzBmPkF/Gzy9a9/3V3CuH/RRRe5a/zT3qdSufKOfq2PQxvBAp4VVljBguhyZgEyaY4itYiT3+rxxx+X1VdfPR8ljMQGxqaNoG0aMWKEs0GwsN7aNtoq+nq+2IIunr3wwgsOxMv1IYccIuiF/Mh884MPPugA2LyLJdZArIFYA7EGYg3EGugdDdQGedQ7aY9jjTXQbzWQHKUDgYzu95PsNA6Y/Q0QL9Lf7nOprtlftk1rbW117A01C3QJDWj48OFucInx0AzsffFTYRqCIaFSYVsdVuoWGkQHwx2wulpWduOpgnkxildKhjWrWbLPj5b2d0dIetwXkl7zS2lomSU7tqhlUMUH87oHdfgDiHfimout+rnqAZZE/S6V4H0dkhAHuYRogIkwGENsEm0J+az4M/q5BrI6/9fxqQJ4lXm3XVl4HYB3pn5Uroqr6OsygzLSsIVurbjrAGlcNTcRXFFAdfJUbZtIssptFyv6FG1gXGsDiJfWRs/98b6ib6+jJ7bcA1ywmm7lDVMZoIAlaXEF2wqy7fCyyy7rGENod4rtplBHVS+VQfeXsQHMPxyVSk/UgcWYdwHJ9pf3leo49tdzGojLbXW6njFjhpu8rjSUvffYRX73x8u13Roua60xrtJg+ry/SZOnyM233ynfP6nrhH5PJnzTTTcVgNcA5j67d5pMmfuObBCBlZc0Ns4ZIIv+k5XkMhkZOD4pieacJaQn01/PuGCYRVgAxVghKDCjAYgNE9jgYI5FAKMasx22VR/Ey3uY2djyGcZQ3LHY3sCIvDe5/PLLu9kYv//97zsgL27YwtnAhQaM5DnbffsgXp6xYOcvf/mLA90ATATQE7b9M2ynBuLFHyzC5BnCpy8J8NIXvoW4AEGzJbhJpTqYPn26BSGrrLJK/poLdARjX5Qtv7t4LHCTbVMQ74sZWfhoRrKMfSNKUgG8AzZMSmqZ+uV/A0waGy9JC4J4j7skZxONmOy6OWPM4YN4iailpUVgUwZwDXOhL+yKgmyzzTYOcOu/4zcGoAXjISCmoNhOKoCqYHT0yw1gMOJjfIdfGKatfATDsXsWP5oEy8yKK64okydPduWz0oUqFnZfP9c0v2GyaNbySvugzL1BwfaUUeBudr6CfBXIa8DejBKA3Hv1/TLlxSkyeuRKcuDBBynAV01SbQB9NRQtr3YvZP1FelRhrwqmK77vfQ1oTSzJJs0zgHQ1DyUGapo0H81vnyePPvqIzO+YJ0NXGCp7HL67e5cYnJCbzvuHvPDaczJr0Zeaf+6WphENkhykFrNOxMW2zZvLz/99hvu4t6a/LuNXHt/tQ6lPYFf16xMcASx9+OGH5bnnnnNM4QbkrVV7C6A1jB2/WwJDHpRb7wIIRbB7nXLKKV1CpO5kwQVp8Rc0dXEUcrPMMss4ICs74PzjH//oBuQ1Nl76ENjfkGHDhglAaIQ5VAPxugf6Bxsdi5UQ9E56g3L88cdHBvHitxZx0pfzQbyECzsvC4fRmYGkeR5FzD39KvToyw477ODmX7F1lCJS8v3F17EGYg3EGog1EGsg1kB9NBADeeuj1zjUWANFNZAas5auCJ4riaHDdWSoTjvxEf3yXPRLK3tpKyUr8710+cKgB2CCbeNYednXmXkr+XVg2wLEy3dGNmBquRrQouCt3TXGJmW4eFqNbizTr0RY6v/FQMl8uZJkJi8r6bFfKjvvLNl+9dkydsfZcsc7zfLSR2rpqbFssvJC2XOD+dICE6+KVRO5aPR79ILqIyfBe3sen2MNLNZA1BXji33EV7EG6qcBJlA6PsnKoncy0qEsvGkF8rpJzConRBIjdOJmc5H5G38pA0YOlQHsXbeESUXtYoU6cK2La3D0D/87mXndfpYaZr94X+G318sbxnGOJVUAPlxyySVL6uf1+e+Kxwa1+4mM0ba/n2unkTikemkgLrfVaRaA46qr6n7aFcoqK42Wow4/RP5y1XWy/957LJHMvDDxAuLlO/ne3hbY2DLHZmX+nWmZe1ubDEhHY1TOvJ+VhfemJakAmyZlX0xE89bbnxspftuaudACfUCEJ5xwQsGwZs6c6cCuPth3u+22c0DAoKctt9wy/+jtt9/Og1zsIcAiAIlB8Z9hgzSB0c2kUB8XplOTl156qRuQFzZ7FoEFhe2iAS4BigwCnXBr4B+2kjapVActCgAF2AIQE7ARdkhsKGuttZYLmvhrYXPNKgiwTXegabsvLdmpluoIZ3ac2VSBvGMXWwIj+KrISRi40gK67N4OgSG1L8hWW20Vmgz7zXxgGHmErdMRWAfD8hPkETvvvLPAkBuUZ5991j0CYA7ACRC8L5RhFs3D6AtjdSnxF9izSwVAPYBjRmBRqC4oFW5/fF8qv9XqmxK6AV6qUcvPsO5l6PFbHpDL3rpcNhq3kRx58Ncl0wnezThAr1o9OAMCBtyrx1P3PyWTX5oigxoGym577K52pybHBjzpxbcl2ZGSkcOXk2UGK0gO0K+a9bMduUWAtfqWOJzCGmDs1qFbfqWaU9pf0IU/2leY+uEHMrd1rgxbYZisvu7qjhn3k88+kf8oy/qCjgWy/zH7y6hVl+sE8SoYV/sZsO4OaV5G/nbbpfLW+2/K5jtuLoceuLcSM+XifuyXD8hjXzzmFgYMWl3RvwEZN27x4rAPP/xQxo/vDuSFvZv5rqBQP1FPASjlsEU3tWhviYv+QaVSTr1L38R2CCi0gJ12lQUZV1xxRVlJOvzwwwUgL7sE0JcxJvb58+e7RUUEduSRR+bDZKFQWBuObmn3rR+GB8IIE3YrKEdqESdtTlBYUMyCKNo4Y3YPuil0T3+KhSd8M+0di6T4rsGDtZOhYudC/uPnsQZiDcQaiDUQayDWQM9pIAby9pyu45hiDeQ1kFpvM2l/8A5JraNAXkTtB5gQwAs66W/3ncmOT72jAQyIGPrYrgXmCdg7MPiFGSV7J4Xlx8ogGqYLvgfh+yKDeC06LUcDxqjBRucL2oYq2+Njur56ur2s4EwB/VIBvc/rMWmkJFabLSsqoPcEPc9fd7a8ray/0+c1yMIOK8jlx9HUkJUVh3bI2iu0y/BmZe2GpdsBqNSIxPa+7j53LnhffrSxj1gDsQZiDfSMBrT6Ss9W5t2pekxWAO/bCuB9Vx/Ork30CcUlDNhet7LbrlGGLDsy1y62xu1i5drVtkc90/yFnvVd6HNz30feV/79sc9YA/1TA/HYoDa/W39h3C2VztpoIw6l3hqIy23lGmYC3wcmVhLS+uuu7Zhqb7/zXnn+pVdk/CYbyZrjWmTkyO7AwkrC7w0/M2d+Ie9Mec99T0rBCzDx9gUQr+kiOVQBiZtpv/3dJkm/rOMB/V9SGEtMEmm9S0GEqYQ0rZsD6JT01w8cAEB5SAFFPtuen2xsfABsfWEnM4AYvgCSNYGxtpTAZGtsdebWQJB2b+dCNkYDFm2++ebdGO7M76hRo/IgWdwHCRwMvGjug2cfYOO/8xl87XmlOqA9vfLKKx3THWEBrORA77vuuqsD9cJ8XEgPFn+xMyDeha+npU3zcGayuoyS7wlQx1epdTTPb5rUrdsZgdVfwsCVgHjtef1TUDqGIEuh+QjbDQSgl0kYI7S922ijjboBeQEGsrU5AjNzGGu2+ef85JNP+reh16Th1FNPFbaWB4hlQH1A7QB7KSPV7F4RGmkffmj5ymeC7vn8lpW5mTmSGpkQXSpSWFvaBF3yt4vkyVefkqGpYXLoPw6QRLvum6frCa655y8ye9ocB+7db999hTLP8c6r78jDtz0sTammzqNZhg4cKqOWW17nPEbLiGVGSEKx4Yo/1S2rcsenUz+TZDYpg5sHy8BmRZbyXKcKItcb6rRfCSpP6aGIhQWt82XBwlZJDUjKsqO1L6bPvpz1hXz00YeyKLNIFqXb9bxQ2vV6obtvk7b0QlmYbtP7hXrdJrsc+DXZdJtNFMibkGt+9Fd5b+q7ssu6X5P1Dj7aAXnvu/wOOffVX7nf/HvHHScNA8KhEstusoxMm/KxtMq6eRCvr9c11ljDv81fRwFEwlhfSPx3kyZNcmzhtWhviW/kyJGFoi35vJx6119YA9t5IamkrvvqV7/q2mfqz1tvvTXP2m/s6cR1wAEHdIsShuDbb79d7rvvvrLZbCvVWzVxFlqsGNbOdfvYkAe0LeyewKKTp59+Wvbff3/nCqZ6a3uCLPEhwcSPYg3EGog1EGsg1kCsgR7QQHjvtAcijqOINbA0a6Bx061l4Y1X6ArdDkmkKIa6otcAejlIRD+6X5p/yb7z7YBcMS5jyAf8ijE/yA7Qd1JbOiUYxvkmBqVMalYjjSupoXvnhCSVpXHhf9RY/p6GliO5rTzYubrS/vXlpWOKMvSO1lW7ys67yWpzpGEVZdoeuUCSjWzK1AlwquasxmIXThnnyj8q9hlrINZArIH6aICJi45PM9L+njJjvKsA3nd0G8OPtHZTVpOaiDKrJMcqM9cOSWneLCWpEcwAxO1i1bql7UGV+lP163PViogDiDXQ/zQQjw2q/836OxOvpb96TcQh9JQG4nJbvqY/+ugjB4AKAhHLD0kcyPXEY4+Q1998W15+7Q156JHHFTRS6bY+laSgtn5GDB8uLWNWlYkTthGAyn1RWHjdsVVW2qYrM2nURdcKYkq/ntVhhF5oX7VpvSWDmRfwnsmMGTO6MfQddthhwuHLmWeeKeedd557ZHazcpnZZs+ufkXlp59+6tIQxqjrp3fFFVd0tkq+r55SjQ723ntveeaZZ9xW59dcc41LL+FxzdHS0iL33HOPjBkzpuxPyC5URunXM9J6Z0bSuiONA+RFDCWxoo51t0pK46qdVJAR/VXrzAdX9jyosnTqAV9HFRiWTYqx3YblY9h8g6B5CyvsPGfOnLDHXZ6R9nPPPdcxY1511VVy2223ufdse8629xwwWMI4WQo43CXgfnzT1/ObqZZdiv7z9MMKAJ0rzaMHSNMY0Kc5eXieggOnvyOrDl9BDt/1QHssm+23rqT2aJcbrr5B/n3z3bJw9kJpVnr55lSzAnubZcxqY+QXv9KFAyOXlyxzFgraveqUS6V1Tpt8daevym6775YD+QLkpfmDFFvPc76cIzf/7SZd29Ig2+ywjYwdN1ay+vzRex6VOTPnyCqrrSIbj984516rnU8/mi5vvfq2zj7qP82D+X963aTzMO3tOlep/0atMEpa1mzJpV/fzfpilrz9+lva7GZls603k6aBTfLpJ5/KpDcnuWfb7rytpBpUDxRJPRJUVXagHr1OcNZj8qTJ8vhDj0laE3rkd49SsK6247jtfK+fIjdffYe8+uarMm6DsXLcQcc5IO9dNz0iN979f9KWUbBuJ2gXwG6r0ia3phX4m9Ujs0CnfNQAqTJ6heGy3S66XZfKPXNvl3dmvCPrDB4rA9YkMq1TV26QLzK59mju/LkyYoBOHIVIPdssdtcsJMOGDcu/grwH6QvtbTn1rl+f+nVw/sM6LyrRMXOHRx99tPz617+Wv//973kg70033eRChdHYXwDFPCn9JhZPhIkx8oe9q/RZLeIsR99R0gkrMe3N9ddfL3/5y18c4zP+nnjiCXf8+Mc/lp/97Gfy05/+NEpwsZtYA7EGYg3EGog1EGugjhqIgbx1VG4cdKyBYhoY8LV9pf3lZ6RhTVYjdhqedGCck/52X+xL43c9qQGM92bA78l4+3pcqWUTytCo4C5dVd/2qILIXlXrVWm7aunPamuQ7HsjJP3BcEmP0KkkZedNrqqr91eeJ6nR8yQxbJEaozQuLeO6Nt+Flyvd9bsvnejYRayBWAOxBnpAAzp5kJ6lzLsfKoD3A2XenZJj383O1Lj1Xc1Edyts2CghzcrEO2BtXbih29/5EreLvjbKvFZVutZKJ6pcXzXk3B/el/nVsfNYA0uUBuI6sPKfk0kzJqtpVVwtaPfBcx9/X7kGYp+9pYG43EbXPAym22+/fXQPEVwCeu2rwNcIye9XTtjuumnjpKR1gd+iBxWVlNuQqfQ3KMDJgXkzuoBa8TpNG3YfA5QKpFaLHdLp3MAmjBm2VBr89/4W3DClnXjiif7r0OsHHnjAPYcJ11hiN9xww7xbtvIuxXSbd1zFBfE/9thj4jPhBoMDCAlAEamEeS8YXrH7anVA+n73u9/JBRdcIK+++qpjSr744ovdFtZsEQ47r8/uWiwt9i6zQEG8r2Sk7W4F8U7SXgXMmlFFd7tu/ErS5XPKTE+LgSvt3NPx1yq+ddZZJx/U1KlT89fBi9dffz34yO16B4MuDPAnn3yyXHjhhd3cVPoAsC4HW7k/99xzcvfdd8tll13mgMOw/7LtOflvaRHLZ3bui98Nc7oBuwELRhLFjm68xUbu+N+LftGtbnnitf/Ik997pEvd8vdPr3SLlT5UYPCBe++5OBqtQgzI++z9L8lZr/1IGhON8uAfHpAh6+g8hTZLN9z6N3nplZdk37X2lW0O3sy5b2ttk6O3/YYD8K640mg54bsnyNrrrC2ALRsbdHW8hnvKf58ik9+YLLuO3VU2OPikXJw69mnODJczDrlUZnw+Q47/2vGyzwH7yF//5xJ56tUnZbf9/kv+67AdO93qCTsSWFkOBefmQLpqOQKoq88m3/SanHXxjxzg9sSDjpYBzQ05Nwy6OuWB6+6UWz64RfbYYA/5/oT/dk+Hft4kd118u7uuRfu26aabWnTu95gwYUL+3i4AYsJcWi+hTikksPCaWP3V19pbS1+hs6Wb9/QRqOvChLqvEoFdFiAvekRfq6yyigOoEtY3v/nNLkECVDUQ78477yw/+tGPhN0QYNmlD0WZrpRxt0tE3k1vxOlFX/CSxSGAoDkAh7OA6YYbbnBMvXhiNwJ0c9BBBxUMI34RayDWQKyBWAOxBmIN1F8DdKdjiTUQa6AXNDBgxz0lkVHWidm6IpyZQaS/nnOpj//GGujTGgDc1bxxSgbvl5Km3XSiZ4wmt1bLWTJqbZo5SDIvjpaOe8fJon+Pk7Z7WmThEytL+7vLSGaeGtKsfDst5Zh2Fyus1veLQ46vYg3EGog10GMa0HouPUcnKd/KyPz7O2TeTR2y4IaMLLxZF1A8pfXcZ5qS3Fx39UnS+jvZohP3/6VbDWq93rxRqhuIt/pIlvIQ9Pd0rZNrv9yNKqTruV+8X8p/xvjzYw3EGqhMAwbyclWgBpG/7+zU5+87g8/f97H3lX197CvWQN/XANuct7W1CVvBxtJ/NdCgi62bt01Kw/pqUynHPmNg3tsUHPm0giNnW21dWhdsC/3JJ5/IBx98UNpxCRcAupCVVlqphMvir22LaFz96le/cjttFfMxbdq0PIuaD4zdeOON895eeeWV/HU9L8aPH++Ch7m2EBuuD1bywVP1SFetdACwh7T+4Ac/cCBkAwUB5n333XcjJ53Fra1PKhPv7ekcE285IF4tEw0bahnZOikQFNRLFKdXVCoBVTYpLrAvCaB2Y2e0chuWvmeffTbssWy11Vbu+VNPPRX6vtqHgwcPFuoBWHrff/99aWlpcUHecsst1Qbdp/xHyRcV5beG6G1ANQr5+OOPBeZukwMOOMAuI5/LrVsAOXbZdVHLK6BYgP0vvvGCzM/OlVmZL2TN8Wu4eqJhVEI+yXwokxa9KdMbPpbG1XXRe0tSnv74cXlu/lPy7Lwn5dCzDpCvHbeDrDZhtAzZoEma1tFFNau3yV+euEQennWfvNc8SZo20PkTjvWTMmjDRlljv9Xl0dkPyp/u/b0sWH62/O7fv5Gn5j4mE47cWgaM0zg4xubiIk4YxNklsWFFPTRN1GGp4Qlpb1ro0rwou1AUf5wD+Jaog1Burep2+6E22GADu5Szzz5bFi3SlUEBASRaTylU3xCn1VMrr7yyA1vzrK+1t6SpmDQ2Nub76Zdccomwk0ZQHnzwQWHRQiXCAgvAzcitt94q999/v7uGXZdFN77YO55deumlAnCbdsEWQtWjz9QbcfrfbIvN/GfBa3Sw1157ybXXXttlQRa7D8QSayDWQKyBWAOxBmIN9K4GYiBv7+o/jn0p10DzN06U9heflGzbfMf0k/ujSrHBq3d2l969U51336vvl/LfMf78fqQBNXQ1rqagr11TMvDAlDRurSUnqWD6WgosvcrQm3liNWm/C1DvGrLwvhZZ9NRK0vG+Pp+HNTvZWeY7C7FazXNluEb3tfyeOKxYA7EGYg0U04DOlzjw7tsK3n0oLfNvAbybltYblYn3gaxklIlX976tqSSW1bp8W2Va13p88C5al6+iQ5p4VFNTHbvAXNuk7ZL7zx/aqn54X3vNxCHGGog1sBRowLax7O/npeCnij9xKdTAyy+/LA8//LDst99+S+HXL3mf3DgmKc0TdbF1i36bdjUji5L4ZiYrSPKOtCzQcUjHdB13RMBybb311i4KQAuwkAHMKveA0ZOtiWFbW2655cQHBEVOv+cQEB8AXgQw7FFHHZVnfPScuUtAvLDJmRx22GF2KYCUAbAgbIs8b968/Du7eOSRR1zZofzUAszsg6u+853v5Be+WHyAo77//e/brWy00Ub563pcVKoDmFC/9a1vOd0D9PYFoA8gSxNbvGP3oWfNi+3TMrLgwbS0/Sut42J1pXk2smhZSI4Tad5RgXFqx6ynHLhV7cMfP7aeKa4s7K985SvO4x//+EcJA9AB7jLwXDAGA9ABqoQ1OygALb/73e+6snX++ecHX3e7v+iii1x+O+2007qCNNUlZbjWbPPdEtBLD4YOFNm0pZyKPlpCx4+tfZjBmGfMmCG77LJLfsEC4Lcov1O1dQttwi9/+ctgcmTKlCkO+M0LmMibmpq6ufEfzJo1K39r7UT+gV7AyFlMDj30UPeadg9QJgLItKcWVFVat7uEhvyh3aWtRWDd/fa3v+3KP+BHdHveeec5ZtIQrzV79PTTT8uVV17ZLbwnn3zSMXPzwhYRcN3X2lvSVEqOO+4454R8DIM1wN3W1lb5/PPPXV1aLevrEUcc4cIn//7zn/901zwLlocvv1w8/xfczTSjOzz89a9/LfUpZb/vjThTKZ387JRge0bf5ZRTTnFtj5Vhc8sZYLQtePGfx9exBmINxBqINRBrINZA72igoXeijWONNRBrAA0kV1hZBp3wY1nw519J46ZbS2LYcAeScIZn7A+w+ShowpGfBe6dcbsvvI9/ylgD/VADyaEKANs8JQ0rJ2TeC/+SjlFbSOJTtZC31pAyQouvzG2SrB7pyctKeuhC6VhxniRGz5OUnpMrLJDUqAWSGNKuW0jlGHkTWqbxVs25H/4ccZJjDcQa6G8aUFZdwLtMlnd8ogxYH2b10Ge6La7M1o8pZ4KynG/XbUVTaySkcTMY1nPMHjGAtxwFlumWNsn1P7UrmtDflv/98b7Mz46dxxqINRBrAA0YSKe/n+NfM9bAkqYBmHgB8R5++OEOQLmkfd/S+D0wCw5YT9kAdRzRtkB3Lvu4DC3QP9VxyKK7M5KZmZXm7VMyQIHBMBUWEkC3u+22m9vC/qabbirkLNJzQLwHH3xwJLelHAHI/f3vf++2+IaF87XXXnMgrc0220xWXHFFt3U0wGNAgrDCIieffLJst912+aBZfMLW0QBnAHDAFgmjLABGgDP//ve/5Yc//KEDCbe0tDgQVt5zhRdsv3zkkUfKVVddJaT7pJNOcltar7/++vL88887MBLALwRw8fDhavuuo1SqAwBp119/vUvZ22+/LX/6058cQDuZTAqgqnPOOce9AzA3bty4ol+QVWLHRe8qW/SjaWl/TjPpYvxQUX/+y8QqCuLdWfOzMmWyPX095fAJSXlnum59/l5tBvLf3CEpm49j4Ni3hK3U77jjDpcogOyUpR133NGVBysbhVJMOQcADLs07Q/bkQOoB1xIfmErcmOVjAJMa25uzuc32OUpp2PHjnWsoOTDq6++2iXlG9/4RqEk9dvn39szKd+7IiOzF2jZqIEcu3NS1lu1NvkNsO5DDz2UTxULEaZPn+6A35dddln+OfUFdUQUqUXdwkIPAHr77ruvY4AHePqLX/wiv+CD/FdKfNDxWWedJYMGDXJtAyypACDPPPPMokFQ93G8+uqr8vOf/9y5PeaYY4Q6siek0rq9WNr+8Ic/OMb1xx57zJVHawPMD6BGwI3ou15y/PHHu0U3MMgCsCb/0U6b+Nd9rb21NBY707eh3gQYTd6h/xUU+iqV9sfwyyIiwuZA/AVOFtdOO+3kmHi5P/30012diz7feOMNufDCC/P1sbmvxbk34gSgTj6aO3euK9cTJ050/cQRI0a4Ms9CJfpq5HXqFNrCUaNGyezZs92CMttZIV4sWYscEIcRayDWQKyBWAOxBqrTQAzkrU5/se9YA1VrIDVuHRl82rnS9vc/6ZbT06Rh7NpqoOu00AHiRcwW0Vfvc6mM/8Ya6F8aUDtT48q6PV3zvZLd/nNJfLaPZN4aKdlpyl6yqMZW8owW4tnNkuWYNFIyQxdJYgXAvPNzx6hWSY7S6+FsK4XhHM7DnEEzV/yj3/evHyFObayBWAP9RQNMRqZ1crxjekY6pilw9xNl29VJ9vTHWlfN0a+ozZxfuDqUWCS5ugJ4N0pI0ya6PZ9u05coTjYSHk78tDwNaAPkWh9AvHRG9dwf78v76Nh1rIFYA7EGchpgshgQb38/x79nrIElRQMATQA3AHgC9ACAMpYlRwPJgbpQb3xSsvOzsvCejNpHy/u27BfKfvqIjk8+S0t6+6w0bai2nmVy1pSwkAC/rrXWWo51D/BCuQJwaaWVVqqaidePF9ZXtjI+9thjHSDwnXfekQMPPNB30uV6jz32CGULBFT74Ycfuq3CKTM+KM0CAOQBe51tKW3PKznTTgJwBOwG8+Rf/vIXdwTDIl2lgGJBP5XeV6IDAGqAJq+77jqBdXXLLbfsFj16M+bkbi87H6RnaR5+JSMLH9UFr2/rOKqtkMvCzxMriDTtlJSmTZWpWstGvWUFxVb/6dik3Pl8Qt7XspezRpYfa1OjyFfWENmkpf5pLj91IltssYVjXTz66KMdqyr5JCiA4H3Apr0HgA5QF3ZMgE6nnnqqvepyhuHTmEu7vAjcfP3rX5c///nPDuBGfBwGvjKna665ppxwwgl2u8Sc11kpITeelpL7XsnKxzMr/6xBahMiv22otqJaCeA3AJXFBFZeFi5E7YdUW7eQn8h7Z599tjuCaQPES3tQSlgQAus4IHEWevjM7vgFsAqzNG1PIYG19nvf+17+Nfm4J6WSur1Y+mBtvfHGG92imCCQFP0A8OSoF5CX/ixM4NQnYXXKNddcI5tuumn+E/pie5tPXJELQOcIYF5fWlpa3AKmmTNnVgzkpRwCOgWcilBvbr755n407ppFG+RxQMW08xy+VAMm9sPxr3sjTuKnXNKmUJ9985vfdEkCrL/33ns7kPj999/v3rEYjCPY9lCfAEKOJdZArIFYA7EGYg3EGuhdDcRA3t7Vfxx7rAGnAcfMe8o5suihf8mi+2+VxLLLS2ql1UQGD5VEKrVYS504ihxlpz7uC/eLUxdfxRronxpILJTkqh9I43pTJTN2lnS8taxk3llWsp8O0Zkgr/zV6uugM5yjTL16pN8ZKemBysgLMy/A3uUVzAuod2SrJPRIDepQ9BoFvZOpN5+GUvd5h/FFrIFYA7EGKtOAAnPTcxWw+7mCdz/Vs7LvGng3o/cyv7Jgy/IFgHeVhDSsn5ABCuIdME4nMgfXbpKmrLQspY5da+P6m/qH/53MvIB6kX7xfin97eLPjjUQa6A6DfR3Jl5Lf3VaiH3HGug9DcCCBzPe1KlT3aQ7W/ACvuypLZx778uX3pgB3jZvk5Ls/2/vTGIkueo8/CIyq3rf273vnjbY7aWN7TFiLI+GmRviwCKQkDhwGQlO5oAQIDhxQJzNCSHBBUtIIIEGicU2lsXYbg0ybZb20u2mF/fm3t1rVWZGzO/3MqLILme5q6syq3L5ngjei8jIyIgvoqL93vve/41pbPVzknnvVPC6ofrL/jzcOCeBUoMOFzymAYBbNABQcmG75Eh7vTaFsIWUX/ziF+Hpp5+O8tCJEyfed+qO7ujIvRYypkqOfLtixYoYcbSMrlbua9HwG9/4RtixY0e5Keat0zHf8kHLSil7jI7eGvJ4ZGQk/PSnP40RIj09teWRMvk7Tz31VHA0VEtIrel2vzl5auzW796uPBMGnuLc4o1lo1aZzdfw5JNPxnti8ahdymtqQjwmgff/sjC+T8/vSe3VrDK1233KbYnGKIz+RxoWfbQSKstv5TXllzrwQaqf+uSjc/d7tzvlyc9Kuf/tnhnvN/n5LL/r3NF0x8bGgiOStv5t+N8WC3uvvfbaxO6Tz2HLli3hueeei5Glf/nLX07s54LlsW9961vh85///C0RSqc6X4vBr7zySvjud78bfvKTn8RzKf9u/DfuKej92dKlapsewLR8UQifebz3nzej978T27Zti3Kgn592kn95i6YaHDGbd4sHnXzzm9+MAp4HS5TJ7yVHa22N2Fp+5ndyu2S5z+9+S+Stz78HMXiQQhlhfqrr+PSnPz0h8vpv5nbRydudw3Qi+Jbv/nbnMZN3e7vjlOe2Zs2a8Mwzz0Qejnbv//70dXmwjpMHkLVLUzEu9239259qX0fLt1ztiNz79u0rvxqfOYvbfp9MTjP993Y63Kfap/VaJp9Puf5B710f19fz9a9/PRw6dChcuHAhRjN3RHPfmx/96EflYd733wkTH3xAwQxLkdcDNdol/zfRiy++GKP3To68/J3vfCd85StfmZCJW5+X6Vy7f6/1O+Xvz/Q3p7oP5XGdf9DfiGcQsLjf+nde/nv2yCOPxBkf/Mz97ne/i/+9Vv7bY9HZg1nMoty/9TcpQwACEIAABCAwtwQS/YfpDJoU5vYk+TUIDBuB2p9fDo0Dr4bGkYMhO3s65OPtK4zDxoXrhUC3CGT/vXHi0Pm1JSE7uTXkR+/RFI0a2n9uQ0hqcxT6sToe8mWXQlilCMGrFQZj1bmQrPRyISTLL2t6SIXELOSpiROmAAEIQKATBDTIIB8bDfmVFSG/pOjkF9WDqCW/eFcIF9aF5KrC9NTbdwZ04udbj5GPqPd+7cmQbHs7JNvfCumm4yFZfL11F8pdJvDJ//lDl3+Bw0MAAhDoXQJP3vhM754cZwaBISFgIcDTwG7dujVGTt2zZ8+QXDmXWX9XMu4fGmH8hRnIvCU+uW+VezWbx0cV1XSPovOukjDWO85YeZa3zS9evBinij5z5kywRGihy9LRdFOWZcHf9eLvWUyarpQy3d+YvF+tVgunTp2Koo6na16/fn1bwWXy97q1PlMG169fD4cPHw4WLs1+yqSetcYFReH9myTeVySRv6ENMxz0mqjqHSXef6+E6l19+MBOCan3Pmg0GsFTjDsSpGUy/3tzJ+natWvBor2lYH9/+fLld/L19+3rv/WjR49GYXT16tXv+5wNg0Nguu8Wv3cs21oytLzqdPXq1WDZ1BKvP28nD06XlGc68Ln4OIsXL57W1958883gCMNOjiTrgSHzlWb6bm8931Ke9b9Vu3btav0olv0blhvN/Gtf+1qwINmtVL4DPKBn48aN0/q3utf+vW3H5sCBA1EWtXi6d+/edrtEcdTR/B19uLwnbXfs0Ea///2+9cBXS/pTidYd+rl4mPn4TT+/nnXCYrCl4snJ1+//XvO/g5bXp/semHycQVj/IBF9EK6Pa4AABCDQDQIe/ETqLgFE3u7y5egQgAAEINCnBBrv5aH2tiJpqBG+runwsqMzb4yfMQK1o6Xrk5BuUL4uCZV16oxaq04o+XWVNdruKfZo258xXr4IgaEmoFdadl1RdtXp6Ki7jXPOtU3RdrPTytWBHubanXWHu6ZFrHxI0XfV8T66UxF45zAK0VA/D1w8BCAAAQhAAAIQgAAEeoRAlHlfKGTeczM8KTeXaMz2yEfSMPoR1S12qW6xmAaUGdLka20IZNfyMO52w1fVfvhnieeqR88kCq8PHSXejysS75NIvG1QswkCQ0egncg73xA+9alPhV//+tdRJD5+/Hjfi3+OKvynP/0pYt2/f3+47777JhDX6/UYFduRip1++9vfxojtEztQmBYBi+jf+9734r4//vGPwxe+8IVbvufnyc+V07e//e243LIDKxCAAAQgAAEIQGBICSDyDumN57IhAAEIQGB6BNwwX5PEO/5mFuoHJbj9QxOJX9R3Ne38nCb3N0lySxWVI11fiL2K1hGFXkm9ldVaPOV8ZU7Pih+DAAT6hUBDkYKuFuLueb3LtERxV8JudkavNMm8MXKQsjlNqTotFXCmsjMJ1XuSMHKPpsDdpvec32ckCEAAAhCAAAQgAAEIQGAoCdRVP7nxx0LmPTULBJpgKd3ZFHoXPKi6xuYkJAuoa8yC6NB/NR9TO+FxReH9SxZqr2YhOyIkswhIlGzSNNmSeBd+TBLvWp7NoX/AAAABEegVkddReC2x/uY3vwnPPvtsvDff//73w1NPPdX39+lXv/pV+OxnPztxHY8//nh47LHHgiPM/f73v4+ReP3ho48+Gp5//vmwcOHCiX0pTI+AI746anmZdu/eHZ544okog1uifumll+JHjjL98ssvx5kHyn3JIQABCEAAAhCAwDATQOQd5rvPtUMAAhCAwLQJ5JrpvX5KkTYOSeg9JAHusMS3UzLetH1ektv2l6hDSo38iSL0WvCtaIbF1FJvKfZq1hw6qObl7vCjEJh3AvlNvacuN6f5bJTi7nm9txR5N1eneKbyvIi7JRm1f6cb9b66O4Tq3YqStVsi74Y0JKPlDuQQgAAEIAABCEAAAhCAwDATaFzKw819jXDzWUU7PS4SsxlQvUyDBz1w8MEkLNiThupG1T1Ghpku136nBPJaCLWTahf8uwTe11Tf1mD/cPVOj9Kyvwa1pvKbFvyXJN5/rYTKCiTeFjoUITDUBHpF5P3BD34QvvrVr07ci8997nPhhz/8YVi0aNHEtn4u/OxnPwtf/vKXw5UrV9pexhe/+MXw9NNPD8z1tr3ILm989dVXw5e+9KXw+uuvt/2lhx9+OPz85z+P8nrbHdgIAQhAAAIQgAAEhpAAIu8Q3nQuGQIQgAAEZkFAHUeNi4rQ+w8JvYfzUFeE3kwRe2OUXkW8nNckAS5ZpY4Ayb2p5N7EQq/XVznXNueaph5Rbl7vEj8OgY4T8ECDxnt6F6mj2++nTEtDkcNzC7yaitbRdvNL+tlZRAnqyEkrYrij76bbJe0qAu/ILi0701BZqQ5LdWKSIAABCEAAAhCAAAQgAAEItBLINKvIzf1ZuPmcIp9anJxlnSZZKaH3w6qH3J+E0Q8VQi+DCVuRU55EINczFwXeNyTw/k117Tf1HGrQ7KySnrnKhySV/6ck3odSZqSZFUy+DIHBI/DMM8+EGzduhIceeig88sgj83aBL7zwQvC5bNu2LZ7LJz7xiZAkgzXo4OzZszEa7BtvvBGOHTsW1q5dGx588MFw//33EyG2Q0/e2NhYePHFF8PBgwfjUq1WwwMPPBCXPXv2hNFR/kOsQ6g5DAQgAAEIQAACA0IAkXdAbiSXAQEIQAACc0/AES9rispbOyKpV0Jv45iEuXfUoO9B3MrmPbldbXEhzq2WyOuIvc4t9brzStE+Ui2V5dpnoXYerHa4ecfPCUCg4wT0Xsn03snUadi47LxYLmj7BZWdS96NAwuu69d75T2kd0y6Re+a7ZrSdoc6zXeow3yDBhX4vUOCAAQgAAEIQAACEIAABCDwAQQ8cHHsLUXmfV5tL4qEOqsoqOXvaNBzVRF6q/dJ6L0nDSObqZ+UaMibBLIbGrx/QgP535LA+3e1+Vkk9wDZ2SZFh67uTcKij1c0Mw2z0swWJ9+HAAQgAAEIQAACEIAABCAAAQgMEgFE3kG6m1wLBCAAAQjMDwG15TeuqIFfEm/taCahVw38mvYxO6kPrumUlPVMctTLJXJ2FQEzjdF6i7LE3tTbCrE3VeTedIk6sphqsmduHScyXAQ8baejT2WKtNt4T+VS2lXHoSPuWtZ1BN4JaXc208x2Gq393KV6p2ySvLtVnZTb1EG+vdk5ni5j0ECncXM8CEAAAhCAAAQgAAEIDDwB1XfGjyky7/9KqtyXhfyMrrgTbS0rNMj5btVXFB11RFLlqOou1FkG/mma+gKL9r2aZt6qHZQ4rui7jbe1UXXyWSdXhTeGMPq4ovD+m+rHW9RAx8w0s8bKASAAAQhAAAIQgAAEIAABCEAAAoNEAJF3kO4m1wIBCEAAAvNPQJ1LDcl1tXfU4H/cQq+WExLuFLm3ZyL1tqO0UB0K6sCKgq+kXpct9aaO1mupV0tFUUMQfNvBYxsEZk7A03Rm1yTsajBAFHYl7rqcF/JubnFXi/NcMm9QNKqeTJZ3HXl3o94Vm9UZvlWd4VvdOal1vUvooOzJu8ZJQQACEIAABCAAAQhAoK8INDQTyc0/Z2Hsj1nIDnWwfrRIVRYPQLTQu0vLzjRU1jK4ua8ejlmcrAfS1s9KEtdsW3WJu/W3VC9Xe164MYuDtn51gerIigA9+oQk3r16tjRTFgkCEIAABCAAAQhAAAIQgAAEIAABCEwmgMg7mQjrEIAABCAAgU4RKKVeTcVXP6FIvYrYm51sSr35Zf1IvVM/1KXjODKIOrMSy7yWei36WvCV1JtY6l3qSDVF7rIjcC5SZ0SlS+fDYSHQrwQa+rvXtJzZVeWOsitRN5Yt7F7RNud6J2RaLOvG98NNXWwvRdltx76qd4LEf8u76SZF3tV0tFVFFXKOvNsOGNsgAAEIQAACEIAABCAAgdkSyDW4cVzRUm++1Aj1/ao/ndcR5Vx2JLmOc5eaNf5FdRtF6h3RzCKxfqN2EAYndoRw7xzEbXYaSFvOrmWBtyE5PD+nU+xUe50eGz9P1b1JWPixShi9Ow2JpF4SBCAAAQhAAAIQgAAEIAABCEAAAhBoRwCRtx0VtkEAAhCAAAQ6TUCdSrGD4LQ6CU5K6i2E3sYp9Ted62CUj06fd7vjOXCIOh6SZRJ6l+cxj5F7vR7FXn22RGLfEvVzKU+KPF2s8ki7A7INAv1PwBF8susSdBVdN7+msqPsKs8t7jq3sKtOQud5mascI+x2qtN5LjBa7r9Lou4G/X1vUq6pQaub1bm9QWW9A4LfDyQIQAACEIAABCAAAQhAAALdJKA6VP3dPIztb4SxfapzOTqvB0N2MnmwsmYYqe5UXWeHpF7POqJ6j9s5qPd0EvQcHkuPievqNc2a5Vm06kck7yoKb6aB90H19o4m1Z0thI8+noQFeyuhqno0z01HCXMwCEAAAhCAAAQgAAEIQAACEIDAwBFA5B24W8oFQQACEIBAPxBwdM7GWXUaqPOgIbnXS3ZGHQpnJPpd0hVICuzL5Gi8C9U3oQi9yVJJvjHXugQ/R+yNUq+F3sUqa4lyrzo3nE+IvoiAfXnrB/qk1aeXj+vv01F1Jevm15tl57FcyruSdnNF3W0uLidR5I3TcfZ6dN2pbqDk+xh1d73+RterI9LCriLwVr2oIzJZyB/sVOjYDgEIQAACEIAABCAAAQh0l0CMzvt2Fsb+Lwu1/VnIT+v3NCNKR5NmK4p1om2qC21XPchyr2cjQertKOauHczyrurqdbW71TRjlmfLahxV3f6Y6uyeLavTdXW1iyUa8DrycBoWPJaG0Z1E4e3aveXAEIAABCAAAQhAAAIQgAAEIACBASOAyDtgN5TLgQAEIACBPiSgTqYYrVcSb5R6nSuyTPauhEDJvvl7uqZOTes3n3js+1kKVITeoCXKvEXEXm9LLPM6d8TPRRYEJfg693pZljSYehpCdaSRINARAuq0yzQ1a25J96b+3m5oXZGcvO5ymWeWdi3sxrwoO2KPo+1qW5DoOxDJU8mu0CJJN71L4q4E3riok7rqsqPuWtgnQQACEIAABCAAAQhAAAIQ6BECjQuKznsgC+OvagakA6q3XejSiVnSXKUmCcm8FUXorWiWkjjI0VLvCrVfUFfqEvg7O2yuNrTG5WYbWxxAf1JlReB15N04eL7TsndxeskaVZf3KArvRyTx3puGyioGvt7ZnWNvCEAAAhCAAAQgAAEIQAACEIDAcBNA5B3u+8/VQwACEIBADxJw5M/GRUULkczbOJtFodfRe7OzEgYt9jpiyCCIve3YSyKMEX0dtXeRwqZY5C2i90ahN25vir2JhF5HA23m6kgrywu0zeLvqPJRHQ/ptx3pwd6mTrlcUa2zMf29WNJ1Ljk3rlvS9TbnFnejtKuyxV1LupZ3Y67n77oi6mp7nKJ1UP/mLNcv11KKu+uaAm91nTodVa6sLP6OBvuJ4eogAAEIQAACEIAABCAAgX4noHpg7bSi8/7F0XnVnnJIdbkrXbwoR+pVXSrVbCXpZgmcymMdSvUoz16SauBywOPs4g1oObRudYy6e073vRgc3/AMWCe0XXl8Djodebfl54Oeg8puCbwPJWHBg6miNevhoC2qlRBlCEAAAhCAAAQgAAEIQAACEIAABKZBAJF3GpDYBQIQgAAEIDCfBCbEXkm8MVKvOiYyL+fVIXFe8uFFnZ3kw6FJ7gizoCtZ18Kul1guovg215syr0XeKPpa+rXY62i+3ha3a90So8sjRTmuq2yhmE4XQZjnpI42C7l5Tc95zFUeL8oS3mPZkXAt5jyPkMYAAAyuSURBVHrdwm7MXW6uW8JtSrvKCym3XI+Crr+vfr2hSY5w7QhSa9WxrGhB6RqLu+potrSrHHF3aJ4ELhQCEIAABCAAAQhAAAIDScB1wdo7Enr/moX6X9WOclgVPs+m0u3k9om1qmMpOm/FEXrXSe5UvctL1fUui720M3TmLqitoHFV91ZtYnHx4HcPhj+tdrLTahc4p5/Rc9D1pFmlKncnYeQBSbwPpGFkS9ocUN71H+YHIAABCEAAAhCAAAQgAAEIQAACEBhEAoi8g3hXuSYIQAACEBhoAjHS6HuK2OtII+q0iFJvKfZq+shcU0rmjjozqBFE7/TuWsq1yKtIvWFB3pR5y/VS6lUeCol3QvQtt1Wbkm/QcSz+lnni7T62tzuvNNfjVJqebrPcVpaHocPOkXCLJTT0HPoZLLfV1XnqsrbF7c4t6HqfWP5nnntfC7mWdy3aWt6Necu2KO9qvyjsap8xRc+VyBs763xMUvOZXqZnUdN5NqXdQtx1R3LRoZwuL55jeEEAAhCAAAQgAAEIQAACEBggAp6BZfyoFgu9B9R+ckT1xatzdIEegOzZhSz2xkGTylUHS1dL7PWiOlplhT53O4X3JU1NQLfNdf3GpebsVQ21eWVe3Cammass8EZx1wPcte+cpKVqAtqpevUeReC9XwLvNtWrNUsUCQIQgAAEIAABCEAAAhCAAAQgAAEIzIYAIu9s6PFdCEAAAhCAQC8QUEdFdk0dGOrIiJFIYqeGtl1054b6MRSxN1c5yr0SKUnTJGAR1+KuBV9Ju2FEoGPk3jLXZ8U+pdAbpV6Ju5Pl3rifhV7LvF7KsveN6+r0KT6Luft/vN6SJy3luN3r3qXIb+n8K7d5h9aOrKKcl9ucF0vuaSbL9bJc5PEzlWPuZyjTjpZy/XmZF+Uo5UY5V8+c97Wk6/XWsgTdpryr4xSRdkNNJ+2yJV5/XuyjEmk6BPQsJupMtLSbrNbj46i7FnhVrij6U+wsdhSoxeLc+nxM59jsAwEIQAACEIAABCAAAQhAoE8JZJqZpXZMQu8BCb2vq93EEXrfm4eLccTelaqnqV6WlLOjeH2lpV7V1ST2eqCl62xxgPA8nOJ8/6TbDrLrasvS4PXsspoRLjvXonYty7t568xUcxFxdzIQ3aMYgfdeReG9VwLvVt0vzQ5FggAEIAABCEAAAhCAAAQgAAEIQAACnSCAyNsJihwDAhCAAAQg0GMELFBmV9RBJYF3IlqJpN7sUrMzJFckk1zl3J1XjnRKmhsChaxrkTcuUeJVOXVHnToT4+fqBIoSb7Fu7zKuez8t7iNyHpM/1H7e5v9z2aUJu1crsnbj1vh/2scW7+Sy1wtpN2SKbBvXi/28PS7abhm3kHjjNq/HbcU+ykhzQMARpZdrUWdv7AguOn8t71rYnYjytFT3288aCQIQgAAEIAABCEAAAhCAwJATcFTX2jsSet+Q0PuW2kreboqhsU47H2xcjy/k3kQyr+tzMVddL9XMKuky1fdUp0s1YNNyb7pEuaO+9nsdz+1VipacXVOuQekxV/tVriXT7FKWeN1mlbndyoPSJfSGm1piO8Y83CjxdmRlC7zVe5Iw+mEJvJt1bxxNmQQBCEAAAhCAAAQgAAEIQAACEIAABDpIAJG3gzA5FAQgAAEIQKCXCcTIJlfVWWWZNy5yMN0pUsq96hzJ3WEiuTdXh4ojppIgAIF5JOAou+qsnZB2HaHJizt546Kyp2R1eYk6Eh0hmgQBCEAAAhCAAAQgAAEIQAACUxLINftL/d0s1A5K7D2YSeiVNHpSlqhl0V5IFnUXuR6oOp4HcFrk9ewrknrjLCyLta4osIn30RLLHuwpsTRRno4qH9Ux5lr4taCrgeIWpvMyF1NLu/kNbdfi6Mhub4qL2qeyqyp7KdqiwnWdtwcS90Iy202qb9+tyZl2S97dLZF3XUq9uxfuDecAAQhAAAIQgAAEIAABCEAAAhAYUAKIvAN6Y7ksCEAAAhCAwLQIqK/KHSkWexsSeZvTF2q9KEep12JvKfi6U0WdXiQIQKCDBEbU6WphV1GXJjprHYXJ06uWucqVKPJqm6fuJPhPB28Ah4IABCAAAQhAAAIQgAAEho6A2kM8uLl2RFKvovPW/6G2kCMSTS+IRK/IpJNvimfnsbSrCL1hca5cZUu9juyrJVjmddn7qJ4ZXNcc0b7OPVDUcm+xxHIx60+c1Md1zLKe6ei3WuJsPS6LR5yhxzPyaIllDf7Oa+LlNiItUd61wGsh2vLuWLOcqx3JS7DMe10H0/ae5Ss2yWoh2qH6984kjCgK78iONA6enWCj0ydBAAIQgAAEIAABCEAAAhCAAAQgAIFuEEDk7QZVjgkBCEAAAhDodwKOpCLBtyGBN0q9l5W7rGkOc0dNcV4uWnfZnTI92xnT7/eD8+9/Au4sdYeqZd04RWqzHKdLjdOmStxVxCUvUdh12Z2z7lglQQACEIAABCAAAQhAAAIQgEDXCDiKbP1UHsYPZ6EumbdxVM0bJySduq3DIms/JtclLfMqOm+Ueau6ENdLq5J9K0U5iryqdyZad5bq/1TMbfDqfyFvrse2nobafizy1rVNZef5uPJC5O3b9iBdTnCdfIvq4tuFR+Lu6C5F390gThKjSRCAAAQgAAEIQAACEIAABCAAAQhAYK4IIPLOFWl+BwIQgAAEIDAIBNRH42kRLfJmV5z/sxynQ7Tk62kSPTVizPUF50TyHYS7zzXcjoCjHCkiUvC0p0vU6ecou+U0qF532YJuFHeda/G2heocpH/wdnT5HAIQgAAEIAABCEAAAhCAQHcJqAnDA5rr7yhS79EsNI5J6j2u9g9Jvm7b6Fupt7vU+u/orn+7Lr5R8u5W+c3bFHl3exqqlnlVT6d+3n+3lDOGAAQgAAEIQAACEIAABCAAAQgMAgFE3kG4i1wDBCAAAQhAoBcIOIqvJV9JvFkUepvlGMG3FHs1jWI5rWLMr6kzzNMsOpqvpmCkU6wXbiTn0JaAO/ocyciirqcvVbTcOI1pLGu9EHfTKO8Wgu5S51q0LdV0p0TXbUuWjRCAAAQgAAEIQAACEIAABHqPQCap96KE3ncUpVdib8PLiULqdaRefU7qIwKOPry8kHc3S97drKi7WyXwbklDZSX19T66k5wqBCAAAQhAAAIQgAAEIAABCEBgYAkg8g7sreXCIAABCEAAAj1EQL5urukWM4m8XhytN5ZjtN5yXed7Q59b6tViuTfXepR8b2jKRpXDmD6js6yHbuyAnIqnF10gGdeRcRflIVmkssRb50FLWq5b0i0k3ijnWua1pKvcS+JpSy38kiAAAQhAAAIQgAAEIAABCEBgcAhY6r2sSL0nvEjsPam2i9NqnjitNovzukwPTCb1HgHX89eozr5BdfYNIVQ3Sd7drMi7kngrmi2Hwba9d8s4IwhAAAIQgAAEIAABCEAAAhCAwDATQOQd5rvPtUMAAhCAAAR6iEBel7jriL6FwHtLHqP2NqXe/GZzP+cWe5vrKuu7ebEeO9HckaYowaQhJWA5VxF0o6DrzjstQaJuslBlLaHIkwXFNsu6FnKjtKuyRN7U0q5zb/N3fUwSBCAAAQhAAAIQgAAEIAABCAwvAY0xzjS7UP1dLacUpdfLGbVlvKv2CW3LLwuN2jdI80BAg2uTFVruUj1+naLurpewu1HyrheVPQCXwbfzcF/4SQhAAAIQgAAEIAABCEAAAhCAAASmRQCRd1qY2AkCEIAABCAAgZ4gUEb2tbTbIvRmE+uF5Guh14tl3ljW/hPlYvu4tilKcJR+a4r4q/XgdS9E/RWEHkme/tKRbt0hN6pOtxHdJwm6Mfqt1hOXLekWsm7cx+ViexR3J2Rdddy1CLzNso5BJN0eudmcBgQgAAEIQAACEIAABCAAgf4i4HaFxiXJvBZ7tWRaXM7Oqe3hfCH2ql2C1AUCrvuvVGDdNarXr5W4K4G3sk6LBV7nK7W92oXf5ZAQgAAEIAABCEAAAhCAAAQgAAEIQKALBBB5uwCVQ0IAAhCAAAQg0BsEYpRfCbz5mDrPWoTepuTbFHkt+DYXCaJluRB883pT7vVxLPhG8deRg/1562eO/Ot9GhKCY67vTWzT9mESgy3eOnKtO8tiro4z5xUx8TYtcd1ibtVirrbF8uRcnxXCrqXcWC7F3YntOnYp8FrWjfJukdNZJ2gkCEAAAhCAAAQgAAEIQAACEJhLAm5fiGLvOQm9XiTzZsqzC2pLuOhFbRFXdEZuOyBNn4DbDZZZ3FWdf5Xl3abAW5HEW1mrxRKvP3MbAwkCEIAABCAAAQhAAAIQgAAEIAABCPQhgf8HyStAG1AbO6sAAAAASUVORK5CYII="
+    }
+   },
    "cell_type": "markdown",
    "id": "871c539b-a1c2-41a3-a33d-37649b603de2",
    "metadata": {},
    "source": [
-    "### Filter to `missense` variants passing filters"
+    "### Filter to `missense` variants passing filters\n",
+    "\n",
+    "![Screenshot 2024-12-20 at 11.12.46 AM.png](attachment:440e8e62-f912-4c99-9687-12d655742ab5.png)"
    ]
   },
   {
@@ -3743,17 +3763,24 @@
     }
    ],
    "source": [
-    "var_ht = filter_by_csqs(['missense'], ht=drd2_interval_ht)\n",
+    "var_ht = filter_by_consequence_category(missense=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
     "print(\"The total number of missense variants passing filters in DRD2 is: \", var_ht.count())"
    ]
   },
   {
+   "attachments": {
+    "f42fc1f8-afe3-4684-9fee-66582eba9080.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "id": "5b980b4f-13bd-4bb4-bdbc-5584a6a59ca5",
    "metadata": {},
    "source": [
-    "### Filter to `synonymous` variants passing filters"
+    "### Filter to `synonymous` variants passing filters\n",
+    "\n",
+    "![Screenshot 2024-12-20 at 11.12.57 AM.png](attachment:f42fc1f8-afe3-4684-9fee-66582eba9080.png)"
    ]
   },
   {
@@ -4832,17 +4859,24 @@
     }
    ],
    "source": [
-    "var_ht = filter_by_csqs(['synonymous'], ht=drd2_interval_ht)\n",
+    "var_ht = filter_by_consequence_category(synonymous=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
     "print(\"The total number of synonymous variants passing filters in DRD2 is: \", var_ht.count())"
    ]
   },
   {
+   "attachments": {
+    "5565afe1-9dbe-42de-b70e-224b9d8651e4.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "id": "cbcd2fa0-5108-4d94-a681-493882c295bf",
    "metadata": {},
    "source": [
-    "### Filter to 'Other' variants passing filters"
+    "### Filter to 'Other' variants passing filters\n",
+    "\n",
+    "![Screenshot 2024-12-20 at 11.13.08 AM.png](attachment:5565afe1-9dbe-42de-b70e-224b9d8651e4.png)"
    ]
   },
   {
@@ -4855,11 +4889,11 @@
      "data": {
       "text/html": [
        "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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A,"A"),(1,NA,NA,"protein_coding",1,NA,1751,1751,NA,NA,NA,["3_prime_UTR_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.*65C>T",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,NA,NA,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"A"),(1,NA,NA,"protein_coding",NA,NA,1664,1664,NA,NA,NA,["3_prime_UTR_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.*65C>T",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"A"),(1,NA,NA,"protein_coding",NA,NA,1479,1479,NA,NA,NA,["3_prime_UTR_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.*65C>T",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"A")]"SNV"5.24e+00"AS_MQ"FalseFalseFalseFalseFalseNATrueFalse"multi-snv"2False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,4139,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,22477,65025,118275,313361,178106,20388,3276,1054,693,645,582,508,355,259,173,113,79,64]0265[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]01[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][653,0,1690,2,6865,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][264,3602,22767,65834,118294,313419,178113,20399,3282,1056,696,645,582,508,355,259,173,113,79,64]0265[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]01[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]006.25e+004.76e-01NA0.00e+002.10e-011.62e+00NANA
showing top 5 rows
\n"
       ],
       "text/plain": [
@@ -4868,11 +4902,11 @@
        "+-----------------+------------+\n",
        "| locus   | array |\n",
        "+-----------------+------------+\n",
-       "| chr11:113409636 | [\"G\",\"C\"]  |\n",
-       "| chr11:113409693 | [\"G\",\"A\"]  |\n",
-       "| chr11:113409717 | [\"C\",\"T\"]  |\n",
-       "| chr11:113409758 | [\"C\",\"T\"]  |\n",
-       "| chr11:113410002 | [\"C\",\"A\"]  |\n",
+       "| chr11:113410657 | [\"C\",\"T\"]  |\n",
+       "| chr11:113410658 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410658 | [\"G\",\"T\"]  |\n",
+       "| chr11:113410660 | [\"A\",\"G\"]  |\n",
+       "| chr11:113410662 | [\"G\",\"A\"]  |\n",
        "+-----------------+------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -4880,11 +4914,11 @@
        "+------------------------------------------------------------------------------+\n",
        "| array    |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [(1,1.87e-03,534,0),(5,7.95e-06,628788,2),(0,0.00e+00,8,0),(0,NA,0,0),(0,... |\n",
-       "| [(1,1.20e-03,834,0),(16,2.54e-05,628790,3),(0,0.00e+00,6,0),(0,0.00e+00,2... |\n",
-       "| [(1,1.01e-03,990,0),(5,7.95e-06,628794,2),(0,0.00e+00,10,0),(0,0.00e+00,4... |\n",
-       "| [(1,1.23e-03,810,0),(4,6.36e-06,628786,1),(0,0.00e+00,12,0),(0,0.00e+00,4... |\n",
-       "| [(128,1.34e-01,952,15),(18463,2.94e-02,628994,6837),(0,0.00e+00,6,0),(17,... |\n",
+       "| [(18,1.25e-05,1445518,0),(24,1.64e-05,1461322,0),(1,3.02e-05,33096,0),(0,... |\n",
+       "| [(23,1.59e-05,1448166,0),(23,1.57e-05,1461456,0),(0,0.00e+00,33180,0),(0,... |\n",
+       "| [(94,6.49e-05,1448162,0),(105,7.18e-05,1461456,0),(24,7.23e-04,33180,0),(... |\n",
+       "| [(1,6.89e-07,1450438,0),(2,1.37e-06,1461588,0),(0,0.00e+00,33208,0),(0,0.... |\n",
+       "| [(2,1.38e-06,1451396,0),(2,1.37e-06,1461538,0),(0,0.00e+00,33252,0),(1,2.... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
        "+------------------+------------------+------------------+\n",
@@ -4892,11 +4926,11 @@
        "+------------------+------------------+------------------+\n",
        "|            int32 |          float64 |            int32 |\n",
        "+------------------+------------------+------------------+\n",
-       "|               NA |               NA |               NA |\n",
-       "|                1 |         2.87e-03 |              348 |\n",
-       "|                1 |         2.17e-03 |              460 |\n",
-       "|               NA |               NA |               NA |\n",
-       "|                2 |         5.00e-01 |                4 |\n",
+       "|                4 |         1.01e-04 |            39606 |\n",
+       "|                3 |         7.57e-05 |            39630 |\n",
+       "|               24 |         7.23e-04 |            33180 |\n",
+       "|                1 |         9.07e-07 |          1103066 |\n",
+       "|                1 |         2.24e-05 |            44696 |\n",
        "+------------------+------------------+------------------+\n",
        "\n",
        "+--------------------------------+-----------------------+-------------------+\n",
@@ -4904,11 +4938,11 @@
        "+--------------------------------+-----------------------+-------------------+\n",
        "|                          int64 | str                   |             int32 |\n",
        "+--------------------------------+-----------------------+-------------------+\n",
-       "|                             NA | NA                    |                NA |\n",
+       "|                              0 | \"eas\"                 |                 4 |\n",
+       "|                              0 | \"eas\"                 |                10 |\n",
+       "|                              0 | \"afr\"                 |                15 |\n",
        "|                              0 | \"nfe\"                 |                 1 |\n",
-       "|                              0 | \"nfe\"                 |                 1 |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                              0 | \"eas\"                 |                 2 |\n",
+       "|                              0 | \"amr\"                 |                 1 |\n",
        "+--------------------------------+-----------------------+-------------------+\n",
        "\n",
        "+-------------------+-------------------+---------------------------------+\n",
@@ -4916,11 +4950,11 @@
        "+-------------------+-------------------+---------------------------------+\n",
        "|           float64 |             int32 |                           int64 |\n",
        "+-------------------+-------------------+---------------------------------+\n",
-       "|                NA |                NA |                              NA |\n",
-       "|          2.87e-03 |               348 |                               0 |\n",
-       "|          2.17e-03 |               460 |                               0 |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|          5.00e-01 |                 4 |                               0 |\n",
+       "|          1.11e-04 |             36050 |                               0 |\n",
+       "|          2.86e-05 |            349848 |                               0 |\n",
+       "|          8.50e-04 |             17650 |                               0 |\n",
+       "|          2.86e-06 |            349920 |                               0 |\n",
+       "|          2.29e-05 |             43728 |                               0 |\n",
        "+-------------------+-------------------+---------------------------------+\n",
        "\n",
        "+------------------------+\n",
@@ -4928,11 +4962,11 @@
        "+------------------------+\n",
        "| str                    |\n",
        "+------------------------+\n",
-       "| NA                     |\n",
+       "| \"eas\"                  |\n",
        "| \"nfe\"                  |\n",
+       "| \"afr\"                  |\n",
        "| \"nfe\"                  |\n",
-       "| NA                     |\n",
-       "| \"eas\"                  |\n",
+       "| \"amr\"                  |\n",
        "+------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -4940,11 +4974,11 @@
        "+------------------------------------------------------------------------------+\n",
        "| array                                |\n",
        "+------------------------------------------------------------------------------+\n",
+       "| [(7.78e-06,6.42e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(3.35e-05,1.... |\n",
+       "| [(1.06e-05,8.83e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(2.01e-05,1.... |\n",
+       "| [(5.41e-05,5.02e-05),(4.99e-04,4.24e-04),(2.99e-05,1.81e-05),(0.00e+00,0.... |\n",
        "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(1.16e-01,1.08e-01),(0.00e+00,0.00e+00),(1.23e-01,1.01e-01),(8.88e-02,3.... |\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
        "+-------------------------+---------------------------------+\n",
@@ -4952,11 +4986,11 @@
        "+-------------------------+---------------------------------+\n",
        "|                 float64 | str                             |\n",
        "+-------------------------+---------------------------------+\n",
+       "|                3.35e-05 | \"eas\"                           |\n",
+       "|                2.01e-05 | \"eas\"                           |\n",
+       "|                4.99e-04 | \"afr\"                           |\n",
        "|                      NA | NA                              |\n",
        "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                1.23e-01 | \"amr\"                           |\n",
        "+-------------------------+---------------------------------+\n",
        "\n",
        "+-------------------------+---------------------------------+\n",
@@ -4964,11 +4998,11 @@
        "+-------------------------+---------------------------------+\n",
        "|                 float64 | str                             |\n",
        "+-------------------------+---------------------------------+\n",
+       "|                1.99e-05 | \"eas\"                           |\n",
+       "|                1.06e-05 | \"eas\"                           |\n",
+       "|                4.24e-04 | \"afr\"                           |\n",
        "|                      NA | NA                              |\n",
        "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                1.02e-01 | \"nfe\"                           |\n",
        "+-------------------------+---------------------------------+\n",
        "\n",
        "+--------------------------+----------------------------------+\n",
@@ -4976,11 +5010,11 @@
        "+--------------------------+----------------------------------+\n",
        "|                  float64 | str                              |\n",
        "+--------------------------+----------------------------------+\n",
+       "|                 3.78e-05 | \"eas\"                            |\n",
+       "|                 1.54e-05 | \"nfe\"                            |\n",
+       "|                 5.23e-04 | \"afr\"                            |\n",
        "|                       NA | NA                               |\n",
        "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                 1.23e-01 | \"amr\"                            |\n",
        "+--------------------------+----------------------------------+\n",
        "\n",
        "+--------------------------+----------------------------------+---------+\n",
@@ -4988,119 +5022,119 @@
        "+--------------------------+----------------------------------+---------+\n",
        "|                  float64 | str                              |   int32 |\n",
        "+--------------------------+----------------------------------+---------+\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
+       "|                 2.24e-05 | \"eas\"                            |       3 |\n",
+       "|                 1.15e-05 | \"nfe\"                            |       1 |\n",
+       "|                 4.23e-04 | \"afr\"                            |       2 |\n",
        "|                       NA | NA                               |       2 |\n",
-       "|                       NA | NA                               |       2 |\n",
-       "|                 1.02e-01 | \"nfe\"                            |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
        "+--------------------------+----------------------------------+---------+\n",
        "\n",
-       "+-----------+-----------------+----------+----------+----------+\n",
-       "| was_split | rsid            | filters  |  info.FS |  info.MQ |\n",
-       "+-----------+-----------------+----------+----------+----------+\n",
-       "|      bool | set        | set |  float64 |  float64 |\n",
-       "+-----------+-----------------+----------+----------+----------+\n",
-       "|      True | {\"rs200733424\"} | {}       | 0.00e+00 | 6.00e+01 |\n",
-       "|      True | NA              | {}       | 5.32e+00 | 6.00e+01 |\n",
-       "|      True | NA              | {}       | 4.82e-16 | 6.00e+01 |\n",
-       "|      True | {\"rs200557458\"} | {}       | 0.00e+00 | 6.00e+01 |\n",
-       "|      True | {\"rs6278\"}      | {}       | 2.80e+00 | 6.00e+01 |\n",
-       "+-----------+-----------------+----------+----------+----------+\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      bool | set         | set |  float64 |  float64 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      True | {\"rs200559334\"}  | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|      True | {\"rs201475538\"}  | {}       | 8.65e-01 | 6.00e+01 |\n",
+       "|      True | {\"rs201475538\"}  | {}       | 8.65e-01 | 6.00e+01 |\n",
+       "|      True | {\"rs1950760213\"} | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.51e-01 | 6.00e+01 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
        "\n",
        "+----------------+-----------------+----------+---------------------+\n",
        "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
        "+----------------+-----------------+----------+---------------------+\n",
        "|        float64 |           int64 |  float64 |             float64 |\n",
        "+----------------+-----------------+----------+---------------------+\n",
-       "|       0.00e+00 |             582 | 1.94e+01 |           -2.11e+00 |\n",
-       "|       0.00e+00 |             898 | 9.76e+00 |            0.00e+00 |\n",
-       "|       0.00e+00 |             209 | 9.09e+00 |            8.42e-01 |\n",
-       "|       0.00e+00 |             947 | 1.26e+01 |           -6.74e-01 |\n",
-       "|       0.00e+00 |         1998239 | 1.99e+01 |            0.00e+00 |\n",
+       "|       0.00e+00 |           12992 | 1.27e+01 |            0.00e+00 |\n",
+       "|       0.00e+00 |          142805 | 1.36e+01 |           -1.80e-02 |\n",
+       "|       0.00e+00 |          142805 | 1.36e+01 |           -1.80e-02 |\n",
+       "|       0.00e+00 |            3619 | 1.78e+01 |            6.74e-01 |\n",
+       "|       0.00e+00 |            3070 | 9.27e+00 |            0.00e+00 |\n",
        "+----------------+-----------------+----------+---------------------+\n",
        "\n",
-       "+---------------------------+----------+------------+------------+------------+\n",
-       "| info.SB                   | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
-       "+---------------------------+----------+------------+------------+------------+\n",
-       "| array              |  float64 |      int32 |    float64 |    float64 |\n",
-       "+---------------------------+----------+------------+------------+------------+\n",
-       "| [8,2,15,5]                | 5.82e-01 |         30 |   0.00e+00 |   6.00e+01 |\n",
-       "| [31,19,21,21]             | 3.30e-01 |         92 |   4.52e+00 |   6.00e+01 |\n",
-       "| [6,7,4,6]                 | 9.17e-01 |         23 |   0.00e+00 |   6.00e+01 |\n",
-       "| [16,19,18,22]             | 7.22e-01 |         75 |   0.00e+00 |   6.00e+01 |\n",
-       "| [19752,12842,45058,22649] | 9.83e-01 |     100292 |   2.80e+00 |   6.00e+01 |\n",
-       "+---------------------------+----------+------------+------------+------------+\n",
+       "+---------------------+----------+------------+------------+------------+\n",
+       "| info.SB             | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+---------------------+----------+------------+------------+------------+\n",
+       "| array        |  float64 |      int32 |    float64 |    float64 |\n",
+       "+---------------------+----------+------------+------------+------------+\n",
+       "| [543,53,386,37]     | 7.04e-01 |       1019 |   4.82e-16 |   6.00e+01 |\n",
+       "| [4798,567,4576,579] | 6.27e-01 |      10518 |   1.55e+00 |   6.00e+01 |\n",
+       "| [4798,567,4576,579] | 6.27e-01 |      10518 |   0.00e+00 |   6.00e+01 |\n",
+       "| [83,21,79,20]       | 6.91e-01 |        203 |   4.82e-16 |   6.00e+01 |\n",
+       "| [174,36,99,22]      | 6.12e-01 |        331 |   1.21e+00 |   6.00e+01 |\n",
+       "+---------------------+----------+------------+------------+------------+\n",
        "\n",
        "+-------------------+-----------------+--------------------+------------+\n",
        "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
        "+-------------------+-----------------+--------------------+------------+\n",
        "|           float64 |         float64 |              int64 |    float64 |\n",
        "+-------------------+-----------------+--------------------+------------+\n",
-       "|          0.00e+00 |        5.41e-01 |                582 |   1.94e+01 |\n",
-       "|          0.00e+00 |        1.00e+00 |                553 |   8.01e+00 |\n",
-       "|          0.00e+00 |        2.67e-01 |                178 |   1.05e+01 |\n",
-       "|          0.00e+00 |        1.00e+00 |                703 |   1.12e+01 |\n",
-       "|          0.00e+00 |        1.00e+00 |            1998092 |   1.99e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |              12891 |   1.32e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |              19189 |   1.44e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |             123616 |   1.35e+01 |\n",
+       "|          0.00e+00 |        4.53e-01 |               3591 |   1.98e+01 |\n",
+       "|          0.00e+00 |        8.45e-01 |               2972 |   1.38e+01 |\n",
        "+-------------------+-----------------+--------------------+------------+\n",
        "\n",
-       "+------------------------+---------------------------+-------------+\n",
-       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE          | info.AS_SOR |\n",
-       "+------------------------+---------------------------+-------------+\n",
-       "|                float64 | array              |     float64 |\n",
-       "+------------------------+---------------------------+-------------+\n",
-       "|              -2.11e+00 | [8,2,15,5]                |    5.82e-01 |\n",
-       "|               0.00e+00 | [31,19,15,15]             |    3.30e-01 |\n",
-       "|               7.20e-02 | [6,7,3,5]                 |    1.00e+00 |\n",
-       "|              -1.13e+00 | [16,19,14,17]             |    7.13e-01 |\n",
-       "|               0.00e+00 | [19752,12842,45055,22646] |    9.83e-01 |\n",
-       "+------------------------+---------------------------+-------------+\n",
+       "+------------------------+---------------------+-------------+---------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE    | info.AS_SOR | info.AS_VarDP |\n",
+       "+------------------------+---------------------+-------------+---------------+\n",
+       "|                float64 | array        |     float64 |         int32 |\n",
+       "+------------------------+---------------------+-------------+---------------+\n",
+       "|              -3.90e-02 | [543,53,379,36]     |    7.13e-01 |           976 |\n",
+       "|               1.34e-01 | [4798,567,588,57]   |    8.94e-01 |          1334 |\n",
+       "|              -2.10e-02 | [4798,567,3988,522] |    5.96e-01 |          9184 |\n",
+       "|               9.51e-01 | [83,21,77,19]       |    7.15e-01 |           181 |\n",
+       "|               1.49e+00 | [174,36,87,20]      |    5.79e-01 |           216 |\n",
+       "+------------------------+---------------------+-------------+---------------+\n",
        "\n",
-       "+---------------+----------------+----------------------------+\n",
-       "| info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
-       "+---------------+----------------+----------------------------+\n",
-       "|         int32 |           bool |                       bool |\n",
-       "+---------------+----------------+----------------------------+\n",
-       "|            30 |          False |                      False |\n",
-       "|            69 |          False |                      False |\n",
-       "|            17 |          False |                      False |\n",
-       "|            63 |          False |                      False |\n",
-       "|        100286 |          False |                         NA |\n",
-       "+---------------+----------------+----------------------------+\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           bool |                       bool |                   bool |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                      False |                  False |\n",
+       "|          False |                         NA |                     NA |\n",
+       "+----------------+----------------------------+------------------------+\n",
        "\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "| info.sibling_singleton | info.omni | info.mills | info.monoallelic |\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "|                   bool |      bool |       bool |             bool |\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "|                  False |     False |      False |            False |\n",
-       "|                  False |     False |      False |            False |\n",
-       "|                  False |     False |      False |            False |\n",
-       "|                  False |     False |      False |            False |\n",
-       "|                     NA |      True |      False |            False |\n",
-       "+------------------------+-----------+------------+------------------+\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|     False |      False |            False |         False |       9.14e+00 |\n",
+       "|     False |      False |            False |         False |       6.96e+00 |\n",
+       "|     False |      False |            False |         False |       9.35e+00 |\n",
+       "|     False |      False |            False |         False |       4.34e+00 |\n",
+       "|     False |      False |            False |         False |       5.24e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
        "\n",
-       "+---------------+----------------+-----------------------+\n",
-       "| info.only_het | info.AS_VQSLOD | info.inbreeding_coeff |\n",
-       "+---------------+----------------+-----------------------+\n",
-       "|          bool |        float64 |               float64 |\n",
-       "+---------------+----------------+-----------------------+\n",
-       "|         False |       4.51e+00 |              8.00e-01 |\n",
-       "|         False |       5.16e+00 |              3.75e-01 |\n",
-       "|         False |       6.19e+00 |              8.00e-01 |\n",
-       "|         False |       6.36e+00 |              5.00e-01 |\n",
-       "|         False |       5.57e+00 |              7.33e-01 |\n",
-       "+---------------+----------------+-----------------------+\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|             -1.64e-05 |\n",
+       "|             -1.57e-05 |\n",
+       "|             -7.19e-05 |\n",
+       "|             -1.37e-06 |\n",
+       "|             -1.37e-06 |\n",
+       "+-----------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
        "| info.vrs.VRS_Allele_IDs                                                      |\n",
        "+------------------------------------------------------------------------------+\n",
        "| array                                                                   |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.BlCz08hG7ZEQzvW20V8Kp4xJMx-pMKes\",\"ga4gh:VA.U4eD7PXtXRCClN6FQt... |\n",
-       "| [\"ga4gh:VA.aXxME51cngsJBnH_3WX01B0Ms-SLay9X\",\"ga4gh:VA.ynryFZCH9dBU67nwPr... |\n",
-       "| [\"ga4gh:VA.Q6p6okAv6qHSPSV8MOvy8Hhl0kZ6tQ4U\",\"ga4gh:VA.WIfYWxgEc5ICUUByiD... |\n",
-       "| [\"ga4gh:VA.k_gesWx8TbLs23MHIkc3NaVscBWVDrSE\",\"ga4gh:VA.NJz3hsVBrqbTn17g5T... |\n",
-       "| [\"ga4gh:VA.HjAJ4gj43VVFJQmV-h5WuPZZPBmNwSFZ\",\"ga4gh:VA.sT14AsCSWlf2AqH0wh... |\n",
+       "| [\"ga4gh:VA.xwHxL6yO3I874HqhfGar1ZIaOp66ifsx\",\"ga4gh:VA.eglRJIN5-izMH1peMx... |\n",
+       "| [\"ga4gh:VA.3n-gYs3xsspnxwI91EMCEDsIcrj9bIqO\",\"ga4gh:VA.9Y9GDmOWCTqMks8dsN... |\n",
+       "| [\"ga4gh:VA.3n-gYs3xsspnxwI91EMCEDsIcrj9bIqO\",\"ga4gh:VA.R13f7bcCv-WGRkCM6W... |\n",
+       "| [\"ga4gh:VA.MHTiWiUO_hsQ_FAaIagdomN6DdnpjqRm\",\"ga4gh:VA.D8-MF1xUzSMIKncRc7... |\n",
+       "| [\"ga4gh:VA.oDr3967BFZ6uu2LJEc9_RVWIOnNqtSF-\",\"ga4gh:VA.N81rCvIHb-9PjhcHLL... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
        "+-----------------------+-----------------------+---------------------+\n",
@@ -5108,11 +5142,11 @@
        "+-----------------------+-----------------------+---------------------+\n",
        "| array          | array          | array          |\n",
        "+-----------------------+-----------------------+---------------------+\n",
-       "| [113409635,113409635] | [113409636,113409636] | [\"G\",\"C\"]           |\n",
-       "| [113409692,113409692] | [113409693,113409693] | [\"G\",\"A\"]           |\n",
-       "| [113409716,113409716] | [113409717,113409717] | [\"C\",\"T\"]           |\n",
-       "| [113409757,113409757] | [113409758,113409758] | [\"C\",\"T\"]           |\n",
-       "| [113410001,113410001] | [113410002,113410002] | [\"C\",\"A\"]           |\n",
+       "| [113410656,113410656] | [113410657,113410657] | [\"C\",\"T\"]           |\n",
+       "| [113410657,113410657] | [113410658,113410658] | [\"G\",\"A\"]           |\n",
+       "| [113410657,113410657] | [113410658,113410658] | [\"G\",\"T\"]           |\n",
+       "| [113410659,113410659] | [113410660,113410660] | [\"A\",\"G\"]           |\n",
+       "| [113410661,113410661] | [113410662,113410662] | [\"G\",\"A\"]           |\n",
        "+-----------------------+-----------------------+---------------------+\n",
        "\n",
        "+-------------------+-----------+--------+--------------------------------+\n",
@@ -5120,11 +5154,11 @@
        "+-------------------+-----------+--------+--------------------------------+\n",
        "| str               |     int32 | str    | str                            |\n",
        "+-------------------+-----------+--------+--------------------------------+\n",
-       "| \"G/C\"             | 113409636 | \".\"    | \"chr11\t113409636\t.\tG\tC\t.\t.\tGT\" |\n",
-       "| \"G/A\"             | 113409693 | \".\"    | \"chr11\t113409693\t.\tG\tA\t.\t.\tGT\" |\n",
-       "| \"C/T\"             | 113409717 | \".\"    | \"chr11\t113409717\t.\tC\tT\t.\t.\tGT\" |\n",
-       "| \"C/T\"             | 113409758 | \".\"    | \"chr11\t113409758\t.\tC\tT\t.\t.\tGT\" |\n",
-       "| \"C/A\"             | 113410002 | \".\"    | \"chr11\t113410002\t.\tC\tA\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113410657 | \".\"    | \"chr11\t113410657\t.\tC\tT\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410658 | \".\"    | \"chr11\t113410658\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/T\"             | 113410658 | \".\"    | \"chr11\t113410658\t.\tG\tT\t.\t.\tGT\" |\n",
+       "| \"A/G\"             | 113410660 | \".\"    | \"chr11\t113410660\t.\tA\tG\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410662 | \".\"    | \"chr11\t113410662\t.\tG\tA\t.\t.\tGT\" |\n",
        "+-------------------+-----------+--------+--------------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -5180,11 +5214,11 @@
        "+---------------------+-----------+------------+\n",
        "| str                 |     int32 |      int32 |\n",
        "+---------------------+-----------+------------+\n",
-       "| \"chr11\"             | 113409636 |          1 |\n",
-       "| \"chr11\"             | 113409693 |          1 |\n",
-       "| \"chr11\"             | 113409717 |          1 |\n",
-       "| \"chr11\"             | 113409758 |          1 |\n",
-       "| \"chr11\"             | 113410002 |          1 |\n",
+       "| \"chr11\"             | 113410657 |          1 |\n",
+       "| \"chr11\"             | 113410658 |          1 |\n",
+       "| \"chr11\"             | 113410658 |          1 |\n",
+       "| \"chr11\"             | 113410660 |          1 |\n",
+       "| \"chr11\"             | 113410662 |          1 |\n",
        "+---------------------+-----------+------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -5192,11 +5226,11 @@
        "+------------------------------------------------------------------------------+\n",
        "| array                                    |\n",
-       "+-------------------------------------------------+\n",
-       "| [0,0,0,0,28,0,76,0,162,0,0,0,0,0,0,0,0,0,0,1]   |\n",
-       "| [0,0,0,0,25,0,186,0,206,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,95,0,197,0,202,0,0,0,0,0,0,0,1,0,0,0]  |\n",
-       "| [0,0,0,0,100,0,111,0,193,0,0,0,0,0,1,0,0,0,0,0] |\n",
-       "| [0,0,0,0,120,1,223,5,34,1,5,0,2,5,7,1,3,2,1,66] |\n",
-       "+-------------------------------------------------+\n",
+       "+------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq                       |\n",
+       "+------------------------------------------------------------------+\n",
+       "| array                                                     |\n",
+       "+------------------------------------------------------------------+\n",
+       "| [0,0,0,0,4430,0,32721,0,357851,0,60423,0,267316,0,0,0,0,0,0,18]  |\n",
+       "| [0,0,0,0,4183,2,30033,1,354765,0,58262,0,276721,0,0,0,1,0,0,115] |\n",
+       "| [0,0,0,0,4183,0,30033,1,354765,0,58262,0,276721,0,0,0,1,0,0,115] |\n",
+       "| [0,0,0,0,3304,0,26636,0,351846,0,54948,0,288484,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,4139,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]   |\n",
+       "+------------------------------------------------------------------+\n",
        "\n",
        "+---------------------------------------------+\n",
        "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
@@ -5343,17 +5377,17 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq       |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,101,28,30,82,26,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,211,29,38,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "| [0,0,286,30,40,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "| [0,0,200,35,36,102,32,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "| [0,0,438,30,5,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
-       "+--------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,29987,74977,120278,300405,170342,18935,3117,1021,684,644,577,507,353... |\n",
+       "| [0,0,26988,70993,120130,305542,173013,19471,3177,1036,688,647,582,509,357... |\n",
+       "| [0,0,26986,70993,120130,305542,173013,19471,3177,1036,688,647,582,509,357... |\n",
+       "| [0,0,24253,67281,120581,309738,175490,19882,3222,1041,692,644,582,508,354... |\n",
+       "| [0,0,22477,65025,118275,313361,178106,20388,3276,1054,693,645,582,508,355... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
        "+---------------------------------------------+\n",
        "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
@@ -5372,11 +5406,11 @@
        "+--------------------------------------------+\n",
        "|                                      int64 |\n",
        "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
+       "|                                        252 |\n",
+       "|                                        265 |\n",
+       "|                                        265 |\n",
+       "|                                        264 |\n",
+       "|                                        265 |\n",
        "+--------------------------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -5391,17 +5425,17 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq  |\n",
-       "+---------------------------------------------+\n",
-       "| array                                |\n",
-       "+---------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
-       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]   |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,1,1,11,5,2,1,5,0,2,5,7,1,3,2,1,66] |\n",
-       "+---------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18] |\n",
+       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,21] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,94] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "+--------------------------------------------+\n",
        "\n",
        "+---------------------------------------------+\n",
        "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
@@ -5439,17 +5473,17 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq  |\n",
-       "+---------------------------------------------+\n",
-       "| array                                |\n",
-       "+---------------------------------------------+\n",
-       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,108,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+---------------------------------------------+\n",
+       "+-----------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq    |\n",
+       "+-----------------------------------------------+\n",
+       "| array                                  |\n",
+       "+-----------------------------------------------+\n",
+       "| [0,0,0,2,3,3,2,1,0,1,1,0,1,0,0,1,1,0,0,1]     |\n",
+       "| [0,0,1,2,4,3,4,1,1,0,0,1,0,1,0,0,0,2,0,1]     |\n",
+       "| [0,0,2,3,11,13,14,10,8,9,1,3,2,0,4,0,0,1,1,1] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
+       "+-----------------------------------------------+\n",
        "\n",
        "+---------------------------------------------+\n",
        "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
@@ -5468,11 +5502,11 @@
        "+--------------------------------------------+\n",
        "|                                      int64 |\n",
        "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
+       "|                                          1 |\n",
+       "|                                          2 |\n",
+       "|                                         11 |\n",
+       "|                                          1 |\n",
+       "|                                          1 |\n",
        "+--------------------------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -5487,17 +5521,17 @@
        "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq  |\n",
-       "+---------------------------------------------+\n",
-       "| array                                |\n",
-       "+---------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,3,8,0,4,8,6,30,15,8,9,3,1,3,0,0,0] |\n",
-       "+---------------------------------------------+\n",
+       "+-----------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq    |\n",
+       "+-----------------------------------------------+\n",
+       "| array                                  |\n",
+       "+-----------------------------------------------+\n",
+       "| [0,0,0,0,0,0,2,0,4,4,4,3,1,0,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,2,0,1,0,3,3,8,5,0,1,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,0,0,6,5,19,14,25,17,6,1,1,0,0,0,0,0] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]     |\n",
+       "+-----------------------------------------------+\n",
        "\n",
        "+---------------------------------------------+\n",
        "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
@@ -5535,17 +5569,17 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [313709,2,2,0,442,0,76,0,162,0,0,0,0,0,0,0,0,0,0,1]                          |\n",
-       "| [313080,4,2,0,906,6,189,2,206,0,0,0,0,0,0,0,0,0,0,0]                         |\n",
-       "| [313071,2,3,0,920,0,198,0,202,0,0,0,0,0,0,0,1,0,0,0]                         |\n",
-       "| [313441,3,2,1,640,0,111,1,193,0,0,0,0,0,1,0,0,0,0,0]                         |\n",
-       "| [301848,5644,543,415,1889,1584,436,212,123,210,485,568,76,48,44,53,135,57... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+-----------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                        |\n",
+       "+-----------------------------------------------------------------------+\n",
+       "| array                                                          |\n",
+       "+-----------------------------------------------------------------------+\n",
+       "| [979,2,2487,1,8860,1,32722,0,357851,0,60423,0,267317,0,0,0,0,0,0,18]  |\n",
+       "| [886,4,2000,0,7936,2,30033,1,354765,1,58262,0,276721,0,0,0,1,0,0,116] |\n",
+       "| [886,4,2000,0,7936,2,30033,1,354765,1,58262,0,276721,0,0,0,1,0,0,116] |\n",
+       "| [684,0,1637,0,6556,2,26636,0,351846,0,54948,0,288484,0,0,0,0,0,0,1]   |\n",
+       "| [653,0,1690,2,6865,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]   |\n",
+       "+-----------------------------------------------------------------------+\n",
        "\n",
        "+-------------------------------------------------+\n",
        "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
@@ -5583,17 +5617,17 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+-----------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq            |\n",
-       "+-----------------------------------------------------------+\n",
-       "| array                                              |\n",
-       "+-----------------------------------------------------------+\n",
-       "| [240827,73298,103,28,30,82,26,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [240299,73676,214,29,38,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "| [240226,73668,294,30,40,105,34,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "| [240307,73671,209,36,36,102,32,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "| [238198,75776,482,33,5,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
-       "+-----------------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [415,5663,30415,76214,120310,300505,170348,18947,3122,1023,686,644,577,50... |\n",
+       "| [362,4790,27388,71922,120182,305624,173020,19481,3184,1039,690,648,582,50... |\n",
+       "| [362,4790,27388,71922,120182,305624,173020,19481,3184,1039,690,648,582,50... |\n",
+       "| [296,4122,24622,67964,120609,309787,175498,19891,3228,1043,694,645,582,50... |\n",
+       "| [264,3602,22767,65834,118294,313419,178113,20399,3282,1056,696,645,582,50... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
        "+-------------------------------------------------+\n",
        "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
@@ -5612,11 +5646,11 @@
        "+------------------------------------------------+\n",
        "|                                          int64 |\n",
        "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
+       "|                                            252 |\n",
+       "|                                            265 |\n",
+       "|                                            265 |\n",
+       "|                                            264 |\n",
+       "|                                            265 |\n",
        "+------------------------------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -5631,17 +5665,17 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]                                    |\n",
-       "| [0,3,0,0,1,5,3,1,0,0,0,0,0,0,0,0,0,0,0,0]                                    |\n",
-       "| [0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]                                    |\n",
-       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]                                    |\n",
-       "| [398,5642,541,415,716,1584,224,212,91,210,485,568,76,48,44,53,135,57,19,108] |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [1,2,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,18]     |\n",
+       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,21]     |\n",
+       "| [2,4,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,95]     |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "+------------------------------------------------+\n",
        "\n",
        "+-------------------------------------------------+\n",
        "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
@@ -5679,17 +5713,17 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq    |\n",
-       "+---------------------------------------------------+\n",
-       "| array                                      |\n",
-       "+---------------------------------------------------+\n",
-       "| [2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
-       "| [8,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
-       "| [2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
-       "| [1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]         |\n",
-       "| [9330,2176,115,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |\n",
-       "+---------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,1,3,6,4,2,1,0,1,1,0,1,0,0,1,1,0,0,1]      |\n",
+       "| [0,0,1,2,4,3,4,1,1,0,0,1,0,1,0,0,0,2,0,1]      |\n",
+       "| [0,2,4,4,17,13,14,10,8,9,1,3,2,0,4,0,0,1,1,1]  |\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
        "\n",
        "+-------------------------------------------------+\n",
        "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
@@ -5708,11 +5742,11 @@
        "+------------------------------------------------+\n",
        "|                                          int64 |\n",
        "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
+       "|                                              1 |\n",
+       "|                                              2 |\n",
+       "|                                             11 |\n",
+       "|                                              1 |\n",
+       "|                                              1 |\n",
        "+------------------------------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
@@ -5727,17 +5761,17 @@
        "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq                   |\n",
-       "+------------------------------------------------------------------+\n",
-       "| array                                                     |\n",
-       "+------------------------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]                        |\n",
-       "| [0,0,0,0,1,1,2,0,1,0,1,0,0,4,0,0,0,0,0,0]                        |\n",
-       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]                        |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]                        |\n",
-       "| [0,0,4,4,27,107,217,41,416,6,1084,414,43,1634,53,495,208,34,2,0] |\n",
-       "+------------------------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,1,3,2,0,0,2,0,4,4,4,3,1,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,2,0,1,0,3,3,8,5,0,1,0,0,0,0,0,0]      |\n",
+       "| [0,2,7,0,0,1,6,5,19,14,26,17,6,1,1,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
        "\n",
        "+-------------------------------------------------+\n",
        "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
@@ -5780,11 +5814,11 @@
        "+--------------------------------------------+\n",
        "| array                               |\n",
        "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,1,1,5,4,3,0,0]                      |\n",
+       "| [1,1,0,2,5,2,1,0,0,0]                      |\n",
+       "| [0,0,1,1,5,3,2,0,0,0]                      |\n",
+       "| [0,1,4,4,2,5,0,1,4,1]                      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,1,0,1,0,0,0,0,0]                      |\n",
        "+--------------------------------------------+\n",
        "\n",
        "+---------------------------------------------+\n",
@@ -5792,9 +5826,9 @@
        "+---------------------------------------------+\n",
        "|                                       int64 |\n",
        "+---------------------------------------------+\n",
+       "|                                           2 |\n",
        "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
+       "|                                           1 |\n",
        "|                                           0 |\n",
        "|                                           0 |\n",
        "+---------------------------------------------+\n",
@@ -5832,7 +5866,7 @@
        "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
        "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
        "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,1,2,2,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
        "+--------------------------------------------+\n",
        "\n",
        "+---------------------------------------------+\n",
@@ -5852,11 +5886,11 @@
        "+--------------------------------------------+---------------------------------+\n",
        "|                                      int64 |                         float32 |\n",
        "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        8.46e+00 |\n",
-       "|                                          0 |                        9.08e+00 |\n",
-       "|                                          0 |                        1.51e+01 |\n",
-       "|                                          0 |                        9.13e+00 |\n",
-       "|                                          0 |                        2.02e+00 |\n",
+       "|                                          0 |                        3.61e+00 |\n",
+       "|                                          0 |                        2.90e+00 |\n",
+       "|                                          0 |                        2.38e+00 |\n",
+       "|                                          0 |                        1.22e+00 |\n",
+       "|                                          0 |                        6.25e+00 |\n",
        "+--------------------------------------------+---------------------------------+\n",
        "\n",
        "+-------------------------------------+--------------------------------+\n",
@@ -5864,11 +5898,11 @@
        "+-------------------------------------+--------------------------------+\n",
        "|                             float32 |                        float64 |\n",
        "+-------------------------------------+--------------------------------+\n",
-       "|                            7.01e-01 |                             NA |\n",
-       "|                            7.68e-01 |                             NA |\n",
-       "|                            1.40e+00 |                             NA |\n",
-       "|                            7.73e-01 |                             NA |\n",
-       "|                            9.23e-02 |                             NA |\n",
+       "|                            2.42e-01 |                             NA |\n",
+       "|                            1.80e-01 |                             NA |\n",
+       "|                            1.31e-01 |                             NA |\n",
+       "|                           -1.85e-02 |                             NA |\n",
+       "|                            4.76e-01 |                             NA |\n",
        "+-------------------------------------+--------------------------------+\n",
        "\n",
        "+--------------------------------------+\n",
@@ -5879,7 +5913,7 @@
        "|                             0.00e+00 |\n",
        "|                             0.00e+00 |\n",
        "|                             0.00e+00 |\n",
-       "|                             1.00e-02 |\n",
+       "|                             0.00e+00 |\n",
        "|                             0.00e+00 |\n",
        "+--------------------------------------+\n",
        "\n",
@@ -5888,11 +5922,11 @@
        "+------------------------------------------+-----------------------------+\n",
        "|                                  float64 |                     float64 |\n",
        "+------------------------------------------+-----------------------------+\n",
-       "|                                 2.00e-02 |                    1.94e+00 |\n",
-       "|                                 0.00e+00 |                    5.70e-01 |\n",
-       "|                                 0.00e+00 |                    1.05e+00 |\n",
-       "|                                 3.00e-02 |                    2.58e+00 |\n",
-       "|                                 0.00e+00 |                    1.61e+00 |\n",
+       "|                                 1.20e-01 |                   -4.68e-01 |\n",
+       "|                                 5.00e-02 |                    7.10e-02 |\n",
+       "|                                 3.00e-02 |                    7.10e-02 |\n",
+       "|                                 4.00e-02 |                   -1.27e+00 |\n",
+       "|                                 2.10e-01 |                    1.62e+00 |\n",
        "+------------------------------------------+-----------------------------+\n",
        "\n",
        "+-------------------------------+-----------------------------------+\n",
@@ -5916,12 +5950,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The total number of other variants passing filters in DRD2 is:  2075\n"
+      "The total number of other variants passing filters in DRD2 is:  783\n"
      ]
     }
    ],
    "source": [
-    "var_ht = filter_by_csqs(['other'], ht=drd2_interval_ht)\n",
+    "var_ht = filter_by_consequence_category(other=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
     "print(\"The total number of other variants passing filters in DRD2 is: \", var_ht.count())"
    ]
@@ -5938,12 +5972,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "a25ddd1b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "drd2_synonymous_ht = filter_by_csqs(['synonymous'], ht=drd2_interval_ht)"
+    "drd2_synonymous_ht = filter_by_consequence_category(synonymous=True, ht=drd2_interval_ht)"
    ]
   },
   {
@@ -5956,7 +5990,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "4f78166f",
    "metadata": {},
    "outputs": [
@@ -6017,7 +6051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "e3a07848",
    "metadata": {},
    "outputs": [
@@ -6152,7 +6186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "4845be1d-d4c0-4b83-9e92-bd72379b8a99",
    "metadata": {},
    "outputs": [
@@ -6200,10 +6234,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "bee28829",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-20 12:31:16.359 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -6252,7 +6293,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.5"
   },
   "toc": {
    "base_numbering": 1,

From 80d33beeb4d4dd49922851f760e8a15a2d97810e Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Fri, 3 Jan 2025 15:21:56 -0500
Subject: [PATCH 056/121] reformat

---
 gnomad_toolbox/filtering/vep.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py
index 3f930da..48ff07e 100644
--- a/gnomad_toolbox/filtering/vep.py
+++ b/gnomad_toolbox/filtering/vep.py
@@ -1,10 +1,13 @@
 """Functions to filter gnomAD sites HT by VEP annotations."""
 
 import hail as hl
-
 from gnomad.resources.grch37.reference_data import gencode as grch37_gencode
 from gnomad.resources.grch38.reference_data import gencode as grch38_gencode
-from gnomad.utils.vep import LOF_CSQ_SET, filter_vep_transcript_csqs, filter_vep_transcript_csqs_expr
+from gnomad.utils.vep import (
+    LOF_CSQ_SET,
+    filter_vep_transcript_csqs,
+    filter_vep_transcript_csqs_expr,
+)
 
 from gnomad_toolbox.load_data import _get_gnomad_release, gnomad_session
 

From e03253e8fc83bd1fb9ff3bcddc805189da1892e8 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Mon, 6 Jan 2025 13:25:35 -0500
Subject: [PATCH 057/121] Modify the code to use browser tables

---
 gnomad_toolbox/filtering/variant.py | 44 ++++++++++++++++++++++++++++-
 1 file changed, 43 insertions(+), 1 deletion(-)

diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index 536c0bd..4d7441a 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -3,6 +3,8 @@
 from typing import Optional, Union
 
 import hail as hl
+from gnomad.resources.grch37.gnomad import browser_gene as browser_gene_grch37
+from gnomad.resources.grch38.gnomad import browser_gene as browser_gene_grch38
 from gnomad.utils.filtering import filter_to_gencode_cds
 from gnomad.utils.reference_genome import get_reference_genome
 
@@ -136,6 +138,46 @@ def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.
     """
     # Load the Hail Table if not provided
     ht = _get_gnomad_release(dataset="variant", **kwargs)
-    ht = filter_to_gencode_cds(ht, genes=gene, padding_bp=exon_padding_bp)
+    if ht.locus.dtype.reference_genome.name == "GRCh37":
+        gene_ht = browser_gene_grch37().ht()
+    else:
+        gene_ht = browser_gene_grch38().ht()
+
+    gene_ht = gene_ht.filter(gene_ht.gencode_symbol.lower() == gene.lower())
+
+    # Get intervals based on feature type (CDS > UTR > Exons) with padding
+    def get_intervals(feature_type: str) -> hl.expr.ArrayExpression:
+        return hl.array(
+            gene_ht.exons.filter(lambda exon: exon.feature_type == feature_type)
+        ).map(
+            lambda exon: hl.locus_interval(
+                hl.if_else(
+                    gene_ht.interval.start.dtype.reference_genome.name == "GRCh38",
+                    "chr" + gene_ht.chrom,
+                    gene_ht.chrom,
+                ),
+                exon.start - exon_padding_bp,
+                exon.stop + exon_padding_bp,
+                reference_genome=gene_ht.interval.start.dtype.reference_genome,
+                includes_end=True,
+            )
+        )
+
+    cds_intervals = get_intervals("CDS")
+    utr_intervals = get_intervals("UTR")
+    exon_intervals = get_intervals("exon")
+
+    # Determine which intervals to use
+    gene_ht = gene_ht.annotate(
+        intervals=hl.if_else(
+            hl.len(cds_intervals) > 0,
+            cds_intervals,
+            hl.if_else(hl.len(utr_intervals) > 0, utr_intervals, exon_intervals),
+        )
+    )
+
+    intervals = gene_ht.intervals.collect()[0]
+
+    ht = hl.filter_intervals(ht, intervals)
 
     return ht

From 427217c7cc8d921bc53f24d19b188ea56e84baa2 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Tue, 7 Jan 2025 12:52:28 -0500
Subject: [PATCH 058/121] Add extra filter steps to match Browser

---
 gnomad_toolbox/filtering/variant.py | 46 ++++++++++++++++-------------
 1 file changed, 26 insertions(+), 20 deletions(-)

diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index 33f2b51..d862219 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -8,6 +8,7 @@
 from gnomad.utils.filtering import filter_to_gencode_cds
 from gnomad.utils.parse import parse_variant
 from gnomad.utils.reference_genome import get_reference_genome
+from gnomad.utils.vep import filter_vep_transcript_csqs
 
 from gnomad_toolbox.load_data import _get_gnomad_release
 
@@ -102,37 +103,36 @@ def filter_by_intervals(
     return ht
 
 
-# TODO: Add a pre-processing step to filter out these genes on chrY to
-# match the gnomAD browser.
 def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.Table:
     """
-    Filter variants in a gene.
+    Filter variants in a gene by gene symbol.
 
     .. note::
-
-           This function is to match the number of variants that you will get in the
-           gnomAD browser, which only focus on variants in "CDS" regions plus
-           75bp (default of `exon_padding_bp`) up- and downstream.
-
-           However, gnomAD browser used a preprocessed Gencode file which excluded
-           46 genes on chrY that share the same gene id as chrX. For example,
-           if you use this function to filter "ASMT" gene, you will get more variants
-           than shown in the gnomAD browser.
+       This is to match the browser display, which includes variants in the CDS +
+       75bp padding for protein-coding genes, and UTR/exons + 75bp padding for
+       non-protein-coding genes.
 
     :param gene: Gene symbol.
-    :param exon_padding_bp: Number of base pairs to pad the CDS intervals. Default is
-        75bp.
+    :param exon_padding_bp: Number of base pairs to pad the CDS intervals. Default is 75bp.
     :param kwargs: Arguments to pass to `_get_gnomad_release`.
     :return: Table with variants in the gene.
     """
     # Load the Hail Table if not provided
     ht = _get_gnomad_release(dataset="variant", **kwargs)
-    if ht.locus.dtype.reference_genome.name == "GRCh37":
-        gene_ht = browser_gene_grch37().ht()
-    else:
-        gene_ht = browser_gene_grch38().ht()
 
-    gene_ht = gene_ht.filter(gene_ht.gencode_symbol.lower() == gene.lower())
+    # Load gene information
+    gene_ht = (
+        browser_gene_grch37().ht()
+        if ht.locus.dtype.reference_genome.name == "GRCh37"
+        else browser_gene_grch38().ht()
+    )
+
+    # Pre-filter to the specified gene (case-insensitive)
+    gene = gene.upper()
+    gene_ht = gene_ht.filter(gene_ht.gencode_symbol == gene)
+
+    # First get the variants in the gene region
+    ht = hl.filter_intervals(ht, hl.array(gene_ht.interval.take(1)))
 
     # Get intervals based on feature type (CDS > UTR > Exons) with padding
     def get_intervals(feature_type: str) -> hl.expr.ArrayExpression:
@@ -148,6 +148,7 @@ def get_intervals(feature_type: str) -> hl.expr.ArrayExpression:
                 exon.start - exon_padding_bp,
                 exon.stop + exon_padding_bp,
                 reference_genome=gene_ht.interval.start.dtype.reference_genome,
+                includes_start=True,
                 includes_end=True,
             )
         )
@@ -165,8 +166,13 @@ def get_intervals(feature_type: str) -> hl.expr.ArrayExpression:
         )
     )
 
-    intervals = gene_ht.intervals.collect()[0]
+    intervals = gene_ht.intervals.take(1)[0]
 
     ht = hl.filter_intervals(ht, intervals)
 
+    # Additional filtering (e.g., with VEP consequences)
+    ht = filter_vep_transcript_csqs(
+        ht, genes=[gene], synonymous=False, match_by_gene_symbol=True
+    )
+
     return ht

From fa0bcfdfeccbf71651121d56ecd43115a1526812 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Tue, 7 Jan 2025 12:55:45 -0500
Subject: [PATCH 059/121] Removed unused imports

---
 gnomad_toolbox/filtering/variant.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index d862219..e32c0a5 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -5,7 +5,6 @@
 import hail as hl
 from gnomad.resources.grch37.gnomad import browser_gene as browser_gene_grch37
 from gnomad.resources.grch38.gnomad import browser_gene as browser_gene_grch38
-from gnomad.utils.filtering import filter_to_gencode_cds
 from gnomad.utils.parse import parse_variant
 from gnomad.utils.reference_genome import get_reference_genome
 from gnomad.utils.vep import filter_vep_transcript_csqs

From 319d9fa571036b625f85b03394aaf56bd144e708 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Wed, 8 Jan 2025 15:42:49 -0500
Subject: [PATCH 060/121] Make the big functions to smaller ones

---
 gnomad_toolbox/filtering/vep.py | 111 ++++++++++++++++++--------------
 gnomad_toolbox/load_data.py     |  19 ++++++
 2 files changed, 80 insertions(+), 50 deletions(-)

diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py
index 48ff07e..a7a1505 100644
--- a/gnomad_toolbox/filtering/vep.py
+++ b/gnomad_toolbox/filtering/vep.py
@@ -1,5 +1,7 @@
 """Functions to filter gnomAD sites HT by VEP annotations."""
 
+from typing import List
+
 import hail as hl
 from gnomad.resources.grch37.reference_data import gencode as grch37_gencode
 from gnomad.resources.grch38.reference_data import gencode as grch38_gencode
@@ -9,7 +11,11 @@
     filter_vep_transcript_csqs_expr,
 )
 
-from gnomad_toolbox.load_data import _get_gnomad_release, gnomad_session
+from gnomad_toolbox.load_data import (
+    _get_gnomad_release,
+    get_coverage_for_variant,
+    gnomad_session,
+)
 
 
 # TODO: Check these csq sets, the ones in the code don't match what is listed on the
@@ -122,46 +128,16 @@ def filter_by_consequence_category(
     return ht.filter(filter_expr)
 
 
-def filter_to_plofs(
-    gene_symbol: str, select_fields: bool = False, **kwargs
-) -> hl.Table:
+def get_gene_intervals(gene_symbol: str, version: str) -> List[hl.utils.Interval]:
     """
-        Filter to observed pLoF variants that we used to calculate the gene constraint metrics.
-
-        .. note::
-
-                pLOF variants meets the following requirements:
-                - High-confidence LOFTEE variants (without any flags),
-                - Only variants in the MANE Select transcript,
-                - PASS variants that are SNVs with MAF ≤ 0.1%,
-                - Exome median depth ≥ 30 (# TODO: This is changing in v4 constraint?)
+    Get the genomic intervals for a given gene symbol.
 
-    **Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**
-
-        :param gene_symbol: Gene symbol.
-        :param select_fields: Boolean if the output should be limited to specific fields.
-        :return: Table with pLoF variants.
+    :param gene_symbol: Gene symbol.
+    :param version: Dataset version.
+    :return: List of intervals for the specified gene.
     """
-    var_version = kwargs.pop("version", gnomad_session.version)
-    var_ht = _get_gnomad_release(dataset="variant", version=var_version, **kwargs)
-
-    # Determine the version of the coverage table
-    if var_version.startswith("4."):
-        cov_ht = _get_gnomad_release(dataset="coverage", version="4.0", **kwargs)
-    elif var_version.startswith("3."):
-        cov_ht = _get_gnomad_release(dataset="coverage", version="3.0.1", **kwargs)
-    elif var_version.startswith("2."):
-        cov_ht = _get_gnomad_release(dataset="coverage", version="2.1", **kwargs)
-    else:
-        raise ValueError(
-            f"Unrecognized version: '{var_version}'. Please specify a valid version."
-        )
-
-    # Get the gene interval from gen_ht
-    gen_ht = (
-        grch37_gencode.ht() if var_version.startswith("2.") else grch38_gencode.ht()
-    )
-    interval = (
+    gen_ht = grch37_gencode.ht() if version.startswith("2.") else grch38_gencode.ht()
+    intervals = (
         gen_ht.filter(
             (gen_ht.feature == "gene")
             & (gen_ht.gene_name.lower() == gene_symbol.lower())
@@ -169,23 +145,24 @@ def filter_to_plofs(
         .select()
         .collect()
     )
-
-    if not interval:
+    if not intervals:
         raise ValueError(f"No interval found for gene: {gene_symbol}")
+    return [row["interval"] for row in intervals]
 
-    # Convert to a list of intervals
-    interval = [row["interval"] for row in interval]
-    var_ht = hl.filter_intervals(var_ht, interval)
-    cov_ht = hl.filter_intervals(cov_ht, interval)
 
-    # Filter to high-confidence LOFTEE variants
-    var_ht = filter_vep_transcript_csqs(
+def filter_hc_variants(var_ht: hl.Table, gene_symbol: str, version: str) -> hl.Table:
+    """
+    Filter variants to high-confidence LOFTEE variants with optional transcript selection.
+
+    :param var_ht: Variants Table.
+    :param gene_symbol: Gene symbol.
+    :param version: Dataset version.
+    :return: Filtered variants Hail Table.
+    """
+    return filter_vep_transcript_csqs(
         var_ht,
         synonymous=False,
-        mane_select=True if var_version.startswith("4.") else False,
-        # TODO: When this function is applied to DRD2 gene in v4.1, it will get 7 pLoF
-        #  variants instead of 8 on the browser and the 4.1 constraint table,
-        #  because one of them is not in mane select, nor in canonical transcript.
+        mane_select=version.startswith("4."),
         genes=[gene_symbol.upper()],
         match_by_gene_symbol=True,
         additional_filtering_criteria=[
@@ -194,6 +171,38 @@ def filter_to_plofs(
         ],
     )
 
+
+def filter_to_plofs(
+    gene_symbol: str, select_fields: bool = False, **kwargs
+) -> hl.Table:
+    """
+    Filter to observed pLoF variants used for gene constraint metrics.
+
+    .. note::
+
+                pLOF variants meets the following requirements:
+                - High-confidence LOFTEE variants (without any flags),
+                - Only variants in the MANE Select transcript,
+                - PASS variants that are SNVs with MAF ≤ 0.1%,
+                - Exome median depth ≥ 30 (# TODO: This is changing in v4 constraint?)
+
+    :param gene_symbol: Gene symbol.
+    :param select_fields: Whether to limit the output to specific fields.
+    :return: Table with pLoF variants.
+    """
+    var_version = kwargs.pop("version", gnomad_session.version)
+    var_ht = _get_gnomad_release(dataset="variant", version=var_version, **kwargs)
+    cov_ht = get_coverage_for_variant(var_version, **kwargs)
+
+    # Get gene intervals and filter tables
+    intervals = get_gene_intervals(gene_symbol, var_version)
+    var_ht = hl.filter_intervals(var_ht, intervals)
+    cov_ht = hl.filter_intervals(cov_ht, intervals)
+
+    # Filter to high-confidence LOFTEE variants
+    var_ht = filter_hc_variants(var_ht, gene_symbol, var_version)
+
+    # Version-specific expressions
     if var_version.startswith("2."):
         allele_type_expr = var_ht.allele_type
         cov_cut_expr = cov_ht[var_ht.locus].median
@@ -201,6 +210,7 @@ def filter_to_plofs(
         allele_type_expr = var_ht.allele_info.allele_type
         cov_cut_expr = cov_ht[var_ht.locus].median_approx
 
+    # Apply final filters
     var_ht = var_ht.filter(
         (hl.len(var_ht.filters) == 0)
         & (allele_type_expr == "snv")
@@ -208,6 +218,7 @@ def filter_to_plofs(
         & (cov_cut_expr >= 30)
     )
 
+    # Select specific fields if requested
     if select_fields:
         var_ht = var_ht.select(
             freq=var_ht.freq[0],
diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index de29e85..8405dfd 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -200,3 +200,22 @@ def get_gnomad_release(
     :return: Hail Table for requested dataset, data type, and version.
     """
     return _get_gnomad_release(dataset=dataset, data_type=data_type, version=version)
+
+
+def get_coverage_for_variant(variant_version: str, **kwargs) -> hl.Table:
+    """
+    Get the appropriate coverage table based on the provided version.
+
+    :param variant_version: Version of the variant dataset.
+    :return: Hail Table for the corresponding coverage dataset.
+    """
+    if variant_version.startswith("4."):
+        return _get_gnomad_release(dataset="coverage", version="4.0", **kwargs)
+    elif variant_version.startswith("3."):
+        return _get_gnomad_release(dataset="coverage", version="3.0.1", **kwargs)
+    elif variant_version.startswith("2."):
+        return _get_gnomad_release(dataset="coverage", version="2.1", **kwargs)
+    else:
+        raise ValueError(
+            f"Unrecognized version: '{variant_version}'. Please specify a valid version."
+        )

From 93184af3f2fa3b738d0f4e5df6f4481e8a7a22a2 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Fri, 10 Jan 2025 10:55:11 -0500
Subject: [PATCH 061/121] Move gnomad_methods to setup.py

---
 gnomad_toolbox/notebooks/needs_a_name.ipynb | 323 +-------------------
 requirements.txt                            |   3 -
 setup.py                                    |   1 +
 3 files changed, 12 insertions(+), 315 deletions(-)

diff --git a/gnomad_toolbox/notebooks/needs_a_name.ipynb b/gnomad_toolbox/notebooks/needs_a_name.ipynb
index c4b688a..d450fcf 100644
--- a/gnomad_toolbox/notebooks/needs_a_name.ipynb
+++ b/gnomad_toolbox/notebooks/needs_a_name.ipynb
@@ -31,316 +31,15 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "    \n",
-       "    
\n",
-       "        \n",
-       "        Loading BokehJS ...\n",
-       "    
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "(function(root) {\n",
-       "  function now() {\n",
-       "    return new Date();\n",
-       "  }\n",
-       "\n",
-       "  const force = true;\n",
-       "\n",
-       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
-       "    root._bokeh_onload_callbacks = [];\n",
-       "    root._bokeh_is_loading = undefined;\n",
-       "  }\n",
-       "\n",
-       "const JS_MIME_TYPE = 'application/javascript';\n",
-       "  const HTML_MIME_TYPE = 'text/html';\n",
-       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
-       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
-       "\n",
-       "  /**\n",
-       "   * Render data to the DOM node\n",
-       "   */\n",
-       "  function render(props, node) {\n",
-       "    const script = document.createElement(\"script\");\n",
-       "    node.appendChild(script);\n",
-       "  }\n",
-       "\n",
-       "  /**\n",
-       "   * Handle when an output is cleared or removed\n",
-       "   */\n",
-       "  function handleClearOutput(event, handle) {\n",
-       "    function drop(id) {\n",
-       "      const view = Bokeh.index.get_by_id(id)\n",
-       "      if (view != null) {\n",
-       "        view.model.document.clear()\n",
-       "        Bokeh.index.delete(view)\n",
-       "      }\n",
-       "    }\n",
-       "\n",
-       "    const cell = handle.cell;\n",
-       "\n",
-       "    const id = cell.output_area._bokeh_element_id;\n",
-       "    const server_id = cell.output_area._bokeh_server_id;\n",
-       "\n",
-       "    // Clean up Bokeh references\n",
-       "    if (id != null) {\n",
-       "      drop(id)\n",
-       "    }\n",
-       "\n",
-       "    if (server_id !== undefined) {\n",
-       "      // Clean up Bokeh references\n",
-       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
-       "      cell.notebook.kernel.execute(cmd_clean, {\n",
-       "        iopub: {\n",
-       "          output: function(msg) {\n",
-       "            const id = msg.content.text.trim()\n",
-       "            drop(id)\n",
-       "          }\n",
-       "        }\n",
-       "      });\n",
-       "      // Destroy server and session\n",
-       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
-       "      cell.notebook.kernel.execute(cmd_destroy);\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  /**\n",
-       "   * Handle when a new output is added\n",
-       "   */\n",
-       "  function handleAddOutput(event, handle) {\n",
-       "    const output_area = handle.output_area;\n",
-       "    const output = handle.output;\n",
-       "\n",
-       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
-       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
-       "      return\n",
-       "    }\n",
-       "\n",
-       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
-       "\n",
-       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
-       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
-       "      // store reference to embed id on output_area\n",
-       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
-       "    }\n",
-       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
-       "      const bk_div = document.createElement(\"div\");\n",
-       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
-       "      const script_attrs = bk_div.children[0].attributes;\n",
-       "      for (let i = 0; i < script_attrs.length; i++) {\n",
-       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
-       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
-       "      }\n",
-       "      // store reference to server id on output_area\n",
-       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  function register_renderer(events, OutputArea) {\n",
-       "\n",
-       "    function append_mime(data, metadata, element) {\n",
-       "      // create a DOM node to render to\n",
-       "      const toinsert = this.create_output_subarea(\n",
-       "        metadata,\n",
-       "        CLASS_NAME,\n",
-       "        EXEC_MIME_TYPE\n",
-       "      );\n",
-       "      this.keyboard_manager.register_events(toinsert);\n",
-       "      // Render to node\n",
-       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
-       "      render(props, toinsert[toinsert.length - 1]);\n",
-       "      element.append(toinsert);\n",
-       "      return toinsert\n",
-       "    }\n",
-       "\n",
-       "    /* Handle when an output is cleared or removed */\n",
-       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
-       "    events.on('delete.Cell', handleClearOutput);\n",
-       "\n",
-       "    /* Handle when a new output is added */\n",
-       "    events.on('output_added.OutputArea', handleAddOutput);\n",
-       "\n",
-       "    /**\n",
-       "     * Register the mime type and append_mime function with output_area\n",
-       "     */\n",
-       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
-       "      /* Is output safe? */\n",
-       "      safe: true,\n",
-       "      /* Index of renderer in `output_area.display_order` */\n",
-       "      index: 0\n",
-       "    });\n",
-       "  }\n",
-       "\n",
-       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
-       "  if (root.Jupyter !== undefined) {\n",
-       "    const events = require('base/js/events');\n",
-       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
-       "\n",
-       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
-       "      register_renderer(events, OutputArea);\n",
-       "    }\n",
-       "  }\n",
-       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
-       "    root._bokeh_timeout = Date.now() + 5000;\n",
-       "    root._bokeh_failed_load = false;\n",
-       "  }\n",
-       "\n",
-       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
-       "     \"
\\n\"+\n",
-       "     \"
\\n\"+\n",
-       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
-       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
-       "     \"
\\n\"+\n",
-       "     \"
    \\n\"+\n",
-       "     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n",
-       "     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n",
-       "     \"
\\n\"+\n",
-       "     \"\\n\"+\n",
-       "     \"from bokeh.resources import INLINE\\n\"+\n",
-       "     \"output_notebook(resources=INLINE)\\n\"+\n",
-       "     \"\\n\"+\n",
-       "     \"
\"}};\n",
-       "\n",
-       "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\");\n",
-       "    if (el != null) {\n",
-       "      el.textContent = \"BokehJS is loading...\";\n",
-       "    }\n",
-       "    if (root.Bokeh !== undefined) {\n",
-       "      if (el != null) {\n",
-       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
-       "      }\n",
-       "    } else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(display_loaded, 100)\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  function run_callbacks() {\n",
-       "    try {\n",
-       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
-       "        if (callback != null)\n",
-       "          callback();\n",
-       "      });\n",
-       "    } finally {\n",
-       "      delete root._bokeh_onload_callbacks\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
-       "  }\n",
-       "\n",
-       "  function load_libs(css_urls, js_urls, callback) {\n",
-       "    if (css_urls == null) css_urls = [];\n",
-       "    if (js_urls == null) js_urls = [];\n",
-       "\n",
-       "    root._bokeh_onload_callbacks.push(callback);\n",
-       "    if (root._bokeh_is_loading > 0) {\n",
-       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
-       "      return null;\n",
-       "    }\n",
-       "    if (js_urls == null || js_urls.length === 0) {\n",
-       "      run_callbacks();\n",
-       "      return null;\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
-       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
-       "\n",
-       "    function on_load() {\n",
-       "      root._bokeh_is_loading--;\n",
-       "      if (root._bokeh_is_loading === 0) {\n",
-       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
-       "        run_callbacks()\n",
-       "      }\n",
-       "    }\n",
-       "\n",
-       "    function on_error(url) {\n",
-       "      console.error(\"failed to load \" + url);\n",
-       "    }\n",
-       "\n",
-       "    for (let i = 0; i < css_urls.length; i++) {\n",
-       "      const url = css_urls[i];\n",
-       "      const element = document.createElement(\"link\");\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error.bind(null, url);\n",
-       "      element.rel = \"stylesheet\";\n",
-       "      element.type = \"text/css\";\n",
-       "      element.href = url;\n",
-       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
-       "      document.body.appendChild(element);\n",
-       "    }\n",
-       "\n",
-       "    for (let i = 0; i < js_urls.length; i++) {\n",
-       "      const url = js_urls[i];\n",
-       "      const element = document.createElement('script');\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error.bind(null, url);\n",
-       "      element.async = false;\n",
-       "      element.src = url;\n",
-       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
-       "      document.head.appendChild(element);\n",
-       "    }\n",
-       "  };\n",
-       "\n",
-       "  function inject_raw_css(css) {\n",
-       "    const element = document.createElement(\"style\");\n",
-       "    element.appendChild(document.createTextNode(css));\n",
-       "    document.body.appendChild(element);\n",
-       "  }\n",
-       "\n",
-       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
-       "  const css_urls = [];\n",
-       "\n",
-       "  const inline_js = [    function(Bokeh) {\n",
-       "      Bokeh.set_log_level(\"info\");\n",
-       "    },\n",
-       "function(Bokeh) {\n",
-       "    }\n",
-       "  ];\n",
-       "\n",
-       "  function run_inline_js() {\n",
-       "    if (root.Bokeh !== undefined || force === true) {\n",
-       "          for (let i = 0; i < inline_js.length; i++) {\n",
-       "      inline_js[i].call(root, root.Bokeh);\n",
-       "    }\n",
-       "if (force === true) {\n",
-       "        display_loaded();\n",
-       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(run_inline_js, 100);\n",
-       "    } else if (!root._bokeh_failed_load) {\n",
-       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
-       "      root._bokeh_failed_load = true;\n",
-       "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\")).parents('.cell').data().cell;\n",
-       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  if (root._bokeh_is_loading === 0) {\n",
-       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
-       "    run_inline_js();\n",
-       "  } else {\n",
-       "    load_libs(css_urls, js_urls, function() {\n",
-       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
-       "      run_inline_js();\n",
-       "    });\n",
-       "  }\n",
-       "}(window));"
-      ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"eb2ddde4-530e-4ce3-9772-cc3703c8cf6d\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "ImportError",
+     "evalue": "cannot import name 'filter_to_plofs' from 'gnomad_toolbox.filtering.vep' (/Users/heqin/miniconda3/envs/hail2132/lib/python3.11/site-packages/gnomad_toolbox/filtering/vep.py)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgnomad_toolbox\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manalysis\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneral\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_variant_count_by_freq_bin\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgnomad_toolbox\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfiltering\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvariant\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m filter_by_intervals\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgnomad_toolbox\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfiltering\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvep\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m filter_by_csqs, filter_to_plofs\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgnomad_toolbox\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mload_data\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_gnomad_release\n",
+      "\u001b[0;31mImportError\u001b[0m: cannot import name 'filter_to_plofs' from 'gnomad_toolbox.filtering.vep' (/Users/heqin/miniconda3/envs/hail2132/lib/python3.11/site-packages/gnomad_toolbox/filtering/vep.py)"
+     ]
     }
    ],
    "source": [
@@ -348,7 +47,7 @@
     "\n",
     "from gnomad_toolbox.analysis.general import get_variant_count_by_freq_bin\n",
     "from gnomad_toolbox.filtering.variant import filter_by_intervals\n",
-    "from gnomad_toolbox.filtering.vep import filter_by_csqs\n",
+    "from gnomad_toolbox.filtering.vep import filter_by_csqs, filter_to_plofs\n",
     "from gnomad_toolbox.load_data import get_gnomad_release"
    ]
   },
@@ -566,7 +265,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.9"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/requirements.txt b/requirements.txt
index 702bdab..49daf5d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,3 @@
-# We're using the main branch of gnomad_method on github rather than the pip version
-# TODO: Decide on how to handle this. We might just need to have more releases of gnomad_methods.
-git+https://github.com/broadinstitute/gnomad_methods@main
 hail
 jupyter
 jupyter_contrib_nbextensions
diff --git a/setup.py b/setup.py
index 78ab1cb..705b7c6 100644
--- a/setup.py
+++ b/setup.py
@@ -37,4 +37,5 @@
     ],
     python_requires=">=3.9",
     install_requires=install_requires,
+    depencency_links=['git+https://github.com/broadinstitute/gnomad_methods@main#egg=gnomad_methods'],
 )

From b271529fbef7b79281ae6381e79a4ea007077d2b Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Fri, 10 Jan 2025 11:00:26 -0500
Subject: [PATCH 062/121] update notebook

---
 gnomad_toolbox/notebooks/needs_a_name.ipynb | 374 ++++++++++++++++++--
 1 file changed, 336 insertions(+), 38 deletions(-)

diff --git a/gnomad_toolbox/notebooks/needs_a_name.ipynb b/gnomad_toolbox/notebooks/needs_a_name.ipynb
index d450fcf..b37f8e5 100644
--- a/gnomad_toolbox/notebooks/needs_a_name.ipynb
+++ b/gnomad_toolbox/notebooks/needs_a_name.ipynb
@@ -31,15 +31,316 @@
    },
    "outputs": [
     {
-     "ename": "ImportError",
-     "evalue": "cannot import name 'filter_to_plofs' from 'gnomad_toolbox.filtering.vep' (/Users/heqin/miniconda3/envs/hail2132/lib/python3.11/site-packages/gnomad_toolbox/filtering/vep.py)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgnomad_toolbox\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manalysis\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneral\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_variant_count_by_freq_bin\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgnomad_toolbox\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfiltering\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvariant\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m filter_by_intervals\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgnomad_toolbox\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfiltering\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvep\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m filter_by_csqs, filter_to_plofs\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgnomad_toolbox\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mload_data\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_gnomad_release\n",
-      "\u001b[0;31mImportError\u001b[0m: cannot import name 'filter_to_plofs' from 'gnomad_toolbox.filtering.vep' (/Users/heqin/miniconda3/envs/hail2132/lib/python3.11/site-packages/gnomad_toolbox/filtering/vep.py)"
-     ]
+     "data": {
+      "text/html": [
+       "    \n",
+       "    
\n",
+       "        \n",
+       "        Loading BokehJS ...\n",
+       "    
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  const force = true;\n",
+       "\n",
+       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+       "    root._bokeh_onload_callbacks = [];\n",
+       "    root._bokeh_is_loading = undefined;\n",
+       "  }\n",
+       "\n",
+       "const JS_MIME_TYPE = 'application/javascript';\n",
+       "  const HTML_MIME_TYPE = 'text/html';\n",
+       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
+       "\n",
+       "  /**\n",
+       "   * Render data to the DOM node\n",
+       "   */\n",
+       "  function render(props, node) {\n",
+       "    const script = document.createElement(\"script\");\n",
+       "    node.appendChild(script);\n",
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+       "\n",
+       "  /**\n",
+       "   * Handle when an output is cleared or removed\n",
+       "   */\n",
+       "  function handleClearOutput(event, handle) {\n",
+       "    function drop(id) {\n",
+       "      const view = Bokeh.index.get_by_id(id)\n",
+       "      if (view != null) {\n",
+       "        view.model.document.clear()\n",
+       "        Bokeh.index.delete(view)\n",
+       "      }\n",
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+       "\n",
+       "    const cell = handle.cell;\n",
+       "\n",
+       "    const id = cell.output_area._bokeh_element_id;\n",
+       "    const server_id = cell.output_area._bokeh_server_id;\n",
+       "\n",
+       "    // Clean up Bokeh references\n",
+       "    if (id != null) {\n",
+       "      drop(id)\n",
+       "    }\n",
+       "\n",
+       "    if (server_id !== undefined) {\n",
+       "      // Clean up Bokeh references\n",
+       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+       "      cell.notebook.kernel.execute(cmd_clean, {\n",
+       "        iopub: {\n",
+       "          output: function(msg) {\n",
+       "            const id = msg.content.text.trim()\n",
+       "            drop(id)\n",
+       "          }\n",
+       "        }\n",
+       "      });\n",
+       "      // Destroy server and session\n",
+       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+       "      cell.notebook.kernel.execute(cmd_destroy);\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when a new output is added\n",
+       "   */\n",
+       "  function handleAddOutput(event, handle) {\n",
+       "    const output_area = handle.output_area;\n",
+       "    const output = handle.output;\n",
+       "\n",
+       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
+       "      return\n",
+       "    }\n",
+       "\n",
+       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+       "      // store reference to embed id on output_area\n",
+       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "    }\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "      const bk_div = document.createElement(\"div\");\n",
+       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "      const script_attrs = bk_div.children[0].attributes;\n",
+       "      for (let i = 0; i < script_attrs.length; i++) {\n",
+       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
+       "      }\n",
+       "      // store reference to server id on output_area\n",
+       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function register_renderer(events, OutputArea) {\n",
+       "\n",
+       "    function append_mime(data, metadata, element) {\n",
+       "      // create a DOM node to render to\n",
+       "      const toinsert = this.create_output_subarea(\n",
+       "        metadata,\n",
+       "        CLASS_NAME,\n",
+       "        EXEC_MIME_TYPE\n",
+       "      );\n",
+       "      this.keyboard_manager.register_events(toinsert);\n",
+       "      // Render to node\n",
+       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "      render(props, toinsert[toinsert.length - 1]);\n",
+       "      element.append(toinsert);\n",
+       "      return toinsert\n",
+       "    }\n",
+       "\n",
+       "    /* Handle when an output is cleared or removed */\n",
+       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
+       "    events.on('delete.Cell', handleClearOutput);\n",
+       "\n",
+       "    /* Handle when a new output is added */\n",
+       "    events.on('output_added.OutputArea', handleAddOutput);\n",
+       "\n",
+       "    /**\n",
+       "     * Register the mime type and append_mime function with output_area\n",
+       "     */\n",
+       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "      /* Is output safe? */\n",
+       "      safe: true,\n",
+       "      /* Index of renderer in `output_area.display_order` */\n",
+       "      index: 0\n",
+       "    });\n",
+       "  }\n",
+       "\n",
+       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+       "  if (root.Jupyter !== undefined) {\n",
+       "    const events = require('base/js/events');\n",
+       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  }\n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
+       "     \"
\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"
    \\n\"+\n",
+       "     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n",
+       "     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"from bokeh.resources import INLINE\\n\"+\n",
+       "     \"output_notebook(resources=INLINE)\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"
\"}};\n",
+       "\n",
+       "  function display_loaded() {\n",
+       "    const el = document.getElementById(\"d7751d80-c376-4274-999c-135812562683\");\n",
+       "    if (el != null) {\n",
+       "      el.textContent = \"BokehJS is loading...\";\n",
+       "    }\n",
+       "    if (root.Bokeh !== undefined) {\n",
+       "      if (el != null) {\n",
+       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+       "      }\n",
+       "    } else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(display_loaded, 100)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls == null || js_urls.length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    function on_error(url) {\n",
+       "      console.error(\"failed to load \" + url);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < css_urls.length; i++) {\n",
+       "      const url = css_urls[i];\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < js_urls.length; i++) {\n",
+       "      const url = js_urls[i];\n",
+       "      const element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
+       "  const css_urls = [];\n",
+       "\n",
+       "  const inline_js = [    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
+       "    },\n",
+       "function(Bokeh) {\n",
+       "    }\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    if (root.Bokeh !== undefined || force === true) {\n",
+       "          for (let i = 0; i < inline_js.length; i++) {\n",
+       "      inline_js[i].call(root, root.Bokeh);\n",
+       "    }\n",
+       "if (force === true) {\n",
+       "        display_loaded();\n",
+       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    } else if (force !== true) {\n",
+       "      const cell = $(document.getElementById(\"d7751d80-c376-4274-999c-135812562683\")).parents('.cell').data().cell;\n",
+       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  if (root._bokeh_is_loading === 0) {\n",
+       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+       "    run_inline_js();\n",
+       "  } else {\n",
+       "    load_libs(css_urls, js_urls, function() {\n",
+       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "      run_inline_js();\n",
+       "    });\n",
+       "  }\n",
+       "}(window));"
+      ],
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"d7751d80-c376-4274-999c-135812562683\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"d7751d80-c376-4274-999c-135812562683\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -47,7 +348,7 @@
     "\n",
     "from gnomad_toolbox.analysis.general import get_variant_count_by_freq_bin\n",
     "from gnomad_toolbox.filtering.variant import filter_by_intervals\n",
-    "from gnomad_toolbox.filtering.vep import filter_by_csqs, filter_to_plofs\n",
+    "from gnomad_toolbox.filtering.vep import filter_by_consequence_category, filter_to_plofs\n",
     "from gnomad_toolbox.load_data import get_gnomad_release"
    ]
   },
@@ -210,43 +511,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "6ce87a77",
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'coverage' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[7], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# TODO: add function for this\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m coverage_ht \u001b[38;5;241m=\u001b[39m coverage(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexomes\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mht()\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m#Filter to PASS SNVs with AF <= 0.1% and median exome depth ≥ 30.\u001b[39;00m\n\u001b[1;32m      6\u001b[0m ht \u001b[38;5;241m=\u001b[39m ht\u001b[38;5;241m.\u001b[39mfilter(\n\u001b[1;32m      7\u001b[0m     (hl\u001b[38;5;241m.\u001b[39mlen(ht\u001b[38;5;241m.\u001b[39mfilters) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m) \n\u001b[1;32m      8\u001b[0m     \u001b[38;5;241m&\u001b[39m (ht\u001b[38;5;241m.\u001b[39mallele_info\u001b[38;5;241m.\u001b[39mallele_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msnv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;241m&\u001b[39m (ht\u001b[38;5;241m.\u001b[39mfreq[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mAF \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.001\u001b[39m)\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;241m&\u001b[39m (coverage_ht[ht\u001b[38;5;241m.\u001b[39mlocus]\u001b[38;5;241m.\u001b[39mmedian_approx \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m30\u001b[39m)\n\u001b[1;32m     11\u001b[0m )\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'coverage' is not defined"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing Hail with default parameters...\n",
+      "Welcome to\n",
+      "     __  __     <>__\n",
+      "    / /_/ /__  __/ /\n",
+      "   / __  / _ `/ / /\n",
+      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
+      "LOGGING: writing to /Users/heqin/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250110-1057-0.2.132-678e1f52b999.log\n",
+      "INFO (gnomad.utils.vep 947): Filtering to canonical transcripts\n",
+      "INFO (gnomad.utils.vep 950): Filtering to MANE Select transcripts...\n",
+      "INFO (gnomad.utils.vep 953): Filtering to Ensembl transcripts...\n",
+      "INFO (gnomad.utils.vep 959): Filtering to genes of interest...\n",
+      "INFO (gnomad.utils.vep 967): Filtering to variants with additional criteria...\n"
      ]
     }
    ],
    "source": [
-    "# TODO: add function for this\n",
-    "\n",
-    "coverage_ht = coverage(\"exomes\").ht()\n",
-    "\n",
-    "#Filter to PASS SNVs with AF <= 0.1% and median exome depth ≥ 30.\n",
-    "ht = ht.filter(\n",
-    "    (hl.len(ht.filters) == 0) \n",
-    "    & (ht.allele_info.allele_type == \"snv\")\n",
-    "    & (ht.freq[0].AF <= 0.001)\n",
-    "    & (coverage_ht[ht.locus].median_approx >= 30)\n",
-    ")\n",
-    "\n",
-    "\n",
-    "print(f\"Number of variants: {ht.count()}\")\n",
-    "ht.select(\n",
-    "    freq=ht.freq[0],\n",
-    "    csq=ht.vep.transcript_consequences[0].consequence_terms,\n",
-    "    coverage=coverage_ht[ht.locus],\n",
-    ").show(-1)"
+    "filter_to_plofs('ash1l').show(-1)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c2212f4-a7cf-45bc-930d-aa18cebff7a3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From bdb41d21113d939cb2331ded8ecce2d0ad01d328 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Fri, 10 Jan 2025 12:07:47 -0500
Subject: [PATCH 063/121] Move modified gnomad_methods back to requirements.txt

---
 gnomad_toolbox/notebooks/needs_a_name.ipynb | 167 ++++++++++++++++++--
 requirements.txt                            |   1 +
 setup.py                                    |   1 -
 3 files changed, 158 insertions(+), 11 deletions(-)

diff --git a/gnomad_toolbox/notebooks/needs_a_name.ipynb b/gnomad_toolbox/notebooks/needs_a_name.ipynb
index b37f8e5..c63ea3a 100644
--- a/gnomad_toolbox/notebooks/needs_a_name.ipynb
+++ b/gnomad_toolbox/notebooks/needs_a_name.ipynb
@@ -497,7 +497,7 @@
     "tags": []
    },
    "source": [
-    "## Filter to pLOF variants that we used to compute constraint metrics\n",
+    "## Filter to pLoF variants that we used to compute constraint metrics\n",
     "pLOF variants meets the following requirements:\n",
     "* High-confidence LOFTEE variants (without any flags),\n",
     "* Only variants in the MANE Select transcript,\n",
@@ -511,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "6ce87a77",
    "metadata": {},
    "outputs": [
@@ -519,23 +519,170 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Initializing Hail with default parameters...\n",
-      "Welcome to\n",
-      "     __  __     <>__\n",
-      "    / /_/ /__  __/ /\n",
-      "   / __  / _ `/ / /\n",
-      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/heqin/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250110-1057-0.2.132-678e1f52b999.log\n",
       "INFO (gnomad.utils.vep 947): Filtering to canonical transcripts\n",
       "INFO (gnomad.utils.vep 950): Filtering to MANE Select transcripts...\n",
       "INFO (gnomad.utils.vep 953): Filtering to Ensembl transcripts...\n",
       "INFO (gnomad.utils.vep 959): Filtering to genes of interest...\n",
       "INFO (gnomad.utils.vep 967): Filtering to variants with additional criteria...\n"
      ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
freq
coverage
locus
alleles
AC
AF
AN
homozygote_count
csq
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>array<str>int32float64int32int64array<str>float64int32int64float64float64float64float64float64float64float64float64float64
chr1:155337668["G","A"]4593.14e-0414612700["stop_gained"]2.99e+0130218875281.00e+001.00e+001.00e+009.97e-019.86e-019.32e-016.07e-018.96e-046.02e-05
chr1:155337704["G","A"]64.10e-0614617140["stop_gained"]3.02e+0130220423721.00e+001.00e+001.00e+009.99e-019.94e-019.48e-016.21e-018.39e-044.93e-05
chr1:155337735["G","C"]16.84e-0714616940["stop_gained"]3.00e+0130219569141.00e+001.00e+001.00e+009.99e-019.92e-019.37e-016.12e-018.29e-044.51e-05
chr1:155338087["A","T"]16.85e-0714596480["splice_donor_variant"]3.16e+0131231113191.00e+001.00e+009.99e-019.95e-019.89e-019.59e-017.28e-011.35e-027.59e-04
chr1:155338161["G","A"]16.84e-0714618840["stop_gained"]3.19e+0131233084341.00e+001.00e+001.00e+001.00e+001.00e+009.76e-017.42e-011.35e-027.59e-04
chr1:155349380["T","A"]16.84e-0714617680["stop_gained"]3.21e+0132234449991.00e+001.00e+001.00e+001.00e+009.99e-019.76e-017.69e-015.01e-031.85e-04
chr1:155354631["C","T"]16.88e-0714534160["splice_acceptor_variant"]3.04e+0131222097141.00e+001.00e+009.95e-019.83e-019.70e-019.28e-016.53e-014.38e-044.93e-05
chr1:155357583["A","T"]16.84e-0714616800["splice_donor_variant"]3.13e+0131228588871.00e+001.00e+001.00e+009.99e-019.97e-019.79e-017.47e-018.14e-046.29e-05
chr1:155370984["C","T"]32.06e-0614557260["splice_acceptor_variant"]3.12e+0131228021811.00e+001.00e+001.00e+009.99e-019.97e-019.54e-016.97e-011.14e-033.69e-05
chr1:155415924["C","A"]21.48e-0613520320["splice_acceptor_variant"]2.73e+0130199323751.00e+009.84e-019.41e-018.72e-018.17e-017.64e-015.70e-011.35e-031.76e-04
chr1:155478020["G","C"]16.84e-0714618880["stop_gained"]3.26e+0133238300811.00e+001.00e+001.00e+001.00e+001.00e+009.72e-017.53e-013.11e-031.40e-03
chr1:155478203["C","T"]16.84e-0714618740["stop_gained"]3.27e+0133239248711.00e+001.00e+001.00e+001.00e+001.00e+009.72e-017.55e-014.07e-032.20e-03
chr1:155478439["C","T"]16.84e-0714618900["stop_gained"]3.28e+0133239948001.00e+001.00e+001.00e+001.00e+001.00e+009.73e-017.59e-014.64e-032.58e-03
chr1:155478528["G","A"]16.84e-0714618760["stop_gained"]3.28e+0133239977761.00e+001.00e+001.00e+001.00e+001.00e+009.73e-017.59e-014.67e-032.60e-03
chr1:155479767["G","A"]16.84e-0714618580["stop_gained"]3.30e+0133241434591.00e+001.00e+001.00e+001.00e+001.00e+009.74e-017.66e-016.55e-033.89e-03
chr1:155479862["G","T"]16.84e-0714611600["stop_gained"]3.24e+0132236512031.00e+001.00e+001.00e+009.98e-019.91e-019.42e-017.13e-016.42e-033.91e-03
chr1:155521291["C","A"]16.84e-0714618620["stop_gained"]3.30e+0132241144561.00e+001.00e+001.00e+001.00e+001.00e+009.81e-017.72e-011.94e-023.68e-03
chr1:155521474["C","A"]16.84e-0714617240["stop_gained"]3.29e+0132240775511.00e+001.00e+001.00e+009.99e-019.98e-019.79e-017.69e-011.94e-023.67e-03
"
+      ],
+      "text/plain": [
+       "+----------------+------------+---------+----------+---------+\n",
+       "| locus          | alleles    | freq.AC |  freq.AF | freq.AN |\n",
+       "+----------------+------------+---------+----------+---------+\n",
+       "| locus  | array |   int32 |  float64 |   int32 |\n",
+       "+----------------+------------+---------+----------+---------+\n",
+       "| chr1:155337668 | [\"G\",\"A\"]  |     459 | 3.14e-04 | 1461270 |\n",
+       "| chr1:155337704 | [\"G\",\"A\"]  |       6 | 4.10e-06 | 1461714 |\n",
+       "| chr1:155337735 | [\"G\",\"C\"]  |       1 | 6.84e-07 | 1461694 |\n",
+       "| chr1:155338087 | [\"A\",\"T\"]  |       1 | 6.85e-07 | 1459648 |\n",
+       "| chr1:155338161 | [\"G\",\"A\"]  |       1 | 6.84e-07 | 1461884 |\n",
+       "| chr1:155349380 | [\"T\",\"A\"]  |       1 | 6.84e-07 | 1461768 |\n",
+       "| chr1:155354631 | [\"C\",\"T\"]  |       1 | 6.88e-07 | 1453416 |\n",
+       "| chr1:155357583 | [\"A\",\"T\"]  |       1 | 6.84e-07 | 1461680 |\n",
+       "| chr1:155370984 | [\"C\",\"T\"]  |       3 | 2.06e-06 | 1455726 |\n",
+       "| chr1:155415924 | [\"C\",\"A\"]  |       2 | 1.48e-06 | 1352032 |\n",
+       "| chr1:155478020 | [\"G\",\"C\"]  |       1 | 6.84e-07 | 1461888 |\n",
+       "| chr1:155478203 | [\"C\",\"T\"]  |       1 | 6.84e-07 | 1461874 |\n",
+       "| chr1:155478439 | [\"C\",\"T\"]  |       1 | 6.84e-07 | 1461890 |\n",
+       "| chr1:155478528 | [\"G\",\"A\"]  |       1 | 6.84e-07 | 1461876 |\n",
+       "| chr1:155479767 | [\"G\",\"A\"]  |       1 | 6.84e-07 | 1461858 |\n",
+       "| chr1:155479862 | [\"G\",\"T\"]  |       1 | 6.84e-07 | 1461160 |\n",
+       "| chr1:155521291 | [\"C\",\"A\"]  |       1 | 6.84e-07 | 1461862 |\n",
+       "| chr1:155521474 | [\"C\",\"A\"]  |       1 | 6.84e-07 | 1461724 |\n",
+       "+----------------+------------+---------+----------+---------+\n",
+       "\n",
+       "+-----------------------+-----------------------------+---------------+\n",
+       "| freq.homozygote_count | csq                         | coverage.mean |\n",
+       "+-----------------------+-----------------------------+---------------+\n",
+       "|                 int64 | array                  |       float64 |\n",
+       "+-----------------------+-----------------------------+---------------+\n",
+       "|                     0 | [\"stop_gained\"]             |      2.99e+01 |\n",
+       "|                     0 | [\"stop_gained\"]             |      3.02e+01 |\n",
+       "|                     0 | [\"stop_gained\"]             |      3.00e+01 |\n",
+       "|                     0 | [\"splice_donor_variant\"]    |      3.16e+01 |\n",
+       "|                     0 | [\"stop_gained\"]             |      3.19e+01 |\n",
+       "|                     0 | [\"stop_gained\"]             |      3.21e+01 |\n",
+       "|                     0 | [\"splice_acceptor_variant\"] |      3.04e+01 |\n",
+       "|                     0 | [\"splice_donor_variant\"]    |      3.13e+01 |\n",
+       "|                     0 | [\"splice_acceptor_variant\"] |      3.12e+01 |\n",
+       "|                     0 | [\"splice_acceptor_variant\"] |      2.73e+01 |\n",
+       "|                     0 | [\"stop_gained\"]             |      3.26e+01 |\n",
+       "|                     0 | [\"stop_gained\"]             |      3.27e+01 |\n",
+       "|                     0 | [\"stop_gained\"]             |      3.28e+01 |\n",
+       "|                     0 | [\"stop_gained\"]             |      3.28e+01 |\n",
+       "|                     0 | [\"stop_gained\"]             |      3.30e+01 |\n",
+       "|                     0 | [\"stop_gained\"]             |      3.24e+01 |\n",
+       "|                     0 | [\"stop_gained\"]             |      3.30e+01 |\n",
+       "|                     0 | [\"stop_gained\"]             |      3.29e+01 |\n",
+       "+-----------------------+-----------------------------+---------------+\n",
+       "\n",
+       "+------------------------+-------------------+-----------------+\n",
+       "| coverage.median_approx | coverage.total_DP | coverage.over_1 |\n",
+       "+------------------------+-------------------+-----------------+\n",
+       "|                  int32 |             int64 |         float64 |\n",
+       "+------------------------+-------------------+-----------------+\n",
+       "|                     30 |          21887528 |        1.00e+00 |\n",
+       "|                     30 |          22042372 |        1.00e+00 |\n",
+       "|                     30 |          21956914 |        1.00e+00 |\n",
+       "|                     31 |          23111319 |        1.00e+00 |\n",
+       "|                     31 |          23308434 |        1.00e+00 |\n",
+       "|                     32 |          23444999 |        1.00e+00 |\n",
+       "|                     31 |          22209714 |        1.00e+00 |\n",
+       "|                     31 |          22858887 |        1.00e+00 |\n",
+       "|                     31 |          22802181 |        1.00e+00 |\n",
+       "|                     30 |          19932375 |        1.00e+00 |\n",
+       "|                     33 |          23830081 |        1.00e+00 |\n",
+       "|                     33 |          23924871 |        1.00e+00 |\n",
+       "|                     33 |          23994800 |        1.00e+00 |\n",
+       "|                     33 |          23997776 |        1.00e+00 |\n",
+       "|                     33 |          24143459 |        1.00e+00 |\n",
+       "|                     32 |          23651203 |        1.00e+00 |\n",
+       "|                     32 |          24114456 |        1.00e+00 |\n",
+       "|                     32 |          24077551 |        1.00e+00 |\n",
+       "+------------------------+-------------------+-----------------+\n",
+       "\n",
+       "+-----------------+------------------+------------------+------------------+\n",
+       "| coverage.over_5 | coverage.over_10 | coverage.over_15 | coverage.over_20 |\n",
+       "+-----------------+------------------+------------------+------------------+\n",
+       "|         float64 |          float64 |          float64 |          float64 |\n",
+       "+-----------------+------------------+------------------+------------------+\n",
+       "|        1.00e+00 |         1.00e+00 |         9.97e-01 |         9.86e-01 |\n",
+       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.94e-01 |\n",
+       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.92e-01 |\n",
+       "|        1.00e+00 |         9.99e-01 |         9.95e-01 |         9.89e-01 |\n",
+       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
+       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         9.99e-01 |\n",
+       "|        1.00e+00 |         9.95e-01 |         9.83e-01 |         9.70e-01 |\n",
+       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.97e-01 |\n",
+       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.97e-01 |\n",
+       "|        9.84e-01 |         9.41e-01 |         8.72e-01 |         8.17e-01 |\n",
+       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
+       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
+       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
+       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
+       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
+       "|        1.00e+00 |         1.00e+00 |         9.98e-01 |         9.91e-01 |\n",
+       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
+       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.98e-01 |\n",
+       "+-----------------+------------------+------------------+------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+-------------------+\n",
+       "| coverage.over_25 | coverage.over_30 | coverage.over_50 | coverage.over_100 |\n",
+       "+------------------+------------------+------------------+-------------------+\n",
+       "|          float64 |          float64 |          float64 |           float64 |\n",
+       "+------------------+------------------+------------------+-------------------+\n",
+       "|         9.32e-01 |         6.07e-01 |         8.96e-04 |          6.02e-05 |\n",
+       "|         9.48e-01 |         6.21e-01 |         8.39e-04 |          4.93e-05 |\n",
+       "|         9.37e-01 |         6.12e-01 |         8.29e-04 |          4.51e-05 |\n",
+       "|         9.59e-01 |         7.28e-01 |         1.35e-02 |          7.59e-04 |\n",
+       "|         9.76e-01 |         7.42e-01 |         1.35e-02 |          7.59e-04 |\n",
+       "|         9.76e-01 |         7.69e-01 |         5.01e-03 |          1.85e-04 |\n",
+       "|         9.28e-01 |         6.53e-01 |         4.38e-04 |          4.93e-05 |\n",
+       "|         9.79e-01 |         7.47e-01 |         8.14e-04 |          6.29e-05 |\n",
+       "|         9.54e-01 |         6.97e-01 |         1.14e-03 |          3.69e-05 |\n",
+       "|         7.64e-01 |         5.70e-01 |         1.35e-03 |          1.76e-04 |\n",
+       "|         9.72e-01 |         7.53e-01 |         3.11e-03 |          1.40e-03 |\n",
+       "|         9.72e-01 |         7.55e-01 |         4.07e-03 |          2.20e-03 |\n",
+       "|         9.73e-01 |         7.59e-01 |         4.64e-03 |          2.58e-03 |\n",
+       "|         9.73e-01 |         7.59e-01 |         4.67e-03 |          2.60e-03 |\n",
+       "|         9.74e-01 |         7.66e-01 |         6.55e-03 |          3.89e-03 |\n",
+       "|         9.42e-01 |         7.13e-01 |         6.42e-03 |          3.91e-03 |\n",
+       "|         9.81e-01 |         7.72e-01 |         1.94e-02 |          3.68e-03 |\n",
+       "|         9.79e-01 |         7.69e-01 |         1.94e-02 |          3.67e-03 |\n",
+       "+------------------+------------------+------------------+-------------------+"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "filter_to_plofs('ash1l').show(-1)"
+    "filter_to_plofs('ash1l', select_fields=True).show(-1)"
    ]
   },
   {
diff --git a/requirements.txt b/requirements.txt
index 49daf5d..c61664c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,4 @@
+gnomad_methods @ git+https://github.com/broadinstitute/gnomad_methods@main
 hail
 jupyter
 jupyter_contrib_nbextensions
diff --git a/setup.py b/setup.py
index 705b7c6..78ab1cb 100644
--- a/setup.py
+++ b/setup.py
@@ -37,5 +37,4 @@
     ],
     python_requires=">=3.9",
     install_requires=install_requires,
-    depencency_links=['git+https://github.com/broadinstitute/gnomad_methods@main#egg=gnomad_methods'],
 )

From bf593fc0bfc85f8c25bfd9a73468452541f960df Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Fri, 10 Jan 2025 12:10:16 -0500
Subject: [PATCH 064/121] rename to gnomad

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index c61664c..b0183e0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-gnomad_methods @ git+https://github.com/broadinstitute/gnomad_methods@main
+gnomad @ git+https://github.com/broadinstitute/gnomad_methods@main
 hail
 jupyter
 jupyter_contrib_nbextensions

From 04903621338431ec8b516a4233699a0c1e52e2a8 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Fri, 10 Jan 2025 10:12:05 -0700
Subject: [PATCH 065/121] Update filter_by_intervals and filter_by_gene_symbol
 to use more gnomad_methods functions

---
 gnomad_toolbox/filtering/variant.py | 118 +++++++++++-----------------
 1 file changed, 46 insertions(+), 72 deletions(-)

diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index e32c0a5..9bb4ddf 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -3,11 +3,10 @@
 from typing import Optional, Union
 
 import hail as hl
-from gnomad.resources.grch37.gnomad import browser_gene as browser_gene_grch37
-from gnomad.resources.grch38.gnomad import browser_gene as browser_gene_grch38
+from gnomad.utils.filtering import filter_by_intervals as interval_filter
+from gnomad.utils.filtering import filter_gencode_ht
 from gnomad.utils.parse import parse_variant
 from gnomad.utils.reference_genome import get_reference_genome
-from gnomad.utils.vep import filter_vep_transcript_csqs
 
 from gnomad_toolbox.load_data import _get_gnomad_release
 
@@ -83,95 +82,70 @@ def filter_by_intervals(
     :param kwargs: Arguments to pass to `_get_gnomad_release`.
     :return: Table with variants in the interval(s).
     """
-    # Load the Hail Table if not provided
+    # Load the Hail Table if not provided.
     ht = _get_gnomad_release(dataset="variant", **kwargs)
 
-    # Determine the reference genome build for the ht.
-    build = get_reference_genome(ht.locus).name
-
-    if isinstance(intervals, str):
-        intervals = [intervals]
-
-    if build == "GRCh38" and any([not i.startswith("chr") for i in intervals]):
-        raise ValueError("Interval must start with 'chr' for GRCh38 reference genome.")
-
-    ht = hl.filter_intervals(
-        ht, [hl.parse_locus_interval(i, reference_genome=build) for i in intervals]
+    return interval_filter(
+        ht, intervals, reference_genome=get_reference_genome(ht.locus).name
     )
 
-    return ht
-
 
 def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.Table:
     """
-    Filter variants in a gene by gene symbol.
+    Filter variants by gene symbol.
 
     .. note::
-       This is to match the browser display, which includes variants in the CDS +
-       75bp padding for protein-coding genes, and UTR/exons + 75bp padding for
-       non-protein-coding genes.
 
-    :param gene: Gene symbol.
-    :param exon_padding_bp: Number of base pairs to pad the CDS intervals. Default is 75bp.
+           This function is to match the number of variants that you will get in the
+           gnomAD browser when you search for a gene symbol. The gnomAD browser
+           filters to only variants located in or within 75 base pairs of a coding exon.
+
+    :param gene: Gencode gene symbol.
+    :param exon_padding_bp: Number of base pairs to pad the intervals. Default is 75bp.
     :param kwargs: Arguments to pass to `_get_gnomad_release`.
-    :return: Table with variants in the gene.
+    :return: Table with variants in the specified gene.
     """
-    # Load the Hail Table if not provided
+    # Load the Hail Table if not provided.
     ht = _get_gnomad_release(dataset="variant", **kwargs)
 
-    # Load gene information
-    gene_ht = (
-        browser_gene_grch37().ht()
-        if ht.locus.dtype.reference_genome.name == "GRCh37"
-        else browser_gene_grch38().ht()
+    # The gnomAD browser will display variants in CDS regions if present, otherwise UTR,
+    # and finally exons.
+    feature_order = ["CDS", "UTR", "exon"]
+    gencode_ht = filter_gencode_ht(
+        reference_genome=get_reference_genome(ht.locus).name,
+        feature=feature_order + ["gene"],
+        genes=gene,
     )
 
-    # Pre-filter to the specified gene (case-insensitive)
-    gene = gene.upper()
-    gene_ht = gene_ht.filter(gene_ht.gencode_symbol == gene)
-
-    # First get the variants in the gene region
-    ht = hl.filter_intervals(ht, hl.array(gene_ht.interval.take(1)))
-
-    # Get intervals based on feature type (CDS > UTR > Exons) with padding
-    def get_intervals(feature_type: str) -> hl.expr.ArrayExpression:
-        return hl.array(
-            gene_ht.exons.filter(lambda exon: exon.feature_type == feature_type)
-        ).map(
-            lambda exon: hl.locus_interval(
-                hl.if_else(
-                    gene_ht.interval.start.dtype.reference_genome.name == "GRCh38",
-                    "chr" + gene_ht.chrom,
-                    gene_ht.chrom,
-                ),
-                exon.start - exon_padding_bp,
-                exon.stop + exon_padding_bp,
-                reference_genome=gene_ht.interval.start.dtype.reference_genome,
-                includes_start=True,
-                includes_end=True,
-            )
-        )
-
-    cds_intervals = get_intervals("CDS")
-    utr_intervals = get_intervals("UTR")
-    exon_intervals = get_intervals("exon")
-
-    # Determine which intervals to use
-    gene_ht = gene_ht.annotate(
-        intervals=hl.if_else(
-            hl.len(cds_intervals) > 0,
-            cds_intervals,
-            hl.if_else(hl.len(utr_intervals) > 0, utr_intervals, exon_intervals),
-        )
+    # The 75bp padding only applies to variants in the specified gene interval
+    # (without padding), so we need to filter the gencode HT to only include the gene
+    # of interest first.
+    ht = filter_by_intervals(
+        ht, gencode_ht.filter(gencode_ht.feature == "gene").interval
     )
 
-    intervals = gene_ht.intervals.take(1)[0]
+    for f in feature_order:
+        filtered_gencode_ht = gencode_ht.filter(gencode_ht.feature == f)
+        filter_count = filtered_gencode_ht.count()
+        if filter_count > 0:
+            break
 
-    ht = hl.filter_intervals(ht, intervals)
+    if filter_count == 0:
+        raise ValueError(f"No intervals match the gene symbol {gene}")
 
-    # Additional filtering (e.g., with VEP consequences)
-    ht = filter_vep_transcript_csqs(
-        ht, genes=[gene], synonymous=False, match_by_gene_symbol=True
+    ht = filter_by_intervals(
+        ht, filtered_gencode_ht.interval, padding_bp=exon_padding_bp
     )
 
+    contigs = filtered_gencode_ht.aggregate(
+        hl.agg.collect_as_set(filtered_gencode_ht.interval.start.contig)
+    )
+    if len(contigs) > 1:
+        hl.utils.warning(
+            "The gnomAD browser excludes genes on chrY that share the same gene symbol "
+            "as chrX. For example, if you use this function to filter 'ASMT' gene, you "
+            "may get more variants than shown in the gnomAD browser because it includes "
+            "both chrX and chrY variants."
+        )
+
     return ht

From 19ace51e73ee435b2cdf8b4dd28355fbdef8c58d Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Fri, 10 Jan 2025 10:32:31 -0700
Subject: [PATCH 066/121] Fix use of filter_by_intervals

---
 gnomad_toolbox/filtering/variant.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index 9bb4ddf..410ba18 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -71,6 +71,7 @@ def get_single_variant(
 
 def filter_by_intervals(
     intervals: Union[str, list[str]],
+    padding_bp: int = 0,
     **kwargs,
 ) -> hl.Table:
     """
@@ -79,6 +80,7 @@ def filter_by_intervals(
     :param intervals: Interval string or list of interval strings. The interval string
         format has to be "contig:start-end", e.g.,"1:1000-2000" (GRCh37) or
         "chr1:1000-2000" (GRCh38).
+    :param padding_bp: Number of base pairs to pad the intervals. Default is 0bp.
     :param kwargs: Arguments to pass to `_get_gnomad_release`.
     :return: Table with variants in the interval(s).
     """
@@ -86,7 +88,10 @@ def filter_by_intervals(
     ht = _get_gnomad_release(dataset="variant", **kwargs)
 
     return interval_filter(
-        ht, intervals, reference_genome=get_reference_genome(ht.locus).name
+        ht,
+        intervals,
+        padding_bp=padding_bp,
+        reference_genome=get_reference_genome(ht.locus).name,
     )
 
 
@@ -121,7 +126,7 @@ def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.
     # (without padding), so we need to filter the gencode HT to only include the gene
     # of interest first.
     ht = filter_by_intervals(
-        ht, gencode_ht.filter(gencode_ht.feature == "gene").interval
+        gencode_ht.filter(gencode_ht.feature == "gene").interval, ht=ht
     )
 
     for f in feature_order:
@@ -134,7 +139,7 @@ def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.
         raise ValueError(f"No intervals match the gene symbol {gene}")
 
     ht = filter_by_intervals(
-        ht, filtered_gencode_ht.interval, padding_bp=exon_padding_bp
+        filtered_gencode_ht.interval, ht=ht, padding_bp=exon_padding_bp
     )
 
     contigs = filtered_gencode_ht.aggregate(

From f44a79f8455460652f896eaa1e0ee0db436a7e27 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Fri, 10 Jan 2025 12:49:24 -0500
Subject: [PATCH 067/121] Add a name for the gnomad_methods main branch so it
 can be used to set up a cluster

---
 requirements.txt | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 702bdab..b0183e0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,4 @@
-# We're using the main branch of gnomad_method on github rather than the pip version
-# TODO: Decide on how to handle this. We might just need to have more releases of gnomad_methods.
-git+https://github.com/broadinstitute/gnomad_methods@main
+gnomad @ git+https://github.com/broadinstitute/gnomad_methods@main
 hail
 jupyter
 jupyter_contrib_nbextensions

From ae2acbcc0a40704c6a0f2c7570655e95643defb2 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Mon, 13 Jan 2025 17:23:25 -0700
Subject: [PATCH 068/121] Updates to loading data to support more data types
 and inclusion of compatible dataset versions and constraint parameters. Also,
 added vep and gencode as supported reference datasets Reduced the toolbox
 supported versions to only v2.1.1 and 4.1, since the others are outdated
 and/or have bugs

---
 gnomad_toolbox/analysis/general.py            |   8 +-
 gnomad_toolbox/filtering/frequency.py         |   6 +-
 gnomad_toolbox/filtering/variant.py           |  32 +-
 gnomad_toolbox/filtering/vep.py               | 206 +++++++----
 gnomad_toolbox/load_data.py                   | 344 ++++++++++++------
 .../notebooks/explore_release_data.ipynb      | 132 +++----
 6 files changed, 427 insertions(+), 301 deletions(-)

diff --git a/gnomad_toolbox/analysis/general.py b/gnomad_toolbox/analysis/general.py
index be9fb7e..923b47e 100644
--- a/gnomad_toolbox/analysis/general.py
+++ b/gnomad_toolbox/analysis/general.py
@@ -5,7 +5,7 @@
 import hail as hl
 from gnomad.assessment.summary_stats import freq_bin_expr
 
-from gnomad_toolbox.load_data import _get_gnomad_release
+from gnomad_toolbox.load_data import _get_dataset
 
 
 def get_variant_count_by_freq_bin(
@@ -33,12 +33,12 @@ def get_variant_count_by_freq_bin(
     :param singletons: Include singletons.
     :param doubletons: Include doubletons.
     :param pass_only: Include only PASS variants.
-    :param kwargs: Keyword arguments to pass to _get_gnomad_release. Includes
-        'ht', 'data_type', and 'version'.
+    :param kwargs: Keyword arguments to pass to `_get_dataset`. Includes 'ht',
+        'data_type', and 'version'.
     :return: Dictionary with counts.
     """
     # Load the Hail Table if not provided
-    ht = _get_gnomad_release(dataset="variant", **kwargs)
+    ht = _get_dataset(dataset="variant", **kwargs)
 
     # Filter to PASS variants.
     if pass_only:
diff --git a/gnomad_toolbox/filtering/frequency.py b/gnomad_toolbox/filtering/frequency.py
index c5a008b..d08e253 100644
--- a/gnomad_toolbox/filtering/frequency.py
+++ b/gnomad_toolbox/filtering/frequency.py
@@ -6,7 +6,7 @@
 from gnomad.utils.filtering import filter_arrays_by_meta
 
 from gnomad_toolbox.filtering.variant import get_single_variant
-from gnomad_toolbox.load_data import _get_gnomad_release
+from gnomad_toolbox.load_data import _get_dataset
 
 
 def get_ancestry_callstats(
@@ -19,11 +19,11 @@ def get_ancestry_callstats(
     :param gen_ancs: Genetic ancestry group(s) (e.g., 'afr', 'amr', 'asj', 'eas',
         'fin', 'nfe', 'oth', 'sas'). Can be a single ancestry group or a list of
         ancestry groups.
-    :param kwargs: Keyword arguments to pass to _get_gnomad_release.
+    :param kwargs: Keyword arguments to pass to _get_dataset.
     :return: Table with callstats for the given ancestry groups and variant.
     """
     # Load the Hail Table if not provided
-    ht = _get_gnomad_release(dataset="variant", **kwargs)
+    ht = _get_dataset(dataset="variant", **kwargs)
 
     # Check if gen_ancs is a single ancestry group.
     one_anc = isinstance(gen_ancs, str)
diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index 7359106..8a90020 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -3,11 +3,12 @@
 from typing import Optional, Union
 
 import hail as hl
+from gnomad.utils.filtering import filter_by_intervals as interval_filter
 from gnomad.utils.filtering import filter_to_gencode_cds
 from gnomad.utils.parse import parse_variant
 from gnomad.utils.reference_genome import get_reference_genome
 
-from gnomad_toolbox.load_data import _get_gnomad_release
+from gnomad_toolbox.load_data import _get_dataset
 
 
 def get_single_variant(
@@ -34,7 +35,7 @@ def get_single_variant(
     :param position: Variant position. Required if `variant` is not provided.
     :param ref: Reference allele. Required if `variant` is not provided.
     :param alt: Alternate allele. Required if `variant` is not provided.
-    :param kwargs: Additional arguments to pass to `_get_gnomad_release`.
+    :param kwargs: Additional arguments to pass to `_get_dataset`.
     :return: Table with the single variant.
     """
     if not variant and not all([contig, position, ref, alt]):
@@ -44,7 +45,7 @@ def get_single_variant(
         )
 
     # Load the Hail Table if not provided
-    ht = _get_gnomad_release(dataset="variant", **kwargs)
+    ht = _get_dataset(dataset="variant", **kwargs)
 
     # Determine the reference genome build for the ht.
     build = get_reference_genome(ht.locus).name
@@ -78,27 +79,18 @@ def filter_by_intervals(
     :param intervals: Interval string or list of interval strings. The interval string
         format has to be "contig:start-end", e.g.,"1:1000-2000" (GRCh37) or
         "chr1:1000-2000" (GRCh38).
-    :param kwargs: Arguments to pass to `_get_gnomad_release`.
+    :param kwargs: Arguments to pass to `_get_dataset`.
     :return: Table with variants in the interval(s).
     """
     # Load the Hail Table if not provided
-    ht = _get_gnomad_release(dataset="variant", **kwargs)
+    ht = _get_dataset(dataset="variant", **kwargs)
 
-    # Determine the reference genome build for the ht.
-    build = get_reference_genome(ht.locus).name
-
-    if isinstance(intervals, str):
-        intervals = [intervals]
-
-    if build == "GRCh38" and any([not i.startswith("chr") for i in intervals]):
-        raise ValueError("Interval must start with 'chr' for GRCh38 reference genome.")
-
-    ht = hl.filter_intervals(
-        ht, [hl.parse_locus_interval(i, reference_genome=build) for i in intervals]
+    return interval_filter(
+        ht,
+        intervals,
+        reference_genome=get_reference_genome(ht.locus).name,
     )
 
-    return ht
-
 
 # TODO: Add a pre-processing step to filter out these genes on chrY to
 # match the gnomAD browser.
@@ -120,11 +112,11 @@ def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.
     :param gene: Gene symbol.
     :param exon_padding_bp: Number of base pairs to pad the CDS intervals. Default is
         75bp.
-    :param kwargs: Arguments to pass to `_get_gnomad_release`.
+    :param kwargs: Arguments to pass to `_get_dataset`.
     :return: Table with variants in the gene.
     """
     # Load the Hail Table if not provided
-    ht = _get_gnomad_release(dataset="variant", **kwargs)
+    ht = _get_dataset(dataset="variant", **kwargs)
     ht = filter_to_gencode_cds(ht, genes=gene, padding_bp=exon_padding_bp)
 
     return ht
diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py
index a7a1505..6e5519d 100644
--- a/gnomad_toolbox/filtering/vep.py
+++ b/gnomad_toolbox/filtering/vep.py
@@ -1,10 +1,9 @@
 """Functions to filter gnomAD sites HT by VEP annotations."""
 
-from typing import List
+from typing import List, Optional
 
 import hail as hl
-from gnomad.resources.grch37.reference_data import gencode as grch37_gencode
-from gnomad.resources.grch38.reference_data import gencode as grch38_gencode
+from gnomad.utils.filtering import filter_gencode_ht
 from gnomad.utils.vep import (
     LOF_CSQ_SET,
     filter_vep_transcript_csqs,
@@ -12,9 +11,9 @@
 )
 
 from gnomad_toolbox.load_data import (
-    _get_gnomad_release,
-    get_coverage_for_variant,
-    gnomad_session,
+    CONSTRAINT_DATA,
+    _get_dataset,
+    get_compatible_dataset_versions,
 )
 
 
@@ -88,7 +87,7 @@ def filter_by_consequence_category(
     :param synonymous: Whether to include synonymous variants.
     :param other: Whether to include other variants.
     :param pass_filters: Boolean if the variants pass the filters.
-    :param kwargs: Arguments to pass to _get_gnomad_release.
+    :param kwargs: Arguments to pass to `_get_dataset`.
     :return: Table with variants with the specified consequences.
     """
     if not any([plof, missense, synonymous, other]):
@@ -97,7 +96,7 @@ def filter_by_consequence_category(
         )
 
     # Load the Hail Table if not provided
-    ht = _get_gnomad_release(dataset="variant", **kwargs)
+    ht = _get_dataset(dataset="variant", **kwargs)
 
     lof_csqs = list(LOF_CSQ_SET)
     missense_csqs = ["missense_variant", "inframe_insertion", "inframe_deletion"]
@@ -128,102 +127,155 @@ def filter_by_consequence_category(
     return ht.filter(filter_expr)
 
 
-def get_gene_intervals(gene_symbol: str, version: str) -> List[hl.utils.Interval]:
+def get_gene_intervals(
+    gene_symbol: str, version: Optional[str] = None
+) -> List[hl.utils.Interval]:
     """
-    Get the genomic intervals for a given gene symbol.
+    Get the GENCODE genomic intervals for a given gene symbol.
 
     :param gene_symbol: Gene symbol.
-    :param version: Dataset version.
-    :return: List of intervals for the specified gene.
+    :param version: Optional gnomAD dataset version. If not provided, uses the gnomAD
+        session version.
+    :return: List of GENCODE intervals for the specified gene.
     """
-    gen_ht = grch37_gencode.ht() if version.startswith("2.") else grch38_gencode.ht()
-    intervals = (
-        gen_ht.filter(
-            (gen_ht.feature == "gene")
-            & (gen_ht.gene_name.lower() == gene_symbol.lower())
-        )
-        .select()
-        .collect()
-    )
+    # Load the Hail Table if not provided.
+    ht = _get_dataset(dataset="gencode", version=version)
+    gene_symbol = gene_symbol.upper()
+
+    intervals = filter_gencode_ht(gencode_ht=ht, feature="gene", genes=gene_symbol)
+    intervals = intervals.interval.collect()
+
     if not intervals:
         raise ValueError(f"No interval found for gene: {gene_symbol}")
-    return [row["interval"] for row in intervals]
 
+    return intervals
 
-def filter_hc_variants(var_ht: hl.Table, gene_symbol: str, version: str) -> hl.Table:
-    """
-    Filter variants to high-confidence LOFTEE variants with optional transcript selection.
 
-    :param var_ht: Variants Table.
-    :param gene_symbol: Gene symbol.
-    :param version: Dataset version.
-    :return: Filtered variants Hail Table.
+def filter_to_high_confidence_loftee(
+    gene_symbol: Optional[str] = None,
+    no_lof_flags: bool = False,
+    mane_select_only: bool = False,
+    canonical_only: bool = False,
+    **kwargs,
+) -> hl.Table:
+    """
+    Filter gnomAD variants to high-confidence LOFTEE variants for a gene.
+
+    :param gene_symbol: Optional gene symbol to filter by.
+    :param no_lof_flags: Whether to exclude variants with LOFTEE flags. Default is
+        False.
+    :param mane_select_only: Whether to include only MANE Select transcripts. Default
+        is False.
+    :param canonical_only: Whether to include only canonical transcripts. Default is
+        False.
+    :param kwargs: Additional arguments to pass to `_get_dataset`.
+    :return: Table with high-confidence LOFTEE variants.
     """
+    # Load the Hail Table if not provided.
+    ht = _get_dataset(dataset="variant", **kwargs)
+    gene_symbol = gene_symbol.upper() if gene_symbol else None
+
+    if gene_symbol:
+        ht = hl.filter_intervals(ht, get_gene_intervals(gene_symbol))
+
     return filter_vep_transcript_csqs(
-        var_ht,
+        ht,
         synonymous=False,
-        mane_select=version.startswith("4."),
-        genes=[gene_symbol.upper()],
+        canonical=canonical_only,
+        mane_select=mane_select_only,
+        genes=[gene_symbol],
         match_by_gene_symbol=True,
-        additional_filtering_criteria=[
-            lambda x: (x.lof == "HC")
-            & (hl.is_missing(x.lof_flags) | (x.lof_flags == ""))
-        ],
+        loftee_labels=["HC"],
+        no_lof_flags=no_lof_flags,
     )
 
 
+# TODO: Let's move this function to constraint.py and change the name to something more
+#  descriptive, like maybe get_observed_plofs_for_gene_constraint.
 def filter_to_plofs(
-    gene_symbol: str, select_fields: bool = False, **kwargs
+    gene_symbol: str,
+    version: str = None,
+    variant_ht: hl.Table = None,
+    coverage_ht: hl.Table = None,
 ) -> hl.Table:
     """
     Filter to observed pLoF variants used for gene constraint metrics.
 
     .. note::
 
-                pLOF variants meets the following requirements:
-                - High-confidence LOFTEE variants (without any flags),
-                - Only variants in the MANE Select transcript,
-                - PASS variants that are SNVs with MAF ≤ 0.1%,
-                - Exome median depth ≥ 30 (# TODO: This is changing in v4 constraint?)
+        pLOF variants meets the following requirements:
+
+            - PASS variant QC
+            - SNV
+            - Allele frequency ≤ 0.1%
+            - High-confidence LOFTEE in the Canonical or MANE Select transcript (depends
+              on the version)
+            - ≥ a specified coverage threshold (depends on the version)
 
     :param gene_symbol: Gene symbol.
-    :param select_fields: Whether to limit the output to specific fields.
+    :param version: Optional gnomAD dataset version. If not provided, uses the gnomAD
+        session version.
+    :param variant_ht: Optional Hail Table with variants. If not provided, uses the
+        exome variant Table for the gnomAD session version.
+    :param coverage_ht: Optional Hail Table with coverage data. If not provided, uses
+        the exome coverage Table for the gnomAD session version.
     :return: Table with pLoF variants.
     """
-    var_version = kwargs.pop("version", gnomad_session.version)
-    var_ht = _get_gnomad_release(dataset="variant", version=var_version, **kwargs)
-    cov_ht = get_coverage_for_variant(var_version, **kwargs)
-
-    # Get gene intervals and filter tables
-    intervals = get_gene_intervals(gene_symbol, var_version)
-    var_ht = hl.filter_intervals(var_ht, intervals)
-    cov_ht = hl.filter_intervals(cov_ht, intervals)
-
-    # Filter to high-confidence LOFTEE variants
-    var_ht = filter_hc_variants(var_ht, gene_symbol, var_version)
-
-    # Version-specific expressions
-    if var_version.startswith("2."):
-        allele_type_expr = var_ht.allele_type
-        cov_cut_expr = cov_ht[var_ht.locus].median
-    else:
-        allele_type_expr = var_ht.allele_info.allele_type
-        cov_cut_expr = cov_ht[var_ht.locus].median_approx
-
-    # Apply final filters
-    var_ht = var_ht.filter(
-        (hl.len(var_ht.filters) == 0)
-        & (allele_type_expr == "snv")
-        & (var_ht.freq[0].AF <= 0.001)
-        & (cov_cut_expr >= 30)
+    if variant_ht is not None and coverage_ht is None:
+        raise ValueError("Variant Hail Table provided without coverage Hail Table.")
+
+    if coverage_ht is not None and variant_ht is None:
+        raise ValueError("Coverage Hail Table provided without variant Hail Table.")
+
+    # Load the variant exomes Hail Table if not provided.
+    variant_ht = _get_dataset(
+        dataset="variant",
+        ht=variant_ht,
+        data_type="exomes",
+        version=version,
     )
 
-    # Select specific fields if requested
-    if select_fields:
-        var_ht = var_ht.select(
-            freq=var_ht.freq[0],
-            csq=var_ht.vep.transcript_consequences[0].consequence_terms,
-            coverage=cov_ht[var_ht.locus],
-        )
+    # Determine the coverage version compatible with the variant version.
+    coverage_version = get_compatible_dataset_versions("coverage", version, "exomes")
+
+    # Load the coverage Hail Table if not provided.
+    coverage_ht = _get_dataset(
+        dataset="coverage",
+        ht=coverage_ht,
+        data_type="exomes",
+        version=coverage_version,
+    )
+
+    # Get gene intervals and filter tables.
+    gencode_version = get_compatible_dataset_versions("gencode", version)
+    intervals = get_gene_intervals(gene_symbol, version=gencode_version)
+    variant_ht = hl.filter_intervals(variant_ht, intervals)
+    coverage_ht = hl.filter_intervals(coverage_ht, intervals)
+
+    # Determine constraint filters.
+    constraint_version = get_compatible_dataset_versions("constraint", version)
+    constraint_info = CONSTRAINT_DATA[constraint_version]
+    cov_field = constraint_info["exome_coverage_field"]
+    cov_cutoff = constraint_info["exome_coverage_cutoff"]
+    af_cutoff = constraint_info["af_cutoff"]
+
+    # Annotate the exome coverage.
+    variant_ht = variant_ht.annotate(
+        exome_coverage=coverage_ht[variant_ht.locus][cov_field]
+    )
+
+    # Apply constraint filters.
+    variant_ht = variant_ht.filter(
+        (hl.len(variant_ht.filters) == 0)
+        & (hl.is_snp(*variant_ht.alleles))
+        & (variant_ht.freq[0].AF <= af_cutoff)
+        & (variant_ht.exome_coverage >= cov_cutoff)
+    )
+
+    # Filter to high-confidence LOFTEE variants.
+    variant_ht = filter_to_high_confidence_loftee(
+        gene_symbol=gene_symbol,
+        ht=variant_ht,
+    )
 
-    return var_ht
+    return variant_ht
diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index 8405dfd..11685bf 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -1,50 +1,115 @@
 """Functions to import gnomAD data."""
 
-import functools
-from typing import Optional
+from typing import Callable, Optional, Union
 
-import gnomad.resources.grch37.gnomad as grch37_gnomad
-import gnomad.resources.grch38.gnomad as grch38_gnomad
+import gnomad.resources.grch37 as grch37_res
+import gnomad.resources.grch38 as grch38_res
 import hail as hl
+from gnomad.resources.resource_utils import VersionedTableResource
 
-GNOMAD_BY_BUILD = {
-    "GRCh37": grch37_gnomad,
-    "GRCh38": grch38_gnomad,
+DATA_TYPES = ["exomes", "genomes", "joint"]
+PEXT_DATA_TYPES = ["base_level", "annotation_level"]
+RESOURCES_BY_BUILD = {
+    "GRCh37": grch37_res,
+    "GRCh38": grch38_res,
 }
-DATASETS = {
-    "variant": "public_release",
-    "all_sites_an": "all_sites_an",
-    "coverage": "coverage",
+VARIANT_DATA = {
+    "2.1.1": {
+        "reference_genome": "GRCh37",
+        "data_types": ["exomes", "genomes"],
+        "dataset_versions": {
+            "vep": "85",
+            "gencode": "v19",
+            "coverage": {
+                "exomes": "2.1",
+                "genomes": "2.1",
+            },
+            "constraint": "2.1.1",
+            "pext": "v7",
+            "liftover": "2.1.1",
+        },
+    },
+    "4.1": {
+        "reference_genome": "GRCh38",
+        "data_types": ["exomes", "genomes", "joint"],
+        "dataset_versions": {
+            "vep": "105",
+            "gencode": "v39",
+            "coverage": {"exomes": "4.0", "genomes": "3.0.1"},
+            "all_sites_an": "4.1",
+            "constraint": "4.1",
+            "pext": "v7",
+            "browser": "4.1",
+        },
+    },
 }
-DATA_TYPES = ["exomes", "genomes", "joint"]
-RELEASES_GLOBAL = {
-    "variant": {
-        "exomes": "EXOME_RELEASES",
-        "genomes": "GENOME_RELEASES",
-        "joint": "JOINT_RELEASES",
+COVERAGE_DATA = {
+    "2.1": {"reference_genome": "GRCh37", "data_types": ["exomes", "genomes"]},
+    "3.0.1": {"reference_genome": "GRCh38", "data_types": ["genomes"]},
+    "4.0": {
+        "reference_genome": "GRCh38",
+        "data_types": ["exomes"],
     },
-    "all_sites_an": {
-        "exomes": "EXOME_AN_RELEASES",
-        "genomes": "GENOME_AN_RELEASES",
+}
+ALL_SITES_AN_DATA = {
+    "4.1": {
+        "reference_genome": "GRCh38",
+        "data_types": ["exomes", "genomes"],
+    }
+}
+CONSTRAINT_DATA = {
+    "2.1.1": {
+        "reference_genome": "GRCh37",
+        "exome_coverage_field": "median",
+        "exome_coverage_cutoff": 30,
+        "af_cutoff": 0.001,
+        "mane_select": False,
+        "canonical": True,
     },
-    "coverage": {
-        "exomes": "EXOME_COVERAGE_RELEASES",
-        "genomes": "GENOME_COVERAGE_RELEASES",
+    "4.1": {
+        "reference_genome": "GRCh38",
+        "exome_coverage_field": "median_approx",
+        "exome_coverage_cutoff": 30,
+        "af_cutoff": 0.001,
+        "mane_select": True,
+        "canonical": False,
     },
 }
-RELEASES = {
-    dataset: {
-        data_type: {
-            build: (
-                None
-                if release_global.get(data_type) is None
-                else getattr(res, release_global.get(data_type), None)
-            )
-            for build, res in GNOMAD_BY_BUILD.items()
-        }
-        for data_type in DATA_TYPES
+LIFTOVER_DATA = {
+    "2.1.1": {
+        "reference_genome": "GRCh37",
+        "data_types": ["exomes", "genomes"],
     }
-    for dataset, release_global in RELEASES_GLOBAL.items()
+}
+PEXT_DATA = {
+    "v7": {"reference_genome": "GRCh37", "data_types": PEXT_DATA_TYPES},
+    "v10": {"reference_genome": "GRCh38", "data_types": PEXT_DATA_TYPES},
+}
+BROWSER_DATA = {"4.1": {"reference_genome": "GRCh38"}}
+SUPPORTED_DATASETS = {
+    "variant": {"resource": "public_release", "versions": VARIANT_DATA},
+    "coverage": {"resource": "coverage", "versions": COVERAGE_DATA},
+    "all_sites_an": {"resource": "all_sites_an", "versions": ALL_SITES_AN_DATA},
+    "constraint": {"resource": "constraint", "versions": CONSTRAINT_DATA},
+    "liftover": {"resource": "liftover", "versions": LIFTOVER_DATA},
+    "pext": {"resource": "pext", "versions": PEXT_DATA},
+    "browser": {"resource": "browser", "versions": BROWSER_DATA},
+}
+SUPPORTED_REFERENCE_DATA = {
+    "vep": {
+        "resource": "vep_context",
+        "versions": {
+            "85": {"reference_genome": "GRCh37"},
+            "105": {"reference_genome": "GRCh38"},
+        },
+    },
+    "gencode": {
+        "resource": "gencode",
+        "versions": {
+            "v19": {"reference_genome": "GRCh37"},
+            "v39": {"reference_genome": "GRCh38"},
+        },
+    },
 }
 
 
@@ -62,8 +127,10 @@ def __init__(self) -> None:
 
         :return: None.
         """
-        self.data_type = "exomes"
-        self.version = grch38_gnomad.CURRENT_EXOME_RELEASE
+        self.set_default_data(
+            data_type="exomes",
+            version="4.1",
+        )
 
     def set_default_data(
         self,
@@ -80,36 +147,26 @@ def set_default_data(
         data_type = data_type or self.data_type
         version = version or self.version
 
-        # Validate data type.
-        if data_type and data_type not in DATA_TYPES:
-            raise ValueError(
-                f"Data type {data_type} is invalid. Choose from {DATA_TYPES}"
-            )
-
-        # Get all possible versions.
-        possible_versions = functools.reduce(
-            lambda x, y: (x or []) + (y or []),
-            [
-                ds[dt][r]
-                for ds in RELEASES.values()
-                for dt in ([data_type] if data_type else DATA_TYPES)
-                for r in GNOMAD_BY_BUILD
-            ],
-        )
+        # Validate version.
+        if version not in VARIANT_DATA:
+            raise ValueError(f"Version {version} is not a supported gnomAD version.")
 
-        # Check version availability.
-        if version not in possible_versions:
+        # Validate data type for the version.
+        version_info = VARIANT_DATA[version]
+        if data_type not in version_info["data_types"]:
             raise ValueError(f"Version {version} for {data_type} is not available.")
 
         self.data_type = data_type
         self.version = version
+        self.reference_genome = version_info["reference_genome"]
+        self.compatible_datasets = version_info["dataset_versions"]
 
 
 # Global gnomad session object
 gnomad_session = GnomADSession()
 
 
-def _get_gnomad_release(
+def _get_dataset(
     ht: hl.Table = None,
     dataset: str = "variant",
     data_type: str = None,
@@ -119,8 +176,8 @@ def _get_gnomad_release(
     Get gnomAD HT using a Hail Table, specific parameters, or session defaults.
 
     :param ht: Pre-loaded Hail Table. If provided, other parameters are ignored.
-    :param dataset: Dataset type. One of "variant", "all_sites_an", "coverage". Default
-        is variant.
+    :param dataset: Dataset type. One of "variant", "all_sites_an", "coverage",
+        "gencode". Default is variant.
     :param data_type: Data type (exomes, genomes, or joint). Default is session value.
     :param version: gnomAD version. Default is session value.
     :return: Hail Table for requested dataset, data type, and version.
@@ -133,36 +190,54 @@ def _get_gnomad_release(
     data_type = data_type or gnomad_session.data_type
     version = version or gnomad_session.version
 
-    # Get all releases for the given dataset.
-    releases = RELEASES.get(dataset)
-
     # Validate dataset.
-    if releases is None:
-        raise ValueError(f"{dataset} is invalid. Choose from {list(RELEASES.keys())}")
+    dataset_info = SUPPORTED_DATASETS.get(dataset)
+    if dataset_info is None:
+        dataset_info = SUPPORTED_REFERENCE_DATA.get(dataset)
 
-    # Get all releases for the given dataset and data_type.
-    data_type_releases = releases.get(data_type)
-
-    # Validate data type.
-    if data_type_releases is None:
-        raise ValueError(f"Invalid data_type '{data_type}' for dataset '{dataset}'.")
+    if dataset_info is None:
+        raise ValueError(
+            f"{dataset} is invalid. Choose from:\n"
+            f"\tgnomAD datasets:{list(SUPPORTED_DATASETS.keys())}\n"
+            f"\treference datasets: {list(SUPPORTED_REFERENCE_DATA.keys())}"
+        )
 
-    # Check version availability for GRCh38 and GRCh37.
-    if data_type_releases["GRCh38"] and version in data_type_releases["GRCh38"]:
-        return (
-            getattr(grch38_gnomad, DATASETS[dataset])(data_type).versions[version].ht()
+    # Validate version.
+    versions = dataset_info["versions"]
+    version_info = versions.get(version)
+    if version_info is None:
+        version_format = "\t".join(
+            f"{v} ({versions[v]['reference_genome']})" for v in versions
         )
-    elif data_type_releases["GRCh37"] and version in data_type_releases["GRCh37"]:
-        return (
-            getattr(grch37_gnomad, DATASETS[dataset])(data_type).versions[version].ht()
+        raise ValueError(
+            f"Version {version} is not in the supported versions for {dataset}. "
+            f"Supported versions: {version_format}"
         )
-    else:
+
+    # Validate data type.
+    data_types = version_info.get("data_types")
+    if data_types and data_type and data_type not in data_types:
         raise ValueError(
-            f"Version {version} is not available for {data_type} in the {dataset} dataset. "
-            f"Available versions: GRCh38 - {data_type_releases['GRCh38']}, "
-            f"GRCh37 - {data_type_releases['GRCh37']}."
+            f"Version {version} is not available for {data_type} in the {dataset} "
+            f"dataset. Available data types: {data_types}."
         )
 
+    # Get the resource for the given build.
+    build = version_info["reference_genome"]
+    res_build = RESOURCES_BY_BUILD[build]
+    res_name = dataset_info["resource"]
+    res = getattr(res_build.gnomad, res_name, None) or getattr(
+        res_build.reference_data, res_name
+    )
+
+    if isinstance(res, Callable):
+        res = res(data_type) if data_type else res()
+
+    if isinstance(res, VersionedTableResource):
+        res = res.versions[version]
+
+    return res.ht()
+
 
 def get_gnomad_release(
     dataset: str = "variant",
@@ -170,52 +245,89 @@ def get_gnomad_release(
     version: Optional[str] = None,
 ) -> hl.Table:
     """
-    Get gnomAD HT by dataset, data type,  and version.
+    Get gnomAD HT by dataset, data type, and version.
+
+    Not all combinations of dataset, data type, and version are available and/or
+    supported by the toolbox. The table below shows what is supported.
 
-    .. table:: Available versions for each dataset and data type are (as of 2024-10-29)
+    .. table:: Available versions for each dataset and data type are (as of 2025-1-13)
         :widths: auto
 
-        +--------------+-----------------+----------------------------------+----------------------+
-        | Dataset      | Data Type       | GRCh38 Versions                  | GRCh37 Versions      |
-        +==============+=================+==================================+======================+
-        | variant      | exomes          | 4.0, 4.1                         | 2.1, 2.1.1           |
-        |              +-----------------+----------------------------------+----------------------+
-        |              | genomes         | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1           |
-        |              +-----------------+----------------------------------+----------------------+
-        |              | joint           | 4.1                              | N/A                  |
-        +--------------+-----------------+----------------------------------+----------------------+
-        | coverage     | exomes          | 4.0                              | 2.1                  |
-        |              +-----------------+----------------------------------+----------------------+
-        |              | genomes         | 3.0.1                            | 2.1                  |
-        +--------------+-----------------+----------------------------------+----------------------+
-        | all_sites_an | exomes          | 4.1                              | N/A                  |
-        |              +-----------------+----------------------------------+----------------------+
-        |              | genomes         | 4.1                              | N/A                  |
-        +--------------+-----------------+----------------------------------+----------------------+
-
-    :param data_type: Data type (exomes, genomes, or joint). Default is "exomes".
-    :param version: gnomAD version. Default is the current exome release.
-    :param dataset: Dataset type. One of "variant", "all_sites_an", "coverage". Default
-        is "variant".
+        +--------------+--------------+---------+------------------------------+
+        | Dataset      | Genome Build | Version | Data Types                   |
+        +==============+==============+=========+==============================+
+        | variant      | GRCh37       | 2.1.1   | exomes, genomes              |
+        |              +--------------+---------+------------------------------+
+        |              | GRCh38       | 4.1     | exomes, genomes, joint       |
+        |--------------+--------------+---------+------------------------------+
+        | coverage     | GRCh37       | 2.1     | exomes, genomes              |
+        |              +--------------+---------+------------------------------+
+        |              | GRCh38       | 3.0.1   | genomes                      |
+        |              |              +---------+------------------------------+
+        |              |              | 4.0     | exomes                       |
+        +--------------+--------------+---------+------------------------------+
+        | all_sites_an | GRCh38       | 4.1     | exomes, genomes              |
+        +--------------+--------------+---------+------------------------------+
+        | constraint   | GRCh37       | 2.1.1   | N/A                          |
+        |              +--------------+---------+------------------------------+
+        |              | GRCh38       | 4.1     | N/A                          |
+        |--------------|--------------+---------+------------------------------+
+        | liftover     | GRCh37       | 2.1.1   | exomes, genomes              |
+        +--------------+--------------+---------+------------------------------+
+        | pext         | GRCh37       | v7      | base_level, annotation_level |
+        |              +--------------+---------+------------------------------+
+        |              | GRCh38       | v10     | base_level, annotation_level |
+        +--------------+--------------+---------+------------------------------+
+        | browser      | GRCh38       | 4.1     | N/A (joint, but doesn't need |
+        |              |              |         | to be specified)             |
+        +--------------+--------------+---------+------------------------------+
+
+    :param dataset: Dataset type. One of "variant", "coverage", "all_sites_an",
+        "constraint", "liftover", "pext", "browser". Default is "variant".
+    :param data_type: Data type. One of "exomes", "genomes", "joint" for all datasets
+        except "pext" where it is one of "base_level", "annotation_level". Default is
+        the current session data type.
+    :param version: gnomAD dataset version. Default is the current session version.
     :return: Hail Table for requested dataset, data type, and version.
     """
-    return _get_gnomad_release(dataset=dataset, data_type=data_type, version=version)
+    return _get_dataset(dataset=dataset, data_type=data_type, version=version)
 
 
-def get_coverage_for_variant(variant_version: str, **kwargs) -> hl.Table:
+def get_compatible_dataset_versions(
+    dataset: str, variant_version: Optional[str] = None, data_type: Optional[str] = None
+) -> Union[str, dict]:
     """
-    Get the appropriate coverage table based on the provided version.
+    Get the compatible version of another datasets for a given gnomAD variant data version.
 
-    :param variant_version: Version of the variant dataset.
-    :return: Hail Table for the corresponding coverage dataset.
+    :param dataset: Dataset to get the compatible version for.
+    :param variant_version: Optional gnomAD variant data version. If not provided, the
+        current session version is used.
+    :param data_type: Optional data type for the dataset if applicable.
+    :return: Compatible version of the dataset for the given variant version.
     """
-    if variant_version.startswith("4."):
-        return _get_gnomad_release(dataset="coverage", version="4.0", **kwargs)
-    elif variant_version.startswith("3."):
-        return _get_gnomad_release(dataset="coverage", version="3.0.1", **kwargs)
-    elif variant_version.startswith("2."):
-        return _get_gnomad_release(dataset="coverage", version="2.1", **kwargs)
+    # Get the dictionary of compatible versions for the given variant version or
+    # the current session version.
+    if variant_version is None:
+        versions = VARIANT_DATA[variant_version]["dataset_versions"]
     else:
+        versions = gnomad_session.compatible_datasets
+
+    # Validate dataset.
+    if dataset not in versions:
         raise ValueError(
-            f"Unrecognized version: '{variant_version}'. Please specify a valid version."
+            f"{dataset} is not available for {variant_version}."
+            f"Available datasets: {list(versions.keys())}"
         )
+
+    # If the dataset has multiple data types and a data type is provided, return the
+    # version for the data type.
+    dataset_version = versions[dataset]
+    if data_type and isinstance(dataset_version, dict):
+        if data_type not in dataset_version:
+            raise ValueError(
+                f"{data_type} is not available for {variant_version} {dataset}."
+                f"Available data types: {list(dataset_version.keys())}"
+            )
+        return dataset_version[data_type]
+
+    return dataset_version
diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
index 1801596..a0959a7 100644
--- a/gnomad_toolbox/notebooks/explore_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -76,7 +76,7 @@
     {
      "data": {
       "text/html": [
-       "    \n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -125,22 +125,14 @@
        "   * Handle when an output is cleared or removed\n",
        "   */\n",
        "  function handleClearOutput(event, handle) {\n",
-       "    function drop(id) {\n",
-       "      const view = Bokeh.index.get_by_id(id)\n",
-       "      if (view != null) {\n",
-       "        view.model.document.clear()\n",
-       "        Bokeh.index.delete(view)\n",
-       "      }\n",
-       "    }\n",
-       "\n",
        "    const cell = handle.cell;\n",
        "\n",
        "    const id = cell.output_area._bokeh_element_id;\n",
        "    const server_id = cell.output_area._bokeh_server_id;\n",
-       "\n",
        "    // Clean up Bokeh references\n",
-       "    if (id != null) {\n",
-       "      drop(id)\n",
+       "    if (id != null && id in Bokeh.index) {\n",
+       "      Bokeh.index[id].model.document.clear();\n",
+       "      delete Bokeh.index[id];\n",
        "    }\n",
        "\n",
        "    if (server_id !== undefined) {\n",
@@ -149,8 +141,11 @@
        "      cell.notebook.kernel.execute(cmd_clean, {\n",
        "        iopub: {\n",
        "          output: function(msg) {\n",
-       "            const id = msg.content.text.trim()\n",
-       "            drop(id)\n",
+       "            const id = msg.content.text.trim();\n",
+       "            if (id in Bokeh.index) {\n",
+       "              Bokeh.index[id].model.document.clear();\n",
+       "              delete Bokeh.index[id];\n",
+       "            }\n",
        "          }\n",
        "        }\n",
        "      });\n",
@@ -258,7 +253,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\");\n",
+       "    const el = document.getElementById(\"d187bdb8-34f9-433d-91a7-1276eafc4685\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -341,7 +336,7 @@
        "    document.body.appendChild(element);\n",
        "  }\n",
        "\n",
-       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
+       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n",
        "  const css_urls = [];\n",
        "\n",
        "  const inline_js = [    function(Bokeh) {\n",
@@ -364,7 +359,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"d187bdb8-34f9-433d-91a7-1276eafc4685\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -380,7 +375,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"ac1134fa-55fa-4b47-b5c7-344dfbc63734\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"d187bdb8-34f9-433d-91a7-1276eafc4685\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"d187bdb8-34f9-433d-91a7-1276eafc4685\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -405,8 +400,8 @@
       "     __  __     <>__\n",
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
-      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241210-1438-0.2.132-678e1f52b999.log\n"
+      "  /_/ /_/\\_,_/_/_/   version 0.2.133-4c60fddb171a\n",
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250113-1435-0.2.133-4c60fddb171a.log\n"
      ]
     }
    ],
@@ -423,13 +418,12 @@
    "source": [
     "## Variant data\n",
     "\n",
-    "Available versions for each data type and reference build are (as of 2024-10-29):\n",
+    "Supported versions for each data type and reference build are (as of 2025-1-13):\n",
     "\n",
-    "| Data Type       | GRCh38 Versions                  | GRCh37 Versions      |\n",
-    "|-----------------|----------------------------------|----------------------|\n",
-    "| exomes          | 4.0, 4.1                         | 2.1, 2.1.1           |\n",
-    "| genomes         | 3.0, 3.1, 3.1.1, 3.1.2, 4.0, 4.1 | 2.1, 2.1.1           |\n",
-    "| joint           | 4.1                              | N/A                  |\n",
+    "| Genome Build    | Version  | Data Types             |\n",
+    "|-----------------|----------|------------------------|\n",
+    "| GRCh37          | 2.1.1    | exomes, genomes        |\n",
+    "| GRCh38          | 4.1      | exomes, genomes, joint |\n",
     "\n",
     "For a description of all fields the the HT, see the [Help/FAQ](https://gnomad.broadinstitute.org/help/v4-hts) page."
    ]
@@ -6407,7 +6401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "a8d0be07-c35d-425a-b554-c86034e367fc",
    "metadata": {
     "ExecuteTime": {
@@ -6418,7 +6412,7 @@
    },
    "outputs": [],
    "source": [
-    "ht = get_gnomad_release(data_type='genomes', version='3.0', dataset=\"coverage\")"
+    "ht = get_gnomad_release(data_type='genomes', version='3.0.1', dataset=\"coverage\")"
    ]
   },
   {
@@ -6431,7 +6425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "18afb20f-7429-4fe2-a6c5-73a22dcbdb76",
    "metadata": {
     "ExecuteTime": {
@@ -6452,8 +6446,8 @@
       "Row fields:\n",
       "    'locus': locus \n",
       "    'mean': float64 \n",
-      "    'median': int32 \n",
-      "    'count_array': array \n",
+      "    'median_approx': int32 \n",
+      "    'total_DP': int64 \n",
       "    'over_1': float32 \n",
       "    'over_5': float32 \n",
       "    'over_10': float32 \n",
@@ -6485,7 +6479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "b27cb655-3abb-4501-bcc9-3f634db64591",
    "metadata": {
     "ExecuteTime": {
@@ -6498,62 +6492,38 @@
     {
      "data": {
       "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "
locus
mean
median
count_array
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32array<int32>float32float32float32float32float32float32float32float32float32
chr1:100011.93e+0116[0,20,65,141,226,328,386,442,415,385,292,198,362,367,337,273,289,266,241,226,191,169,168,151,227,194,179,190,177,166,143,131,150,130,125,121,136,94,93,83,67,60,68,68,59,33,39,39,39,38,20,18,21,25,10,9,16,8,7,6,2,3,2,4,1,2,2,2,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]1.25e-011.19e-019.12e-026.95e-025.15e-023.88e-022.62e-022.27e-030.00e+00
chr1:100022.10e+0118[0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,435,417,366,346,320,437,415,405,359,333,308,283,266,272,248,218,231,241,184,176,162,138,119,127,137,118,63,82,87,66,66,46,33,39,43,22,25,26,19,19,11,7,6,7,5,3,5,2,4,2,6,2,3,2,0,1,1,1,0,0,0,1,0,0,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]2.20e-012.15e-011.79e-011.39e-011.02e-017.60e-025.06e-024.83e-032.79e-05
chr1:100032.44e+0123[0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,658,649,590,548,486,679,656,640,571,533,491,439,398,412,404,349,383,360,298,263,242,207,182,186,194,159,118,123,116,96,96,67,59,61,64,34,33,34,31,30,15,12,11,13,10,7,7,3,7,5,10,3,3,5,0,2,2,1,0,0,2,1,1,1,4,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,4]2.62e-012.59e-012.41e-012.07e-011.59e-011.18e-017.77e-027.61e-035.58e-05
chr1:100042.43e+0123[0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1242,1181,1162,1083,966,845,1149,1088,1047,922,857,804,725,645,633,658,525,610,537,451,411,369,343,285,290,260,235,184,190,174,151,152,96,83,96,91,52,52,56,43,47,30,20,19,22,16,13,9,9,10,10,14,8,7,8,0,5,3,3,2,1,2,1,1,1,5,3,3,2,1,0,2,0,0,1,0,1,0,1,1,0,0,12]4.27e-014.24e-013.99e-013.43e-012.60e-011.87e-011.21e-011.20e-021.67e-04
chr1:100052.45e+0123[0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1401,1341,1300,1266,1105,966,1288,1243,1198,1068,976,905,842,725,728,740,600,678,613,515,464,414,396,338,324,300,268,213,210,198,175,165,113,100,108,102,61,58,61,50,53,35,22,22,27,22,15,11,12,10,13,14,10,8,9,1,6,4,6,5,3,3,4,2,2,5,3,7,3,1,0,2,0,1,2,2,2,0,1,1,0,0,17]4.83e-014.80e-014.56e-013.92e-012.96e-012.14e-011.38e-011.42e-022.37e-04
\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "
locus
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32int64float32float32float32float32float32float32float32float32float32
chr1:100012.41e+0001729261.25e-011.19e-019.12e-026.95e-025.15e-023.88e-022.62e-022.27e-030.00e+00
chr1:100024.61e+0003307602.20e-012.15e-011.79e-011.39e-011.02e-017.60e-025.06e-024.83e-032.79e-05
chr1:100036.38e+0004576112.62e-012.59e-012.41e-012.07e-011.59e-011.18e-017.77e-027.61e-035.58e-05
chr1:100041.04e+0107448404.27e-014.24e-013.99e-013.43e-012.60e-011.87e-011.21e-011.20e-021.67e-04
chr1:100051.18e+0108494414.83e-014.80e-014.56e-013.92e-012.96e-012.14e-011.38e-011.42e-022.37e-04
showing top 5 rows
\n"
       ],
       "text/plain": [
-       "+---------------+----------+--------+\n",
-       "| locus         |     mean | median |\n",
-       "+---------------+----------+--------+\n",
-       "| locus |  float64 |  int32 |\n",
-       "+---------------+----------+--------+\n",
-       "| chr1:10001    | 1.93e+01 |     16 |\n",
-       "| chr1:10002    | 2.10e+01 |     18 |\n",
-       "| chr1:10003    | 2.44e+01 |     23 |\n",
-       "| chr1:10004    | 2.43e+01 |     23 |\n",
-       "| chr1:10005    | 2.45e+01 |     23 |\n",
-       "+---------------+----------+--------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| count_array                                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,20,65,141,226,328,386,442,415,385,292,198,362,367,337,273,289,266,241,... |\n",
-       "| [0,10,41,108,209,339,482,570,581,575,483,390,702,689,639,556,587,534,516,... |\n",
-       "| [0,6,16,62,100,162,233,289,294,346,342,310,537,612,664,649,661,711,723,65... |\n",
-       "| [0,4,21,70,101,178,270,396,417,515,550,486,864,1003,1098,1138,1183,1270,1... |\n",
-       "| [0,4,22,67,98,177,257,381,423,511,580,560,1013,1158,1257,1314,1338,1472,1... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| locus         |     mean | median_approx | total_DP |   over_1 |   over_5 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| locus |  float64 |         int32 |    int64 |  float32 |  float32 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| chr1:10001    | 2.41e+00 |             0 |   172926 | 1.25e-01 | 1.19e-01 |\n",
+       "| chr1:10002    | 4.61e+00 |             0 |   330760 | 2.20e-01 | 2.15e-01 |\n",
+       "| chr1:10003    | 6.38e+00 |             0 |   457611 | 2.62e-01 | 2.59e-01 |\n",
+       "| chr1:10004    | 1.04e+01 |             0 |   744840 | 4.27e-01 | 4.24e-01 |\n",
+       "| chr1:10005    | 1.18e+01 |             0 |   849441 | 4.83e-01 | 4.80e-01 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
        "\n",
        "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "|   over_1 |   over_5 |  over_10 |  over_15 |  over_20 |  over_25 |  over_30 |\n",
+       "|  over_10 |  over_15 |  over_20 |  over_25 |  over_30 |  over_50 | over_100 |\n",
        "+----------+----------+----------+----------+----------+----------+----------+\n",
        "|  float32 |  float32 |  float32 |  float32 |  float32 |  float32 |  float32 |\n",
        "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "| 1.25e-01 | 1.19e-01 | 9.12e-02 | 6.95e-02 | 5.15e-02 | 3.88e-02 | 2.62e-02 |\n",
-       "| 2.20e-01 | 2.15e-01 | 1.79e-01 | 1.39e-01 | 1.02e-01 | 7.60e-02 | 5.06e-02 |\n",
-       "| 2.62e-01 | 2.59e-01 | 2.41e-01 | 2.07e-01 | 1.59e-01 | 1.18e-01 | 7.77e-02 |\n",
-       "| 4.27e-01 | 4.24e-01 | 3.99e-01 | 3.43e-01 | 2.60e-01 | 1.87e-01 | 1.21e-01 |\n",
-       "| 4.83e-01 | 4.80e-01 | 4.56e-01 | 3.92e-01 | 2.96e-01 | 2.14e-01 | 1.38e-01 |\n",
+       "| 9.12e-02 | 6.95e-02 | 5.15e-02 | 3.88e-02 | 2.62e-02 | 2.27e-03 | 0.00e+00 |\n",
+       "| 1.79e-01 | 1.39e-01 | 1.02e-01 | 7.60e-02 | 5.06e-02 | 4.83e-03 | 2.79e-05 |\n",
+       "| 2.41e-01 | 2.07e-01 | 1.59e-01 | 1.18e-01 | 7.77e-02 | 7.61e-03 | 5.58e-05 |\n",
+       "| 3.99e-01 | 3.43e-01 | 2.60e-01 | 1.87e-01 | 1.21e-01 | 1.20e-02 | 1.67e-04 |\n",
+       "| 4.56e-01 | 3.92e-01 | 2.96e-01 | 2.14e-01 | 1.38e-01 | 1.42e-02 | 2.37e-04 |\n",
        "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "\n",
-       "+----------+----------+\n",
-       "|  over_50 | over_100 |\n",
-       "+----------+----------+\n",
-       "|  float32 |  float32 |\n",
-       "+----------+----------+\n",
-       "| 2.27e-03 | 0.00e+00 |\n",
-       "| 4.83e-03 | 2.79e-05 |\n",
-       "| 7.61e-03 | 5.58e-05 |\n",
-       "| 1.20e-02 | 1.67e-04 |\n",
-       "| 1.42e-02 | 2.37e-04 |\n",
-       "+----------+----------+\n",
        "showing top 5 rows"
       ]
      },
@@ -6582,7 +6552,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.5"
   },
   "toc": {
    "base_numbering": 1,

From 8eb7296089acad1d0efd340b2cf3ecd0f3c25b09 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Mon, 13 Jan 2025 22:39:33 -0700
Subject: [PATCH 069/121] Fixes during testing

---
 gnomad_toolbox/filtering/vep.py | 22 ++++++++++++++--------
 gnomad_toolbox/load_data.py     | 15 +++++++++------
 2 files changed, 23 insertions(+), 14 deletions(-)

diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py
index 6e5519d..36cdbfa 100644
--- a/gnomad_toolbox/filtering/vep.py
+++ b/gnomad_toolbox/filtering/vep.py
@@ -128,18 +128,18 @@ def filter_by_consequence_category(
 
 
 def get_gene_intervals(
-    gene_symbol: str, version: Optional[str] = None
+    gene_symbol: str, gencode_version: Optional[str] = None
 ) -> List[hl.utils.Interval]:
     """
     Get the GENCODE genomic intervals for a given gene symbol.
 
     :param gene_symbol: Gene symbol.
-    :param version: Optional gnomAD dataset version. If not provided, uses the gnomAD
-        session version.
+    :param gencode_version: Optional GENCODE version. If not provided, uses the gencode
+        version associated with the gnomAD session.
     :return: List of GENCODE intervals for the specified gene.
     """
     # Load the Hail Table if not provided.
-    ht = _get_dataset(dataset="gencode", version=version)
+    ht = _get_dataset(dataset="gencode", version=gencode_version)
     gene_symbol = gene_symbol.upper()
 
     intervals = filter_gencode_ht(gencode_ht=ht, feature="gene", genes=gene_symbol)
@@ -156,6 +156,7 @@ def filter_to_high_confidence_loftee(
     no_lof_flags: bool = False,
     mane_select_only: bool = False,
     canonical_only: bool = False,
+    version: Optional[str] = None,
     **kwargs,
 ) -> hl.Table:
     """
@@ -172,11 +173,14 @@ def filter_to_high_confidence_loftee(
     :return: Table with high-confidence LOFTEE variants.
     """
     # Load the Hail Table if not provided.
-    ht = _get_dataset(dataset="variant", **kwargs)
+    ht = _get_dataset(dataset="variant", version=version, **kwargs)
     gene_symbol = gene_symbol.upper() if gene_symbol else None
 
     if gene_symbol:
-        ht = hl.filter_intervals(ht, get_gene_intervals(gene_symbol))
+        gencode_version = get_compatible_dataset_versions("gencode", version)
+        ht = hl.filter_intervals(
+            ht, get_gene_intervals(gene_symbol, gencode_version=gencode_version)
+        )
 
     return filter_vep_transcript_csqs(
         ht,
@@ -248,7 +252,7 @@ def filter_to_plofs(
 
     # Get gene intervals and filter tables.
     gencode_version = get_compatible_dataset_versions("gencode", version)
-    intervals = get_gene_intervals(gene_symbol, version=gencode_version)
+    intervals = get_gene_intervals(gene_symbol, gencode_version=gencode_version)
     variant_ht = hl.filter_intervals(variant_ht, intervals)
     coverage_ht = hl.filter_intervals(coverage_ht, intervals)
 
@@ -267,7 +271,7 @@ def filter_to_plofs(
     # Apply constraint filters.
     variant_ht = variant_ht.filter(
         (hl.len(variant_ht.filters) == 0)
-        & (hl.is_snp(*variant_ht.alleles))
+        & (hl.is_snp(variant_ht.alleles[0], variant_ht.alleles[1]))
         & (variant_ht.freq[0].AF <= af_cutoff)
         & (variant_ht.exome_coverage >= cov_cutoff)
     )
@@ -276,6 +280,8 @@ def filter_to_plofs(
     variant_ht = filter_to_high_confidence_loftee(
         gene_symbol=gene_symbol,
         ht=variant_ht,
+        mane_select_only=constraint_info["mane_select"],
+        canonical_only=constraint_info["canonical"],
     )
 
     return variant_ht
diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index 11685bf..564a33c 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -186,10 +186,6 @@ def _get_dataset(
     if ht is not None:
         return ht
 
-    # Use session defaults if parameters are not provided.
-    data_type = data_type or gnomad_session.data_type
-    version = version or gnomad_session.version
-
     # Validate dataset.
     dataset_info = SUPPORTED_DATASETS.get(dataset)
     if dataset_info is None:
@@ -202,6 +198,13 @@ def _get_dataset(
             f"\treference datasets: {list(SUPPORTED_REFERENCE_DATA.keys())}"
         )
 
+    # If version or data_type are not provided, use the session information.
+    data_type = data_type or gnomad_session.data_type
+    if dataset == "variant":
+        version = version or gnomad_session.version
+    else:
+        version = version or gnomad_session.compatible_datasets[dataset]
+
     # Validate version.
     versions = dataset_info["versions"]
     version_info = versions.get(version)
@@ -308,9 +311,9 @@ def get_compatible_dataset_versions(
     # Get the dictionary of compatible versions for the given variant version or
     # the current session version.
     if variant_version is None:
-        versions = VARIANT_DATA[variant_version]["dataset_versions"]
-    else:
         versions = gnomad_session.compatible_datasets
+    else:
+        versions = VARIANT_DATA[variant_version]["dataset_versions"]
 
     # Validate dataset.
     if dataset not in versions:

From 87906ee2267beb970aef5aad1e6b5bc72bedda3d Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 14 Jan 2025 08:21:06 -0700
Subject: [PATCH 070/121] Fix GnomADSession init

---
 gnomad_toolbox/load_data.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index 564a33c..4532c01 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -127,10 +127,9 @@ def __init__(self) -> None:
 
         :return: None.
         """
-        self.set_default_data(
-            data_type="exomes",
-            version="4.1",
-        )
+        self.data_type = "exomes"
+        self.version = "4.1"
+        self.set_default_data()
 
     def set_default_data(
         self,

From 95daf522756d6e8f0eb6eb26b3040641b8d8fbab Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 14 Jan 2025 08:26:55 -0700
Subject: [PATCH 071/121] Fix table in `get_gnomad_release` docstring

---
 gnomad_toolbox/load_data.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index 4532c01..a0b23eb 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -261,7 +261,7 @@ def get_gnomad_release(
         | variant      | GRCh37       | 2.1.1   | exomes, genomes              |
         |              +--------------+---------+------------------------------+
         |              | GRCh38       | 4.1     | exomes, genomes, joint       |
-        |--------------+--------------+---------+------------------------------+
+        +--------------+--------------+---------+------------------------------+
         | coverage     | GRCh37       | 2.1     | exomes, genomes              |
         |              +--------------+---------+------------------------------+
         |              | GRCh38       | 3.0.1   | genomes                      |
@@ -273,7 +273,7 @@ def get_gnomad_release(
         | constraint   | GRCh37       | 2.1.1   | N/A                          |
         |              +--------------+---------+------------------------------+
         |              | GRCh38       | 4.1     | N/A                          |
-        |--------------|--------------+---------+------------------------------+
+        +--------------+--------------+---------+------------------------------+
         | liftover     | GRCh37       | 2.1.1   | exomes, genomes              |
         +--------------+--------------+---------+------------------------------+
         | pext         | GRCh37       | v7      | base_level, annotation_level |

From 2853978aa068a793c229b160f0c17f156c2b4a4a Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 14 Jan 2025 10:14:06 -0700
Subject: [PATCH 072/121] Fix use of `data_type` in `_get_dataset`

---
 gnomad_toolbox/load_data.py | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index a0b23eb..108d5a4 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -197,8 +197,7 @@ def _get_dataset(
             f"\treference datasets: {list(SUPPORTED_REFERENCE_DATA.keys())}"
         )
 
-    # If version or data_type are not provided, use the session information.
-    data_type = data_type or gnomad_session.data_type
+    # If version is not provided, use the session information.
     if dataset == "variant":
         version = version or gnomad_session.version
     else:
@@ -218,11 +217,13 @@ def _get_dataset(
 
     # Validate data type.
     data_types = version_info.get("data_types")
-    if data_types and data_type and data_type not in data_types:
-        raise ValueError(
-            f"Version {version} is not available for {data_type} in the {dataset} "
-            f"dataset. Available data types: {data_types}."
-        )
+    if data_types:
+        data_type = data_type or gnomad_session.data_type
+        if data_type and data_type not in data_types:
+            raise ValueError(
+                f"Version {version} is not available for {data_type} in the {dataset} "
+                f"dataset. Available data types: {data_types}."
+            )
 
     # Get the resource for the given build.
     build = version_info["reference_genome"]

From 23e0fe25bd0ccec55e89cdee9e628ebc5fe14d42 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 14 Jan 2025 10:17:50 -0700
Subject: [PATCH 073/121] split with comma instead of tab

---
 gnomad_toolbox/load_data.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index 108d5a4..96e8501 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -207,7 +207,7 @@ def _get_dataset(
     versions = dataset_info["versions"]
     version_info = versions.get(version)
     if version_info is None:
-        version_format = "\t".join(
+        version_format = ", ".join(
             f"{v} ({versions[v]['reference_genome']})" for v in versions
         )
         raise ValueError(

From 41c6739122967ea054ecdb20b1d54ad5a66ba603 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 14 Jan 2025 11:39:55 -0700
Subject: [PATCH 074/121] Use correct browser resource, browser_variant

---
 gnomad_toolbox/load_data.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index 96e8501..70c718b 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -93,7 +93,7 @@
     "constraint": {"resource": "constraint", "versions": CONSTRAINT_DATA},
     "liftover": {"resource": "liftover", "versions": LIFTOVER_DATA},
     "pext": {"resource": "pext", "versions": PEXT_DATA},
-    "browser": {"resource": "browser", "versions": BROWSER_DATA},
+    "browser": {"resource": "browser_variant", "versions": BROWSER_DATA},
 }
 SUPPORTED_REFERENCE_DATA = {
     "vep": {

From 93cc5cd42dc8438d67341a67746d44c8ff7140ba Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 14 Jan 2025 12:08:05 -0700
Subject: [PATCH 075/121] Add additional datasets to the data exploration

---
 .../notebooks/explore_release_data.ipynb      | 6016 ++++++++++++++++-
 1 file changed, 5986 insertions(+), 30 deletions(-)

diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
index a0959a7..3017c9f 100644
--- a/gnomad_toolbox/notebooks/explore_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -51,15 +51,28 @@
    },
    "source": [
     "
Table of Contents
\n",
-    "
"
+    "
"
    ]
   },
   {
+   "attachments": {
+    "53765947-f8a0-4821-aed7-99c4b6de5c25.png": {
+     "image/png": 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"
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"
+    }
+   },
    "cell_type": "markdown",
    "id": "8e713032",
    "metadata": {},
    "source": [
-    "## Import modules"
+    "## Import the loading function `get_gnomad_release`\n",
+    "\n",
+    "The main toolbox function that loads gnomAD release data is `get_gnomad_release`.\n",
+    "\n",
+    "![Screenshot 2025-01-14 at 11.02.35 AM.png](attachment:8fbdba40-9387-4520-a2d0-370aaeed6f00.png)\n",
+    "![Screenshot 2025-01-14 at 11.07.19 AM.png](attachment:53765947-f8a0-4821-aed7-99c4b6de5c25.png)"
    ]
   },
   {
@@ -76,7 +89,7 @@
     {
      "data": {
       "text/html": [
-       "\n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -125,14 +138,22 @@
        "   * Handle when an output is cleared or removed\n",
        "   */\n",
        "  function handleClearOutput(event, handle) {\n",
+       "    function drop(id) {\n",
+       "      const view = Bokeh.index.get_by_id(id)\n",
+       "      if (view != null) {\n",
+       "        view.model.document.clear()\n",
+       "        Bokeh.index.delete(view)\n",
+       "      }\n",
+       "    }\n",
+       "\n",
        "    const cell = handle.cell;\n",
        "\n",
        "    const id = cell.output_area._bokeh_element_id;\n",
        "    const server_id = cell.output_area._bokeh_server_id;\n",
+       "\n",
        "    // Clean up Bokeh references\n",
-       "    if (id != null && id in Bokeh.index) {\n",
-       "      Bokeh.index[id].model.document.clear();\n",
-       "      delete Bokeh.index[id];\n",
+       "    if (id != null) {\n",
+       "      drop(id)\n",
        "    }\n",
        "\n",
        "    if (server_id !== undefined) {\n",
@@ -141,11 +162,8 @@
        "      cell.notebook.kernel.execute(cmd_clean, {\n",
        "        iopub: {\n",
        "          output: function(msg) {\n",
-       "            const id = msg.content.text.trim();\n",
-       "            if (id in Bokeh.index) {\n",
-       "              Bokeh.index[id].model.document.clear();\n",
-       "              delete Bokeh.index[id];\n",
-       "            }\n",
+       "            const id = msg.content.text.trim()\n",
+       "            drop(id)\n",
        "          }\n",
        "        }\n",
        "      });\n",
@@ -253,7 +271,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"d187bdb8-34f9-433d-91a7-1276eafc4685\");\n",
+       "    const el = document.getElementById(\"da0da3b0-b5fd-4d97-96c0-ec7eae06d97a\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -336,7 +354,7 @@
        "    document.body.appendChild(element);\n",
        "  }\n",
        "\n",
-       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n",
+       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
        "  const css_urls = [];\n",
        "\n",
        "  const inline_js = [    function(Bokeh) {\n",
@@ -359,7 +377,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"d187bdb8-34f9-433d-91a7-1276eafc4685\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"da0da3b0-b5fd-4d97-96c0-ec7eae06d97a\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -375,7 +393,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"d187bdb8-34f9-433d-91a7-1276eafc4685\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"d187bdb8-34f9-433d-91a7-1276eafc4685\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"da0da3b0-b5fd-4d97-96c0-ec7eae06d97a\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"da0da3b0-b5fd-4d97-96c0-ec7eae06d97a\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -400,8 +418,8 @@
       "     __  __     <>__\n",
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
-      "  /_/ /_/\\_,_/_/_/   version 0.2.133-4c60fddb171a\n",
-      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250113-1435-0.2.133-4c60fddb171a.log\n"
+      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250114-1205-0.2.132-678e1f52b999.log\n"
      ]
     }
    ],
@@ -409,6 +427,38 @@
     "hl.init(backend=\"local\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3347caa3-1656-4cec-87a3-727a30185f13",
+   "metadata": {},
+   "source": [
+    "## Table of all currently supported gnomAD release datasets\n",
+    "\n",
+    "The following are the datasets, reference genome build, version, and data type combinations are currently supported for loading in the gnomAD toolbox with the `get_gnomad_release` function.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4b633dd-9b92-4e3f-b6c7-1100fa813885",
+   "metadata": {},
+   "source": [
+    "\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "
Genome BuildDataset (dataset)Version (version)Data Type (data_type)
GRCh37variant2.1.1exomes, genomes
coverage2.1exomes, genomes
constraint2.1.1N/A
liftover2.1.1exomes, genomes
pextv7base_level, annotation_level
GRCh38variant4.1exomes, genomes, joint
coverage3.0.1genomes
4.0exomes
all_sites_an4.1exomes, genomes
constraint4.1N/A
pextv10base_level, annotation_level
browser_variant4.1N/A (This includes information for the joint, exomes, and genomes, but doesn't need to be specified)
\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "5335a135",
@@ -418,13 +468,6 @@
    "source": [
     "## Variant data\n",
     "\n",
-    "Supported versions for each data type and reference build are (as of 2025-1-13):\n",
-    "\n",
-    "| Genome Build    | Version  | Data Types             |\n",
-    "|-----------------|----------|------------------------|\n",
-    "| GRCh37          | 2.1.1    | exomes, genomes        |\n",
-    "| GRCh38          | 4.1      | exomes, genomes, joint |\n",
-    "\n",
     "For a description of all fields the the HT, see the [Help/FAQ](https://gnomad.broadinstitute.org/help/v4-hts) page."
    ]
   },
@@ -6401,7 +6444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "a8d0be07-c35d-425a-b554-c86034e367fc",
    "metadata": {
     "ExecuteTime": {
@@ -6425,7 +6468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "18afb20f-7429-4fe2-a6c5-73a22dcbdb76",
    "metadata": {
     "ExecuteTime": {
@@ -6479,7 +6522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "b27cb655-3abb-4501-bcc9-3f634db64591",
    "metadata": {
     "ExecuteTime": {
@@ -6534,6 +6577,5919 @@
    "source": [
     "ht.show(5)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4ae4713-f542-46e3-979f-0bcfb44ec56c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Constraint\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed92af6-aacc-4cb2-890d-7ff76a00196d",
+   "metadata": {},
+   "source": [
+    "### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "3de343dc-37ae-4b17-a056-9d30e71e217c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(version='4.1', dataset=\"constraint\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b59b2f6-aadd-408d-8663-b676d41565bf",
+   "metadata": {},
+   "source": [
+    "### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c401dc94-0db0-4902-933d-e46c8aa447ce",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'version': str \n",
+      "    'apply_model_params': struct {\n",
+      "        max_af: float64, \n",
+      "        pops: tuple (\n",
+      "        ), \n",
+      "        plateau_models: struct {\n",
+      "            total: dict>\n",
+      "        }, \n",
+      "        coverage_model: str\n",
+      "    } \n",
+      "    'sd_raw_z': struct {\n",
+      "        lof: float64, \n",
+      "        mis: float64, \n",
+      "        syn: float64\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'gene': str \n",
+      "    'gene_id': str \n",
+      "    'transcript': str \n",
+      "    'canonical': bool \n",
+      "    'mane_select': bool \n",
+      "    'lof_hc_lc': struct {\n",
+      "        obs: int64, \n",
+      "        exp: float64, \n",
+      "        possible: int64, \n",
+      "        oe: float64, \n",
+      "        mu: float64, \n",
+      "        pLI: float64, \n",
+      "        pNull: float64, \n",
+      "        pRec: float64\n",
+      "    } \n",
+      "    'lof': struct {\n",
+      "        obs: int64, \n",
+      "        exp: float64, \n",
+      "        possible: int64, \n",
+      "        oe: float64, \n",
+      "        mu: float64, \n",
+      "        pLI: float64, \n",
+      "        pNull: float64, \n",
+      "        pRec: float64, \n",
+      "        oe_ci: struct {\n",
+      "            lower: float64, \n",
+      "            upper: float64, \n",
+      "            upper_rank: int64, \n",
+      "            upper_bin_decile: int32\n",
+      "        }, \n",
+      "        z_raw: float64, \n",
+      "        z_score: float64\n",
+      "    } \n",
+      "    'mis': struct {\n",
+      "        obs: int64, \n",
+      "        exp: float64, \n",
+      "        possible: int64, \n",
+      "        oe: float64, \n",
+      "        mu: float64, \n",
+      "        oe_ci: struct {\n",
+      "            lower: float64, \n",
+      "            upper: float64\n",
+      "        }, \n",
+      "        z_raw: float64, \n",
+      "        z_score: float64\n",
+      "    } \n",
+      "    'mis_pphen': struct {\n",
+      "        obs: int64, \n",
+      "        exp: float64, \n",
+      "        possible: int64, \n",
+      "        oe: float64\n",
+      "    } \n",
+      "    'syn': struct {\n",
+      "        obs: int64, \n",
+      "        exp: float64, \n",
+      "        possible: int64, \n",
+      "        oe: float64, \n",
+      "        mu: float64, \n",
+      "        oe_ci: struct {\n",
+      "            lower: float64, \n",
+      "            upper: float64\n",
+      "        }, \n",
+      "        z_raw: float64, \n",
+      "        z_score: float64\n",
+      "    } \n",
+      "    'constraint_flags': set \n",
+      "    'level': str \n",
+      "    'transcript_type': str \n",
+      "    'chromosome': str \n",
+      "    'cds_length': int64 \n",
+      "    'num_coding_exons': int64 \n",
+      "----------------------------------------\n",
+      "Key: ['gene', 'gene_id', 'transcript', 'canonical', 'mane_select']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b0e64c9-744c-4ed7-a832-9d68c548540b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "262cbbf1-9fc7-4819-9b2a-d1a1e27c10bf",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
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"A1BG""1""NM_130786.4"TrueTrue454.30e+011931.05e+007.06e-071.63e-168.49e-011.51e-01454.30e+011931.05e+007.06e-071.77e-168.43e-011.57e-018.22e-011.34e+00NANA-2.98e-01-2.52e-017076.47e+0228701.09e+007.67e-061.03e+001.16e+00-2.36e+00-8.61e-012201.91e+028901.15e+003162.96e+029941.07e+003.02e-069.73e-011.17e+00-1.17e+00-6.35e-01{}NANANANANA
"A1BG""ENSG00000121410""ENST00000263100"TrueTrue454.30e+011931.05e+007.06e-071.63e-168.49e-011.51e-01454.30e+011931.05e+007.06e-071.77e-168.43e-011.57e-018.22e-011.34e+00140577-2.98e-01-2.52e-017076.47e+0228701.09e+007.67e-061.03e+001.16e+00-2.36e+00-8.61e-012201.91e+028901.15e+003162.96e+029941.07e+003.02e-069.73e-011.17e+00-1.17e+00-6.35e-01{}"2""protein_coding""chr19"14858
"A1BG""ENSG00000121410""ENST00000600966"FalseFalse242.63e+011239.14e-014.20e-072.53e-083.43e-016.57e-01222.50e+011198.80e-013.96e-071.84e-072.65e-017.35e-016.28e-011.26e+00NANA6.01e-015.09e-013994.06e+0218589.84e-014.91e-069.06e-011.07e+003.24e-011.18e-011031.10e+025129.40e-011661.79e+026379.28e-011.91e-068.17e-011.06e+009.64e-015.26e-01{}"1""protein_coding""chr19"9175
"A1CF""29974""NM_001198818.2"FalseFalse457.00e+013526.43e-019.55e-077.32e-102.06e-039.98e-01457.00e+013526.43e-019.55e-077.66e-101.96e-039.98e-015.06e-018.25e-01NANA2.98e+002.53e+006537.46e+0237208.76e-014.38e-068.21e-019.34e-013.39e+001.24e+002362.77e+0213998.53e-012722.73e+0211579.98e-012.01e-069.03e-011.10e+003.27e-021.78e-02{}NANANANANA
"A1CF""29974""NM_001198819.2"FalseFalse487.28e+013676.59e-011.02e-067.64e-112.53e-039.97e-01477.03e+013556.69e-019.72e-077.70e-113.58e-039.96e-015.28e-018.53e-01NANA2.78e+002.35e+006707.62e+0238378.80e-014.43e-068.25e-019.38e-013.33e+001.21e+003093.65e+0218418.47e-012712.76e+0211889.82e-011.91e-068.88e-011.09e+003.00e-011.64e-01{}NANANANANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+--------+-------------------+-------------------+-----------+-------------+\n",
+       "| gene   | gene_id           | transcript        | canonical | mane_select |\n",
+       "+--------+-------------------+-------------------+-----------+-------------+\n",
+       "| str    | str               | str               |      bool |        bool |\n",
+       "+--------+-------------------+-------------------+-----------+-------------+\n",
+       "| \"A1BG\" | \"1\"               | \"NM_130786.4\"     |      True |        True |\n",
+       "| \"A1BG\" | \"ENSG00000121410\" | \"ENST00000263100\" |      True |        True |\n",
+       "| \"A1BG\" | \"ENSG00000121410\" | \"ENST00000600966\" |     False |       False |\n",
+       "| \"A1CF\" | \"29974\"           | \"NM_001198818.2\"  |     False |       False |\n",
+       "| \"A1CF\" | \"29974\"           | \"NM_001198819.2\"  |     False |       False |\n",
+       "+--------+-------------------+-------------------+-----------+-------------+\n",
+       "\n",
+       "+---------------+---------------+--------------------+--------------+\n",
+       "| lof_hc_lc.obs | lof_hc_lc.exp | lof_hc_lc.possible | lof_hc_lc.oe |\n",
+       "+---------------+---------------+--------------------+--------------+\n",
+       "|         int64 |       float64 |              int64 |      float64 |\n",
+       "+---------------+---------------+--------------------+--------------+\n",
+       "|            45 |      4.30e+01 |                193 |     1.05e+00 |\n",
+       "|            45 |      4.30e+01 |                193 |     1.05e+00 |\n",
+       "|            24 |      2.63e+01 |                123 |     9.14e-01 |\n",
+       "|            45 |      7.00e+01 |                352 |     6.43e-01 |\n",
+       "|            48 |      7.28e+01 |                367 |     6.59e-01 |\n",
+       "+---------------+---------------+--------------------+--------------+\n",
+       "\n",
+       "+--------------+---------------+-----------------+----------------+---------+\n",
+       "| lof_hc_lc.mu | lof_hc_lc.pLI | lof_hc_lc.pNull | lof_hc_lc.pRec | lof.obs |\n",
+       "+--------------+---------------+-----------------+----------------+---------+\n",
+       "|      float64 |       float64 |         float64 |        float64 |   int64 |\n",
+       "+--------------+---------------+-----------------+----------------+---------+\n",
+       "|     7.06e-07 |      1.63e-16 |        8.49e-01 |       1.51e-01 |      45 |\n",
+       "|     7.06e-07 |      1.63e-16 |        8.49e-01 |       1.51e-01 |      45 |\n",
+       "|     4.20e-07 |      2.53e-08 |        3.43e-01 |       6.57e-01 |      22 |\n",
+       "|     9.55e-07 |      7.32e-10 |        2.06e-03 |       9.98e-01 |      45 |\n",
+       "|     1.02e-06 |      7.64e-11 |        2.53e-03 |       9.97e-01 |      47 |\n",
+       "+--------------+---------------+-----------------+----------------+---------+\n",
+       "\n",
+       "+----------+--------------+----------+----------+----------+-----------+\n",
+       "|  lof.exp | lof.possible |   lof.oe |   lof.mu |  lof.pLI | lof.pNull |\n",
+       "+----------+--------------+----------+----------+----------+-----------+\n",
+       "|  float64 |        int64 |  float64 |  float64 |  float64 |   float64 |\n",
+       "+----------+--------------+----------+----------+----------+-----------+\n",
+       "| 4.30e+01 |          193 | 1.05e+00 | 7.06e-07 | 1.77e-16 |  8.43e-01 |\n",
+       "| 4.30e+01 |          193 | 1.05e+00 | 7.06e-07 | 1.77e-16 |  8.43e-01 |\n",
+       "| 2.50e+01 |          119 | 8.80e-01 | 3.96e-07 | 1.84e-07 |  2.65e-01 |\n",
+       "| 7.00e+01 |          352 | 6.43e-01 | 9.55e-07 | 7.66e-10 |  1.96e-03 |\n",
+       "| 7.03e+01 |          355 | 6.69e-01 | 9.72e-07 | 7.70e-11 |  3.58e-03 |\n",
+       "+----------+--------------+----------+----------+----------+-----------+\n",
+       "\n",
+       "+----------+-----------------+-----------------+----------------------+\n",
+       "| lof.pRec | lof.oe_ci.lower | lof.oe_ci.upper | lof.oe_ci.upper_rank |\n",
+       "+----------+-----------------+-----------------+----------------------+\n",
+       "|  float64 |         float64 |         float64 |                int64 |\n",
+       "+----------+-----------------+-----------------+----------------------+\n",
+       "| 1.57e-01 |        8.22e-01 |        1.34e+00 |                   NA |\n",
+       "| 1.57e-01 |        8.22e-01 |        1.34e+00 |                14057 |\n",
+       "| 7.35e-01 |        6.28e-01 |        1.26e+00 |                   NA |\n",
+       "| 9.98e-01 |        5.06e-01 |        8.25e-01 |                   NA |\n",
+       "| 9.96e-01 |        5.28e-01 |        8.53e-01 |                   NA |\n",
+       "+----------+-----------------+-----------------+----------------------+\n",
+       "\n",
+       "+----------------------------+-----------+-------------+---------+----------+\n",
+       "| lof.oe_ci.upper_bin_decile | lof.z_raw | lof.z_score | mis.obs |  mis.exp |\n",
+       "+----------------------------+-----------+-------------+---------+----------+\n",
+       "|                      int32 |   float64 |     float64 |   int64 |  float64 |\n",
+       "+----------------------------+-----------+-------------+---------+----------+\n",
+       "|                         NA | -2.98e-01 |   -2.52e-01 |     707 | 6.47e+02 |\n",
+       "|                          7 | -2.98e-01 |   -2.52e-01 |     707 | 6.47e+02 |\n",
+       "|                         NA |  6.01e-01 |    5.09e-01 |     399 | 4.06e+02 |\n",
+       "|                         NA |  2.98e+00 |    2.53e+00 |     653 | 7.46e+02 |\n",
+       "|                         NA |  2.78e+00 |    2.35e+00 |     670 | 7.62e+02 |\n",
+       "+----------------------------+-----------+-------------+---------+----------+\n",
+       "\n",
+       "+--------------+----------+----------+-----------------+-----------------+\n",
+       "| mis.possible |   mis.oe |   mis.mu | mis.oe_ci.lower | mis.oe_ci.upper |\n",
+       "+--------------+----------+----------+-----------------+-----------------+\n",
+       "|        int64 |  float64 |  float64 |         float64 |         float64 |\n",
+       "+--------------+----------+----------+-----------------+-----------------+\n",
+       "|         2870 | 1.09e+00 | 7.67e-06 |        1.03e+00 |        1.16e+00 |\n",
+       "|         2870 | 1.09e+00 | 7.67e-06 |        1.03e+00 |        1.16e+00 |\n",
+       "|         1858 | 9.84e-01 | 4.91e-06 |        9.06e-01 |        1.07e+00 |\n",
+       "|         3720 | 8.76e-01 | 4.38e-06 |        8.21e-01 |        9.34e-01 |\n",
+       "|         3837 | 8.80e-01 | 4.43e-06 |        8.25e-01 |        9.38e-01 |\n",
+       "+--------------+----------+----------+-----------------+-----------------+\n",
+       "\n",
+       "+-----------+-------------+---------------+---------------+--------------------+\n",
+       "| mis.z_raw | mis.z_score | mis_pphen.obs | mis_pphen.exp | mis_pphen.possible |\n",
+       "+-----------+-------------+---------------+---------------+--------------------+\n",
+       "|   float64 |     float64 |         int64 |       float64 |              int64 |\n",
+       "+-----------+-------------+---------------+---------------+--------------------+\n",
+       "| -2.36e+00 |   -8.61e-01 |           220 |      1.91e+02 |                890 |\n",
+       "| -2.36e+00 |   -8.61e-01 |           220 |      1.91e+02 |                890 |\n",
+       "|  3.24e-01 |    1.18e-01 |           103 |      1.10e+02 |                512 |\n",
+       "|  3.39e+00 |    1.24e+00 |           236 |      2.77e+02 |               1399 |\n",
+       "|  3.33e+00 |    1.21e+00 |           309 |      3.65e+02 |               1841 |\n",
+       "+-----------+-------------+---------------+---------------+--------------------+\n",
+       "\n",
+       "+--------------+---------+----------+--------------+----------+----------+\n",
+       "| mis_pphen.oe | syn.obs |  syn.exp | syn.possible |   syn.oe |   syn.mu |\n",
+       "+--------------+---------+----------+--------------+----------+----------+\n",
+       "|      float64 |   int64 |  float64 |        int64 |  float64 |  float64 |\n",
+       "+--------------+---------+----------+--------------+----------+----------+\n",
+       "|     1.15e+00 |     316 | 2.96e+02 |          994 | 1.07e+00 | 3.02e-06 |\n",
+       "|     1.15e+00 |     316 | 2.96e+02 |          994 | 1.07e+00 | 3.02e-06 |\n",
+       "|     9.40e-01 |     166 | 1.79e+02 |          637 | 9.28e-01 | 1.91e-06 |\n",
+       "|     8.53e-01 |     272 | 2.73e+02 |         1157 | 9.98e-01 | 2.01e-06 |\n",
+       "|     8.47e-01 |     271 | 2.76e+02 |         1188 | 9.82e-01 | 1.91e-06 |\n",
+       "+--------------+---------+----------+--------------+----------+----------+\n",
+       "\n",
+       "+-----------------+-----------------+-----------+-------------+\n",
+       "| syn.oe_ci.lower | syn.oe_ci.upper | syn.z_raw | syn.z_score |\n",
+       "+-----------------+-----------------+-----------+-------------+\n",
+       "|         float64 |         float64 |   float64 |     float64 |\n",
+       "+-----------------+-----------------+-----------+-------------+\n",
+       "|        9.73e-01 |        1.17e+00 | -1.17e+00 |   -6.35e-01 |\n",
+       "|        9.73e-01 |        1.17e+00 | -1.17e+00 |   -6.35e-01 |\n",
+       "|        8.17e-01 |        1.06e+00 |  9.64e-01 |    5.26e-01 |\n",
+       "|        9.03e-01 |        1.10e+00 |  3.27e-02 |    1.78e-02 |\n",
+       "|        8.88e-01 |        1.09e+00 |  3.00e-01 |    1.64e-01 |\n",
+       "+-----------------+-----------------+-----------+-------------+\n",
+       "\n",
+       "+------------------+-------+------------------+------------+------------+\n",
+       "| constraint_flags | level | transcript_type  | chromosome | cds_length |\n",
+       "+------------------+-------+------------------+------------+------------+\n",
+       "| set         | str   | str              | str        |      int64 |\n",
+       "+------------------+-------+------------------+------------+------------+\n",
+       "| {}               | NA    | NA               | NA         |         NA |\n",
+       "| {}               | \"2\"   | \"protein_coding\" | \"chr19\"    |       1485 |\n",
+       "| {}               | \"1\"   | \"protein_coding\" | \"chr19\"    |        917 |\n",
+       "| {}               | NA    | NA               | NA         |         NA |\n",
+       "| {}               | NA    | NA               | NA         |         NA |\n",
+       "+------------------+-------+------------------+------------+------------+\n",
+       "\n",
+       "+------------------+\n",
+       "| num_coding_exons |\n",
+       "+------------------+\n",
+       "|            int64 |\n",
+       "+------------------+\n",
+       "|               NA |\n",
+       "|                8 |\n",
+       "|                5 |\n",
+       "|               NA |\n",
+       "|               NA |\n",
+       "+------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72ce8dac-994f-42ae-a6a0-948f5517e427",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Proportion expressed across transcripts (pext) score\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea178a83-1bb6-4f9b-8334-0ca1f6096021",
+   "metadata": {},
+   "source": [
+    "### Base level pext\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeb8bb32-d258-4b3a-ab5e-edc76502fa5b",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "1f448e05-7f99-4c63-b101-b33143d9d58b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(version='v10', data_type=\"base_level\", dataset=\"pext\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0773d5d3-21f1-4892-b16b-dc31eb2a7bf5",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "b9f99311-91e6-454e-b940-3d036bddf3a5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'tissues': array \n",
+      "    'exp_prop_mean_tissues': array \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'gene_id': str \n",
+      "    'gene_symbol': str \n",
+      "    'exp_prop_mean': float64 \n",
+      "    'Adipose_Subcutaneous': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Adipose_Visceral_Omentum': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'AdrenalGland': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Artery_Aorta': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Artery_Coronary': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Artery_Tibial': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Bladder': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Amygdala': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Anteriorcingulatecortex_BA24': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Caudate_basalganglia': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_CerebellarHemisphere': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Cerebellum': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Cortex': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_FrontalCortex_BA9': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Hippocampus': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Hypothalamus': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Nucleusaccumbens_basalganglia': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Putamen_basalganglia': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Spinalcord_cervicalc_1': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Substantianigra': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Breast_MammaryTissue': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Cells_Culturedfibroblasts': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Cells_EBV_transformedlymphocytes': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Colon_Sigmoid': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Colon_Transverse': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Esophagus_GastroesophagealJunction': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Esophagus_Mucosa': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Esophagus_Muscularis': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Heart_AtrialAppendage': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Heart_LeftVentricle': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Kidney_Cortex': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Liver': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Lung': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'MinorSalivaryGland': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Muscle_Skeletal': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Nerve_Tibial': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Ovary': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Pancreas': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Pituitary': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Prostate': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Skin_NotSunExposed_Suprapubic': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Skin_SunExposed_Lowerleg': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'SmallIntestine_TerminalIleum': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Spleen': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Stomach': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Testis': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Thyroid': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Uterus': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Vagina': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'WholeBlood': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Key: ['locus']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a39f9dca-5db4-4ca3-8eac-e6c76db7c7bc",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a6ac695f-cfb2-4aa7-877c-b204351a77da",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
Adipose_Subcutaneous
Adipose_Visceral_Omentum
AdrenalGland
Artery_Aorta
Artery_Coronary
Artery_Tibial
Bladder
Brain_Amygdala
Brain_Anteriorcingulatecortex_BA24
Brain_Caudate_basalganglia
Brain_CerebellarHemisphere
Brain_Cerebellum
Brain_Cortex
Brain_FrontalCortex_BA9
Brain_Hippocampus
Brain_Hypothalamus
Brain_Nucleusaccumbens_basalganglia
Brain_Putamen_basalganglia
Brain_Spinalcord_cervicalc_1
Brain_Substantianigra
Breast_MammaryTissue
Cells_Culturedfibroblasts
Cells_EBV_transformedlymphocytes
Colon_Sigmoid
Colon_Transverse
Esophagus_GastroesophagealJunction
Esophagus_Mucosa
Esophagus_Muscularis
Heart_AtrialAppendage
Heart_LeftVentricle
Kidney_Cortex
Liver
Lung
MinorSalivaryGland
Muscle_Skeletal
Nerve_Tibial
Ovary
Pancreas
Pituitary
Prostate
Skin_NotSunExposed_Suprapubic
Skin_SunExposed_Lowerleg
SmallIntestine_TerminalIleum
Spleen
Stomach
Testis
Thyroid
Uterus
Vagina
WholeBlood
locus
gene_id
gene_symbol
exp_prop_mean
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
locus<GRCh38>strstrfloat64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64
chr1:65565"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65566"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65567"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65568"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65569"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+-------------------+-------------+---------------+\n",
+       "| locus         | gene_id           | gene_symbol | exp_prop_mean |\n",
+       "+---------------+-------------------+-------------+---------------+\n",
+       "| locus | str               | str         |       float64 |\n",
+       "+---------------+-------------------+-------------+---------------+\n",
+       "| chr1:65565    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
+       "| chr1:65566    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
+       "| chr1:65567    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
+       "| chr1:65568    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
+       "| chr1:65569    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
+       "+---------------+-------------------+-------------+---------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Adipose_Subcutaneous.transcript_expression |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Adipose_Subcutaneous.expression_proportion |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| Adipose_Visceral_Omentum.transcript_expression |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| Adipose_Visceral_Omentum.expression_proportion |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------+------------------------------------+\n",
+       "| AdrenalGland.transcript_expression | AdrenalGland.expression_proportion |\n",
+       "+------------------------------------+------------------------------------+\n",
+       "|                            float64 |                            float64 |\n",
+       "+------------------------------------+------------------------------------+\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "+------------------------------------+------------------------------------+\n",
+       "\n",
+       "+------------------------------------+------------------------------------+\n",
+       "| Artery_Aorta.transcript_expression | Artery_Aorta.expression_proportion |\n",
+       "+------------------------------------+------------------------------------+\n",
+       "|                            float64 |                            float64 |\n",
+       "+------------------------------------+------------------------------------+\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "+------------------------------------+------------------------------------+\n",
+       "\n",
+       "+---------------------------------------+\n",
+       "| Artery_Coronary.transcript_expression |\n",
+       "+---------------------------------------+\n",
+       "|                               float64 |\n",
+       "+---------------------------------------+\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "+---------------------------------------+\n",
+       "\n",
+       "+---------------------------------------+-------------------------------------+\n",
+       "| Artery_Coronary.expression_proportion | Artery_Tibial.transcript_expression |\n",
+       "+---------------------------------------+-------------------------------------+\n",
+       "|                               float64 |                             float64 |\n",
+       "+---------------------------------------+-------------------------------------+\n",
+       "|                              0.00e+00 |                            0.00e+00 |\n",
+       "|                              0.00e+00 |                            0.00e+00 |\n",
+       "|                              0.00e+00 |                            0.00e+00 |\n",
+       "|                              0.00e+00 |                            0.00e+00 |\n",
+       "|                              0.00e+00 |                            0.00e+00 |\n",
+       "+---------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+-------------------------------------+-------------------------------+\n",
+       "| Artery_Tibial.expression_proportion | Bladder.transcript_expression |\n",
+       "+-------------------------------------+-------------------------------+\n",
+       "|                             float64 |                       float64 |\n",
+       "+-------------------------------------+-------------------------------+\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "+-------------------------------------+-------------------------------+\n",
+       "\n",
+       "+-------------------------------+--------------------------------------+\n",
+       "| Bladder.expression_proportion | Brain_Amygdala.transcript_expression |\n",
+       "+-------------------------------+--------------------------------------+\n",
+       "|                       float64 |                              float64 |\n",
+       "+-------------------------------+--------------------------------------+\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "+-------------------------------+--------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| Brain_Amygdala.expression_proportion |\n",
+       "+--------------------------------------+\n",
+       "|                              float64 |\n",
+       "+--------------------------------------+\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| Brain_Anteriorcingulatecortex_BA24.transcript_expression |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                  float64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| Brain_Anteriorcingulatecortex_BA24.expression_proportion |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                  float64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_Caudate_basalganglia.transcript_expression |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_Caudate_basalganglia.expression_proportion |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_CerebellarHemisphere.transcript_expression |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_CerebellarHemisphere.expression_proportion |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Brain_Cerebellum.transcript_expression |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "| Brain_Cerebellum.expression_proportion | Brain_Cortex.transcript_expression |\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "|                                float64 |                            float64 |\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "\n",
+       "+------------------------------------+\n",
+       "| Brain_Cortex.expression_proportion |\n",
+       "+------------------------------------+\n",
+       "|                            float64 |\n",
+       "+------------------------------------+\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "+------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------+\n",
+       "| Brain_FrontalCortex_BA9.transcript_expression |\n",
+       "+-----------------------------------------------+\n",
+       "|                                       float64 |\n",
+       "+-----------------------------------------------+\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "+-----------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------+\n",
+       "| Brain_FrontalCortex_BA9.expression_proportion |\n",
+       "+-----------------------------------------------+\n",
+       "|                                       float64 |\n",
+       "+-----------------------------------------------+\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "+-----------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------+\n",
+       "| Brain_Hippocampus.transcript_expression |\n",
+       "+-----------------------------------------+\n",
+       "|                                 float64 |\n",
+       "+-----------------------------------------+\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "+-----------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------+\n",
+       "| Brain_Hippocampus.expression_proportion |\n",
+       "+-----------------------------------------+\n",
+       "|                                 float64 |\n",
+       "+-----------------------------------------+\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "+-----------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+\n",
+       "| Brain_Hypothalamus.transcript_expression |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+\n",
+       "| Brain_Hypothalamus.expression_proportion |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------------+\n",
+       "| Brain_Nucleusaccumbens_basalganglia.transcript_expression |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                   float64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "+-----------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------------+\n",
+       "| Brain_Nucleusaccumbens_basalganglia.expression_proportion |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                   float64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "+-----------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_Putamen_basalganglia.transcript_expression |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_Putamen_basalganglia.expression_proportion |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| Brain_Spinalcord_cervicalc_1.transcript_expression |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| Brain_Spinalcord_cervicalc_1.expression_proportion |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| Brain_Substantianigra.transcript_expression |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| Brain_Substantianigra.expression_proportion |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Breast_MammaryTissue.transcript_expression |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Breast_MammaryTissue.expression_proportion |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| Cells_Culturedfibroblasts.transcript_expression |\n",
+       "+-------------------------------------------------+\n",
+       "|                                         float64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| Cells_Culturedfibroblasts.expression_proportion |\n",
+       "+-------------------------------------------------+\n",
+       "|                                         float64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| Cells_EBV_transformedlymphocytes.transcript_expression |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                float64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| Cells_EBV_transformedlymphocytes.expression_proportion |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                float64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "| Colon_Sigmoid.transcript_expression | Colon_Sigmoid.expression_proportion |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                             float64 |                             float64 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Colon_Transverse.transcript_expression |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Colon_Transverse.expression_proportion |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| Esophagus_GastroesophagealJunction.transcript_expression |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                  float64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| Esophagus_GastroesophagealJunction.expression_proportion |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                  float64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Esophagus_Mucosa.transcript_expression |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Esophagus_Mucosa.expression_proportion |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Esophagus_Muscularis.transcript_expression |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Esophagus_Muscularis.expression_proportion |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| Heart_AtrialAppendage.transcript_expression |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| Heart_AtrialAppendage.expression_proportion |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------+\n",
+       "| Heart_LeftVentricle.transcript_expression |\n",
+       "+-------------------------------------------+\n",
+       "|                                   float64 |\n",
+       "+-------------------------------------------+\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "+-------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------+\n",
+       "| Heart_LeftVentricle.expression_proportion |\n",
+       "+-------------------------------------------+\n",
+       "|                                   float64 |\n",
+       "+-------------------------------------------+\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "+-------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "| Kidney_Cortex.transcript_expression | Kidney_Cortex.expression_proportion |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                             float64 |                             float64 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+-----------------------------+-----------------------------+\n",
+       "| Liver.transcript_expression | Liver.expression_proportion |\n",
+       "+-----------------------------+-----------------------------+\n",
+       "|                     float64 |                     float64 |\n",
+       "+-----------------------------+-----------------------------+\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "+-----------------------------+-----------------------------+\n",
+       "\n",
+       "+----------------------------+----------------------------+\n",
+       "| Lung.transcript_expression | Lung.expression_proportion |\n",
+       "+----------------------------+----------------------------+\n",
+       "|                    float64 |                    float64 |\n",
+       "+----------------------------+----------------------------+\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "+----------------------------+----------------------------+\n",
+       "\n",
+       "+------------------------------------------+\n",
+       "| MinorSalivaryGland.transcript_expression |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+\n",
+       "| MinorSalivaryGland.expression_proportion |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------+\n",
+       "| Muscle_Skeletal.transcript_expression |\n",
+       "+---------------------------------------+\n",
+       "|                               float64 |\n",
+       "+---------------------------------------+\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "+---------------------------------------+\n",
+       "\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "| Muscle_Skeletal.expression_proportion | Nerve_Tibial.transcript_expression |\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "|                               float64 |                            float64 |\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "\n",
+       "+------------------------------------+-----------------------------+\n",
+       "| Nerve_Tibial.expression_proportion | Ovary.transcript_expression |\n",
+       "+------------------------------------+-----------------------------+\n",
+       "|                            float64 |                     float64 |\n",
+       "+------------------------------------+-----------------------------+\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "+------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-----------------------------+--------------------------------+\n",
+       "| Ovary.expression_proportion | Pancreas.transcript_expression |\n",
+       "+-----------------------------+--------------------------------+\n",
+       "|                     float64 |                        float64 |\n",
+       "+-----------------------------+--------------------------------+\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "+-----------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------+---------------------------------+\n",
+       "| Pancreas.expression_proportion | Pituitary.transcript_expression |\n",
+       "+--------------------------------+---------------------------------+\n",
+       "|                        float64 |                         float64 |\n",
+       "+--------------------------------+---------------------------------+\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "+--------------------------------+---------------------------------+\n",
+       "\n",
+       "+---------------------------------+--------------------------------+\n",
+       "| Pituitary.expression_proportion | Prostate.transcript_expression |\n",
+       "+---------------------------------+--------------------------------+\n",
+       "|                         float64 |                        float64 |\n",
+       "+---------------------------------+--------------------------------+\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "+---------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------+\n",
+       "| Prostate.expression_proportion |\n",
+       "+--------------------------------+\n",
+       "|                        float64 |\n",
+       "+--------------------------------+\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "+--------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| Skin_NotSunExposed_Suprapubic.transcript_expression |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                             float64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| Skin_NotSunExposed_Suprapubic.expression_proportion |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                             float64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| Skin_SunExposed_Lowerleg.transcript_expression |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| Skin_SunExposed_Lowerleg.expression_proportion |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| SmallIntestine_TerminalIleum.transcript_expression |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| SmallIntestine_TerminalIleum.expression_proportion |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------+------------------------------+\n",
+       "| Spleen.transcript_expression | Spleen.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
+       "\n",
+       "+-------------------------------+-------------------------------+\n",
+       "| Stomach.transcript_expression | Stomach.expression_proportion |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "|                       float64 |                       float64 |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "\n",
+       "+------------------------------+------------------------------+\n",
+       "| Testis.transcript_expression | Testis.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
+       "\n",
+       "+-------------------------------+-------------------------------+\n",
+       "| Thyroid.transcript_expression | Thyroid.expression_proportion |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "|                       float64 |                       float64 |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "\n",
+       "+------------------------------+------------------------------+\n",
+       "| Uterus.transcript_expression | Uterus.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
+       "\n",
+       "+------------------------------+------------------------------+\n",
+       "| Vagina.transcript_expression | Vagina.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
+       "\n",
+       "+----------------------------------+----------------------------------+\n",
+       "| WholeBlood.transcript_expression | WholeBlood.expression_proportion |\n",
+       "+----------------------------------+----------------------------------+\n",
+       "|                          float64 |                          float64 |\n",
+       "+----------------------------------+----------------------------------+\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "+----------------------------------+----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32528515-1021-40e4-8f4c-3ca06792e0be",
+   "metadata": {},
+   "source": [
+    "### Annotation level pext\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afac2ca3-f3dc-4174-aa98-d9eb70bf343f",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "ce25b9e0-9118-4bf6-ba3c-eca75468b9f1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(version='v10', data_type=\"annotation_level\", dataset=\"pext\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9265ccc-0048-40f0-8f1c-d62e0d3f98ab",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "818f3fc2-09fa-479c-91e1-e19f5991a22d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'tissues': array \n",
+      "    'exp_prop_mean_tissues': array \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'gene_id': str \n",
+      "    'alleles': array \n",
+      "    'gene_symbol': str \n",
+      "    'most_severe_consequence': str \n",
+      "    'lof': str \n",
+      "    'lof_flags': str \n",
+      "    'exp_prop_mean': float64 \n",
+      "    'Adipose_Subcutaneous': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Adipose_Visceral_Omentum': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'AdrenalGland': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Artery_Aorta': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Artery_Coronary': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Artery_Tibial': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Bladder': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Amygdala': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Anteriorcingulatecortex_BA24': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Caudate_basalganglia': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_CerebellarHemisphere': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Cerebellum': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Cortex': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_FrontalCortex_BA9': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Hippocampus': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Hypothalamus': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Nucleusaccumbens_basalganglia': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Putamen_basalganglia': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Spinalcord_cervicalc_1': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Substantianigra': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Breast_MammaryTissue': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Cells_Culturedfibroblasts': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Cells_EBV_transformedlymphocytes': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Colon_Sigmoid': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Colon_Transverse': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Esophagus_GastroesophagealJunction': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Esophagus_Mucosa': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Esophagus_Muscularis': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Heart_AtrialAppendage': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Heart_LeftVentricle': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Kidney_Cortex': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Liver': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Lung': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'MinorSalivaryGland': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Muscle_Skeletal': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Nerve_Tibial': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Ovary': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Pancreas': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Pituitary': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Prostate': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Skin_NotSunExposed_Suprapubic': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Skin_SunExposed_Lowerleg': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'SmallIntestine_TerminalIleum': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Spleen': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Stomach': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Testis': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Thyroid': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Uterus': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Vagina': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'WholeBlood': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Key: ['locus', 'alleles']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77d8e1eb-d07d-4338-a401-ae9191ad2901",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "35371de7-9332-4aa6-a3b9-c573fb2e144f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
Adipose_Subcutaneous
Adipose_Visceral_Omentum
AdrenalGland
Artery_Aorta
Artery_Coronary
Artery_Tibial
Bladder
Brain_Amygdala
Brain_Anteriorcingulatecortex_BA24
Brain_Caudate_basalganglia
Brain_CerebellarHemisphere
Brain_Cerebellum
Brain_Cortex
Brain_FrontalCortex_BA9
Brain_Hippocampus
Brain_Hypothalamus
Brain_Nucleusaccumbens_basalganglia
Brain_Putamen_basalganglia
Brain_Spinalcord_cervicalc_1
Brain_Substantianigra
Breast_MammaryTissue
Cells_Culturedfibroblasts
Cells_EBV_transformedlymphocytes
Colon_Sigmoid
Colon_Transverse
Esophagus_GastroesophagealJunction
Esophagus_Mucosa
Esophagus_Muscularis
Heart_AtrialAppendage
Heart_LeftVentricle
Kidney_Cortex
Liver
Lung
MinorSalivaryGland
Muscle_Skeletal
Nerve_Tibial
Ovary
Pancreas
Pituitary
Prostate
Skin_NotSunExposed_Suprapubic
Skin_SunExposed_Lowerleg
SmallIntestine_TerminalIleum
Spleen
Stomach
Testis
Thyroid
Uterus
Vagina
WholeBlood
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alleles
gene_symbol
most_severe_consequence
lof
lof_flags
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locus<GRCh38>strarray<str>strstrstrstrfloat64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64
chr1:65565"ENSG00000186092"["A","C"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65565"ENSG00000186092"["A","G"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65565"ENSG00000186092"["A","T"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65566"ENSG00000186092"["T","A"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65566"ENSG00000186092"["T","C"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+-------------------+------------+-------------+\n",
+       "| locus         | gene_id           | alleles    | gene_symbol |\n",
+       "+---------------+-------------------+------------+-------------+\n",
+       "| locus | str               | array | str         |\n",
+       "+---------------+-------------------+------------+-------------+\n",
+       "| chr1:65565    | \"ENSG00000186092\" | [\"A\",\"C\"]  | \"OR4F5\"     |\n",
+       "| chr1:65565    | \"ENSG00000186092\" | [\"A\",\"G\"]  | \"OR4F5\"     |\n",
+       "| chr1:65565    | \"ENSG00000186092\" | [\"A\",\"T\"]  | \"OR4F5\"     |\n",
+       "| chr1:65566    | \"ENSG00000186092\" | [\"T\",\"A\"]  | \"OR4F5\"     |\n",
+       "| chr1:65566    | \"ENSG00000186092\" | [\"T\",\"C\"]  | \"OR4F5\"     |\n",
+       "+---------------+-------------------+------------+-------------+\n",
+       "\n",
+       "+-------------------------+-----+-----------+---------------+\n",
+       "| most_severe_consequence | lof | lof_flags | exp_prop_mean |\n",
+       "+-------------------------+-----+-----------+---------------+\n",
+       "| str                     | str | str       |       float64 |\n",
+       "+-------------------------+-----+-----------+---------------+\n",
+       "| \"start_lost\"            | NA  | NA        |           NaN |\n",
+       "| \"start_lost\"            | NA  | NA        |           NaN |\n",
+       "| \"start_lost\"            | NA  | NA        |           NaN |\n",
+       "| \"start_lost\"            | NA  | NA        |           NaN |\n",
+       "| \"start_lost\"            | NA  | NA        |           NaN |\n",
+       "+-------------------------+-----+-----------+---------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Adipose_Subcutaneous.transcript_expression |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Adipose_Subcutaneous.expression_proportion |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| Adipose_Visceral_Omentum.transcript_expression |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| Adipose_Visceral_Omentum.expression_proportion |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------+------------------------------------+\n",
+       "| AdrenalGland.transcript_expression | AdrenalGland.expression_proportion |\n",
+       "+------------------------------------+------------------------------------+\n",
+       "|                            float64 |                            float64 |\n",
+       "+------------------------------------+------------------------------------+\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "+------------------------------------+------------------------------------+\n",
+       "\n",
+       "+------------------------------------+------------------------------------+\n",
+       "| Artery_Aorta.transcript_expression | Artery_Aorta.expression_proportion |\n",
+       "+------------------------------------+------------------------------------+\n",
+       "|                            float64 |                            float64 |\n",
+       "+------------------------------------+------------------------------------+\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "|                           0.00e+00 |                           0.00e+00 |\n",
+       "+------------------------------------+------------------------------------+\n",
+       "\n",
+       "+---------------------------------------+\n",
+       "| Artery_Coronary.transcript_expression |\n",
+       "+---------------------------------------+\n",
+       "|                               float64 |\n",
+       "+---------------------------------------+\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "+---------------------------------------+\n",
+       "\n",
+       "+---------------------------------------+-------------------------------------+\n",
+       "| Artery_Coronary.expression_proportion | Artery_Tibial.transcript_expression |\n",
+       "+---------------------------------------+-------------------------------------+\n",
+       "|                               float64 |                             float64 |\n",
+       "+---------------------------------------+-------------------------------------+\n",
+       "|                              0.00e+00 |                            0.00e+00 |\n",
+       "|                              0.00e+00 |                            0.00e+00 |\n",
+       "|                              0.00e+00 |                            0.00e+00 |\n",
+       "|                              0.00e+00 |                            0.00e+00 |\n",
+       "|                              0.00e+00 |                            0.00e+00 |\n",
+       "+---------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+-------------------------------------+-------------------------------+\n",
+       "| Artery_Tibial.expression_proportion | Bladder.transcript_expression |\n",
+       "+-------------------------------------+-------------------------------+\n",
+       "|                             float64 |                       float64 |\n",
+       "+-------------------------------------+-------------------------------+\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "+-------------------------------------+-------------------------------+\n",
+       "\n",
+       "+-------------------------------+--------------------------------------+\n",
+       "| Bladder.expression_proportion | Brain_Amygdala.transcript_expression |\n",
+       "+-------------------------------+--------------------------------------+\n",
+       "|                       float64 |                              float64 |\n",
+       "+-------------------------------+--------------------------------------+\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "+-------------------------------+--------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| Brain_Amygdala.expression_proportion |\n",
+       "+--------------------------------------+\n",
+       "|                              float64 |\n",
+       "+--------------------------------------+\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| Brain_Anteriorcingulatecortex_BA24.transcript_expression |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                  float64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| Brain_Anteriorcingulatecortex_BA24.expression_proportion |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                  float64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_Caudate_basalganglia.transcript_expression |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_Caudate_basalganglia.expression_proportion |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_CerebellarHemisphere.transcript_expression |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_CerebellarHemisphere.expression_proportion |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Brain_Cerebellum.transcript_expression |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "| Brain_Cerebellum.expression_proportion | Brain_Cortex.transcript_expression |\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "|                                float64 |                            float64 |\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "\n",
+       "+------------------------------------+\n",
+       "| Brain_Cortex.expression_proportion |\n",
+       "+------------------------------------+\n",
+       "|                            float64 |\n",
+       "+------------------------------------+\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "+------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------+\n",
+       "| Brain_FrontalCortex_BA9.transcript_expression |\n",
+       "+-----------------------------------------------+\n",
+       "|                                       float64 |\n",
+       "+-----------------------------------------------+\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "+-----------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------+\n",
+       "| Brain_FrontalCortex_BA9.expression_proportion |\n",
+       "+-----------------------------------------------+\n",
+       "|                                       float64 |\n",
+       "+-----------------------------------------------+\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "+-----------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------+\n",
+       "| Brain_Hippocampus.transcript_expression |\n",
+       "+-----------------------------------------+\n",
+       "|                                 float64 |\n",
+       "+-----------------------------------------+\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "+-----------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------+\n",
+       "| Brain_Hippocampus.expression_proportion |\n",
+       "+-----------------------------------------+\n",
+       "|                                 float64 |\n",
+       "+-----------------------------------------+\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "+-----------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+\n",
+       "| Brain_Hypothalamus.transcript_expression |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+\n",
+       "| Brain_Hypothalamus.expression_proportion |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------------+\n",
+       "| Brain_Nucleusaccumbens_basalganglia.transcript_expression |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                   float64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "+-----------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------------+\n",
+       "| Brain_Nucleusaccumbens_basalganglia.expression_proportion |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                   float64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "+-----------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_Putamen_basalganglia.transcript_expression |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_Putamen_basalganglia.expression_proportion |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| Brain_Spinalcord_cervicalc_1.transcript_expression |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| Brain_Spinalcord_cervicalc_1.expression_proportion |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| Brain_Substantianigra.transcript_expression |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| Brain_Substantianigra.expression_proportion |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Breast_MammaryTissue.transcript_expression |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Breast_MammaryTissue.expression_proportion |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| Cells_Culturedfibroblasts.transcript_expression |\n",
+       "+-------------------------------------------------+\n",
+       "|                                         float64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| Cells_Culturedfibroblasts.expression_proportion |\n",
+       "+-------------------------------------------------+\n",
+       "|                                         float64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| Cells_EBV_transformedlymphocytes.transcript_expression |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                float64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| Cells_EBV_transformedlymphocytes.expression_proportion |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                float64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "| Colon_Sigmoid.transcript_expression | Colon_Sigmoid.expression_proportion |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                             float64 |                             float64 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Colon_Transverse.transcript_expression |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Colon_Transverse.expression_proportion |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| Esophagus_GastroesophagealJunction.transcript_expression |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                  float64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| Esophagus_GastroesophagealJunction.expression_proportion |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                  float64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Esophagus_Mucosa.transcript_expression |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Esophagus_Mucosa.expression_proportion |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Esophagus_Muscularis.transcript_expression |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Esophagus_Muscularis.expression_proportion |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| Heart_AtrialAppendage.transcript_expression |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| Heart_AtrialAppendage.expression_proportion |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------+\n",
+       "| Heart_LeftVentricle.transcript_expression |\n",
+       "+-------------------------------------------+\n",
+       "|                                   float64 |\n",
+       "+-------------------------------------------+\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "+-------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------+\n",
+       "| Heart_LeftVentricle.expression_proportion |\n",
+       "+-------------------------------------------+\n",
+       "|                                   float64 |\n",
+       "+-------------------------------------------+\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "+-------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "| Kidney_Cortex.transcript_expression | Kidney_Cortex.expression_proportion |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                             float64 |                             float64 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+-----------------------------+-----------------------------+\n",
+       "| Liver.transcript_expression | Liver.expression_proportion |\n",
+       "+-----------------------------+-----------------------------+\n",
+       "|                     float64 |                     float64 |\n",
+       "+-----------------------------+-----------------------------+\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "+-----------------------------+-----------------------------+\n",
+       "\n",
+       "+----------------------------+----------------------------+\n",
+       "| Lung.transcript_expression | Lung.expression_proportion |\n",
+       "+----------------------------+----------------------------+\n",
+       "|                    float64 |                    float64 |\n",
+       "+----------------------------+----------------------------+\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "+----------------------------+----------------------------+\n",
+       "\n",
+       "+------------------------------------------+\n",
+       "| MinorSalivaryGland.transcript_expression |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+\n",
+       "| MinorSalivaryGland.expression_proportion |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------+\n",
+       "| Muscle_Skeletal.transcript_expression |\n",
+       "+---------------------------------------+\n",
+       "|                               float64 |\n",
+       "+---------------------------------------+\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "+---------------------------------------+\n",
+       "\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "| Muscle_Skeletal.expression_proportion | Nerve_Tibial.transcript_expression |\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "|                               float64 |                            float64 |\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "\n",
+       "+------------------------------------+-----------------------------+\n",
+       "| Nerve_Tibial.expression_proportion | Ovary.transcript_expression |\n",
+       "+------------------------------------+-----------------------------+\n",
+       "|                            float64 |                     float64 |\n",
+       "+------------------------------------+-----------------------------+\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "+------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-----------------------------+--------------------------------+\n",
+       "| Ovary.expression_proportion | Pancreas.transcript_expression |\n",
+       "+-----------------------------+--------------------------------+\n",
+       "|                     float64 |                        float64 |\n",
+       "+-----------------------------+--------------------------------+\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "+-----------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------+---------------------------------+\n",
+       "| Pancreas.expression_proportion | Pituitary.transcript_expression |\n",
+       "+--------------------------------+---------------------------------+\n",
+       "|                        float64 |                         float64 |\n",
+       "+--------------------------------+---------------------------------+\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "+--------------------------------+---------------------------------+\n",
+       "\n",
+       "+---------------------------------+--------------------------------+\n",
+       "| Pituitary.expression_proportion | Prostate.transcript_expression |\n",
+       "+---------------------------------+--------------------------------+\n",
+       "|                         float64 |                        float64 |\n",
+       "+---------------------------------+--------------------------------+\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "+---------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------+\n",
+       "| Prostate.expression_proportion |\n",
+       "+--------------------------------+\n",
+       "|                        float64 |\n",
+       "+--------------------------------+\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "+--------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| Skin_NotSunExposed_Suprapubic.transcript_expression |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                             float64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| Skin_NotSunExposed_Suprapubic.expression_proportion |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                             float64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| Skin_SunExposed_Lowerleg.transcript_expression |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| Skin_SunExposed_Lowerleg.expression_proportion |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| SmallIntestine_TerminalIleum.transcript_expression |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------+\n",
+       "| SmallIntestine_TerminalIleum.expression_proportion |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------+------------------------------+\n",
+       "| Spleen.transcript_expression | Spleen.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
+       "\n",
+       "+-------------------------------+-------------------------------+\n",
+       "| Stomach.transcript_expression | Stomach.expression_proportion |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "|                       float64 |                       float64 |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "\n",
+       "+------------------------------+------------------------------+\n",
+       "| Testis.transcript_expression | Testis.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
+       "\n",
+       "+-------------------------------+-------------------------------+\n",
+       "| Thyroid.transcript_expression | Thyroid.expression_proportion |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "|                       float64 |                       float64 |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "\n",
+       "+------------------------------+------------------------------+\n",
+       "| Uterus.transcript_expression | Uterus.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
+       "\n",
+       "+------------------------------+------------------------------+\n",
+       "| Vagina.transcript_expression | Vagina.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
+       "\n",
+       "+----------------------------------+----------------------------------+\n",
+       "| WholeBlood.transcript_expression | WholeBlood.expression_proportion |\n",
+       "+----------------------------------+----------------------------------+\n",
+       "|                          float64 |                          float64 |\n",
+       "+----------------------------------+----------------------------------+\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "+----------------------------------+----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae3e5ba8-075a-455c-8e5c-e244970d5217",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Browser variant data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7c13865-814a-46e8-a4f3-c823a910cfa3",
+   "metadata": {},
+   "source": [
+    "### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "329f08ef-6dbf-42cd-bbbc-d3f5396708ac",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(version='4.1', dataset=\"browser\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ec344e0-4951-44d8-8f7b-1283a427914d",
+   "metadata": {},
+   "source": [
+    "### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "17efacb6-84c4-4345-9bf9-1dca259dd7fa",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'mane_select_version': str \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'alleles': array \n",
+      "    'exome': struct {\n",
+      "        colocated_variants: struct {\n",
+      "            all: array, \n",
+      "            non_ukb: array\n",
+      "        }, \n",
+      "        subsets: set, \n",
+      "        flags: set, \n",
+      "        freq: struct {\n",
+      "            all: struct {\n",
+      "                ac: int32, \n",
+      "                ac_raw: int32, \n",
+      "                an: int32, \n",
+      "                hemizygote_count: int32, \n",
+      "                homozygote_count: int64, \n",
+      "                ancestry_groups: array\n",
+      "            }, \n",
+      "            non_ukb: struct {\n",
+      "                ac: int32, \n",
+      "                ac_raw: int32, \n",
+      "                an: int32, \n",
+      "                hemizygote_count: int32, \n",
+      "                homozygote_count: int64, \n",
+      "                ancestry_groups: array\n",
+      "            }\n",
+      "        }, \n",
+      "        fafmax: struct {\n",
+      "            gnomad: struct {\n",
+      "                faf95_max: float64, \n",
+      "                faf95_max_gen_anc: str, \n",
+      "                faf99_max: float64, \n",
+      "                faf99_max_gen_anc: str\n",
+      "            }, \n",
+      "            non_ukb: struct {\n",
+      "                faf95_max: float64, \n",
+      "                faf95_max_gen_anc: str, \n",
+      "                faf99_max: float64, \n",
+      "                faf99_max_gen_anc: str\n",
+      "            }\n",
+      "        }, \n",
+      "        age_distribution: struct {\n",
+      "            het: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            hom: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        filters: set, \n",
+      "        quality_metrics: struct {\n",
+      "            allele_balance: struct {\n",
+      "                alt_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            genotype_depth: struct {\n",
+      "                all_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                all_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            genotype_quality: struct {\n",
+      "                all_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                all_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            site_quality_metrics: array\n",
+      "        }\n",
+      "    } \n",
+      "    'genome': struct {\n",
+      "        colocated_variants: struct {\n",
+      "            hgdp: array, \n",
+      "            tgp: array, \n",
+      "            all: array\n",
+      "        }, \n",
+      "        subsets: set, \n",
+      "        flags: set, \n",
+      "        freq: struct {\n",
+      "            hgdp: struct {\n",
+      "                ac: int32, \n",
+      "                ac_raw: int32, \n",
+      "                an: int32, \n",
+      "                hemizygote_count: int32, \n",
+      "                homozygote_count: int32, \n",
+      "                ancestry_groups: array\n",
+      "            }, \n",
+      "            tgp: struct {\n",
+      "                ac: int32, \n",
+      "                ac_raw: int32, \n",
+      "                an: int32, \n",
+      "                hemizygote_count: int32, \n",
+      "                homozygote_count: int32, \n",
+      "                ancestry_groups: array\n",
+      "            }, \n",
+      "            all: struct {\n",
+      "                ac: int32, \n",
+      "                ac_raw: int32, \n",
+      "                an: int32, \n",
+      "                hemizygote_count: int32, \n",
+      "                homozygote_count: int32, \n",
+      "                ancestry_groups: array\n",
+      "            }\n",
+      "        }, \n",
+      "        fafmax: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }, \n",
+      "        age_distribution: struct {\n",
+      "            het: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            hom: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        filters: set, \n",
+      "        quality_metrics: struct {\n",
+      "            allele_balance: struct {\n",
+      "                alt_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            genotype_depth: struct {\n",
+      "                all_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                all_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            genotype_quality: struct {\n",
+      "                all_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                all_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            site_quality_metrics: array\n",
+      "        }\n",
+      "    } \n",
+      "    'rsids': set \n",
+      "    'in_silico_predictors': struct {\n",
+      "        cadd: struct {\n",
+      "            phred: float32, \n",
+      "            raw_score: float32\n",
+      "        }, \n",
+      "        revel_max: float64, \n",
+      "        spliceai_ds_max: float32, \n",
+      "        pangolin_largest_ds: float64, \n",
+      "        phylop: float64, \n",
+      "        sift_max: float64, \n",
+      "        polyphen_max: float64\n",
+      "    } \n",
+      "    'variant_id': str \n",
+      "    'colocated_variants': struct {\n",
+      "        all: array, \n",
+      "        non_ukb: array, \n",
+      "        hgdp: array, \n",
+      "        tgp: array\n",
+      "    } \n",
+      "    'joint': struct {\n",
+      "        freq: struct {\n",
+      "            all: struct {\n",
+      "                ac: int32, \n",
+      "                ac_raw: int32, \n",
+      "                an: int32, \n",
+      "                hemizygote_count: int32, \n",
+      "                homozygote_count: int32, \n",
+      "                ancestry_groups: array\n",
+      "            }\n",
+      "        }, \n",
+      "        faf: array, \n",
+      "        fafmax: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }, \n",
+      "        grpmax: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int32, \n",
+      "            gen_anc: str\n",
+      "        }, \n",
+      "        histograms: struct {\n",
+      "            qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            raw_qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            age_hists: struct {\n",
+      "                age_hist_het: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                age_hist_hom: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }\n",
+      "        }, \n",
+      "        flags: set, \n",
+      "        freq_comparison_stats: struct {\n",
+      "            contingency_table_test: array, \n",
+      "            cochran_mantel_haenszel_test: struct {\n",
+      "                p_value: float64, \n",
+      "                chisq: float64\n",
+      "            }, \n",
+      "            stat_union: struct {\n",
+      "                p_value: float64, \n",
+      "                stat_test_name: str, \n",
+      "                gen_ancs: array\n",
+      "            }\n",
+      "        }\n",
+      "    } \n",
+      "    'coverage': struct {\n",
+      "        exome: struct {\n",
+      "            mean: float64, \n",
+      "            median_approx: int32, \n",
+      "            total_DP: int64, \n",
+      "            over_1: float64, \n",
+      "            over_5: float64, \n",
+      "            over_10: float64, \n",
+      "            over_15: float64, \n",
+      "            over_20: float64, \n",
+      "            over_25: float64, \n",
+      "            over_30: float64, \n",
+      "            over_50: float64, \n",
+      "            over_100: float64\n",
+      "        }, \n",
+      "        genome: struct {\n",
+      "            mean: float64, \n",
+      "            median_approx: int32, \n",
+      "            total_DP: int64, \n",
+      "            over_1: float32, \n",
+      "            over_5: float32, \n",
+      "            over_10: float32, \n",
+      "            over_15: float32, \n",
+      "            over_20: float32, \n",
+      "            over_25: float32, \n",
+      "            over_30: float32, \n",
+      "            over_50: float32, \n",
+      "            over_100: float32\n",
+      "        }\n",
+      "    } \n",
+      "    'transcript_consequences': array, \n",
+      "        domains: set, \n",
+      "        gene_id: str, \n",
+      "        gene_symbol: str, \n",
+      "        hgvsc: str, \n",
+      "        hgvsp: str, \n",
+      "        is_canonical: bool, \n",
+      "        lof_filter: str, \n",
+      "        lof_flags: str, \n",
+      "        lof: str, \n",
+      "        major_consequence: str, \n",
+      "        transcript_id: str, \n",
+      "        transcript_version: str, \n",
+      "        gene_version: str, \n",
+      "        is_mane_select: bool, \n",
+      "        is_mane_select_version: bool, \n",
+      "        refseq_id: str, \n",
+      "        refseq_version: str\n",
+      "    }> \n",
+      "    'caid': str \n",
+      "    'vrs': struct {\n",
+      "        ref: struct {\n",
+      "            allele_id: str, \n",
+      "            start: int32, \n",
+      "            end: int32, \n",
+      "            state: str\n",
+      "        }, \n",
+      "        alt: struct {\n",
+      "            allele_id: str, \n",
+      "            start: int32, \n",
+      "            end: int32, \n",
+      "            state: str\n",
+      "        }\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Key: ['locus', 'alleles']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e113c4a2-25c2-4f55-bbb9-382c87cd770c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "d03dd50b-37ac-49ec-b766-a86870ee6573",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
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chr1:10031["T","C"]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA[][][]{}{"lcr","segdup"}0081200[]00107800[]025664200[("remaining",0,782,0,0),("remaining_XX",0,402,0,0),("remaining_XY",0,380,0,0),("amr",0,6420,0,0),("amr_XX",0,2770,0,0),("amr_XY",0,3650,0,0),("fin",0,4326,0,0),("fin_XX",0,1060,0,0),("fin_XY",0,3266,0,0),("ami",0,352,0,0),("ami_XX",0,154,0,0),("ami_XY",0,198,0,0),("eas",0,1712,0,0),("eas_XX",0,770,0,0),("eas_XY",0,942,0,0),("mid",0,192,0,0),("mid_XX",0,102,0,0),("mid_XY",0,90,0,0),("sas",0,1120,0,0),("sas_XX",0,260,0,0),("sas_XY",0,860,0,0),("asj",0,1550,0,0),("asj_XX",0,814,0,0),("asj_XY",0,736,0,0),("afr",0,14642,0,0),("afr_XX",0,7978,0,0),("afr_XY",0,6664,0,0),("nfe",0,25546,0,0),("nfe_XX",0,14998,0,0),("nfe_XY",0,10548,0,0),("XX",0,29308,0,0),("XY",0,27334,0,0)]NANANANA[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00{"AC0","AS_VQSR"}[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[("SiteQuality",9.60e+01),("inbreeding_coeff",-1.65e-05),("AS_MQ",3.51e+01),("AS_FS",5.10e+00),("AS_MQRankSum",-5.72e-01),("AS_pab_max",6.87e-01),("AS_QUALapprox",7.70e+01),("AS_QD",2.96e+00),("AS_ReadPosRankSum",-1.38e+00),("AS_SOR",9.64e-02),("AS_VarDP",2.60e+01),("AS_VQSLOD",-4.57e+00)]{"rs1639542312"}8.97e+007.57e-01NANANANANANA"1-10031-T-C"[][][][]025664200[("remaining",0,782,0,0),("remaining_XX",0,402,0,0),("remaining_XY",0,380,0,0),("amr",0,6420,0,0),("amr_XX",0,2770,0,0),("amr_XY",0,3650,0,0),("fin",0,4326,0,0),("fin_XX",0,1060,0,0),("fin_XY",0,3266,0,0),("ami",0,352,0,0),("ami_XX",0,154,0,0),("ami_XY",0,198,0,0),("eas",0,1712,0,0),("eas_XX",0,770,0,0),("eas_XY",0,942,0,0),("mid",0,192,0,0),("mid_XX",0,102,0,0),("mid_XY",0,90,0,0),("sas",0,1120,0,0),("sas_XX",0,260,0,0),("sas_XY",0,860,0,0),("asj",0,1550,0,0),("asj_XX",0,814,0,0),("asj_XY",0,736,0,0),("afr",0,14642,0,0),("afr_XX",0,7978,0,0),("afr_XY",0,6664,0,0),("nfe",0,25546,0,0),("nfe_XX",0,14998,0,0),("nfe_XY",0,10548,0,0),("",0,56642,0,0),("XX",0,29308,0,0),("XY",0,27334,0,0),("XX",0,29308,0,0),("XY",0,27334,0,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANANANANANA[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3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chr1:10055["T","C"]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA[][][]{"hgdp"}{"lcr","segdup"}01131200[("japanese",0,50,0,0),("japanese_XX",0,12,0,0),("japanese_XY",0,38,0,0),("adygei",0,22,0,0),("adygei_XX",0,16,0,0),("adygei_XY",0,6,0,0),("orcadian",0,26,0,0),("orcadian_XX",0,14,0,0),("orcadian_XY",0,12,0,0),("bantusouthafrica",0,8,0,0),("bantusouthafrica_XX",0,0,0,0),("bantusouthafrica_XY",0,8,0,0),("yakut",0,36,0,0),("yakut_XX",0,14,0,0),("yakut_XY",0,22,0,0),("han",0,58,0,0),("han_XX",0,32,0,0),("han_XY",0,26,0,0),("uygur",0,10,0,0),("uygur_XX",0,4,0,0),("uygur_XY",0,6,0,0),("balochi",0,40,0,0),("balochi_XX",0,0,0,0),("balochi_XY",0,40,0,0),("bedouin",0,72,0,0),("bedouin_XX",0,28,0,0),("bedouin_XY",0,44,0,0),("russian",0,48,0,0),("russian_XX",0,18,0,0),("russian_XY",0,30,0,0),("daur",0,10,0,0),("daur_XX",0,2,0,0),("daur_XY",0,8,0,0),("pima",0,16,0,0),("pima_XX",0,6,0,0),("pima_XY",0,10,0,0),("hezhen",0,14,0,0),("hezhen_XX",0,4,0,0),("hezhen_XY",0,10,0,0),("biaka",0,20,0,0),("biaka_XX",0,0,0,0),("biaka_XY",0,20,0,0),("miao",0,8,0,0),("miao_XX",0,2,0,0),("miao_XY",0,6,0,0),("sindhi",0,40,0,0),("sindhi_XX",0,8,0,0),("sindhi_XY",0,32,0,0),("northernhan",0,14,0,0),("northernhan_XX",0,2,0,0),("northernhan_XY",0,12,0,0),("oroqen",0,14,0,0),("oroqen_XX",0,6,0,0),("oroqen_XY",0,8,0,0),("san",0,12,0,0),("san_XX",0,0,0,0),("san_XY",0,12,0,0),("tu",0,18,0,0),("tu_XX",0,4,0,0),("tu_XY",0,14,0,0),("tuscan",0,12,0,0),("tuscan_XX",0,4,0,0),("tuscan_XY",0,8,0,0),("mbuti",0,16,0,0),("mbuti_XX",0,4,0,0),("mbuti_XY",0,12,0,0),("palestinian",0,58,0,0),("palestinian_XX",0,38,0,0),("palestinian_XY",0,20,0,0),("tujia",0,16,0,0),("tujia_XX",0,2,0,0),("tujia_XY",0,14,0,0),("druze",0,58,0,0),("druze_XX",0,42,0,0),("druze_XY",0,16,0,0),("pathan",0,34,0,0),("pathan_XX",0,4,0,0),("pathan_XY",0,30,0,0),("basque",0,38,0,0),("basque_XX",0,12,0,0),("basque_XY",0,26,0,0),("makrani",0,38,0,0),("makrani_XX",0,6,0,0),("makrani_XY",0,32,0,0),("burusho",0,40,0,0),("burusho_XX",0,6,0,0),("burusho_XY",0,34,0,0),("mongolian",0,10,0,0),("mongolian_XX",0,4,0,0),("mongolian_XY",0,6,0,0),("bougainville",0,10,0,0),("bougainville_XX",0,10,0,0),("bougainville_XY",0,0,0,0),("papuansepik",0,2,0,0),("papuansepik_XX",0,2,0,0),("papuansepik_XY",0,0,0,0),("yi",0,18,0,0),("yi_XX",0,2,0,0),("yi_XY",0,16,0,0),("naxi",0,8,0,0),("naxi_XX",0,2,0,0),("naxi_XY",0,6,0,0),("lahu",0,8,0,0),("lahu_XX",0,0,0,0),("lahu_XY",0,8,0,0),("sardinian",0,52,0,0),("sardinian_XX",0,24,0,0),("sardinian_XY",0,28,0,0),("karitiana",0,14,0,0),("karitiana_XX",0,12,0,0),("karitiana_XY",0,2,0,0),("mozabite",0,36,0,0),("mozabite_XX",0,12,0,0),("mozabite_XY",0,24,0,0),("yoruba",0,20,0,0),("yoruba_XX",0,10,0,0),("yoruba_XY",0,10,0,0),("dai",0,12,0,0),("dai_XX",0,6,0,0),("dai_XY",0,6,0,0),("bergamoitalian",0,14,0,0),("bergamoitalian_XX",0,8,0,0),("bergamoitalian_XY",0,6,0,0),("cambodian",0,14,0,0),("cambodian_XX",0,8,0,0),("cambodian_XY",0,6,0,0),("french",0,48,0,0),("french_XX",0,26,0,0),("french_XY",0,22,0,0),("mandenka",0,26,0,0),("mandenka_XX",0,10,0,0),("mandenka_XY",0,16,0,0),("surui",0,12,0,0),("surui_XX",0,8,0,0),("surui_XY",0,4,0,0),("brahui",0,30,0,0),("brahui_XX",0,0,0,0),("brahui_XY",0,30,0,0),("hazara",0,22,0,0),("hazara_XX",0,0,0,0),("hazara_XY",0,22,0,0),("kalash",0,28,0,0),("kalash_XX",0,6,0,0),("kalash_XY",0,22,0,0),("papuanhighlands",0,4,0,0),("papuanhighlands_XX",0,2,0,0),("papuanhighlands_XY",0,2,0,0),("xibo",0,10,0,0),("xibo_XX",0,0,0,0),("xibo_XY",0,10,0,0),("colombian",0,6,0,0),("colombian_XX",0,4,0,0),("colombian_XY",0,2,0,0),("bantukenya",0,10,0,0),("bantukenya_XX",0,2,0,0),("bantukenya_XY",0,8,0,0),("she",0,18,0,0),("she_XX",0,6,0,0),("she_XY",0,12,0,0),("maya",0,34,0,0),("maya_XX",0,30,0,0),("maya_XY",0,4,0,0),("XX",0,484,0,0),("XY",0,828,0,0)]00147800[]159422400[("remaining",0,1228,0,0),("remaining_XX",0,624,0,0),("remaining_XY",0,604,0,0),("amr",0,8056,0,0),("amr_XX",0,3902,0,0),("amr_XY",0,4154,0,0),("fin",1,5570,0,0),("fin_XX",1,1034,0,0),("fin_XY",0,4536,0,0),("ami",0,682,0,0),("ami_XX",0,344,0,0),("ami_XY",0,338,0,0),("eas",0,3020,0,0),("eas_XX",0,1256,0,0),("eas_XY",0,1764,0,0),("mid",0,216,0,0),("mid_XX",0,110,0,0),("mid_XY",0,106,0,0),("sas",0,2668,0,0),("sas_XX",0,600,0,0),("sas_XY",0,2068,0,0),("asj",0,2370,0,0),("asj_XX",0,1246,0,0),("asj_XY",0,1124,0,0),("afr",0,26032,0,0),("afr_XX",0,14152,0,0),("afr_XY",0,11880,0,0),("nfe",0,44382,0,0),("nfe_XX",0,26312,0,0),("nfe_XY",0,18070,0,0),("XX",1,49580,0,0),("XY",0,44644,0,0)]NANANANA[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00{"AS_VQSR"}[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.5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chr1:10057["A","C"]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA[][][]{}{"lcr","segdup"}00130000[]00281000[]3311353600[("remaining",0,1504,0,0),("remaining_XX",0,764,0,0),("remaining_XY",0,740,0,0),("amr",0,10448,0,0),("amr_XX",0,4944,0,0),("amr_XY",0,5504,0,0),("fin",0,6886,0,0),("fin_XX",0,1472,0,0),("fin_XY",0,5414,0,0),("ami",0,742,0,0),("ami_XX",0,378,0,0),("ami_XY",0,364,0,0),("eas",0,3850,0,0),("eas_XX",0,1670,0,0),("eas_XY",0,2180,0,0),("mid",0,266,0,0),("mid_XX",0,132,0,0),("mid_XY",0,134,0,0),("sas",0,3424,0,0),("sas_XX",0,784,0,0),("sas_XY",0,2640,0,0),("asj",0,2768,0,0),("asj_XX",0,1532,0,0),("asj_XY",0,1236,0,0),("afr",1,30736,0,0),("afr_XX",0,16598,0,0),("afr_XY",1,14138,0,0),("nfe",2,52912,0,0),("nfe_XX",2,31106,0,0),("nfe_XY",0,21806,0,0),("XX",2,59380,0,0),("XY",1,54156,0,0)]6.27e-06"nfe"2.35e-06"nfe"[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00{"AS_VQSR"}[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[("SiteQuality",2.92e+02),("inbreeding_coeff",-2.41e-05),("AS_MQ",3.61e+01),("AS_FS",3.79e+01),("AS_MQRankSum",7.88e-01),("AS_pab_max",1.00e+00),("AS_QUALapprox",2.64e+02),("AS_QD",2.06e+00),("AS_ReadPosRankSum",-6.84e-01),("AS_SOR",7.58e-01),("AS_VarDP",1.28e+02),("AS_VQSLOD",-3.31e+00)]{"rs1570391741"}8.53e+007.09e-01NANANANANANA"1-10057-A-C"[][][][]3311353600[("remaining",0,1504,0,0),("remaining_XX",0,764,0,0),("remaining_XY",0,740,0,0),("amr",0,10448,0,0),("amr_XX",0,4944,0,0),("amr_XY",0,5504,0,0),("fin",0,6886,0,0),("fin_XX",0,1472,0,0),("fin_XY",0,5414,0,0),("ami",0,742,0,0),("ami_XX",0,378,0,0),("ami_XY",0,364,0,0),("eas",0,3850,0,0),("eas_XX",0,1670,0,0),("eas_XY",0,2180,0,0),("mid",0,266,0,0),("mid_XX",0,132,0,0),("mid_XY",0,134,0,0),("sas",0,3424,0,0),("sas_XX",0,784,0,0),("sas_XY",0,2640,0,0),("asj",0,2768,0,0),("asj_XX",0,1532,0,0),("asj_XY",0,1236,0,0),("afr",1,30736,0,0),("afr_XX",0,16598,0,0),("afr_XY",1,14138,0,0),("nfe",2,52912,0,0),("nfe_XX",2,31106,0,0),("nfe_XY",0,21806,0,0),("",3,113536,0,0),("XX",2,59380,0,0),("XY",1,54156,0,0),("XX",2,59380,0,0),("XY",1,54156,0,0)][(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]6.27e-06"nfe"2.35e-06"nfe"23.78e-05529120"nfe"[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00{"not_called_in_exomes"}[NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANANANANANANANANANANANANANA5.43e+015838917008.20e-018.20e-018.20e-018.20e-018.18e-018.15e-018.07e-016.45e-015.38e-02[]"CA997563815""ga4gh:VA.TV0UyS-jCvsVkEOMbipUnzTc4hYuVNTg"1005610057"A""ga4gh:VA.33zGTKcHyj5wWpTfc3OfD8-UlPQ-YOfx"1005610057"C"
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+------------+------------------------------+\n",
+       "| locus         | alleles    | exome.colocated_variants.all |\n",
+       "+---------------+------------+------------------------------+\n",
+       "| locus | array | array                   |\n",
+       "+---------------+------------+------------------------------+\n",
+       "| chr1:10031    | [\"T\",\"C\"]  | NA                           |\n",
+       "| chr1:10037    | [\"T\",\"C\"]  | NA                           |\n",
+       "| chr1:10043    | [\"T\",\"C\"]  | NA                           |\n",
+       "| chr1:10055    | [\"T\",\"C\"]  | NA                           |\n",
+       "| chr1:10057    | [\"A\",\"C\"]  | NA                           |\n",
+       "+---------------+------------+------------------------------+\n",
+       "\n",
+       "+----------------------------------+---------------+-------------+\n",
+       "| exome.colocated_variants.non_ukb | exome.subsets | exome.flags |\n",
+       "+----------------------------------+---------------+-------------+\n",
+       "| array                       | set      | set    |\n",
+       "+----------------------------------+---------------+-------------+\n",
+       "| NA                               | NA            | NA          |\n",
+       "| NA                               | NA            | NA          |\n",
+       "| NA                               | NA            | NA          |\n",
+       "| NA                               | NA            | NA          |\n",
+       "| NA                               | NA            | NA          |\n",
+       "+----------------------------------+---------------+-------------+\n",
+       "\n",
+       "+-------------------+-----------------------+-------------------+\n",
+       "| exome.freq.all.ac | exome.freq.all.ac_raw | exome.freq.all.an |\n",
+       "+-------------------+-----------------------+-------------------+\n",
+       "|             int32 |                 int32 |             int32 |\n",
+       "+-------------------+-----------------------+-------------------+\n",
+       "|                NA |                    NA |                NA |\n",
+       "|                NA |                    NA |                NA |\n",
+       "|                NA |                    NA |                NA |\n",
+       "|                NA |                    NA |                NA |\n",
+       "|                NA |                    NA |                NA |\n",
+       "+-------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+---------------------------------+---------------------------------+\n",
+       "| exome.freq.all.hemizygote_count | exome.freq.all.homozygote_count |\n",
+       "+---------------------------------+---------------------------------+\n",
+       "|                           int32 |                           int64 |\n",
+       "+---------------------------------+---------------------------------+\n",
+       "|                              NA |                              NA |\n",
+       "|                              NA |                              NA |\n",
+       "|                              NA |                              NA |\n",
+       "|                              NA |                              NA |\n",
+       "|                              NA |                              NA |\n",
+       "+---------------------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| exome.freq.all.ancestry_groups                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                       | array                        |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "| exome.age_distribution.het.n_smaller | exome.age_distribution.het.n_larger |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "|                                int64 |                               int64 |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "| exome.age_distribution.hom.bin_edges | exome.age_distribution.hom.bin_freq |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "| array                       | array                        |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "| exome.age_distribution.hom.n_smaller | exome.age_distribution.hom.n_larger |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "|                                int64 |                               int64 |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+---------------+--------------------------------------------------------+\n",
+       "| exome.filters | exome.quality_metrics.allele_balance.alt_adj.bin_edges |\n",
+       "+---------------+--------------------------------------------------------+\n",
+       "| set      | array                                         |\n",
+       "+---------------+--------------------------------------------------------+\n",
+       "| NA            | NA                                                     |\n",
+       "| NA            | NA                                                     |\n",
+       "| NA            | NA                                                     |\n",
+       "| NA            | NA                                                     |\n",
+       "| NA            | NA                                                     |\n",
+       "+---------------+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.allele_balance.alt_adj.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.allele_balance.alt_adj.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.allele_balance.alt_adj.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.allele_balance.alt_raw.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.allele_balance.alt_raw.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.allele_balance.alt_raw.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.allele_balance.alt_raw.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_adj.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_adj.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_adj.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_adj.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_raw.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_raw.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_raw.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_raw.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_adj.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_adj.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_adj.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_adj.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_raw.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_raw.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_raw.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_raw.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_adj.bin_edges |\n",
+       "+----------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+----------------------------------------------------------+\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_adj.bin_freq |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_adj.n_smaller |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_adj.n_larger |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_raw.bin_edges |\n",
+       "+----------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+----------------------------------------------------------+\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_raw.bin_freq |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_raw.n_smaller |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_raw.n_larger |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_adj.bin_edges |\n",
+       "+----------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+----------------------------------------------------------+\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_adj.bin_freq |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_adj.n_smaller |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_adj.n_larger |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_raw.bin_edges |\n",
+       "+----------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+----------------------------------------------------------+\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_raw.bin_freq |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_raw.n_smaller |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_raw.n_larger |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+--------------------------------+\n",
+       "| exome.quality_metrics.site_quality_metrics | genome.colocated_variants.hgdp |\n",
+       "+--------------------------------------------+--------------------------------+\n",
+       "| array | array                     |\n",
+       "+--------------------------------------------+--------------------------------+\n",
+       "| NA                                         | []                             |\n",
+       "| NA                                         | []                             |\n",
+       "| NA                                         | []                             |\n",
+       "| NA                                         | []                             |\n",
+       "| NA                                         | []                             |\n",
+       "+--------------------------------------------+--------------------------------+\n",
+       "\n",
+       "+-------------------------------+-------------------------------+\n",
+       "| genome.colocated_variants.tgp | genome.colocated_variants.all |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "| array                    | array                    |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "| []                            | []                            |\n",
+       "| []                            | []                            |\n",
+       "| []                            | []                            |\n",
+       "| []                            | []                            |\n",
+       "| []                            | []                            |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "\n",
+       "+----------------+------------------+---------------------+\n",
+       "| genome.subsets | genome.flags     | genome.freq.hgdp.ac |\n",
+       "+----------------+------------------+---------------------+\n",
+       "| set       | set         |               int32 |\n",
+       "+----------------+------------------+---------------------+\n",
+       "| {}             | {\"lcr\",\"segdup\"} |                   0 |\n",
+       "| {}             | {\"lcr\",\"segdup\"} |                   0 |\n",
+       "| {}             | {\"lcr\",\"segdup\"} |                   0 |\n",
+       "| {\"hgdp\"}       | {\"lcr\",\"segdup\"} |                   0 |\n",
+       "| {}             | {\"lcr\",\"segdup\"} |                   0 |\n",
+       "+----------------+------------------+---------------------+\n",
+       "\n",
+       "+-------------------------+---------------------+\n",
+       "| genome.freq.hgdp.ac_raw | genome.freq.hgdp.an |\n",
+       "+-------------------------+---------------------+\n",
+       "|                   int32 |               int32 |\n",
+       "+-------------------------+---------------------+\n",
+       "|                       0 |                 812 |\n",
+       "|                       0 |                1028 |\n",
+       "|                       0 |                1154 |\n",
+       "|                       1 |                1312 |\n",
+       "|                       0 |                1300 |\n",
+       "+-------------------------+---------------------+\n",
+       "\n",
+       "+-----------------------------------+-----------------------------------+\n",
+       "| genome.freq.hgdp.hemizygote_count | genome.freq.hgdp.homozygote_count |\n",
+       "+-----------------------------------+-----------------------------------+\n",
+       "|                             int32 |                             int32 |\n",
+       "+-----------------------------------+-----------------------------------+\n",
+       "|                                 0 |                                 0 |\n",
+       "|                                 0 |                                 0 |\n",
+       "|                                 0 |                                 0 |\n",
+       "|                                 0 |                                 0 |\n",
+       "|                                 0 |                                 0 |\n",
+       "+-----------------------------------+-----------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.freq.hgdp.ancestry_groups                                             |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "| genome.age_distribution.het.bin_freq | genome.age_distribution.het.n_smaller |\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "| array                         |                                 int64 |\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,1,0,0,1,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,0,1,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,0,1,0,1,0,0,0,0,0]                |                                     0 |\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| genome.age_distribution.het.n_larger |\n",
+       "+--------------------------------------+\n",
+       "|                                int64 |\n",
+       "+--------------------------------------+\n",
+       "|                                    0 |\n",
+       "|                                    0 |\n",
+       "|                                    0 |\n",
+       "|                                    0 |\n",
+       "|                                    0 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.age_distribution.hom.bin_edges                                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "| genome.age_distribution.hom.bin_freq | genome.age_distribution.hom.n_smaller |\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "| array                         |                                 int64 |\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+-------------------+\n",
+       "| genome.age_distribution.hom.n_larger | genome.filters    |\n",
+       "+--------------------------------------+-------------------+\n",
+       "|                                int64 | set          |\n",
+       "+--------------------------------------+-------------------+\n",
+       "|                                    0 | {\"AC0\",\"AS_VQSR\"} |\n",
+       "|                                    0 | {\"AS_VQSR\"}       |\n",
+       "|                                    0 | {\"AS_VQSR\"}       |\n",
+       "|                                    0 | {\"AS_VQSR\"}       |\n",
+       "|                                    0 | {\"AS_VQSR\"}       |\n",
+       "+--------------------------------------+-------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_adj.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_adj.bin_freq |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_adj.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_adj.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_raw.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_raw.bin_freq |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_raw.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_raw.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_adj.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_adj.bin_freq                       |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
+       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
+       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
+       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
+       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_adj.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_adj.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                    527 |\n",
+       "|                                                    888 |\n",
+       "|                                                   1232 |\n",
+       "|                                                   2440 |\n",
+       "|                                                   3622 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_raw.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_raw.bin_freq                       |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
+       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
+       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
+       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
+       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_raw.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_raw.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                    744 |\n",
+       "|                                                   1130 |\n",
+       "|                                                   1487 |\n",
+       "|                                                   2622 |\n",
+       "|                                                   3743 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_adj.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_adj.bin_freq |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_adj.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_adj.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_raw.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_raw.bin_freq |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_raw.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_raw.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_adj.bin_edges                    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_adj.bin_freq                     |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
+       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
+       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
+       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
+       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_adj.n_smaller |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                     int64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "+-----------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_adj.n_larger |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_raw.bin_edges                    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_raw.bin_freq                     |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
+       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
+       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
+       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
+       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_raw.n_smaller |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                     int64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "+-----------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_raw.n_larger |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_adj.bin_edges                    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_adj.bin_freq |\n",
+       "+----------------------------------------------------------+\n",
+       "| array                                             |\n",
+       "+----------------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]                |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_adj.n_smaller |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                     int64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "+-----------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_adj.n_larger |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_raw.bin_edges                    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_raw.bin_freq |\n",
+       "+----------------------------------------------------------+\n",
+       "| array                                             |\n",
+       "+----------------------------------------------------------+\n",
+       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]                |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_raw.n_smaller |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                     int64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "+-----------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_raw.n_larger |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.site_quality_metrics                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(\"SiteQuality\",9.60e+01),(\"inbreeding_coeff\",-1.65e-05),(\"AS_MQ\",3.51e+0... |\n",
+       "| [(\"SiteQuality\",1.80e+02),(\"inbreeding_coeff\",-3.15e-05),(\"AS_MQ\",3.83e+0... |\n",
+       "| [(\"SiteQuality\",9.70e+01),(\"inbreeding_coeff\",-8.24e-06),(\"AS_MQ\",3.52e+0... |\n",
+       "| [(\"SiteQuality\",2.20e+02),(\"inbreeding_coeff\",-4.64e-05),(\"AS_MQ\",3.48e+0... |\n",
+       "| [(\"SiteQuality\",2.92e+02),(\"inbreeding_coeff\",-2.41e-05),(\"AS_MQ\",3.61e+0... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+---------------------------------+\n",
+       "| rsids            | in_silico_predictors.cadd.phred |\n",
+       "+------------------+---------------------------------+\n",
+       "| set         |                         float32 |\n",
+       "+------------------+---------------------------------+\n",
+       "| {\"rs1639542312\"} |                        8.97e+00 |\n",
+       "| {\"rs1639542418\"} |                        8.90e+00 |\n",
+       "| NA               |                        8.90e+00 |\n",
+       "| {\"rs892501864\"}  |                        8.88e+00 |\n",
+       "| {\"rs1570391741\"} |                        8.53e+00 |\n",
+       "+------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            7.57e-01 |                             NA |\n",
+       "|                            7.49e-01 |                             NA |\n",
+       "|                            7.48e-01 |                             NA |\n",
+       "|                            7.46e-01 |                             NA |\n",
+       "|                            7.09e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "\n",
+       "+---------------+------------------------+----------------------------+\n",
+       "| variant_id    | colocated_variants.all | colocated_variants.non_ukb |\n",
+       "+---------------+------------------------+----------------------------+\n",
+       "| str           | array             | array                 |\n",
+       "+---------------+------------------------+----------------------------+\n",
+       "| \"1-10031-T-C\" | []                     | []                         |\n",
+       "| \"1-10037-T-C\" | []                     | []                         |\n",
+       "| \"1-10043-T-C\" | []                     | []                         |\n",
+       "| \"1-10055-T-C\" | []                     | []                         |\n",
+       "| \"1-10057-A-C\" | []                     | []                         |\n",
+       "+---------------+------------------------+----------------------------+\n",
+       "\n",
+       "+-------------------------+------------------------+-------------------+\n",
+       "| colocated_variants.hgdp | colocated_variants.tgp | joint.freq.all.ac |\n",
+       "+-------------------------+------------------------+-------------------+\n",
+       "| array              | array             |             int32 |\n",
+       "+-------------------------+------------------------+-------------------+\n",
+       "| []                      | []                     |                 0 |\n",
+       "| []                      | []                     |                 2 |\n",
+       "| []                      | []                     |                 1 |\n",
+       "| []                      | []                     |                 1 |\n",
+       "| []                      | []                     |                 3 |\n",
+       "+-------------------------+------------------------+-------------------+\n",
+       "\n",
+       "+-----------------------+-------------------+---------------------------------+\n",
+       "| joint.freq.all.ac_raw | joint.freq.all.an | joint.freq.all.hemizygote_count |\n",
+       "+-----------------------+-------------------+---------------------------------+\n",
+       "|                 int32 |             int32 |                           int32 |\n",
+       "+-----------------------+-------------------+---------------------------------+\n",
+       "|                     2 |             56642 |                               0 |\n",
+       "|                     4 |             76882 |                               0 |\n",
+       "|                     1 |             85634 |                               0 |\n",
+       "|                     5 |             94224 |                               0 |\n",
+       "|                     3 |            113536 |                               0 |\n",
+       "+-----------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+---------------------------------+\n",
+       "| joint.freq.all.homozygote_count |\n",
+       "+---------------------------------+\n",
+       "|                           int32 |\n",
+       "+---------------------------------+\n",
+       "|                               0 |\n",
+       "|                               0 |\n",
+       "|                               0 |\n",
+       "|                               0 |\n",
+       "|                               0 |\n",
+       "+---------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.freq.all.ancestry_groups                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------+--------------------------------+\n",
+       "| joint.fafmax.faf95_max | joint.fafmax.faf95_max_gen_anc |\n",
+       "+------------------------+--------------------------------+\n",
+       "|                float64 | str                            |\n",
+       "+------------------------+--------------------------------+\n",
+       "|                     NA | NA                             |\n",
+       "|                     NA | NA                             |\n",
+       "|                     NA | NA                             |\n",
+       "|                     NA | NA                             |\n",
+       "|               6.27e-06 | \"nfe\"                          |\n",
+       "+------------------------+--------------------------------+\n",
+       "\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "| joint.fafmax.faf99_max | joint.fafmax.faf99_max_gen_anc | joint.grpmax.AC |\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "|                float64 | str                            |           int32 |\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "|                     NA | NA                             |              NA |\n",
+       "|                     NA | NA                             |               1 |\n",
+       "|                     NA | NA                             |               1 |\n",
+       "|                     NA | NA                             |              NA |\n",
+       "|               2.35e-06 | \"nfe\"                          |               2 |\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "| joint.grpmax.AF | joint.grpmax.AN | joint.grpmax.homozygote_count |\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "|         float64 |           int32 |                         int32 |\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "|              NA |              NA |                            NA |\n",
+       "|        4.07e-04 |            2456 |                             0 |\n",
+       "|        4.39e-05 |           22760 |                             0 |\n",
+       "|              NA |              NA |                            NA |\n",
+       "|        3.78e-05 |           52912 |                             0 |\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "\n",
+       "+----------------------+\n",
+       "| joint.grpmax.gen_anc |\n",
+       "+----------------------+\n",
+       "| str                  |\n",
+       "+----------------------+\n",
+       "| NA                   |\n",
+       "| \"eas\"                |\n",
+       "| \"afr\"                |\n",
+       "| NA                   |\n",
+       "| \"nfe\"                |\n",
+       "+----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.bin_freq                             |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
+       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
+       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
+       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
+       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.bin_freq                             |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
+       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
+       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
+       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
+       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                              527 |\n",
+       "|                                              888 |\n",
+       "|                                             1232 |\n",
+       "|                                             2440 |\n",
+       "|                                             3622 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]        |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.bin_freq                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
+       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
+       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
+       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
+       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.bin_freq                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
+       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
+       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
+       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
+       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                  744 |\n",
+       "|                                                 1130 |\n",
+       "|                                                 1487 |\n",
+       "|                                                 2622 |\n",
+       "|                                                 3743 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+------------------------------------------------------+\n",
+       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]            |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+------------------------------------------------------+\n",
+       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+------------------------------------------------------+\n",
+       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,1,0,0,1,0,0,0,0,0]                            |\n",
+       "| [0,0,1,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,1,0,1,0,0,0,0,0]                            |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------+--------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.n_larger | joint.flags              |\n",
+       "+--------------------------------------------------+--------------------------+\n",
+       "|                                            int64 | set                 |\n",
+       "+--------------------------------------------------+--------------------------+\n",
+       "|                                                0 | {\"not_called_in_exomes\"} |\n",
+       "|                                                0 | {\"not_called_in_exomes\"} |\n",
+       "|                                                0 | {\"not_called_in_exomes\"} |\n",
+       "|                                                0 | {\"not_called_in_exomes\"} |\n",
+       "|                                                0 | {\"not_called_in_exomes\"} |\n",
+       "+--------------------------------------------------+--------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.freq_comparison_stats.contingency_table_test                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------+\n",
+       "| joint.freq_comparison_stats.cochran_mantel_haenszel_test.p_value |\n",
+       "+------------------------------------------------------------------+\n",
+       "|                                                          float64 |\n",
+       "+------------------------------------------------------------------+\n",
+       "|                                                               NA |\n",
+       "|                                                               NA |\n",
+       "|                                                               NA |\n",
+       "|                                                               NA |\n",
+       "|                                                               NA |\n",
+       "+------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------------+\n",
+       "| joint.freq_comparison_stats.cochran_mantel_haenszel_test.chisq |\n",
+       "+----------------------------------------------------------------+\n",
+       "|                                                        float64 |\n",
+       "+----------------------------------------------------------------+\n",
+       "|                                                             NA |\n",
+       "|                                                             NA |\n",
+       "|                                                             NA |\n",
+       "|                                                             NA |\n",
+       "|                                                             NA |\n",
+       "+----------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| joint.freq_comparison_stats.stat_union.p_value |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                             NA |\n",
+       "|                                             NA |\n",
+       "|                                             NA |\n",
+       "|                                             NA |\n",
+       "|                                             NA |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.freq_comparison_stats.stat_union.stat_test_name |\n",
+       "+-------------------------------------------------------+\n",
+       "| str                                                   |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+---------------------+\n",
+       "| joint.freq_comparison_stats.stat_union.gen_ancs | coverage.exome.mean |\n",
+       "+-------------------------------------------------+---------------------+\n",
+       "| array                                      |             float64 |\n",
+       "+-------------------------------------------------+---------------------+\n",
+       "| NA                                              |                  NA |\n",
+       "| NA                                              |                  NA |\n",
+       "| NA                                              |                  NA |\n",
+       "| NA                                              |                  NA |\n",
+       "| NA                                              |                  NA |\n",
+       "+-------------------------------------------------+---------------------+\n",
+       "\n",
+       "+------------------------------+-------------------------+\n",
+       "| coverage.exome.median_approx | coverage.exome.total_DP |\n",
+       "+------------------------------+-------------------------+\n",
+       "|                        int32 |                   int64 |\n",
+       "+------------------------------+-------------------------+\n",
+       "|                           NA |                      NA |\n",
+       "|                           NA |                      NA |\n",
+       "|                           NA |                      NA |\n",
+       "|                           NA |                      NA |\n",
+       "|                           NA |                      NA |\n",
+       "+------------------------------+-------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+------------------------+\n",
+       "| coverage.exome.over_1 | coverage.exome.over_5 | coverage.exome.over_10 |\n",
+       "+-----------------------+-----------------------+------------------------+\n",
+       "|               float64 |               float64 |                float64 |\n",
+       "+-----------------------+-----------------------+------------------------+\n",
+       "|                    NA |                    NA |                     NA |\n",
+       "|                    NA |                    NA |                     NA |\n",
+       "|                    NA |                    NA |                     NA |\n",
+       "|                    NA |                    NA |                     NA |\n",
+       "|                    NA |                    NA |                     NA |\n",
+       "+-----------------------+-----------------------+------------------------+\n",
+       "\n",
+       "+------------------------+------------------------+------------------------+\n",
+       "| coverage.exome.over_15 | coverage.exome.over_20 | coverage.exome.over_25 |\n",
+       "+------------------------+------------------------+------------------------+\n",
+       "|                float64 |                float64 |                float64 |\n",
+       "+------------------------+------------------------+------------------------+\n",
+       "|                     NA |                     NA |                     NA |\n",
+       "|                     NA |                     NA |                     NA |\n",
+       "|                     NA |                     NA |                     NA |\n",
+       "|                     NA |                     NA |                     NA |\n",
+       "|                     NA |                     NA |                     NA |\n",
+       "+------------------------+------------------------+------------------------+\n",
+       "\n",
+       "+------------------------+------------------------+-------------------------+\n",
+       "| coverage.exome.over_30 | coverage.exome.over_50 | coverage.exome.over_100 |\n",
+       "+------------------------+------------------------+-------------------------+\n",
+       "|                float64 |                float64 |                 float64 |\n",
+       "+------------------------+------------------------+-------------------------+\n",
+       "|                     NA |                     NA |                      NA |\n",
+       "|                     NA |                     NA |                      NA |\n",
+       "|                     NA |                     NA |                      NA |\n",
+       "|                     NA |                     NA |                      NA |\n",
+       "|                     NA |                     NA |                      NA |\n",
+       "+------------------------+------------------------+-------------------------+\n",
+       "\n",
+       "+----------------------+-------------------------------+\n",
+       "| coverage.genome.mean | coverage.genome.median_approx |\n",
+       "+----------------------+-------------------------------+\n",
+       "|              float64 |                         int32 |\n",
+       "+----------------------+-------------------------------+\n",
+       "|             3.74e+01 |                            38 |\n",
+       "|             4.29e+01 |                            44 |\n",
+       "|             4.42e+01 |                            47 |\n",
+       "|             4.54e+01 |                            51 |\n",
+       "|             5.43e+01 |                            58 |\n",
+       "+----------------------+-------------------------------+\n",
+       "\n",
+       "+--------------------------+------------------------+------------------------+\n",
+       "| coverage.genome.total_DP | coverage.genome.over_1 | coverage.genome.over_5 |\n",
+       "+--------------------------+------------------------+------------------------+\n",
+       "|                    int64 |                float32 |                float32 |\n",
+       "+--------------------------+------------------------+------------------------+\n",
+       "|                  2684436 |               8.08e-01 |               8.08e-01 |\n",
+       "|                  3077955 |               8.43e-01 |               8.43e-01 |\n",
+       "|                  3165823 |               8.06e-01 |               8.06e-01 |\n",
+       "|                  3252282 |               7.16e-01 |               7.16e-01 |\n",
+       "|                  3891700 |               8.20e-01 |               8.20e-01 |\n",
+       "+--------------------------+------------------------+------------------------+\n",
+       "\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
+       "| coverage.genome.over_10 | coverage.genome.over_15 | coverage.genome.over_20 |\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
+       "|                 float32 |                 float32 |                 float32 |\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
+       "|                8.07e-01 |                8.01e-01 |                7.82e-01 |\n",
+       "|                8.42e-01 |                8.39e-01 |                8.27e-01 |\n",
+       "|                8.05e-01 |                8.03e-01 |                7.97e-01 |\n",
+       "|                7.15e-01 |                7.15e-01 |                7.13e-01 |\n",
+       "|                8.20e-01 |                8.20e-01 |                8.18e-01 |\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
+       "\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
+       "| coverage.genome.over_25 | coverage.genome.over_30 | coverage.genome.over_50 |\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
+       "|                 float32 |                 float32 |                 float32 |\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
+       "|                7.43e-01 |                6.67e-01 |                2.89e-01 |\n",
+       "|                8.02e-01 |                7.48e-01 |                3.93e-01 |\n",
+       "|                7.82e-01 |                7.48e-01 |                4.50e-01 |\n",
+       "|                7.10e-01 |                7.00e-01 |                5.30e-01 |\n",
+       "|                8.15e-01 |                8.07e-01 |                6.45e-01 |\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
+       "\n",
+       "+--------------------------+\n",
+       "| coverage.genome.over_100 |\n",
+       "+--------------------------+\n",
+       "|                  float32 |\n",
+       "+--------------------------+\n",
+       "|                 1.13e-02 |\n",
+       "|                 1.66e-02 |\n",
+       "|                 2.14e-02 |\n",
+       "|                 3.76e-02 |\n",
+       "|                 5.38e-02 |\n",
+       "+--------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| transcript_consequences                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, domains: set
Date: Tue, 14 Jan 2025 14:21:44 -0700
Subject: [PATCH 076/121] Update gnomad_toolbox/load_data.py

---
 gnomad_toolbox/load_data.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index 70c718b..1a29ea2 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -38,7 +38,7 @@
             "coverage": {"exomes": "4.0", "genomes": "3.0.1"},
             "all_sites_an": "4.1",
             "constraint": "4.1",
-            "pext": "v7",
+            "pext": "v10",
             "browser": "4.1",
         },
     },

From 82b9dbb1c38267f7840a7c708804cccea1494006 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 14 Jan 2025 15:02:56 -0700
Subject: [PATCH 077/121] Add links for data download to notebook summary table

---
 .../notebooks/explore_release_data.ipynb      | 38 +++++++++----------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
index 3017c9f..a36b06d 100644
--- a/gnomad_toolbox/notebooks/explore_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -99,7 +99,7 @@
        "    \n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -271,7 +271,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"da0da3b0-b5fd-4d97-96c0-ec7eae06d97a\");\n",
+       "    const el = document.getElementById(\"edebb21b-b3fc-4e55-b6bd-7218f358f8ee\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -377,7 +377,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"da0da3b0-b5fd-4d97-96c0-ec7eae06d97a\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"edebb21b-b3fc-4e55-b6bd-7218f358f8ee\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -393,7 +393,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"da0da3b0-b5fd-4d97-96c0-ec7eae06d97a\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"da0da3b0-b5fd-4d97-96c0-ec7eae06d97a\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"edebb21b-b3fc-4e55-b6bd-7218f358f8ee\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"edebb21b-b3fc-4e55-b6bd-7218f358f8ee\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -419,7 +419,7 @@
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
       "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250114-1205-0.2.132-678e1f52b999.log\n"
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250114-1427-0.2.132-678e1f52b999.log\n"
      ]
     }
    ],
@@ -442,20 +442,20 @@
    "id": "b4b633dd-9b92-4e3f-b6c7-1100fa813885",
    "metadata": {},
    "source": [
-    "\n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
+    "
Genome BuildDataset (dataset)Version (version)Data Type (data_type)
GRCh37variant2.1.1exomes, genomes
coverage2.1exomes, genomes
constraint2.1.1N/A
liftover2.1.1exomes, genomes
pextv7base_level, annotation_level
GRCh38variant4.1exomes, genomes, joint
coverage3.0.1genomes
4.0exomes
all_sites_an4.1exomes, genomes
constraint4.1N/A
pextv10base_level, annotation_level
browser_variant4.1N/A (This includes information for the joint, exomes, and genomes, but doesn't need to be specified)
\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
     "
Genome BuildDataset (dataset)Version (version)Data Type (data_type)
GRCh37variant2.1.1exomes, genomes
coverage2.1exomes, genomes
constraint2.1.1N/A
pextv7base_level, annotation_level
liftover2.1.1exomes, genomes
GRCh38variant4.1exomes, genomes, joint
all_sites_an4.1exomes, genomes
browser_variant4.1N/A
(This includes information for the joint, exomes, and 
genomes, but doesn't need to be specified)
coverage3.0.1genomes
4.0exomes
constraint4.1N/A
pextv10base_level, annotation_level
\n"
    ]
   },

From 1d081eaa663bfa2a117791a4acb7968d8f2fb0ef Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 14 Jan 2025 15:08:40 -0700
Subject: [PATCH 078/121] browser_variant -> browser in notebook table

---
 gnomad_toolbox/notebooks/explore_release_data.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
index a36b06d..f04b9c0 100644
--- a/gnomad_toolbox/notebooks/explore_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -451,7 +451,7 @@
     "    liftover2.1.1exomes, genomes\n",
     "    GRCh38variant4.1exomes, genomes, joint\n",
     "    all_sites_an4.1exomes, genomes\n",
-    "    browser_variant4.1N/A
(This includes information for the joint, exomes, and 
genomes, but doesn't need to be specified)\n",
+    "    browser4.1N/A
(This includes information for the joint, exomes, and 
genomes, but doesn't need to be specified)\n",
     "    coverage3.0.1genomes\n",
     "    4.0exomes\n",
     "    constraint4.1N/A\n",
@@ -12526,7 +12526,7 @@
     "height": "calc(100% - 180px)",
     "left": "10px",
     "top": "150px",
-    "width": "202.432px"
+    "width": "418.428px"
    },
    "toc_section_display": true,
    "toc_window_display": true

From 8700831aebcb60477bc1de45f2b4d3f127cc4e06 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 15 Jan 2025 07:27:14 -0700
Subject: [PATCH 079/121] Changes to some headers and added links to the
 notebook

---
 .../notebooks/explore_release_data.ipynb      | 72 ++++++++++++-------
 1 file changed, 46 insertions(+), 26 deletions(-)

diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
index f04b9c0..f93e1ca 100644
--- a/gnomad_toolbox/notebooks/explore_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -51,7 +51,7 @@
    },
    "source": [
     "
Table of Contents
\n",
-    "
"
+    "
"
    ]
   },
   {
@@ -443,19 +443,19 @@
    "metadata": {},
    "source": [
     "\n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
     "
Genome BuildDataset (dataset)Version (version)Data Type (data_type)
GRCh37variant2.1.1exomes, genomes
coverage2.1exomes, genomes
constraint2.1.1N/A
pextv7base_level, annotation_level
liftover2.1.1exomes, genomes
GRCh38variant4.1exomes, genomes, joint
all_sites_an4.1exomes, genomes
browser4.1N/A
(This includes information for the joint, exomes, and 
genomes, but doesn't need to be specified)
coverage3.0.1genomes
4.0exomes
constraint4.1N/A
pextv10base_level, annotation_level
Genome BuildDataset (dataset)Version (version)Data Type (data_type)
GRCh37variant2.1.1exomes, genomes
coverage2.1exomes, genomes
constraint2.1.1N/A
pextv7base_level, annotation_level
liftover2.1.1exomes, genomes
GRCh38variant4.1exomes, genomes, joint
all_sites_an4.1exomes, genomes
browser4.1N/A
Note: This includes information for 
the joint, exomes, and genomes, 
but doesn't need to be specified
coverage3.0.1genomes
4.0exomes
constraint4.1N/A
pextv10base_level, annotation_level
\n"
    ]
   },
@@ -478,7 +478,9 @@
     "tags": []
    },
    "source": [
-    "### v4.1 exomes Hail Table"
+    "### Exomes Hail Table\n",
+    "\n",
+    "Showing gnomAD **v4.1** exomes frequency. "
    ]
   },
   {
@@ -1978,7 +1980,9 @@
     "tags": []
    },
    "source": [
-    "### v4.1 genomes Hail Table"
+    "### Genomes Hail Table\n",
+    "\n",
+    "Showing gnomAD **v4.1** genomes frequency. "
    ]
   },
   {
@@ -3349,7 +3353,7 @@
     "tags": []
    },
    "source": [
-    "### v4.1 Joint Frequency Hail Table"
+    "### Joint Frequency Hail Table"
    ]
   },
   {
@@ -3357,7 +3361,7 @@
    "id": "0a569b77-d3d2-45a4-803a-1214c77e46f2",
    "metadata": {},
    "source": [
-    "The joint frequency Hail table includes frequency for the exomes, genomes, and the exomes+genomes. We have also added statistics for the combined exomes and genomes frequencies, more details on these stats can be found on our [Help](https://gnomad.broadinstitute.org/help/combined-freq-stats) page."
+    "Showing gnomAD **v4.1** joint frequency. The joint frequency Hail table includes frequency for the exomes, genomes, and the exomes+genomes. We have also added statistics for the combined exomes and genomes frequencies, more details on these stats can be found on our [Help](https://gnomad.broadinstitute.org/help/combined-freq-stats) page."
    ]
   },
   {
@@ -5953,7 +5957,9 @@
     "tags": []
    },
    "source": [
-    "### Exomes all sites allele number Hail Table"
+    "### Exomes all sites allele number Hail Table\n",
+    "\n",
+    "Showing gnomAD **v4.1** exomes all sites allele numbers."
    ]
   },
   {
@@ -6124,7 +6130,9 @@
     "tags": []
    },
    "source": [
-    "### Genomes all sites allele number Hail Table"
+    "### Genomes all sites allele number Hail Table\n",
+    "\n",
+    "Showing gnomAD **v4.1** genomes all sites allele numbers."
    ]
   },
   {
@@ -6268,7 +6276,9 @@
     "tags": []
    },
    "source": [
-    "## Coverage\n"
+    "## Coverage\n",
+    "\n",
+    "For details on how coverage was calculated see the [Help/FAQ](https://gnomad.broadinstitute.org/help#coverage) pages."
    ]
   },
   {
@@ -6278,7 +6288,9 @@
     "tags": []
    },
    "source": [
-    "### Exomes coverage Hail Table"
+    "### Exomes coverage Hail Table\n",
+    "\n",
+    "Showing gnomAD **v4.0** coverage."
    ]
   },
   {
@@ -6431,7 +6443,9 @@
     "tags": []
    },
    "source": [
-    "### Genomes coverage Hail Table"
+    "### Genomes coverage Hail Table\n",
+    "\n",
+    "Showing gnomAD **v3.0.1** coverage."
    ]
   },
   {
@@ -6585,7 +6599,9 @@
     "tags": []
    },
    "source": [
-    "## Constraint\n"
+    "## Gene constraint\n",
+    "\n",
+    "For details on gene constraint see the [Help](https://gnomad.broadinstitute.org/help/constraint) and [FAQ](https://gnomad.broadinstitute.org/help#constraint)."
    ]
   },
   {
@@ -6930,7 +6946,9 @@
     "tags": []
    },
    "source": [
-    "## Proportion expressed across transcripts (pext) score\n"
+    "## Proportion expressed across transcripts (pext) score\n",
+    "\n",
+    "For more information about the pext score see the [Help](https://gnomad.broadinstitute.org/help/pext) page."
    ]
   },
   {
@@ -9490,7 +9508,9 @@
     "tags": []
    },
    "source": [
-    "## Browser variant data\n"
+    "## Browser variant data\n",
+    "\n",
+    "For more information about these files, see the [changelog entry](https://gnomad.broadinstitute.org/new/2024-08-release-gnomad-browser-tables) on the browser tables, and the [Help](https://gnomad.broadinstitute.org/help/v4-browser-hts) page."
    ]
   },
   {

From 883377109afdb638b541c4462dded024e4f41478 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 15 Jan 2025 10:17:50 -0700
Subject: [PATCH 080/121] Add liftover and internal links to the notebook

---
 .../notebooks/explore_release_data.ipynb      | 13392 +++++++++-------
 1 file changed, 7754 insertions(+), 5638 deletions(-)

diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
index f93e1ca..bc5c022 100644
--- a/gnomad_toolbox/notebooks/explore_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -51,7 +51,7 @@
    },
    "source": [
     "
Table of Contents
\n",
-    "
"
+    "
"
    ]
   },
   {
@@ -443,19 +443,84 @@
    "metadata": {},
    "source": [
     "\n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
-    "    \n",
+    "    \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "    \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "    \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "    \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "    \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "    \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "    \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "    \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "    \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "    \n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "    \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "    \n",
+    "    \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "        \n",
+    "    \n",
     "
Genome BuildDataset (dataset)Version (version)Data Type (data_type)
GRCh37variant2.1.1exomes, genomes
coverage2.1exomes, genomes
constraint2.1.1N/A
pextv7base_level, annotation_level
liftover2.1.1exomes, genomes
GRCh38variant4.1exomes, genomes, joint
all_sites_an4.1exomes, genomes
browser4.1N/A
Note: This includes information for 
the joint, exomes, and genomes, 
but doesn't need to be specified
coverage3.0.1genomes
4.0exomes
constraint4.1N/A
pextv10base_level, annotation_level
Genome BuildDatasetdatasetversiondata_type
GRCh37Variant datavariant2.1.1exomes, genomes
Coveragecoverage2.1exomes, genomes
Gene constraintconstraint2.1.1N/A
Proportion expressed
across transcripts score		pextv7base_level, annotation_level
Lifted over variant data
(GRCh37 --> GRCh38)		liftover2.1.1exomes, genomes
GRCh38Variant datavariant4.1exomes, genomes, joint
All sites allele numbersall_sites_an4.1exomes, genomes
Browser variant databrowser4.1N/A
Note: This includes information for 
the joint, exomes, and genomes, 
but doesn't need to be specified
Coveragecoverage3.0.1genomes
4.0exomes
Gene constraintconstraint4.1N/A
Proportion expressed
across transcripts score		pextv10base_level, annotation_level
\n"
    ]
   },
@@ -6271,40 +6336,28 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1bf9f31f-34ff-4385-a80e-985cbb0acfe8",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Coverage\n",
-    "\n",
-    "For details on how coverage was calculated see the [Help/FAQ](https://gnomad.broadinstitute.org/help#coverage) pages."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "de70c319-787b-4d6c-9058-255a1137d81f",
+   "id": "ae3e5ba8-075a-455c-8e5c-e244970d5217",
    "metadata": {
     "tags": []
    },
    "source": [
-    "### Exomes coverage Hail Table\n",
+    "## Browser variant data\n",
     "\n",
-    "Showing gnomAD **v4.0** coverage."
+    "For more information about these files, see the [changelog entry](https://gnomad.broadinstitute.org/new/2024-08-release-gnomad-browser-tables) on the browser tables, and the [Help](https://gnomad.broadinstitute.org/help/v4-browser-hts) page."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3278430c-4279-4d89-85e7-276184ec42b8",
+   "id": "d7c13865-814a-46e8-a4f3-c823a910cfa3",
    "metadata": {},
    "source": [
-    "#### Load the Hail Table\n"
+    "### Load the Hail Table\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "7f005af0-df6d-4a0a-a2e0-9cb4ff6782c2",
+   "execution_count": 33,
+   "id": "329f08ef-6dbf-42cd-bbbc-d3f5396708ac",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-12-06T20:08:59.197890Z",
@@ -6313,177 +6366,21 @@
    },
    "outputs": [],
    "source": [
-    "ht = get_gnomad_release(data_type='exomes', version='4.0', dataset=\"coverage\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "128e58ce-c219-472a-88be-6babc2ba5a15",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "6058bf38-4ce9-4d37-89a7-d11af0e4d9f1",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    None\n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'mean': float64 \n",
-      "    'median_approx': int32 \n",
-      "    'total_DP': int64 \n",
-      "    'over_1': float64 \n",
-      "    'over_5': float64 \n",
-      "    'over_10': float64 \n",
-      "    'over_15': float64 \n",
-      "    'over_20': float64 \n",
-      "    'over_25': float64 \n",
-      "    'over_30': float64 \n",
-      "    'over_50': float64 \n",
-      "    'over_100': float64 \n",
-      "----------------------------------------\n",
-      "Key: ['locus']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5969ab0c-7cee-4061-8740-8b82366ae806",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "49fcf64b-1a91-4052-bb98-b329a0e8031b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
locus
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32int64float64float64float64float64float64float64float64float64float64
chr1:118191.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118201.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118211.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118221.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118231.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "| locus         |     mean | median_approx | total_DP |   over_1 |   over_5 |\n",
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "| locus |  float64 |         int32 |    int64 |  float64 |  float64 |\n",
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "| chr1:11819    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "| chr1:11820    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "| chr1:11821    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "| chr1:11822    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "| chr1:11823    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "|  over_10 |  over_15 |  over_20 |  over_25 |  over_30 |  over_50 | over_100 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "|  float64 |  float64 |  float64 |  float64 |  float64 |  float64 |  float64 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "19b2e9be-48fd-4859-af3d-a0775656d24c",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Genomes coverage Hail Table\n",
-    "\n",
-    "Showing gnomAD **v3.0.1** coverage."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5918f8e0-a723-439a-a11f-558d5ed13be8",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "a8d0be07-c35d-425a-b554-c86034e367fc",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(data_type='genomes', version='3.0.1', dataset=\"coverage\")"
+    "ht = get_gnomad_release(version='4.1', dataset=\"browser\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d129b898-d642-44d1-8243-66a9cca8d1b1",
+   "id": "6ec344e0-4951-44d8-8f7b-1283a427914d",
    "metadata": {},
    "source": [
-    "#### Print the schema of the Hail Table\n"
+    "### Print the schema of the Hail Table\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "18afb20f-7429-4fe2-a6c5-73a22dcbdb76",
+   "execution_count": 34,
+   "id": "17efacb6-84c4-4345-9bf9-1dca259dd7fa",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-12-06T20:09:00.032524Z",
@@ -6498,1994 +6395,512 @@
      "text": [
       "----------------------------------------\n",
       "Global fields:\n",
-      "    None\n",
+      "    'mane_select_version': str \n",
       "----------------------------------------\n",
       "Row fields:\n",
       "    'locus': locus \n",
-      "    'mean': float64 \n",
-      "    'median_approx': int32 \n",
-      "    'total_DP': int64 \n",
-      "    'over_1': float32 \n",
-      "    'over_5': float32 \n",
-      "    'over_10': float32 \n",
-      "    'over_15': float32 \n",
-      "    'over_20': float32 \n",
-      "    'over_25': float32 \n",
-      "    'over_30': float32 \n",
-      "    'over_50': float32 \n",
-      "    'over_100': float32 \n",
-      "----------------------------------------\n",
-      "Key: ['locus']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05d8e22c-93d6-4ecf-b9e7-711945268c82",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "b27cb655-3abb-4501-bcc9-3f634db64591",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
locus
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32int64float32float32float32float32float32float32float32float32float32
chr1:100012.41e+0001729261.25e-011.19e-019.12e-026.95e-025.15e-023.88e-022.62e-022.27e-030.00e+00
chr1:100024.61e+0003307602.20e-012.15e-011.79e-011.39e-011.02e-017.60e-025.06e-024.83e-032.79e-05
chr1:100036.38e+0004576112.62e-012.59e-012.41e-012.07e-011.59e-011.18e-017.77e-027.61e-035.58e-05
chr1:100041.04e+0107448404.27e-014.24e-013.99e-013.43e-012.60e-011.87e-011.21e-011.20e-021.67e-04
chr1:100051.18e+0108494414.83e-014.80e-014.56e-013.92e-012.96e-012.14e-011.38e-011.42e-022.37e-04
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "| locus         |     mean | median_approx | total_DP |   over_1 |   over_5 |\n",
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "| locus |  float64 |         int32 |    int64 |  float32 |  float32 |\n",
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "| chr1:10001    | 2.41e+00 |             0 |   172926 | 1.25e-01 | 1.19e-01 |\n",
-       "| chr1:10002    | 4.61e+00 |             0 |   330760 | 2.20e-01 | 2.15e-01 |\n",
-       "| chr1:10003    | 6.38e+00 |             0 |   457611 | 2.62e-01 | 2.59e-01 |\n",
-       "| chr1:10004    | 1.04e+01 |             0 |   744840 | 4.27e-01 | 4.24e-01 |\n",
-       "| chr1:10005    | 1.18e+01 |             0 |   849441 | 4.83e-01 | 4.80e-01 |\n",
-       "+---------------+----------+---------------+----------+----------+----------+\n",
-       "\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "|  over_10 |  over_15 |  over_20 |  over_25 |  over_30 |  over_50 | over_100 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "|  float32 |  float32 |  float32 |  float32 |  float32 |  float32 |  float32 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "| 9.12e-02 | 6.95e-02 | 5.15e-02 | 3.88e-02 | 2.62e-02 | 2.27e-03 | 0.00e+00 |\n",
-       "| 1.79e-01 | 1.39e-01 | 1.02e-01 | 7.60e-02 | 5.06e-02 | 4.83e-03 | 2.79e-05 |\n",
-       "| 2.41e-01 | 2.07e-01 | 1.59e-01 | 1.18e-01 | 7.77e-02 | 7.61e-03 | 5.58e-05 |\n",
-       "| 3.99e-01 | 3.43e-01 | 2.60e-01 | 1.87e-01 | 1.21e-01 | 1.20e-02 | 1.67e-04 |\n",
-       "| 4.56e-01 | 3.92e-01 | 2.96e-01 | 2.14e-01 | 1.38e-01 | 1.42e-02 | 2.37e-04 |\n",
-       "+----------+----------+----------+----------+----------+----------+----------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c4ae4713-f542-46e3-979f-0bcfb44ec56c",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Gene constraint\n",
-    "\n",
-    "For details on gene constraint see the [Help](https://gnomad.broadinstitute.org/help/constraint) and [FAQ](https://gnomad.broadinstitute.org/help#constraint)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4ed92af6-aacc-4cb2-890d-7ff76a00196d",
-   "metadata": {},
-   "source": [
-    "### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "3de343dc-37ae-4b17-a056-9d30e71e217c",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(version='4.1', dataset=\"constraint\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b59b2f6-aadd-408d-8663-b676d41565bf",
-   "metadata": {},
-   "source": [
-    "### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "c401dc94-0db0-4902-933d-e46c8aa447ce",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'version': str \n",
-      "    'apply_model_params': struct {\n",
-      "        max_af: float64, \n",
-      "        pops: tuple (\n",
-      "        ), \n",
-      "        plateau_models: struct {\n",
-      "            total: dict>\n",
+      "    'alleles': array \n",
+      "    'exome': struct {\n",
+      "        colocated_variants: struct {\n",
+      "            all: array, \n",
+      "            non_ukb: array\n",
       "        }, \n",
-      "        coverage_model: str\n",
-      "    } \n",
-      "    'sd_raw_z': struct {\n",
-      "        lof: float64, \n",
-      "        mis: float64, \n",
-      "        syn: float64\n",
-      "    } \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'gene': str \n",
-      "    'gene_id': str \n",
-      "    'transcript': str \n",
-      "    'canonical': bool \n",
-      "    'mane_select': bool \n",
-      "    'lof_hc_lc': struct {\n",
-      "        obs: int64, \n",
-      "        exp: float64, \n",
-      "        possible: int64, \n",
-      "        oe: float64, \n",
-      "        mu: float64, \n",
-      "        pLI: float64, \n",
-      "        pNull: float64, \n",
-      "        pRec: float64\n",
-      "    } \n",
-      "    'lof': struct {\n",
-      "        obs: int64, \n",
-      "        exp: float64, \n",
-      "        possible: int64, \n",
-      "        oe: float64, \n",
-      "        mu: float64, \n",
-      "        pLI: float64, \n",
-      "        pNull: float64, \n",
-      "        pRec: float64, \n",
-      "        oe_ci: struct {\n",
-      "            lower: float64, \n",
-      "            upper: float64, \n",
-      "            upper_rank: int64, \n",
-      "            upper_bin_decile: int32\n",
+      "        subsets: set, \n",
+      "        flags: set, \n",
+      "        freq: struct {\n",
+      "            all: struct {\n",
+      "                ac: int32, \n",
+      "                ac_raw: int32, \n",
+      "                an: int32, \n",
+      "                hemizygote_count: int32, \n",
+      "                homozygote_count: int64, \n",
+      "                ancestry_groups: array\n",
+      "            }, \n",
+      "            non_ukb: struct {\n",
+      "                ac: int32, \n",
+      "                ac_raw: int32, \n",
+      "                an: int32, \n",
+      "                hemizygote_count: int32, \n",
+      "                homozygote_count: int64, \n",
+      "                ancestry_groups: array\n",
+      "            }\n",
       "        }, \n",
-      "        z_raw: float64, \n",
-      "        z_score: float64\n",
-      "    } \n",
-      "    'mis': struct {\n",
-      "        obs: int64, \n",
-      "        exp: float64, \n",
-      "        possible: int64, \n",
-      "        oe: float64, \n",
-      "        mu: float64, \n",
-      "        oe_ci: struct {\n",
-      "            lower: float64, \n",
-      "            upper: float64\n",
+      "        fafmax: struct {\n",
+      "            gnomad: struct {\n",
+      "                faf95_max: float64, \n",
+      "                faf95_max_gen_anc: str, \n",
+      "                faf99_max: float64, \n",
+      "                faf99_max_gen_anc: str\n",
+      "            }, \n",
+      "            non_ukb: struct {\n",
+      "                faf95_max: float64, \n",
+      "                faf95_max_gen_anc: str, \n",
+      "                faf99_max: float64, \n",
+      "                faf99_max_gen_anc: str\n",
+      "            }\n",
       "        }, \n",
-      "        z_raw: float64, \n",
-      "        z_score: float64\n",
-      "    } \n",
-      "    'mis_pphen': struct {\n",
-      "        obs: int64, \n",
-      "        exp: float64, \n",
-      "        possible: int64, \n",
-      "        oe: float64\n",
-      "    } \n",
-      "    'syn': struct {\n",
-      "        obs: int64, \n",
-      "        exp: float64, \n",
-      "        possible: int64, \n",
-      "        oe: float64, \n",
-      "        mu: float64, \n",
-      "        oe_ci: struct {\n",
-      "            lower: float64, \n",
-      "            upper: float64\n",
+      "        age_distribution: struct {\n",
+      "            het: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            hom: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
       "        }, \n",
-      "        z_raw: float64, \n",
-      "        z_score: float64\n",
-      "    } \n",
-      "    'constraint_flags': set \n",
-      "    'level': str \n",
-      "    'transcript_type': str \n",
-      "    'chromosome': str \n",
-      "    'cds_length': int64 \n",
-      "    'num_coding_exons': int64 \n",
-      "----------------------------------------\n",
-      "Key: ['gene', 'gene_id', 'transcript', 'canonical', 'mane_select']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5b0e64c9-744c-4ed7-a832-9d68c548540b",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "262cbbf1-9fc7-4819-9b2a-d1a1e27c10bf",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
lof
mis
syn
lof_hc_lc
oe_ci
oe_ci
mis_pphen
oe_ci
gene
gene_id
transcript
canonical
mane_select
obs
exp
possible
oe
mu
pLI
pNull
pRec
obs
exp
possible
oe
mu
pLI
pNull
pRec
lower
upper
upper_rank
upper_bin_decile
z_raw
z_score
obs
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upper
z_raw
z_score
obs
exp
possible
oe
obs
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possible
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upper
z_raw
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constraint_flags
level
transcript_type
chromosome
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strstrstrboolboolint64float64int64float64float64float64float64float64int64float64int64float64float64float64float64float64float64float64int64int32float64float64int64float64int64float64float64float64float64float64float64int64float64int64float64int64float64int64float64float64float64float64float64float64set<str>strstrstrint64int64
"A1BG""1""NM_130786.4"TrueTrue454.30e+011931.05e+007.06e-071.63e-168.49e-011.51e-01454.30e+011931.05e+007.06e-071.77e-168.43e-011.57e-018.22e-011.34e+00NANA-2.98e-01-2.52e-017076.47e+0228701.09e+007.67e-061.03e+001.16e+00-2.36e+00-8.61e-012201.91e+028901.15e+003162.96e+029941.07e+003.02e-069.73e-011.17e+00-1.17e+00-6.35e-01{}NANANANANA
"A1BG""ENSG00000121410""ENST00000263100"TrueTrue454.30e+011931.05e+007.06e-071.63e-168.49e-011.51e-01454.30e+011931.05e+007.06e-071.77e-168.43e-011.57e-018.22e-011.34e+00140577-2.98e-01-2.52e-017076.47e+0228701.09e+007.67e-061.03e+001.16e+00-2.36e+00-8.61e-012201.91e+028901.15e+003162.96e+029941.07e+003.02e-069.73e-011.17e+00-1.17e+00-6.35e-01{}"2""protein_coding""chr19"14858
"A1BG""ENSG00000121410""ENST00000600966"FalseFalse242.63e+011239.14e-014.20e-072.53e-083.43e-016.57e-01222.50e+011198.80e-013.96e-071.84e-072.65e-017.35e-016.28e-011.26e+00NANA6.01e-015.09e-013994.06e+0218589.84e-014.91e-069.06e-011.07e+003.24e-011.18e-011031.10e+025129.40e-011661.79e+026379.28e-011.91e-068.17e-011.06e+009.64e-015.26e-01{}"1""protein_coding""chr19"9175
"A1CF""29974""NM_001198818.2"FalseFalse457.00e+013526.43e-019.55e-077.32e-102.06e-039.98e-01457.00e+013526.43e-019.55e-077.66e-101.96e-039.98e-015.06e-018.25e-01NANA2.98e+002.53e+006537.46e+0237208.76e-014.38e-068.21e-019.34e-013.39e+001.24e+002362.77e+0213998.53e-012722.73e+0211579.98e-012.01e-069.03e-011.10e+003.27e-021.78e-02{}NANANANANA
"A1CF""29974""NM_001198819.2"FalseFalse487.28e+013676.59e-011.02e-067.64e-112.53e-039.97e-01477.03e+013556.69e-019.72e-077.70e-113.58e-039.96e-015.28e-018.53e-01NANA2.78e+002.35e+006707.62e+0238378.80e-014.43e-068.25e-019.38e-013.33e+001.21e+003093.65e+0218418.47e-012712.76e+0211889.82e-011.91e-068.88e-011.09e+003.00e-011.64e-01{}NANANANANA
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+--------+-------------------+-------------------+-----------+-------------+\n",
-       "| gene   | gene_id           | transcript        | canonical | mane_select |\n",
-       "+--------+-------------------+-------------------+-----------+-------------+\n",
-       "| str    | str               | str               |      bool |        bool |\n",
-       "+--------+-------------------+-------------------+-----------+-------------+\n",
-       "| \"A1BG\" | \"1\"               | \"NM_130786.4\"     |      True |        True |\n",
-       "| \"A1BG\" | \"ENSG00000121410\" | \"ENST00000263100\" |      True |        True |\n",
-       "| \"A1BG\" | \"ENSG00000121410\" | \"ENST00000600966\" |     False |       False |\n",
-       "| \"A1CF\" | \"29974\"           | \"NM_001198818.2\"  |     False |       False |\n",
-       "| \"A1CF\" | \"29974\"           | \"NM_001198819.2\"  |     False |       False |\n",
-       "+--------+-------------------+-------------------+-----------+-------------+\n",
-       "\n",
-       "+---------------+---------------+--------------------+--------------+\n",
-       "| lof_hc_lc.obs | lof_hc_lc.exp | lof_hc_lc.possible | lof_hc_lc.oe |\n",
-       "+---------------+---------------+--------------------+--------------+\n",
-       "|         int64 |       float64 |              int64 |      float64 |\n",
-       "+---------------+---------------+--------------------+--------------+\n",
-       "|            45 |      4.30e+01 |                193 |     1.05e+00 |\n",
-       "|            45 |      4.30e+01 |                193 |     1.05e+00 |\n",
-       "|            24 |      2.63e+01 |                123 |     9.14e-01 |\n",
-       "|            45 |      7.00e+01 |                352 |     6.43e-01 |\n",
-       "|            48 |      7.28e+01 |                367 |     6.59e-01 |\n",
-       "+---------------+---------------+--------------------+--------------+\n",
-       "\n",
-       "+--------------+---------------+-----------------+----------------+---------+\n",
-       "| lof_hc_lc.mu | lof_hc_lc.pLI | lof_hc_lc.pNull | lof_hc_lc.pRec | lof.obs |\n",
-       "+--------------+---------------+-----------------+----------------+---------+\n",
-       "|      float64 |       float64 |         float64 |        float64 |   int64 |\n",
-       "+--------------+---------------+-----------------+----------------+---------+\n",
-       "|     7.06e-07 |      1.63e-16 |        8.49e-01 |       1.51e-01 |      45 |\n",
-       "|     7.06e-07 |      1.63e-16 |        8.49e-01 |       1.51e-01 |      45 |\n",
-       "|     4.20e-07 |      2.53e-08 |        3.43e-01 |       6.57e-01 |      22 |\n",
-       "|     9.55e-07 |      7.32e-10 |        2.06e-03 |       9.98e-01 |      45 |\n",
-       "|     1.02e-06 |      7.64e-11 |        2.53e-03 |       9.97e-01 |      47 |\n",
-       "+--------------+---------------+-----------------+----------------+---------+\n",
-       "\n",
-       "+----------+--------------+----------+----------+----------+-----------+\n",
-       "|  lof.exp | lof.possible |   lof.oe |   lof.mu |  lof.pLI | lof.pNull |\n",
-       "+----------+--------------+----------+----------+----------+-----------+\n",
-       "|  float64 |        int64 |  float64 |  float64 |  float64 |   float64 |\n",
-       "+----------+--------------+----------+----------+----------+-----------+\n",
-       "| 4.30e+01 |          193 | 1.05e+00 | 7.06e-07 | 1.77e-16 |  8.43e-01 |\n",
-       "| 4.30e+01 |          193 | 1.05e+00 | 7.06e-07 | 1.77e-16 |  8.43e-01 |\n",
-       "| 2.50e+01 |          119 | 8.80e-01 | 3.96e-07 | 1.84e-07 |  2.65e-01 |\n",
-       "| 7.00e+01 |          352 | 6.43e-01 | 9.55e-07 | 7.66e-10 |  1.96e-03 |\n",
-       "| 7.03e+01 |          355 | 6.69e-01 | 9.72e-07 | 7.70e-11 |  3.58e-03 |\n",
-       "+----------+--------------+----------+----------+----------+-----------+\n",
-       "\n",
-       "+----------+-----------------+-----------------+----------------------+\n",
-       "| lof.pRec | lof.oe_ci.lower | lof.oe_ci.upper | lof.oe_ci.upper_rank |\n",
-       "+----------+-----------------+-----------------+----------------------+\n",
-       "|  float64 |         float64 |         float64 |                int64 |\n",
-       "+----------+-----------------+-----------------+----------------------+\n",
-       "| 1.57e-01 |        8.22e-01 |        1.34e+00 |                   NA |\n",
-       "| 1.57e-01 |        8.22e-01 |        1.34e+00 |                14057 |\n",
-       "| 7.35e-01 |        6.28e-01 |        1.26e+00 |                   NA |\n",
-       "| 9.98e-01 |        5.06e-01 |        8.25e-01 |                   NA |\n",
-       "| 9.96e-01 |        5.28e-01 |        8.53e-01 |                   NA |\n",
-       "+----------+-----------------+-----------------+----------------------+\n",
-       "\n",
-       "+----------------------------+-----------+-------------+---------+----------+\n",
-       "| lof.oe_ci.upper_bin_decile | lof.z_raw | lof.z_score | mis.obs |  mis.exp |\n",
-       "+----------------------------+-----------+-------------+---------+----------+\n",
-       "|                      int32 |   float64 |     float64 |   int64 |  float64 |\n",
-       "+----------------------------+-----------+-------------+---------+----------+\n",
-       "|                         NA | -2.98e-01 |   -2.52e-01 |     707 | 6.47e+02 |\n",
-       "|                          7 | -2.98e-01 |   -2.52e-01 |     707 | 6.47e+02 |\n",
-       "|                         NA |  6.01e-01 |    5.09e-01 |     399 | 4.06e+02 |\n",
-       "|                         NA |  2.98e+00 |    2.53e+00 |     653 | 7.46e+02 |\n",
-       "|                         NA |  2.78e+00 |    2.35e+00 |     670 | 7.62e+02 |\n",
-       "+----------------------------+-----------+-------------+---------+----------+\n",
-       "\n",
-       "+--------------+----------+----------+-----------------+-----------------+\n",
-       "| mis.possible |   mis.oe |   mis.mu | mis.oe_ci.lower | mis.oe_ci.upper |\n",
-       "+--------------+----------+----------+-----------------+-----------------+\n",
-       "|        int64 |  float64 |  float64 |         float64 |         float64 |\n",
-       "+--------------+----------+----------+-----------------+-----------------+\n",
-       "|         2870 | 1.09e+00 | 7.67e-06 |        1.03e+00 |        1.16e+00 |\n",
-       "|         2870 | 1.09e+00 | 7.67e-06 |        1.03e+00 |        1.16e+00 |\n",
-       "|         1858 | 9.84e-01 | 4.91e-06 |        9.06e-01 |        1.07e+00 |\n",
-       "|         3720 | 8.76e-01 | 4.38e-06 |        8.21e-01 |        9.34e-01 |\n",
-       "|         3837 | 8.80e-01 | 4.43e-06 |        8.25e-01 |        9.38e-01 |\n",
-       "+--------------+----------+----------+-----------------+-----------------+\n",
-       "\n",
-       "+-----------+-------------+---------------+---------------+--------------------+\n",
-       "| mis.z_raw | mis.z_score | mis_pphen.obs | mis_pphen.exp | mis_pphen.possible |\n",
-       "+-----------+-------------+---------------+---------------+--------------------+\n",
-       "|   float64 |     float64 |         int64 |       float64 |              int64 |\n",
-       "+-----------+-------------+---------------+---------------+--------------------+\n",
-       "| -2.36e+00 |   -8.61e-01 |           220 |      1.91e+02 |                890 |\n",
-       "| -2.36e+00 |   -8.61e-01 |           220 |      1.91e+02 |                890 |\n",
-       "|  3.24e-01 |    1.18e-01 |           103 |      1.10e+02 |                512 |\n",
-       "|  3.39e+00 |    1.24e+00 |           236 |      2.77e+02 |               1399 |\n",
-       "|  3.33e+00 |    1.21e+00 |           309 |      3.65e+02 |               1841 |\n",
-       "+-----------+-------------+---------------+---------------+--------------------+\n",
-       "\n",
-       "+--------------+---------+----------+--------------+----------+----------+\n",
-       "| mis_pphen.oe | syn.obs |  syn.exp | syn.possible |   syn.oe |   syn.mu |\n",
-       "+--------------+---------+----------+--------------+----------+----------+\n",
-       "|      float64 |   int64 |  float64 |        int64 |  float64 |  float64 |\n",
-       "+--------------+---------+----------+--------------+----------+----------+\n",
-       "|     1.15e+00 |     316 | 2.96e+02 |          994 | 1.07e+00 | 3.02e-06 |\n",
-       "|     1.15e+00 |     316 | 2.96e+02 |          994 | 1.07e+00 | 3.02e-06 |\n",
-       "|     9.40e-01 |     166 | 1.79e+02 |          637 | 9.28e-01 | 1.91e-06 |\n",
-       "|     8.53e-01 |     272 | 2.73e+02 |         1157 | 9.98e-01 | 2.01e-06 |\n",
-       "|     8.47e-01 |     271 | 2.76e+02 |         1188 | 9.82e-01 | 1.91e-06 |\n",
-       "+--------------+---------+----------+--------------+----------+----------+\n",
-       "\n",
-       "+-----------------+-----------------+-----------+-------------+\n",
-       "| syn.oe_ci.lower | syn.oe_ci.upper | syn.z_raw | syn.z_score |\n",
-       "+-----------------+-----------------+-----------+-------------+\n",
-       "|         float64 |         float64 |   float64 |     float64 |\n",
-       "+-----------------+-----------------+-----------+-------------+\n",
-       "|        9.73e-01 |        1.17e+00 | -1.17e+00 |   -6.35e-01 |\n",
-       "|        9.73e-01 |        1.17e+00 | -1.17e+00 |   -6.35e-01 |\n",
-       "|        8.17e-01 |        1.06e+00 |  9.64e-01 |    5.26e-01 |\n",
-       "|        9.03e-01 |        1.10e+00 |  3.27e-02 |    1.78e-02 |\n",
-       "|        8.88e-01 |        1.09e+00 |  3.00e-01 |    1.64e-01 |\n",
-       "+-----------------+-----------------+-----------+-------------+\n",
-       "\n",
-       "+------------------+-------+------------------+------------+------------+\n",
-       "| constraint_flags | level | transcript_type  | chromosome | cds_length |\n",
-       "+------------------+-------+------------------+------------+------------+\n",
-       "| set         | str   | str              | str        |      int64 |\n",
-       "+------------------+-------+------------------+------------+------------+\n",
-       "| {}               | NA    | NA               | NA         |         NA |\n",
-       "| {}               | \"2\"   | \"protein_coding\" | \"chr19\"    |       1485 |\n",
-       "| {}               | \"1\"   | \"protein_coding\" | \"chr19\"    |        917 |\n",
-       "| {}               | NA    | NA               | NA         |         NA |\n",
-       "| {}               | NA    | NA               | NA         |         NA |\n",
-       "+------------------+-------+------------------+------------+------------+\n",
-       "\n",
-       "+------------------+\n",
-       "| num_coding_exons |\n",
-       "+------------------+\n",
-       "|            int64 |\n",
-       "+------------------+\n",
-       "|               NA |\n",
-       "|                8 |\n",
-       "|                5 |\n",
-       "|               NA |\n",
-       "|               NA |\n",
-       "+------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72ce8dac-994f-42ae-a6a0-948f5517e427",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Proportion expressed across transcripts (pext) score\n",
-    "\n",
-    "For more information about the pext score see the [Help](https://gnomad.broadinstitute.org/help/pext) page."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea178a83-1bb6-4f9b-8334-0ca1f6096021",
-   "metadata": {},
-   "source": [
-    "### Base level pext\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eeb8bb32-d258-4b3a-ab5e-edc76502fa5b",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "1f448e05-7f99-4c63-b101-b33143d9d58b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(version='v10', data_type=\"base_level\", dataset=\"pext\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0773d5d3-21f1-4892-b16b-dc31eb2a7bf5",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "b9f99311-91e6-454e-b940-3d036bddf3a5",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'tissues': array \n",
-      "    'exp_prop_mean_tissues': array \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'gene_id': str \n",
-      "    'gene_symbol': str \n",
-      "    'exp_prop_mean': float64 \n",
-      "    'Adipose_Subcutaneous': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Adipose_Visceral_Omentum': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'AdrenalGland': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Artery_Aorta': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Artery_Coronary': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Artery_Tibial': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Bladder': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Amygdala': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Anteriorcingulatecortex_BA24': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Caudate_basalganglia': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_CerebellarHemisphere': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Cerebellum': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Cortex': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_FrontalCortex_BA9': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Hippocampus': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Hypothalamus': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Nucleusaccumbens_basalganglia': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Putamen_basalganglia': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Spinalcord_cervicalc_1': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Substantianigra': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Breast_MammaryTissue': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Cells_Culturedfibroblasts': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Cells_EBV_transformedlymphocytes': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Colon_Sigmoid': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Colon_Transverse': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Esophagus_GastroesophagealJunction': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Esophagus_Mucosa': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Esophagus_Muscularis': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Heart_AtrialAppendage': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Heart_LeftVentricle': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Kidney_Cortex': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Liver': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Lung': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'MinorSalivaryGland': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Muscle_Skeletal': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Nerve_Tibial': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Ovary': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Pancreas': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Pituitary': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Prostate': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Skin_NotSunExposed_Suprapubic': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Skin_SunExposed_Lowerleg': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'SmallIntestine_TerminalIleum': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Spleen': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Stomach': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Testis': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Thyroid': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Uterus': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Vagina': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'WholeBlood': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "----------------------------------------\n",
-      "Key: ['locus']\n",
-      "----------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "ht.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a39f9dca-5db4-4ca3-8eac-e6c76db7c7bc",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "a6ac695f-cfb2-4aa7-877c-b204351a77da",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:20.949958Z",
-     "start_time": "2024-12-06T20:09:05.622171Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
Adipose_Subcutaneous
Adipose_Visceral_Omentum
AdrenalGland
Artery_Aorta
Artery_Coronary
Artery_Tibial
Bladder
Brain_Amygdala
Brain_Anteriorcingulatecortex_BA24
Brain_Caudate_basalganglia
Brain_CerebellarHemisphere
Brain_Cerebellum
Brain_Cortex
Brain_FrontalCortex_BA9
Brain_Hippocampus
Brain_Hypothalamus
Brain_Nucleusaccumbens_basalganglia
Brain_Putamen_basalganglia
Brain_Spinalcord_cervicalc_1
Brain_Substantianigra
Breast_MammaryTissue
Cells_Culturedfibroblasts
Cells_EBV_transformedlymphocytes
Colon_Sigmoid
Colon_Transverse
Esophagus_GastroesophagealJunction
Esophagus_Mucosa
Esophagus_Muscularis
Heart_AtrialAppendage
Heart_LeftVentricle
Kidney_Cortex
Liver
Lung
MinorSalivaryGland
Muscle_Skeletal
Nerve_Tibial
Ovary
Pancreas
Pituitary
Prostate
Skin_NotSunExposed_Suprapubic
Skin_SunExposed_Lowerleg
SmallIntestine_TerminalIleum
Spleen
Stomach
Testis
Thyroid
Uterus
Vagina
WholeBlood
locus
gene_id
gene_symbol
exp_prop_mean
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
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expression_proportion
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locus<GRCh38>strstrfloat64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64
chr1:65565"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65566"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65567"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65568"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65569"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+---------------+-------------------+-------------+---------------+\n",
-       "| locus         | gene_id           | gene_symbol | exp_prop_mean |\n",
-       "+---------------+-------------------+-------------+---------------+\n",
-       "| locus | str               | str         |       float64 |\n",
-       "+---------------+-------------------+-------------+---------------+\n",
-       "| chr1:65565    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
-       "| chr1:65566    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
-       "| chr1:65567    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
-       "| chr1:65568    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
-       "| chr1:65569    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
-       "+---------------+-------------------+-------------+---------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| Adipose_Subcutaneous.transcript_expression |\n",
-       "+--------------------------------------------+\n",
-       "|                                    float64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| Adipose_Subcutaneous.expression_proportion |\n",
-       "+--------------------------------------------+\n",
-       "|                                    float64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| Adipose_Visceral_Omentum.transcript_expression |\n",
-       "+------------------------------------------------+\n",
-       "|                                        float64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| Adipose_Visceral_Omentum.expression_proportion |\n",
-       "+------------------------------------------------+\n",
-       "|                                        float64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------+------------------------------------+\n",
-       "| AdrenalGland.transcript_expression | AdrenalGland.expression_proportion |\n",
-       "+------------------------------------+------------------------------------+\n",
-       "|                            float64 |                            float64 |\n",
-       "+------------------------------------+------------------------------------+\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "+------------------------------------+------------------------------------+\n",
-       "\n",
-       "+------------------------------------+------------------------------------+\n",
-       "| Artery_Aorta.transcript_expression | Artery_Aorta.expression_proportion |\n",
-       "+------------------------------------+------------------------------------+\n",
-       "|                            float64 |                            float64 |\n",
-       "+------------------------------------+------------------------------------+\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "+------------------------------------+------------------------------------+\n",
-       "\n",
-       "+---------------------------------------+\n",
-       "| Artery_Coronary.transcript_expression |\n",
-       "+---------------------------------------+\n",
-       "|                               float64 |\n",
-       "+---------------------------------------+\n",
-       "|                              0.00e+00 |\n",
-       "|                              0.00e+00 |\n",
-       "|                              0.00e+00 |\n",
-       "|                              0.00e+00 |\n",
-       "|                              0.00e+00 |\n",
-       "+---------------------------------------+\n",
-       "\n",
-       "+---------------------------------------+-------------------------------------+\n",
-       "| Artery_Coronary.expression_proportion | Artery_Tibial.transcript_expression |\n",
-       "+---------------------------------------+-------------------------------------+\n",
-       "|                               float64 |                             float64 |\n",
-       "+---------------------------------------+-------------------------------------+\n",
-       "|                              0.00e+00 |                            0.00e+00 |\n",
-       "|                              0.00e+00 |                            0.00e+00 |\n",
-       "|                              0.00e+00 |                            0.00e+00 |\n",
-       "|                              0.00e+00 |                            0.00e+00 |\n",
-       "|                              0.00e+00 |                            0.00e+00 |\n",
-       "+---------------------------------------+-------------------------------------+\n",
-       "\n",
-       "+-------------------------------------+-------------------------------+\n",
-       "| Artery_Tibial.expression_proportion | Bladder.transcript_expression |\n",
-       "+-------------------------------------+-------------------------------+\n",
-       "|                             float64 |                       float64 |\n",
-       "+-------------------------------------+-------------------------------+\n",
-       "|                            0.00e+00 |                      0.00e+00 |\n",
-       "|                            0.00e+00 |                      0.00e+00 |\n",
-       "|                            0.00e+00 |                      0.00e+00 |\n",
-       "|                            0.00e+00 |                      0.00e+00 |\n",
-       "|                            0.00e+00 |                      0.00e+00 |\n",
-       "+-------------------------------------+-------------------------------+\n",
-       "\n",
-       "+-------------------------------+--------------------------------------+\n",
-       "| Bladder.expression_proportion | Brain_Amygdala.transcript_expression |\n",
-       "+-------------------------------+--------------------------------------+\n",
-       "|                       float64 |                              float64 |\n",
-       "+-------------------------------+--------------------------------------+\n",
-       "|                      0.00e+00 |                             0.00e+00 |\n",
-       "|                      0.00e+00 |                             0.00e+00 |\n",
-       "|                      0.00e+00 |                             0.00e+00 |\n",
-       "|                      0.00e+00 |                             0.00e+00 |\n",
-       "|                      0.00e+00 |                             0.00e+00 |\n",
-       "+-------------------------------+--------------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| Brain_Amygdala.expression_proportion |\n",
-       "+--------------------------------------+\n",
-       "|                              float64 |\n",
-       "+--------------------------------------+\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------------+\n",
-       "| Brain_Anteriorcingulatecortex_BA24.transcript_expression |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                  float64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "+----------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------------+\n",
-       "| Brain_Anteriorcingulatecortex_BA24.expression_proportion |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                  float64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "+----------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| Brain_Caudate_basalganglia.transcript_expression |\n",
-       "+--------------------------------------------------+\n",
-       "|                                          float64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| Brain_Caudate_basalganglia.expression_proportion |\n",
-       "+--------------------------------------------------+\n",
-       "|                                          float64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| Brain_CerebellarHemisphere.transcript_expression |\n",
-       "+--------------------------------------------------+\n",
-       "|                                          float64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| Brain_CerebellarHemisphere.expression_proportion |\n",
-       "+--------------------------------------------------+\n",
-       "|                                          float64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------+\n",
-       "| Brain_Cerebellum.transcript_expression |\n",
-       "+----------------------------------------+\n",
-       "|                                float64 |\n",
-       "+----------------------------------------+\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "+----------------------------------------+\n",
-       "\n",
-       "+----------------------------------------+------------------------------------+\n",
-       "| Brain_Cerebellum.expression_proportion | Brain_Cortex.transcript_expression |\n",
-       "+----------------------------------------+------------------------------------+\n",
-       "|                                float64 |                            float64 |\n",
-       "+----------------------------------------+------------------------------------+\n",
-       "|                               0.00e+00 |                           0.00e+00 |\n",
-       "|                               0.00e+00 |                           0.00e+00 |\n",
-       "|                               0.00e+00 |                           0.00e+00 |\n",
-       "|                               0.00e+00 |                           0.00e+00 |\n",
-       "|                               0.00e+00 |                           0.00e+00 |\n",
-       "+----------------------------------------+------------------------------------+\n",
-       "\n",
-       "+------------------------------------+\n",
-       "| Brain_Cortex.expression_proportion |\n",
-       "+------------------------------------+\n",
-       "|                            float64 |\n",
-       "+------------------------------------+\n",
-       "|                           0.00e+00 |\n",
-       "|                           0.00e+00 |\n",
-       "|                           0.00e+00 |\n",
-       "|                           0.00e+00 |\n",
-       "|                           0.00e+00 |\n",
-       "+------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------+\n",
-       "| Brain_FrontalCortex_BA9.transcript_expression |\n",
-       "+-----------------------------------------------+\n",
-       "|                                       float64 |\n",
-       "+-----------------------------------------------+\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "+-----------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------+\n",
-       "| Brain_FrontalCortex_BA9.expression_proportion |\n",
-       "+-----------------------------------------------+\n",
-       "|                                       float64 |\n",
-       "+-----------------------------------------------+\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "+-----------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------+\n",
-       "| Brain_Hippocampus.transcript_expression |\n",
-       "+-----------------------------------------+\n",
-       "|                                 float64 |\n",
-       "+-----------------------------------------+\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "+-----------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------+\n",
-       "| Brain_Hippocampus.expression_proportion |\n",
-       "+-----------------------------------------+\n",
-       "|                                 float64 |\n",
-       "+-----------------------------------------+\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "+-----------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+\n",
-       "| Brain_Hypothalamus.transcript_expression |\n",
-       "+------------------------------------------+\n",
-       "|                                  float64 |\n",
-       "+------------------------------------------+\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "+------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+\n",
-       "| Brain_Hypothalamus.expression_proportion |\n",
-       "+------------------------------------------+\n",
-       "|                                  float64 |\n",
-       "+------------------------------------------+\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "+------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------------+\n",
-       "| Brain_Nucleusaccumbens_basalganglia.transcript_expression |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                   float64 |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "+-----------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------------+\n",
-       "| Brain_Nucleusaccumbens_basalganglia.expression_proportion |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                   float64 |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "+-----------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| Brain_Putamen_basalganglia.transcript_expression |\n",
-       "+--------------------------------------------------+\n",
-       "|                                          float64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| Brain_Putamen_basalganglia.expression_proportion |\n",
-       "+--------------------------------------------------+\n",
-       "|                                          float64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| Brain_Spinalcord_cervicalc_1.transcript_expression |\n",
-       "+----------------------------------------------------+\n",
-       "|                                            float64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| Brain_Spinalcord_cervicalc_1.expression_proportion |\n",
-       "+----------------------------------------------------+\n",
-       "|                                            float64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| Brain_Substantianigra.transcript_expression |\n",
-       "+---------------------------------------------+\n",
-       "|                                     float64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| Brain_Substantianigra.expression_proportion |\n",
-       "+---------------------------------------------+\n",
-       "|                                     float64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| Breast_MammaryTissue.transcript_expression |\n",
-       "+--------------------------------------------+\n",
-       "|                                    float64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| Breast_MammaryTissue.expression_proportion |\n",
-       "+--------------------------------------------+\n",
-       "|                                    float64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| Cells_Culturedfibroblasts.transcript_expression |\n",
-       "+-------------------------------------------------+\n",
-       "|                                         float64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| Cells_Culturedfibroblasts.expression_proportion |\n",
-       "+-------------------------------------------------+\n",
-       "|                                         float64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| Cells_EBV_transformedlymphocytes.transcript_expression |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                float64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| Cells_EBV_transformedlymphocytes.expression_proportion |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                float64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------+-------------------------------------+\n",
-       "| Colon_Sigmoid.transcript_expression | Colon_Sigmoid.expression_proportion |\n",
-       "+-------------------------------------+-------------------------------------+\n",
-       "|                             float64 |                             float64 |\n",
-       "+-------------------------------------+-------------------------------------+\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "+-------------------------------------+-------------------------------------+\n",
-       "\n",
-       "+----------------------------------------+\n",
-       "| Colon_Transverse.transcript_expression |\n",
-       "+----------------------------------------+\n",
-       "|                                float64 |\n",
-       "+----------------------------------------+\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "+----------------------------------------+\n",
-       "\n",
-       "+----------------------------------------+\n",
-       "| Colon_Transverse.expression_proportion |\n",
-       "+----------------------------------------+\n",
-       "|                                float64 |\n",
-       "+----------------------------------------+\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "+----------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------------+\n",
-       "| Esophagus_GastroesophagealJunction.transcript_expression |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                  float64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "+----------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------------+\n",
-       "| Esophagus_GastroesophagealJunction.expression_proportion |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                  float64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "+----------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------+\n",
-       "| Esophagus_Mucosa.transcript_expression |\n",
-       "+----------------------------------------+\n",
-       "|                                float64 |\n",
-       "+----------------------------------------+\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "+----------------------------------------+\n",
-       "\n",
-       "+----------------------------------------+\n",
-       "| Esophagus_Mucosa.expression_proportion |\n",
-       "+----------------------------------------+\n",
-       "|                                float64 |\n",
-       "+----------------------------------------+\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "+----------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| Esophagus_Muscularis.transcript_expression |\n",
-       "+--------------------------------------------+\n",
-       "|                                    float64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| Esophagus_Muscularis.expression_proportion |\n",
-       "+--------------------------------------------+\n",
-       "|                                    float64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| Heart_AtrialAppendage.transcript_expression |\n",
-       "+---------------------------------------------+\n",
-       "|                                     float64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| Heart_AtrialAppendage.expression_proportion |\n",
-       "+---------------------------------------------+\n",
-       "|                                     float64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------+\n",
-       "| Heart_LeftVentricle.transcript_expression |\n",
-       "+-------------------------------------------+\n",
-       "|                                   float64 |\n",
-       "+-------------------------------------------+\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "+-------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------+\n",
-       "| Heart_LeftVentricle.expression_proportion |\n",
-       "+-------------------------------------------+\n",
-       "|                                   float64 |\n",
-       "+-------------------------------------------+\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "+-------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------+-------------------------------------+\n",
-       "| Kidney_Cortex.transcript_expression | Kidney_Cortex.expression_proportion |\n",
-       "+-------------------------------------+-------------------------------------+\n",
-       "|                             float64 |                             float64 |\n",
-       "+-------------------------------------+-------------------------------------+\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "+-------------------------------------+-------------------------------------+\n",
-       "\n",
-       "+-----------------------------+-----------------------------+\n",
-       "| Liver.transcript_expression | Liver.expression_proportion |\n",
-       "+-----------------------------+-----------------------------+\n",
-       "|                     float64 |                     float64 |\n",
-       "+-----------------------------+-----------------------------+\n",
-       "|                    0.00e+00 |                    0.00e+00 |\n",
-       "|                    0.00e+00 |                    0.00e+00 |\n",
-       "|                    0.00e+00 |                    0.00e+00 |\n",
-       "|                    0.00e+00 |                    0.00e+00 |\n",
-       "|                    0.00e+00 |                    0.00e+00 |\n",
-       "+-----------------------------+-----------------------------+\n",
-       "\n",
-       "+----------------------------+----------------------------+\n",
-       "| Lung.transcript_expression | Lung.expression_proportion |\n",
-       "+----------------------------+----------------------------+\n",
-       "|                    float64 |                    float64 |\n",
-       "+----------------------------+----------------------------+\n",
-       "|                   0.00e+00 |                   0.00e+00 |\n",
-       "|                   0.00e+00 |                   0.00e+00 |\n",
-       "|                   0.00e+00 |                   0.00e+00 |\n",
-       "|                   0.00e+00 |                   0.00e+00 |\n",
-       "|                   0.00e+00 |                   0.00e+00 |\n",
-       "+----------------------------+----------------------------+\n",
-       "\n",
-       "+------------------------------------------+\n",
-       "| MinorSalivaryGland.transcript_expression |\n",
-       "+------------------------------------------+\n",
-       "|                                  float64 |\n",
-       "+------------------------------------------+\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "+------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+\n",
-       "| MinorSalivaryGland.expression_proportion |\n",
-       "+------------------------------------------+\n",
-       "|                                  float64 |\n",
-       "+------------------------------------------+\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "+------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------+\n",
-       "| Muscle_Skeletal.transcript_expression |\n",
-       "+---------------------------------------+\n",
-       "|                               float64 |\n",
-       "+---------------------------------------+\n",
-       "|                              0.00e+00 |\n",
-       "|                              0.00e+00 |\n",
-       "|                              0.00e+00 |\n",
-       "|                              0.00e+00 |\n",
-       "|                              0.00e+00 |\n",
-       "+---------------------------------------+\n",
-       "\n",
-       "+---------------------------------------+------------------------------------+\n",
-       "| Muscle_Skeletal.expression_proportion | Nerve_Tibial.transcript_expression |\n",
-       "+---------------------------------------+------------------------------------+\n",
-       "|                               float64 |                            float64 |\n",
-       "+---------------------------------------+------------------------------------+\n",
-       "|                              0.00e+00 |                           0.00e+00 |\n",
-       "|                              0.00e+00 |                           0.00e+00 |\n",
-       "|                              0.00e+00 |                           0.00e+00 |\n",
-       "|                              0.00e+00 |                           0.00e+00 |\n",
-       "|                              0.00e+00 |                           0.00e+00 |\n",
-       "+---------------------------------------+------------------------------------+\n",
-       "\n",
-       "+------------------------------------+-----------------------------+\n",
-       "| Nerve_Tibial.expression_proportion | Ovary.transcript_expression |\n",
-       "+------------------------------------+-----------------------------+\n",
-       "|                            float64 |                     float64 |\n",
-       "+------------------------------------+-----------------------------+\n",
-       "|                           0.00e+00 |                    0.00e+00 |\n",
-       "|                           0.00e+00 |                    0.00e+00 |\n",
-       "|                           0.00e+00 |                    0.00e+00 |\n",
-       "|                           0.00e+00 |                    0.00e+00 |\n",
-       "|                           0.00e+00 |                    0.00e+00 |\n",
-       "+------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-----------------------------+--------------------------------+\n",
-       "| Ovary.expression_proportion | Pancreas.transcript_expression |\n",
-       "+-----------------------------+--------------------------------+\n",
-       "|                     float64 |                        float64 |\n",
-       "+-----------------------------+--------------------------------+\n",
-       "|                    0.00e+00 |                       0.00e+00 |\n",
-       "|                    0.00e+00 |                       0.00e+00 |\n",
-       "|                    0.00e+00 |                       0.00e+00 |\n",
-       "|                    0.00e+00 |                       0.00e+00 |\n",
-       "|                    0.00e+00 |                       0.00e+00 |\n",
-       "+-----------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------+---------------------------------+\n",
-       "| Pancreas.expression_proportion | Pituitary.transcript_expression |\n",
-       "+--------------------------------+---------------------------------+\n",
-       "|                        float64 |                         float64 |\n",
-       "+--------------------------------+---------------------------------+\n",
-       "|                       0.00e+00 |                        0.00e+00 |\n",
-       "|                       0.00e+00 |                        0.00e+00 |\n",
-       "|                       0.00e+00 |                        0.00e+00 |\n",
-       "|                       0.00e+00 |                        0.00e+00 |\n",
-       "|                       0.00e+00 |                        0.00e+00 |\n",
-       "+--------------------------------+---------------------------------+\n",
-       "\n",
-       "+---------------------------------+--------------------------------+\n",
-       "| Pituitary.expression_proportion | Prostate.transcript_expression |\n",
-       "+---------------------------------+--------------------------------+\n",
-       "|                         float64 |                        float64 |\n",
-       "+---------------------------------+--------------------------------+\n",
-       "|                        0.00e+00 |                       0.00e+00 |\n",
-       "|                        0.00e+00 |                       0.00e+00 |\n",
-       "|                        0.00e+00 |                       0.00e+00 |\n",
-       "|                        0.00e+00 |                       0.00e+00 |\n",
-       "|                        0.00e+00 |                       0.00e+00 |\n",
-       "+---------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------+\n",
-       "| Prostate.expression_proportion |\n",
-       "+--------------------------------+\n",
-       "|                        float64 |\n",
-       "+--------------------------------+\n",
-       "|                       0.00e+00 |\n",
-       "|                       0.00e+00 |\n",
-       "|                       0.00e+00 |\n",
-       "|                       0.00e+00 |\n",
-       "|                       0.00e+00 |\n",
-       "+--------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| Skin_NotSunExposed_Suprapubic.transcript_expression |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                             float64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| Skin_NotSunExposed_Suprapubic.expression_proportion |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                             float64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| Skin_SunExposed_Lowerleg.transcript_expression |\n",
-       "+------------------------------------------------+\n",
-       "|                                        float64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| Skin_SunExposed_Lowerleg.expression_proportion |\n",
-       "+------------------------------------------------+\n",
-       "|                                        float64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| SmallIntestine_TerminalIleum.transcript_expression |\n",
-       "+----------------------------------------------------+\n",
-       "|                                            float64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------+\n",
-       "| SmallIntestine_TerminalIleum.expression_proportion |\n",
-       "+----------------------------------------------------+\n",
-       "|                                            float64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "+----------------------------------------------------+\n",
-       "\n",
-       "+------------------------------+------------------------------+\n",
-       "| Spleen.transcript_expression | Spleen.expression_proportion |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                      float64 |                      float64 |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "+------------------------------+------------------------------+\n",
-       "\n",
-       "+-------------------------------+-------------------------------+\n",
-       "| Stomach.transcript_expression | Stomach.expression_proportion |\n",
-       "+-------------------------------+-------------------------------+\n",
-       "|                       float64 |                       float64 |\n",
-       "+-------------------------------+-------------------------------+\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "+-------------------------------+-------------------------------+\n",
-       "\n",
-       "+------------------------------+------------------------------+\n",
-       "| Testis.transcript_expression | Testis.expression_proportion |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                      float64 |                      float64 |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "+------------------------------+------------------------------+\n",
-       "\n",
-       "+-------------------------------+-------------------------------+\n",
-       "| Thyroid.transcript_expression | Thyroid.expression_proportion |\n",
-       "+-------------------------------+-------------------------------+\n",
-       "|                       float64 |                       float64 |\n",
-       "+-------------------------------+-------------------------------+\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "+-------------------------------+-------------------------------+\n",
-       "\n",
-       "+------------------------------+------------------------------+\n",
-       "| Uterus.transcript_expression | Uterus.expression_proportion |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                      float64 |                      float64 |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "+------------------------------+------------------------------+\n",
-       "\n",
-       "+------------------------------+------------------------------+\n",
-       "| Vagina.transcript_expression | Vagina.expression_proportion |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                      float64 |                      float64 |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "+------------------------------+------------------------------+\n",
-       "\n",
-       "+----------------------------------+----------------------------------+\n",
-       "| WholeBlood.transcript_expression | WholeBlood.expression_proportion |\n",
-       "+----------------------------------+----------------------------------+\n",
-       "|                          float64 |                          float64 |\n",
-       "+----------------------------------+----------------------------------+\n",
-       "|                         0.00e+00 |                         0.00e+00 |\n",
-       "|                         0.00e+00 |                         0.00e+00 |\n",
-       "|                         0.00e+00 |                         0.00e+00 |\n",
-       "|                         0.00e+00 |                         0.00e+00 |\n",
-       "|                         0.00e+00 |                         0.00e+00 |\n",
-       "+----------------------------------+----------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ht.show(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "32528515-1021-40e4-8f4c-3ca06792e0be",
-   "metadata": {},
-   "source": [
-    "### Annotation level pext\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "afac2ca3-f3dc-4174-aa98-d9eb70bf343f",
-   "metadata": {},
-   "source": [
-    "#### Load the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "ce25b9e0-9118-4bf6-ba3c-eca75468b9f1",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:08:59.197890Z",
-     "start_time": "2024-12-06T20:08:50.859108Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ht = get_gnomad_release(version='v10', data_type=\"annotation_level\", dataset=\"pext\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c9265ccc-0048-40f0-8f1c-d62e0d3f98ab",
-   "metadata": {},
-   "source": [
-    "#### Print the schema of the Hail Table\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "818f3fc2-09fa-479c-91e1-e19f5991a22d",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T20:09:00.032524Z",
-     "start_time": "2024-12-06T20:09:00.029271Z"
-    },
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------------------------------------\n",
-      "Global fields:\n",
-      "    'tissues': array \n",
-      "    'exp_prop_mean_tissues': array \n",
-      "----------------------------------------\n",
-      "Row fields:\n",
-      "    'locus': locus \n",
-      "    'gene_id': str \n",
-      "    'alleles': array \n",
-      "    'gene_symbol': str \n",
-      "    'most_severe_consequence': str \n",
-      "    'lof': str \n",
-      "    'lof_flags': str \n",
-      "    'exp_prop_mean': float64 \n",
-      "    'Adipose_Subcutaneous': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Adipose_Visceral_Omentum': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'AdrenalGland': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Artery_Aorta': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Artery_Coronary': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Artery_Tibial': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Bladder': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Amygdala': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Anteriorcingulatecortex_BA24': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Caudate_basalganglia': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_CerebellarHemisphere': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Cerebellum': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Cortex': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_FrontalCortex_BA9': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Hippocampus': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Hypothalamus': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Nucleusaccumbens_basalganglia': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Putamen_basalganglia': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Spinalcord_cervicalc_1': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Brain_Substantianigra': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Breast_MammaryTissue': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Cells_Culturedfibroblasts': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Cells_EBV_transformedlymphocytes': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Colon_Sigmoid': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Colon_Transverse': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Esophagus_GastroesophagealJunction': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Esophagus_Mucosa': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Esophagus_Muscularis': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Heart_AtrialAppendage': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Heart_LeftVentricle': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Kidney_Cortex': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Liver': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Lung': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'MinorSalivaryGland': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Muscle_Skeletal': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Nerve_Tibial': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Ovary': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Pancreas': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Pituitary': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Prostate': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Skin_NotSunExposed_Suprapubic': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Skin_SunExposed_Lowerleg': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'SmallIntestine_TerminalIleum': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
-      "    } \n",
-      "    'Spleen': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
+      "        filters: set, \n",
+      "        quality_metrics: struct {\n",
+      "            allele_balance: struct {\n",
+      "                alt_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            genotype_depth: struct {\n",
+      "                all_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                all_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            genotype_quality: struct {\n",
+      "                all_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                all_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            site_quality_metrics: array\n",
+      "        }\n",
       "    } \n",
-      "    'Stomach': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
+      "    'genome': struct {\n",
+      "        colocated_variants: struct {\n",
+      "            hgdp: array, \n",
+      "            tgp: array, \n",
+      "            all: array\n",
+      "        }, \n",
+      "        subsets: set, \n",
+      "        flags: set, \n",
+      "        freq: struct {\n",
+      "            hgdp: struct {\n",
+      "                ac: int32, \n",
+      "                ac_raw: int32, \n",
+      "                an: int32, \n",
+      "                hemizygote_count: int32, \n",
+      "                homozygote_count: int32, \n",
+      "                ancestry_groups: array\n",
+      "            }, \n",
+      "            tgp: struct {\n",
+      "                ac: int32, \n",
+      "                ac_raw: int32, \n",
+      "                an: int32, \n",
+      "                hemizygote_count: int32, \n",
+      "                homozygote_count: int32, \n",
+      "                ancestry_groups: array\n",
+      "            }, \n",
+      "            all: struct {\n",
+      "                ac: int32, \n",
+      "                ac_raw: int32, \n",
+      "                an: int32, \n",
+      "                hemizygote_count: int32, \n",
+      "                homozygote_count: int32, \n",
+      "                ancestry_groups: array\n",
+      "            }\n",
+      "        }, \n",
+      "        fafmax: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }, \n",
+      "        age_distribution: struct {\n",
+      "            het: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }, \n",
+      "            hom: struct {\n",
+      "                bin_edges: array, \n",
+      "                bin_freq: array, \n",
+      "                n_smaller: int64, \n",
+      "                n_larger: int64\n",
+      "            }\n",
+      "        }, \n",
+      "        filters: set, \n",
+      "        quality_metrics: struct {\n",
+      "            allele_balance: struct {\n",
+      "                alt_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            genotype_depth: struct {\n",
+      "                all_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                all_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            genotype_quality: struct {\n",
+      "                all_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                all_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_adj: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                alt_raw: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            site_quality_metrics: array\n",
+      "        }\n",
       "    } \n",
-      "    'Testis': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
+      "    'rsids': set \n",
+      "    'in_silico_predictors': struct {\n",
+      "        cadd: struct {\n",
+      "            phred: float32, \n",
+      "            raw_score: float32\n",
+      "        }, \n",
+      "        revel_max: float64, \n",
+      "        spliceai_ds_max: float32, \n",
+      "        pangolin_largest_ds: float64, \n",
+      "        phylop: float64, \n",
+      "        sift_max: float64, \n",
+      "        polyphen_max: float64\n",
       "    } \n",
-      "    'Thyroid': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
+      "    'variant_id': str \n",
+      "    'colocated_variants': struct {\n",
+      "        all: array, \n",
+      "        non_ukb: array, \n",
+      "        hgdp: array, \n",
+      "        tgp: array\n",
       "    } \n",
-      "    'Uterus': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
+      "    'joint': struct {\n",
+      "        freq: struct {\n",
+      "            all: struct {\n",
+      "                ac: int32, \n",
+      "                ac_raw: int32, \n",
+      "                an: int32, \n",
+      "                hemizygote_count: int32, \n",
+      "                homozygote_count: int32, \n",
+      "                ancestry_groups: array\n",
+      "            }\n",
+      "        }, \n",
+      "        faf: array, \n",
+      "        fafmax: struct {\n",
+      "            faf95_max: float64, \n",
+      "            faf95_max_gen_anc: str, \n",
+      "            faf99_max: float64, \n",
+      "            faf99_max_gen_anc: str\n",
+      "        }, \n",
+      "        grpmax: struct {\n",
+      "            AC: int32, \n",
+      "            AF: float64, \n",
+      "            AN: int32, \n",
+      "            homozygote_count: int32, \n",
+      "            gen_anc: str\n",
+      "        }, \n",
+      "        histograms: struct {\n",
+      "            qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            raw_qual_hists: struct {\n",
+      "                gq_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_all: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                gq_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                dp_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                ab_hist_alt: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }, \n",
+      "            age_hists: struct {\n",
+      "                age_hist_het: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }, \n",
+      "                age_hist_hom: struct {\n",
+      "                    bin_edges: array, \n",
+      "                    bin_freq: array, \n",
+      "                    n_smaller: int64, \n",
+      "                    n_larger: int64\n",
+      "                }\n",
+      "            }\n",
+      "        }, \n",
+      "        flags: set, \n",
+      "        freq_comparison_stats: struct {\n",
+      "            contingency_table_test: array, \n",
+      "            cochran_mantel_haenszel_test: struct {\n",
+      "                p_value: float64, \n",
+      "                chisq: float64\n",
+      "            }, \n",
+      "            stat_union: struct {\n",
+      "                p_value: float64, \n",
+      "                stat_test_name: str, \n",
+      "                gen_ancs: array\n",
+      "            }\n",
+      "        }\n",
       "    } \n",
-      "    'Vagina': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
+      "    'coverage': struct {\n",
+      "        exome: struct {\n",
+      "            mean: float64, \n",
+      "            median_approx: int32, \n",
+      "            total_DP: int64, \n",
+      "            over_1: float64, \n",
+      "            over_5: float64, \n",
+      "            over_10: float64, \n",
+      "            over_15: float64, \n",
+      "            over_20: float64, \n",
+      "            over_25: float64, \n",
+      "            over_30: float64, \n",
+      "            over_50: float64, \n",
+      "            over_100: float64\n",
+      "        }, \n",
+      "        genome: struct {\n",
+      "            mean: float64, \n",
+      "            median_approx: int32, \n",
+      "            total_DP: int64, \n",
+      "            over_1: float32, \n",
+      "            over_5: float32, \n",
+      "            over_10: float32, \n",
+      "            over_15: float32, \n",
+      "            over_20: float32, \n",
+      "            over_25: float32, \n",
+      "            over_30: float32, \n",
+      "            over_50: float32, \n",
+      "            over_100: float32\n",
+      "        }\n",
       "    } \n",
-      "    'WholeBlood': struct {\n",
-      "        transcript_expression: float64, \n",
-      "        expression_proportion: float64\n",
+      "    'transcript_consequences': array, \n",
+      "        domains: set, \n",
+      "        gene_id: str, \n",
+      "        gene_symbol: str, \n",
+      "        hgvsc: str, \n",
+      "        hgvsp: str, \n",
+      "        is_canonical: bool, \n",
+      "        lof_filter: str, \n",
+      "        lof_flags: str, \n",
+      "        lof: str, \n",
+      "        major_consequence: str, \n",
+      "        transcript_id: str, \n",
+      "        transcript_version: str, \n",
+      "        gene_version: str, \n",
+      "        is_mane_select: bool, \n",
+      "        is_mane_select_version: bool, \n",
+      "        refseq_id: str, \n",
+      "        refseq_version: str\n",
+      "    }> \n",
+      "    'caid': str \n",
+      "    'vrs': struct {\n",
+      "        ref: struct {\n",
+      "            allele_id: str, \n",
+      "            start: int32, \n",
+      "            end: int32, \n",
+      "            state: str\n",
+      "        }, \n",
+      "        alt: struct {\n",
+      "            allele_id: str, \n",
+      "            start: int32, \n",
+      "            end: int32, \n",
+      "            state: str\n",
+      "        }\n",
       "    } \n",
       "----------------------------------------\n",
       "Key: ['locus', 'alleles']\n",
@@ -8499,18 +6914,18 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77d8e1eb-d07d-4338-a401-ae9191ad2901",
+   "id": "e113c4a2-25c2-4f55-bbb9-382c87cd770c",
    "metadata": {
     "tags": []
    },
    "source": [
-    "#### Show the first 5 variants in the Hail Table\n"
+    "### Show the first 5 variants in the Hail Table\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "35371de7-9332-4aa6-a3b9-c573fb2e144f",
+   "execution_count": 35,
+   "id": "d03dd50b-37ac-49ec-b766-a86870ee6573",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-12-06T20:09:20.949958Z",
@@ -8522,974 +6937,3073 @@
     {
      "data": {
       "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
+       "
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Brain_Caudate_basalganglia
Brain_CerebellarHemisphere
Brain_Cerebellum
Brain_Cortex
Brain_FrontalCortex_BA9
Brain_Hippocampus
Brain_Hypothalamus
Brain_Nucleusaccumbens_basalganglia
Brain_Putamen_basalganglia
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locus<GRCh38>strarray<str>strstrstrstrfloat64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64
chr1:65565"ENSG00000186092"["A","C"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65565"ENSG00000186092"["A","G"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65565"ENSG00000186092"["A","T"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65566"ENSG00000186092"["T","A"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65566"ENSG00000186092"["T","C"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "
exome
genome
joint
quality_metrics
quality_metrics
histograms
freq
fafmax
age_distribution
allele_balance
genotype_depth
genotype_quality
freq
age_distribution
allele_balance
genotype_depth
genotype_quality
in_silico_predictors
freq
qual_hists
raw_qual_hists
age_hists
freq_comparison_stats
coverage
vrs
colocated_variants
all
non_ukb
gnomad
non_ukb
het
hom
alt_adj
alt_raw
all_adj
all_raw
alt_adj
alt_raw
all_adj
all_raw
alt_adj
alt_raw
colocated_variants
hgdp
tgp
all
fafmax
het
hom
alt_adj
alt_raw
all_adj
all_raw
alt_adj
alt_raw
all_adj
all_raw
alt_adj
alt_raw
cadd
colocated_variants
all
fafmax
grpmax
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cochran_mantel_haenszel_test
stat_union
exome
genome
ref
alt
locus
alleles
all
non_ukb
subsets
flags
ac
ac_raw
an
hemizygote_count
homozygote_count
ancestry_groups
ac
ac_raw
an
hemizygote_count
homozygote_count
ancestry_groups
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
filters
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
site_quality_metrics
hgdp
tgp
all
subsets
flags
ac
ac_raw
an
hemizygote_count
homozygote_count
ancestry_groups
ac
ac_raw
an
hemizygote_count
homozygote_count
ancestry_groups
ac
ac_raw
an
hemizygote_count
homozygote_count
ancestry_groups
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
filters
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
site_quality_metrics
rsids
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
variant_id
all
non_ukb
hgdp
tgp
ac
ac_raw
an
hemizygote_count
homozygote_count
ancestry_groups
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
AC
AF
AN
homozygote_count
gen_anc
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
flags
contingency_table_test
p_value
chisq
p_value
stat_test_name
gen_ancs
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
transcript_consequences
caid
allele_id
start
end
state
allele_id
start
end
state
locus<GRCh38>array<str>array<str>array<str>set<str>set<str>int32int32int32int32int64array<struct{id: str, ac: int32, an: int32, hemizygote_count: int32, homozygote_count: int64}>int32int32int32int32int64array<struct{id: str, ac: int32, an: int32, hemizygote_count: int32, homozygote_count: int64}>float64strfloat64strfloat64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64set<str>array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{metric: str, value: float64}>array<str>array<str>array<str>set<str>set<str>int32int32int32int32int32array<struct{id: str, ac: int32, an: int32, hemizygote_count: int32, homozygote_count: int32}>int32int32int32int32int32array<struct{id: str, ac: int32, an: int32, hemizygote_count: int32, homozygote_count: int32}>int32int32int32int32int32array<struct{id: str, ac: int32, an: int32, hemizygote_count: int32, homozygote_count: int32}>float64strfloat64strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64set<str>array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<struct{metric: str, value: float64}>set<str>float32float32float64float32float64float64float64float64strarray<str>array<str>array<str>array<str>int32int32int32int32int32array<struct{id: str, ac: int32, an: int32, hemizygote_count: int32, homozygote_count: int32}>array<struct{faf95: float64, faf99: float64}>float64strfloat64strint32float64int32int32strarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64set<str>array<struct{p_value: float64, odds_ratio: float64}>float64float64float64strarray<str>float64int32int64float64float64float64float64float64float64float64float64float64float64int32int64float32float32float32float32float32float32float32float32float32array<struct{biotype: str, consequence_terms: array<str>, domains: set<str>, gene_id: str, gene_symbol: str, hgvsc: str, hgvsp: str, is_canonical: bool, lof_filter: str, lof_flags: str, lof: str, major_consequence: str, transcript_id: str, transcript_version: str, gene_version: str, is_mane_select: bool, is_mane_select_version: bool, refseq_id: str, refseq_version: str}>strstrint32int32strstrint32int32str
chr1:10031["T","C"]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA[][][]{}{"lcr","segdup"}0081200[]00107800[]025664200[("remaining",0,782,0,0),("remaining_XX",0,402,0,0),("remaining_XY",0,380,0,0),("amr",0,6420,0,0),("amr_XX",0,2770,0,0),("amr_XY",0,3650,0,0),("fin",0,4326,0,0),("fin_XX",0,1060,0,0),("fin_XY",0,3266,0,0),("ami",0,352,0,0),("ami_XX",0,154,0,0),("ami_XY",0,198,0,0),("eas",0,1712,0,0),("eas_XX",0,770,0,0),("eas_XY",0,942,0,0),("mid",0,192,0,0),("mid_XX",0,102,0,0),("mid_XY",0,90,0,0),("sas",0,1120,0,0),("sas_XX",0,260,0,0),("sas_XY",0,860,0,0),("asj",0,1550,0,0),("asj_XX",0,814,0,0),("asj_XY",0,736,0,0),("afr",0,14642,0,0),("afr_XX",0,7978,0,0),("afr_XY",0,6664,0,0),("nfe",0,25546,0,0),("nfe_XX",0,14998,0,0),("nfe_XY",0,10548,0,0),("XX",0,29308,0,0),("XY",0,27334,0,0)]NANANANA[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00{"AC0","AS_VQSR"}[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[("SiteQuality",9.60e+01),("inbreeding_coeff",-1.65e-05),("AS_MQ",3.51e+01),("AS_FS",5.10e+00),("AS_MQRankSum",-5.72e-01),("AS_pab_max",6.87e-01),("AS_QUALapprox",7.70e+01),("AS_QD",2.96e+00),("AS_ReadPosRankSum",-1.38e+00),("AS_SOR",9.64e-02),("AS_VarDP",2.60e+01),("AS_VQSLOD",-4.57e+00)]{"rs1639542312"}8.97e+007.57e-01NANANANANANA"1-10031-T-C"[][][][]025664200[("remaining",0,782,0,0),("remaining_XX",0,402,0,0),("remaining_XY",0,380,0,0),("amr",0,6420,0,0),("amr_XX",0,2770,0,0),("amr_XY",0,3650,0,0),("fin",0,4326,0,0),("fin_XX",0,1060,0,0),("fin_XY",0,3266,0,0),("ami",0,352,0,0),("ami_XX",0,154,0,0),("ami_XY",0,198,0,0),("eas",0,1712,0,0),("eas_XX",0,770,0,0),("eas_XY",0,942,0,0),("mid",0,192,0,0),("mid_XX",0,102,0,0),("mid_XY",0,90,0,0),("sas",0,1120,0,0),("sas_XX",0,260,0,0),("sas_XY",0,860,0,0),("asj",0,1550,0,0),("asj_XX",0,814,0,0),("asj_XY",0,736,0,0),("afr",0,14642,0,0),("afr_XX",0,7978,0,0),("afr_XY",0,6664,0,0),("nfe",0,25546,0,0),("nfe_XX",0,14998,0,0),("nfe_XY",0,10548,0,0),("",0,56642,0,0),("XX",0,29308,0,0),("XY",0,27334,0,0),("XX",0,29308,0,0),("XY",0,27334,0,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANANANANANA[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3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chr1:10055["T","C"]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA[][][]{"hgdp"}{"lcr","segdup"}01131200[("japanese",0,50,0,0),("japanese_XX",0,12,0,0),("japanese_XY",0,38,0,0),("adygei",0,22,0,0),("adygei_XX",0,16,0,0),("adygei_XY",0,6,0,0),("orcadian",0,26,0,0),("orcadian_XX",0,14,0,0),("orcadian_XY",0,12,0,0),("bantusouthafrica",0,8,0,0),("bantusouthafrica_XX",0,0,0,0),("bantusouthafrica_XY",0,8,0,0),("yakut",0,36,0,0),("yakut_XX",0,14,0,0),("yakut_XY",0,22,0,0),("han",0,58,0,0),("han_XX",0,32,0,0),("han_XY",0,26,0,0),("uygur",0,10,0,0),("uygur_XX",0,4,0,0),("uygur_XY",0,6,0,0),("balochi",0,40,0,0),("balochi_XX",0,0,0,0),("balochi_XY",0,40,0,0),("bedouin",0,72,0,0),("bedouin_XX",0,28,0,0),("bedouin_XY",0,44,0,0),("russian",0,48,0,0),("russian_XX",0,18,0,0),("russian_XY",0,30,0,0),("daur",0,10,0,0),("daur_XX",0,2,0,0),("daur_XY",0,8,0,0),("pima",0,16,0,0),("pima_XX",0,6,0,0),("pima_XY",0,10,0,0),("hezhen",0,14,0,0),("hezhen_XX",0,4,0,0),("hezhen_XY",0,10,0,0),("biaka",0,20,0,0),("biaka_XX",0,0,0,0),("biaka_XY",0,20,0,0),("miao",0,8,0,0),("miao_XX",0,2,0,0),("miao_XY",0,6,0,0),("sindhi",0,40,0,0),("sindhi_XX",0,8,0,0),("sindhi_XY",0,32,0,0),("northernhan",0,14,0,0),("northernhan_XX",0,2,0,0),("northernhan_XY",0,12,0,0),("oroqen",0,14,0,0),("oroqen_XX",0,6,0,0),("oroqen_XY",0,8,0,0),("san",0,12,0,0),("san_XX",0,0,0,0),("san_XY",0,12,0,0),("tu",0,18,0,0),("tu_XX",0,4,0,0),("tu_XY",0,14,0,0),("tuscan",0,12,0,0),("tuscan_XX",0,4,0,0),("tuscan_XY",0,8,0,0),("mbuti",0,16,0,0),("mbuti_XX",0,4,0,0),("mbuti_XY",0,12,0,0),("palestinian",0,58,0,0),("palestinian_XX",0,38,0,0),("palestinian_XY",0,20,0,0),("tujia",0,16,0,0),("tujia_XX",0,2,0,0),("tujia_XY",0,14,0,0),("druze",0,58,0,0),("druze_XX",0,42,0,0),("druze_XY",0,16,0,0),("pathan",0,34,0,0),("pathan_XX",0,4,0,0),("pathan_XY",0,30,0,0),("basque",0,38,0,0),("basque_XX",0,12,0,0),("basque_XY",0,26,0,0),("makrani",0,38,0,0),("makrani_XX",0,6,0,0),("makrani_XY",0,32,0,0),("burusho",0,40,0,0),("burusho_XX",0,6,0,0),("burusho_XY",0,34,0,0),("mongolian",0,10,0,0),("mongolian_XX",0,4,0,0),("mongolian_XY",0,6,0,0),("bougainville",0,10,0,0),("bougainville_XX",0,10,0,0),("bougainville_XY",0,0,0,0),("papuansepik",0,2,0,0),("papuansepik_XX",0,2,0,0),("papuansepik_XY",0,0,0,0),("yi",0,18,0,0),("yi_XX",0,2,0,0),("yi_XY",0,16,0,0),("naxi",0,8,0,0),("naxi_XX",0,2,0,0),("naxi_XY",0,6,0,0),("lahu",0,8,0,0),("lahu_XX",0,0,0,0),("lahu_XY",0,8,0,0),("sardinian",0,52,0,0),("sardinian_XX",0,24,0,0),("sardinian_XY",0,28,0,0),("karitiana",0,14,0,0),("karitiana_XX",0,12,0,0),("karitiana_XY",0,2,0,0),("mozabite",0,36,0,0),("mozabite_XX",0,12,0,0),("mozabite_XY",0,24,0,0),("yoruba",0,20,0,0),("yoruba_XX",0,10,0,0),("yoruba_XY",0,10,0,0),("dai",0,12,0,0),("dai_XX",0,6,0,0),("dai_XY",0,6,0,0),("bergamoitalian",0,14,0,0),("bergamoitalian_XX",0,8,0,0),("bergamoitalian_XY",0,6,0,0),("cambodian",0,14,0,0),("cambodian_XX",0,8,0,0),("cambodian_XY",0,6,0,0),("french",0,48,0,0),("french_XX",0,26,0,0),("french_XY",0,22,0,0),("mandenka",0,26,0,0),("mandenka_XX",0,10,0,0),("mandenka_XY",0,16,0,0),("surui",0,12,0,0),("surui_XX",0,8,0,0),("surui_XY",0,4,0,0),("brahui",0,30,0,0),("brahui_XX",0,0,0,0),("brahui_XY",0,30,0,0),("hazara",0,22,0,0),("hazara_XX",0,0,0,0),("hazara_XY",0,22,0,0),("kalash",0,28,0,0),("kalash_XX",0,6,0,0),("kalash_XY",0,22,0,0),("papuanhighlands",0,4,0,0),("papuanhighlands_XX",0,2,0,0),("papuanhighlands_XY",0,2,0,0),("xibo",0,10,0,0),("xibo_XX",0,0,0,0),("xibo_XY",0,10,0,0),("colombian",0,6,0,0),("colombian_XX",0,4,0,0),("colombian_XY",0,2,0,0),("bantukenya",0,10,0,0),("bantukenya_XX",0,2,0,0),("bantukenya_XY",0,8,0,0),("she",0,18,0,0),("she_XX",0,6,0,0),("she_XY",0,12,0,0),("maya",0,34,0,0),("maya_XX",0,30,0,0),("maya_XY",0,4,0,0),("XX",0,484,0,0),("XY",0,828,0,0)]00147800[]159422400[("remaining",0,1228,0,0),("remaining_XX",0,624,0,0),("remaining_XY",0,604,0,0),("amr",0,8056,0,0),("amr_XX",0,3902,0,0),("amr_XY",0,4154,0,0),("fin",1,5570,0,0),("fin_XX",1,1034,0,0),("fin_XY",0,4536,0,0),("ami",0,682,0,0),("ami_XX",0,344,0,0),("ami_XY",0,338,0,0),("eas",0,3020,0,0),("eas_XX",0,1256,0,0),("eas_XY",0,1764,0,0),("mid",0,216,0,0),("mid_XX",0,110,0,0),("mid_XY",0,106,0,0),("sas",0,2668,0,0),("sas_XX",0,600,0,0),("sas_XY",0,2068,0,0),("asj",0,2370,0,0),("asj_XX",0,1246,0,0),("asj_XY",0,1124,0,0),("afr",0,26032,0,0),("afr_XX",0,14152,0,0),("afr_XY",0,11880,0,0),("nfe",0,44382,0,0),("nfe_XX",0,26312,0,0),("nfe_XY",0,18070,0,0),("XX",1,49580,0,0),("XY",0,44644,0,0)]NANANANA[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00{"AS_VQSR"}[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.5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chr1:10057["A","C"]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA[][][]{}{"lcr","segdup"}00130000[]00281000[]3311353600[("remaining",0,1504,0,0),("remaining_XX",0,764,0,0),("remaining_XY",0,740,0,0),("amr",0,10448,0,0),("amr_XX",0,4944,0,0),("amr_XY",0,5504,0,0),("fin",0,6886,0,0),("fin_XX",0,1472,0,0),("fin_XY",0,5414,0,0),("ami",0,742,0,0),("ami_XX",0,378,0,0),("ami_XY",0,364,0,0),("eas",0,3850,0,0),("eas_XX",0,1670,0,0),("eas_XY",0,2180,0,0),("mid",0,266,0,0),("mid_XX",0,132,0,0),("mid_XY",0,134,0,0),("sas",0,3424,0,0),("sas_XX",0,784,0,0),("sas_XY",0,2640,0,0),("asj",0,2768,0,0),("asj_XX",0,1532,0,0),("asj_XY",0,1236,0,0),("afr",1,30736,0,0),("afr_XX",0,16598,0,0),("afr_XY",1,14138,0,0),("nfe",2,52912,0,0),("nfe_XX",2,31106,0,0),("nfe_XY",0,21806,0,0),("XX",2,59380,0,0),("XY",1,54156,0,0)]6.27e-06"nfe"2.35e-06"nfe"[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00{"AS_VQSR"}[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[("SiteQuality",2.92e+02),("inbreeding_coeff",-2.41e-05),("AS_MQ",3.61e+01),("AS_FS",3.79e+01),("AS_MQRankSum",7.88e-01),("AS_pab_max",1.00e+00),("AS_QUALapprox",2.64e+02),("AS_QD",2.06e+00),("AS_ReadPosRankSum",-6.84e-01),("AS_SOR",7.58e-01),("AS_VarDP",1.28e+02),("AS_VQSLOD",-3.31e+00)]{"rs1570391741"}8.53e+007.09e-01NANANANANANA"1-10057-A-C"[][][][]3311353600[("remaining",0,1504,0,0),("remaining_XX",0,764,0,0),("remaining_XY",0,740,0,0),("amr",0,10448,0,0),("amr_XX",0,4944,0,0),("amr_XY",0,5504,0,0),("fin",0,6886,0,0),("fin_XX",0,1472,0,0),("fin_XY",0,5414,0,0),("ami",0,742,0,0),("ami_XX",0,378,0,0),("ami_XY",0,364,0,0),("eas",0,3850,0,0),("eas_XX",0,1670,0,0),("eas_XY",0,2180,0,0),("mid",0,266,0,0),("mid_XX",0,132,0,0),("mid_XY",0,134,0,0),("sas",0,3424,0,0),("sas_XX",0,784,0,0),("sas_XY",0,2640,0,0),("asj",0,2768,0,0),("asj_XX",0,1532,0,0),("asj_XY",0,1236,0,0),("afr",1,30736,0,0),("afr_XX",0,16598,0,0),("afr_XY",1,14138,0,0),("nfe",2,52912,0,0),("nfe_XX",2,31106,0,0),("nfe_XY",0,21806,0,0),("",3,113536,0,0),("XX",2,59380,0,0),("XY",1,54156,0,0),("XX",2,59380,0,0),("XY",1,54156,0,0)][(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]6.27e-06"nfe"2.35e-06"nfe"23.78e-05529120"nfe"[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00{"not_called_in_exomes"}[NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANANANANANANANANANANANANANA5.43e+015838917008.20e-018.20e-018.20e-018.20e-018.18e-018.15e-018.07e-016.45e-015.38e-02[]"CA997563815""ga4gh:VA.TV0UyS-jCvsVkEOMbipUnzTc4hYuVNTg"1005610057"A""ga4gh:VA.33zGTKcHyj5wWpTfc3OfD8-UlPQ-YOfx"1005610057"C"
showing top 5 rows
\n"
       ],
       "text/plain": [
-       "+---------------+-------------------+------------+-------------+\n",
-       "| locus         | gene_id           | alleles    | gene_symbol |\n",
-       "+---------------+-------------------+------------+-------------+\n",
-       "| locus | str               | array | str         |\n",
-       "+---------------+-------------------+------------+-------------+\n",
-       "| chr1:65565    | \"ENSG00000186092\" | [\"A\",\"C\"]  | \"OR4F5\"     |\n",
-       "| chr1:65565    | \"ENSG00000186092\" | [\"A\",\"G\"]  | \"OR4F5\"     |\n",
-       "| chr1:65565    | \"ENSG00000186092\" | [\"A\",\"T\"]  | \"OR4F5\"     |\n",
-       "| chr1:65566    | \"ENSG00000186092\" | [\"T\",\"A\"]  | \"OR4F5\"     |\n",
-       "| chr1:65566    | \"ENSG00000186092\" | [\"T\",\"C\"]  | \"OR4F5\"     |\n",
-       "+---------------+-------------------+------------+-------------+\n",
+       "+---------------+------------+------------------------------+\n",
+       "| locus         | alleles    | exome.colocated_variants.all |\n",
+       "+---------------+------------+------------------------------+\n",
+       "| locus | array | array                   |\n",
+       "+---------------+------------+------------------------------+\n",
+       "| chr1:10031    | [\"T\",\"C\"]  | NA                           |\n",
+       "| chr1:10037    | [\"T\",\"C\"]  | NA                           |\n",
+       "| chr1:10043    | [\"T\",\"C\"]  | NA                           |\n",
+       "| chr1:10055    | [\"T\",\"C\"]  | NA                           |\n",
+       "| chr1:10057    | [\"A\",\"C\"]  | NA                           |\n",
+       "+---------------+------------+------------------------------+\n",
+       "\n",
+       "+----------------------------------+---------------+-------------+\n",
+       "| exome.colocated_variants.non_ukb | exome.subsets | exome.flags |\n",
+       "+----------------------------------+---------------+-------------+\n",
+       "| array                       | set      | set    |\n",
+       "+----------------------------------+---------------+-------------+\n",
+       "| NA                               | NA            | NA          |\n",
+       "| NA                               | NA            | NA          |\n",
+       "| NA                               | NA            | NA          |\n",
+       "| NA                               | NA            | NA          |\n",
+       "| NA                               | NA            | NA          |\n",
+       "+----------------------------------+---------------+-------------+\n",
+       "\n",
+       "+-------------------+-----------------------+-------------------+\n",
+       "| exome.freq.all.ac | exome.freq.all.ac_raw | exome.freq.all.an |\n",
+       "+-------------------+-----------------------+-------------------+\n",
+       "|             int32 |                 int32 |             int32 |\n",
+       "+-------------------+-----------------------+-------------------+\n",
+       "|                NA |                    NA |                NA |\n",
+       "|                NA |                    NA |                NA |\n",
+       "|                NA |                    NA |                NA |\n",
+       "|                NA |                    NA |                NA |\n",
+       "|                NA |                    NA |                NA |\n",
+       "+-------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+---------------------------------+---------------------------------+\n",
+       "| exome.freq.all.hemizygote_count | exome.freq.all.homozygote_count |\n",
+       "+---------------------------------+---------------------------------+\n",
+       "|                           int32 |                           int64 |\n",
+       "+---------------------------------+---------------------------------+\n",
+       "|                              NA |                              NA |\n",
+       "|                              NA |                              NA |\n",
+       "|                              NA |                              NA |\n",
+       "|                              NA |                              NA |\n",
+       "|                              NA |                              NA |\n",
+       "+---------------------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| exome.freq.all.ancestry_groups                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                       | array                        |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "| exome.age_distribution.het.n_smaller | exome.age_distribution.het.n_larger |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "|                                int64 |                               int64 |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "| exome.age_distribution.hom.bin_edges | exome.age_distribution.hom.bin_freq |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "| array                       | array                        |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "| NA                                   | NA                                  |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "| exome.age_distribution.hom.n_smaller | exome.age_distribution.hom.n_larger |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "|                                int64 |                               int64 |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "|                                   NA |                                  NA |\n",
+       "+--------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+---------------+--------------------------------------------------------+\n",
+       "| exome.filters | exome.quality_metrics.allele_balance.alt_adj.bin_edges |\n",
+       "+---------------+--------------------------------------------------------+\n",
+       "| set      | array                                         |\n",
+       "+---------------+--------------------------------------------------------+\n",
+       "| NA            | NA                                                     |\n",
+       "| NA            | NA                                                     |\n",
+       "| NA            | NA                                                     |\n",
+       "| NA            | NA                                                     |\n",
+       "| NA            | NA                                                     |\n",
+       "+---------------+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.allele_balance.alt_adj.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.allele_balance.alt_adj.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.allele_balance.alt_adj.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.allele_balance.alt_raw.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.allele_balance.alt_raw.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.allele_balance.alt_raw.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.allele_balance.alt_raw.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_adj.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_adj.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_adj.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_adj.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_raw.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_raw.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_raw.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.all_raw.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_adj.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_adj.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_adj.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_adj.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_raw.bin_edges |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+--------------------------------------------------------+\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "| NA                                                     |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_raw.bin_freq |\n",
+       "+-------------------------------------------------------+\n",
+       "| array                                          |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_raw.n_smaller |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "|                                                     NA |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_depth.alt_raw.n_larger |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "|                                                    NA |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_adj.bin_edges |\n",
+       "+----------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+----------------------------------------------------------+\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_adj.bin_freq |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_adj.n_smaller |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_adj.n_larger |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_raw.bin_edges |\n",
+       "+----------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+----------------------------------------------------------+\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_raw.bin_freq |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_raw.n_smaller |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.all_raw.n_larger |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_adj.bin_edges |\n",
+       "+----------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+----------------------------------------------------------+\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_adj.bin_freq |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_adj.n_smaller |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_adj.n_larger |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_raw.bin_edges |\n",
+       "+----------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+----------------------------------------------------------+\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "| NA                                                       |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_raw.bin_freq |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "| NA                                                      |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_raw.n_smaller |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "|                                                       NA |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| exome.quality_metrics.genotype_quality.alt_raw.n_larger |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "|                                                      NA |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+--------------------------------+\n",
+       "| exome.quality_metrics.site_quality_metrics | genome.colocated_variants.hgdp |\n",
+       "+--------------------------------------------+--------------------------------+\n",
+       "| array | array                     |\n",
+       "+--------------------------------------------+--------------------------------+\n",
+       "| NA                                         | []                             |\n",
+       "| NA                                         | []                             |\n",
+       "| NA                                         | []                             |\n",
+       "| NA                                         | []                             |\n",
+       "| NA                                         | []                             |\n",
+       "+--------------------------------------------+--------------------------------+\n",
+       "\n",
+       "+-------------------------------+-------------------------------+\n",
+       "| genome.colocated_variants.tgp | genome.colocated_variants.all |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "| array                    | array                    |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "| []                            | []                            |\n",
+       "| []                            | []                            |\n",
+       "| []                            | []                            |\n",
+       "| []                            | []                            |\n",
+       "| []                            | []                            |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "\n",
+       "+----------------+------------------+---------------------+\n",
+       "| genome.subsets | genome.flags     | genome.freq.hgdp.ac |\n",
+       "+----------------+------------------+---------------------+\n",
+       "| set       | set         |               int32 |\n",
+       "+----------------+------------------+---------------------+\n",
+       "| {}             | {\"lcr\",\"segdup\"} |                   0 |\n",
+       "| {}             | {\"lcr\",\"segdup\"} |                   0 |\n",
+       "| {}             | {\"lcr\",\"segdup\"} |                   0 |\n",
+       "| {\"hgdp\"}       | {\"lcr\",\"segdup\"} |                   0 |\n",
+       "| {}             | {\"lcr\",\"segdup\"} |                   0 |\n",
+       "+----------------+------------------+---------------------+\n",
+       "\n",
+       "+-------------------------+---------------------+\n",
+       "| genome.freq.hgdp.ac_raw | genome.freq.hgdp.an |\n",
+       "+-------------------------+---------------------+\n",
+       "|                   int32 |               int32 |\n",
+       "+-------------------------+---------------------+\n",
+       "|                       0 |                 812 |\n",
+       "|                       0 |                1028 |\n",
+       "|                       0 |                1154 |\n",
+       "|                       1 |                1312 |\n",
+       "|                       0 |                1300 |\n",
+       "+-------------------------+---------------------+\n",
+       "\n",
+       "+-----------------------------------+-----------------------------------+\n",
+       "| genome.freq.hgdp.hemizygote_count | genome.freq.hgdp.homozygote_count |\n",
+       "+-----------------------------------+-----------------------------------+\n",
+       "|                             int32 |                             int32 |\n",
+       "+-----------------------------------+-----------------------------------+\n",
+       "|                                 0 |                                 0 |\n",
+       "|                                 0 |                                 0 |\n",
+       "|                                 0 |                                 0 |\n",
+       "|                                 0 |                                 0 |\n",
+       "|                                 0 |                                 0 |\n",
+       "+-----------------------------------+-----------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.freq.hgdp.ancestry_groups                                             |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "| genome.age_distribution.het.bin_freq | genome.age_distribution.het.n_smaller |\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "| array                         |                                 int64 |\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,1,0,0,1,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,0,1,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,0,1,0,1,0,0,0,0,0]                |                                     0 |\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| genome.age_distribution.het.n_larger |\n",
+       "+--------------------------------------+\n",
+       "|                                int64 |\n",
+       "+--------------------------------------+\n",
+       "|                                    0 |\n",
+       "|                                    0 |\n",
+       "|                                    0 |\n",
+       "|                                    0 |\n",
+       "|                                    0 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.age_distribution.hom.bin_edges                                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "| genome.age_distribution.hom.bin_freq | genome.age_distribution.hom.n_smaller |\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "| array                         |                                 int64 |\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
+       "+--------------------------------------+---------------------------------------+\n",
+       "\n",
+       "+--------------------------------------+-------------------+\n",
+       "| genome.age_distribution.hom.n_larger | genome.filters    |\n",
+       "+--------------------------------------+-------------------+\n",
+       "|                                int64 | set          |\n",
+       "+--------------------------------------+-------------------+\n",
+       "|                                    0 | {\"AC0\",\"AS_VQSR\"} |\n",
+       "|                                    0 | {\"AS_VQSR\"}       |\n",
+       "|                                    0 | {\"AS_VQSR\"}       |\n",
+       "|                                    0 | {\"AS_VQSR\"}       |\n",
+       "|                                    0 | {\"AS_VQSR\"}       |\n",
+       "+--------------------------------------+-------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_adj.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_adj.bin_freq |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_adj.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_adj.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_raw.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_raw.bin_freq |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_raw.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.allele_balance.alt_raw.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_adj.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_adj.bin_freq                       |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
+       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
+       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
+       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
+       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_adj.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_adj.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                    527 |\n",
+       "|                                                    888 |\n",
+       "|                                                   1232 |\n",
+       "|                                                   2440 |\n",
+       "|                                                   3622 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_raw.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_raw.bin_freq                       |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
+       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
+       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
+       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
+       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_raw.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.all_raw.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                    744 |\n",
+       "|                                                   1130 |\n",
+       "|                                                   1487 |\n",
+       "|                                                   2622 |\n",
+       "|                                                   3743 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_adj.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_adj.bin_freq |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_adj.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_adj.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_raw.bin_edges                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_raw.bin_freq |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_raw.n_smaller |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                   int64 |\n",
+       "+---------------------------------------------------------+\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "|                                                       0 |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_depth.alt_raw.n_larger |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                  int64 |\n",
+       "+--------------------------------------------------------+\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "|                                                      0 |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_adj.bin_edges                    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_adj.bin_freq                     |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
+       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
+       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
+       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
+       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+-------------------------+-----+-----------+---------------+\n",
-       "| most_severe_consequence | lof | lof_flags | exp_prop_mean |\n",
-       "+-------------------------+-----+-----------+---------------+\n",
-       "| str                     | str | str       |       float64 |\n",
-       "+-------------------------+-----+-----------+---------------+\n",
-       "| \"start_lost\"            | NA  | NA        |           NaN |\n",
-       "| \"start_lost\"            | NA  | NA        |           NaN |\n",
-       "| \"start_lost\"            | NA  | NA        |           NaN |\n",
-       "| \"start_lost\"            | NA  | NA        |           NaN |\n",
-       "| \"start_lost\"            | NA  | NA        |           NaN |\n",
-       "+-------------------------+-----+-----------+---------------+\n",
+       "+-----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_adj.n_smaller |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                     int64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "+-----------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------+\n",
-       "| Adipose_Subcutaneous.transcript_expression |\n",
-       "+--------------------------------------------+\n",
-       "|                                    float64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "+--------------------------------------------+\n",
+       "+----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_adj.n_larger |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_raw.bin_edges                    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_raw.bin_freq                     |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
+       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
+       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
+       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
+       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_raw.n_smaller |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                     int64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "+-----------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.all_raw.n_larger |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_adj.bin_edges                    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_adj.bin_freq |\n",
+       "+----------------------------------------------------------+\n",
+       "| array                                             |\n",
+       "+----------------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]                |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_adj.n_smaller |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                     int64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "+-----------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_adj.n_larger |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_raw.bin_edges                    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_raw.bin_freq |\n",
+       "+----------------------------------------------------------+\n",
+       "| array                                             |\n",
+       "+----------------------------------------------------------+\n",
+       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]                |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_raw.n_smaller |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                     int64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "|                                                         0 |\n",
+       "+-----------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------+\n",
+       "| genome.quality_metrics.genotype_quality.alt_raw.n_larger |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                    int64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "|                                                        0 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| genome.quality_metrics.site_quality_metrics                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(\"SiteQuality\",9.60e+01),(\"inbreeding_coeff\",-1.65e-05),(\"AS_MQ\",3.51e+0... |\n",
+       "| [(\"SiteQuality\",1.80e+02),(\"inbreeding_coeff\",-3.15e-05),(\"AS_MQ\",3.83e+0... |\n",
+       "| [(\"SiteQuality\",9.70e+01),(\"inbreeding_coeff\",-8.24e-06),(\"AS_MQ\",3.52e+0... |\n",
+       "| [(\"SiteQuality\",2.20e+02),(\"inbreeding_coeff\",-4.64e-05),(\"AS_MQ\",3.48e+0... |\n",
+       "| [(\"SiteQuality\",2.92e+02),(\"inbreeding_coeff\",-2.41e-05),(\"AS_MQ\",3.61e+0... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+---------------------------------+\n",
+       "| rsids            | in_silico_predictors.cadd.phred |\n",
+       "+------------------+---------------------------------+\n",
+       "| set         |                         float32 |\n",
+       "+------------------+---------------------------------+\n",
+       "| {\"rs1639542312\"} |                        8.97e+00 |\n",
+       "| {\"rs1639542418\"} |                        8.90e+00 |\n",
+       "| NA               |                        8.90e+00 |\n",
+       "| {\"rs892501864\"}  |                        8.88e+00 |\n",
+       "| {\"rs1570391741\"} |                        8.53e+00 |\n",
+       "+------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            7.57e-01 |                             NA |\n",
+       "|                            7.49e-01 |                             NA |\n",
+       "|                            7.48e-01 |                             NA |\n",
+       "|                            7.46e-01 |                             NA |\n",
+       "|                            7.09e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "|                                   NA |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "|                                       NA |                          NA |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
        "\n",
-       "+--------------------------------------------+\n",
-       "| Adipose_Subcutaneous.expression_proportion |\n",
-       "+--------------------------------------------+\n",
-       "|                                    float64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "+--------------------------------------------+\n",
+       "+---------------+------------------------+----------------------------+\n",
+       "| variant_id    | colocated_variants.all | colocated_variants.non_ukb |\n",
+       "+---------------+------------------------+----------------------------+\n",
+       "| str           | array             | array                 |\n",
+       "+---------------+------------------------+----------------------------+\n",
+       "| \"1-10031-T-C\" | []                     | []                         |\n",
+       "| \"1-10037-T-C\" | []                     | []                         |\n",
+       "| \"1-10043-T-C\" | []                     | []                         |\n",
+       "| \"1-10055-T-C\" | []                     | []                         |\n",
+       "| \"1-10057-A-C\" | []                     | []                         |\n",
+       "+---------------+------------------------+----------------------------+\n",
        "\n",
-       "+------------------------------------------------+\n",
-       "| Adipose_Visceral_Omentum.transcript_expression |\n",
-       "+------------------------------------------------+\n",
-       "|                                        float64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "+------------------------------------------------+\n",
+       "+-------------------------+------------------------+-------------------+\n",
+       "| colocated_variants.hgdp | colocated_variants.tgp | joint.freq.all.ac |\n",
+       "+-------------------------+------------------------+-------------------+\n",
+       "| array              | array             |             int32 |\n",
+       "+-------------------------+------------------------+-------------------+\n",
+       "| []                      | []                     |                 0 |\n",
+       "| []                      | []                     |                 2 |\n",
+       "| []                      | []                     |                 1 |\n",
+       "| []                      | []                     |                 1 |\n",
+       "| []                      | []                     |                 3 |\n",
+       "+-------------------------+------------------------+-------------------+\n",
        "\n",
-       "+------------------------------------------------+\n",
-       "| Adipose_Visceral_Omentum.expression_proportion |\n",
-       "+------------------------------------------------+\n",
-       "|                                        float64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "+------------------------------------------------+\n",
+       "+-----------------------+-------------------+---------------------------------+\n",
+       "| joint.freq.all.ac_raw | joint.freq.all.an | joint.freq.all.hemizygote_count |\n",
+       "+-----------------------+-------------------+---------------------------------+\n",
+       "|                 int32 |             int32 |                           int32 |\n",
+       "+-----------------------+-------------------+---------------------------------+\n",
+       "|                     2 |             56642 |                               0 |\n",
+       "|                     4 |             76882 |                               0 |\n",
+       "|                     1 |             85634 |                               0 |\n",
+       "|                     5 |             94224 |                               0 |\n",
+       "|                     3 |            113536 |                               0 |\n",
+       "+-----------------------+-------------------+---------------------------------+\n",
        "\n",
-       "+------------------------------------+------------------------------------+\n",
-       "| AdrenalGland.transcript_expression | AdrenalGland.expression_proportion |\n",
-       "+------------------------------------+------------------------------------+\n",
-       "|                            float64 |                            float64 |\n",
-       "+------------------------------------+------------------------------------+\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "+------------------------------------+------------------------------------+\n",
+       "+---------------------------------+\n",
+       "| joint.freq.all.homozygote_count |\n",
+       "+---------------------------------+\n",
+       "|                           int32 |\n",
+       "+---------------------------------+\n",
+       "|                               0 |\n",
+       "|                               0 |\n",
+       "|                               0 |\n",
+       "|                               0 |\n",
+       "|                               0 |\n",
+       "+---------------------------------+\n",
        "\n",
-       "+------------------------------------+------------------------------------+\n",
-       "| Artery_Aorta.transcript_expression | Artery_Aorta.expression_proportion |\n",
-       "+------------------------------------+------------------------------------+\n",
-       "|                            float64 |                            float64 |\n",
-       "+------------------------------------+------------------------------------+\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "|                           0.00e+00 |                           0.00e+00 |\n",
-       "+------------------------------------+------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.freq.all.ancestry_groups                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------+-------------------------------------+\n",
-       "| Artery_Coronary.expression_proportion | Artery_Tibial.transcript_expression |\n",
-       "+---------------------------------------+-------------------------------------+\n",
-       "|                               float64 |                             float64 |\n",
-       "+---------------------------------------+-------------------------------------+\n",
-       "|                              0.00e+00 |                            0.00e+00 |\n",
-       "|                              0.00e+00 |                            0.00e+00 |\n",
-       "|                              0.00e+00 |                            0.00e+00 |\n",
-       "|                              0.00e+00 |                            0.00e+00 |\n",
-       "|                              0.00e+00 |                            0.00e+00 |\n",
-       "+---------------------------------------+-------------------------------------+\n",
+       "+------------------------+--------------------------------+\n",
+       "| joint.fafmax.faf95_max | joint.fafmax.faf95_max_gen_anc |\n",
+       "+------------------------+--------------------------------+\n",
+       "|                float64 | str                            |\n",
+       "+------------------------+--------------------------------+\n",
+       "|                     NA | NA                             |\n",
+       "|                     NA | NA                             |\n",
+       "|                     NA | NA                             |\n",
+       "|                     NA | NA                             |\n",
+       "|               6.27e-06 | \"nfe\"                          |\n",
+       "+------------------------+--------------------------------+\n",
        "\n",
-       "+-------------------------------------+-------------------------------+\n",
-       "| Artery_Tibial.expression_proportion | Bladder.transcript_expression |\n",
-       "+-------------------------------------+-------------------------------+\n",
-       "|                             float64 |                       float64 |\n",
-       "+-------------------------------------+-------------------------------+\n",
-       "|                            0.00e+00 |                      0.00e+00 |\n",
-       "|                            0.00e+00 |                      0.00e+00 |\n",
-       "|                            0.00e+00 |                      0.00e+00 |\n",
-       "|                            0.00e+00 |                      0.00e+00 |\n",
-       "|                            0.00e+00 |                      0.00e+00 |\n",
-       "+-------------------------------------+-------------------------------+\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "| joint.fafmax.faf99_max | joint.fafmax.faf99_max_gen_anc | joint.grpmax.AC |\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "|                float64 | str                            |           int32 |\n",
+       "+------------------------+--------------------------------+-----------------+\n",
+       "|                     NA | NA                             |              NA |\n",
+       "|                     NA | NA                             |               1 |\n",
+       "|                     NA | NA                             |               1 |\n",
+       "|                     NA | NA                             |              NA |\n",
+       "|               2.35e-06 | \"nfe\"                          |               2 |\n",
+       "+------------------------+--------------------------------+-----------------+\n",
        "\n",
-       "+-------------------------------+--------------------------------------+\n",
-       "| Bladder.expression_proportion | Brain_Amygdala.transcript_expression |\n",
-       "+-------------------------------+--------------------------------------+\n",
-       "|                       float64 |                              float64 |\n",
-       "+-------------------------------+--------------------------------------+\n",
-       "|                      0.00e+00 |                             0.00e+00 |\n",
-       "|                      0.00e+00 |                             0.00e+00 |\n",
-       "|                      0.00e+00 |                             0.00e+00 |\n",
-       "|                      0.00e+00 |                             0.00e+00 |\n",
-       "|                      0.00e+00 |                             0.00e+00 |\n",
-       "+-------------------------------+--------------------------------------+\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "| joint.grpmax.AF | joint.grpmax.AN | joint.grpmax.homozygote_count |\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "|         float64 |           int32 |                         int32 |\n",
+       "+-----------------+-----------------+-------------------------------+\n",
+       "|              NA |              NA |                            NA |\n",
+       "|        4.07e-04 |            2456 |                             0 |\n",
+       "|        4.39e-05 |           22760 |                             0 |\n",
+       "|              NA |              NA |                            NA |\n",
+       "|        3.78e-05 |           52912 |                             0 |\n",
+       "+-----------------+-----------------+-------------------------------+\n",
        "\n",
-       "+--------------------------------------+\n",
-       "| Brain_Amygdala.expression_proportion |\n",
-       "+--------------------------------------+\n",
-       "|                              float64 |\n",
-       "+--------------------------------------+\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "+--------------------------------------+\n",
+       "+----------------------+\n",
+       "| joint.grpmax.gen_anc |\n",
+       "+----------------------+\n",
+       "| str                  |\n",
+       "+----------------------+\n",
+       "| NA                   |\n",
+       "| \"eas\"                |\n",
+       "| \"afr\"                |\n",
+       "| NA                   |\n",
+       "| \"nfe\"                |\n",
+       "+----------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| Brain_Anteriorcingulatecortex_BA24.transcript_expression |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                  float64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "+----------------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.bin_freq                             |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
+       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
+       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
+       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
+       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| Brain_Anteriorcingulatecortex_BA24.expression_proportion |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                  float64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "+----------------------------------------------------------+\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
        "\n",
        "+--------------------------------------------------+\n",
-       "| Brain_Caudate_basalganglia.transcript_expression |\n",
+       "| joint.histograms.qual_hists.gq_hist_all.n_larger |\n",
        "+--------------------------------------------------+\n",
-       "|                                          float64 |\n",
+       "|                                            int64 |\n",
        "+--------------------------------------------------+\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
        "+--------------------------------------------------+\n",
        "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.bin_freq                             |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
+       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
+       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
+       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
+       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
        "+--------------------------------------------------+\n",
-       "| Brain_Caudate_basalganglia.expression_proportion |\n",
+       "| joint.histograms.qual_hists.dp_hist_all.n_larger |\n",
        "+--------------------------------------------------+\n",
-       "|                                          float64 |\n",
+       "|                                            int64 |\n",
        "+--------------------------------------------------+\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
+       "|                                              527 |\n",
+       "|                                              888 |\n",
+       "|                                             1232 |\n",
+       "|                                             2440 |\n",
+       "|                                             3622 |\n",
        "+--------------------------------------------------+\n",
        "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
        "+--------------------------------------------------+\n",
-       "| Brain_CerebellarHemisphere.transcript_expression |\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
        "+--------------------------------------------------+\n",
-       "|                                          float64 |\n",
+       "| array                                     |\n",
        "+--------------------------------------------------+\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]        |\n",
        "+--------------------------------------------------+\n",
        "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
        "+--------------------------------------------------+\n",
-       "| Brain_CerebellarHemisphere.expression_proportion |\n",
+       "| joint.histograms.qual_hists.gq_hist_alt.n_larger |\n",
        "+--------------------------------------------------+\n",
-       "|                                          float64 |\n",
+       "|                                            int64 |\n",
        "+--------------------------------------------------+\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
        "+--------------------------------------------------+\n",
        "\n",
-       "+----------------------------------------+\n",
-       "| Brain_Cerebellum.transcript_expression |\n",
-       "+----------------------------------------+\n",
-       "|                                float64 |\n",
-       "+----------------------------------------+\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "+----------------------------------------+\n",
-       "\n",
-       "+----------------------------------------+------------------------------------+\n",
-       "| Brain_Cerebellum.expression_proportion | Brain_Cortex.transcript_expression |\n",
-       "+----------------------------------------+------------------------------------+\n",
-       "|                                float64 |                            float64 |\n",
-       "+----------------------------------------+------------------------------------+\n",
-       "|                               0.00e+00 |                           0.00e+00 |\n",
-       "|                               0.00e+00 |                           0.00e+00 |\n",
-       "|                               0.00e+00 |                           0.00e+00 |\n",
-       "|                               0.00e+00 |                           0.00e+00 |\n",
-       "|                               0.00e+00 |                           0.00e+00 |\n",
-       "+----------------------------------------+------------------------------------+\n",
-       "\n",
-       "+------------------------------------+\n",
-       "| Brain_Cortex.expression_proportion |\n",
-       "+------------------------------------+\n",
-       "|                            float64 |\n",
-       "+------------------------------------+\n",
-       "|                           0.00e+00 |\n",
-       "|                           0.00e+00 |\n",
-       "|                           0.00e+00 |\n",
-       "|                           0.00e+00 |\n",
-       "|                           0.00e+00 |\n",
-       "+------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------+\n",
-       "| Brain_FrontalCortex_BA9.transcript_expression |\n",
-       "+-----------------------------------------------+\n",
-       "|                                       float64 |\n",
-       "+-----------------------------------------------+\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "+-----------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------+\n",
-       "| Brain_FrontalCortex_BA9.expression_proportion |\n",
-       "+-----------------------------------------------+\n",
-       "|                                       float64 |\n",
-       "+-----------------------------------------------+\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "|                                      0.00e+00 |\n",
-       "+-----------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------+\n",
-       "| Brain_Hippocampus.transcript_expression |\n",
-       "+-----------------------------------------+\n",
-       "|                                 float64 |\n",
-       "+-----------------------------------------+\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "+-----------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------+\n",
-       "| Brain_Hippocampus.expression_proportion |\n",
-       "+-----------------------------------------+\n",
-       "|                                 float64 |\n",
-       "+-----------------------------------------+\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "|                                0.00e+00 |\n",
-       "+-----------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------+\n",
-       "| Brain_Hypothalamus.transcript_expression |\n",
-       "+------------------------------------------+\n",
-       "|                                  float64 |\n",
-       "+------------------------------------------+\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "+------------------------------------------+\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "+--------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------+\n",
-       "| Brain_Hypothalamus.expression_proportion |\n",
-       "+------------------------------------------+\n",
-       "|                                  float64 |\n",
-       "+------------------------------------------+\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "+------------------------------------------+\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
        "\n",
-       "+-----------------------------------------------------------+\n",
-       "| Brain_Nucleusaccumbens_basalganglia.transcript_expression |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                   float64 |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "+-----------------------------------------------------------+\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
        "\n",
-       "+-----------------------------------------------------------+\n",
-       "| Brain_Nucleusaccumbens_basalganglia.expression_proportion |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                   float64 |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "|                                                  0.00e+00 |\n",
-       "+-----------------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
        "+--------------------------------------------------+\n",
-       "| Brain_Putamen_basalganglia.transcript_expression |\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
        "+--------------------------------------------------+\n",
-       "|                                          float64 |\n",
+       "| array                                     |\n",
        "+--------------------------------------------------+\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
        "+--------------------------------------------------+\n",
        "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
+       "\n",
        "+--------------------------------------------------+\n",
-       "| Brain_Putamen_basalganglia.expression_proportion |\n",
+       "| joint.histograms.qual_hists.ab_hist_alt.n_larger |\n",
        "+--------------------------------------------------+\n",
-       "|                                          float64 |\n",
+       "|                                            int64 |\n",
        "+--------------------------------------------------+\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
-       "|                                         0.00e+00 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
        "+--------------------------------------------------+\n",
        "\n",
-       "+----------------------------------------------------+\n",
-       "| Brain_Spinalcord_cervicalc_1.transcript_expression |\n",
-       "+----------------------------------------------------+\n",
-       "|                                            float64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "+----------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+----------------------------------------------------+\n",
-       "| Brain_Spinalcord_cervicalc_1.expression_proportion |\n",
-       "+----------------------------------------------------+\n",
-       "|                                            float64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "+----------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.bin_freq                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
+       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
+       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
+       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
+       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.bin_freq                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
+       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
+       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
+       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
+       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                  744 |\n",
+       "|                                                 1130 |\n",
+       "|                                                 1487 |\n",
+       "|                                                 2622 |\n",
+       "|                                                 3743 |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+------------------------------------------------------+\n",
+       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]            |\n",
+       "+------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------+\n",
-       "| Brain_Substantianigra.transcript_expression |\n",
-       "+---------------------------------------------+\n",
-       "|                                     float64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "+---------------------------------------------+\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------+\n",
-       "| Brain_Substantianigra.expression_proportion |\n",
-       "+---------------------------------------------+\n",
-       "|                                     float64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "+---------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------+\n",
-       "| Breast_MammaryTissue.transcript_expression |\n",
-       "+--------------------------------------------+\n",
-       "|                                    float64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "+--------------------------------------------+\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+------------------------------------------------------+\n",
+       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "+------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------+\n",
-       "| Breast_MammaryTissue.expression_proportion |\n",
-       "+--------------------------------------------+\n",
-       "|                                    float64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "+--------------------------------------------+\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------+\n",
-       "| Cells_Culturedfibroblasts.transcript_expression |\n",
-       "+-------------------------------------------------+\n",
-       "|                                         float64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "+-------------------------------------------------+\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------+\n",
-       "| Cells_Culturedfibroblasts.expression_proportion |\n",
-       "+-------------------------------------------------+\n",
-       "|                                         float64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "|                                        0.00e+00 |\n",
-       "+-------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.bin_edges                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| Cells_EBV_transformedlymphocytes.transcript_expression |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                float64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "+--------------------------------------------------------+\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------------+\n",
+       "| array                                         |\n",
+       "+------------------------------------------------------+\n",
+       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
+       "+------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| Cells_EBV_transformedlymphocytes.expression_proportion |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                float64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "|                                               0.00e+00 |\n",
-       "+--------------------------------------------------------+\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                 int64 |\n",
+       "+-------------------------------------------------------+\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "|                                                     0 |\n",
+       "+-------------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------+-------------------------------------+\n",
-       "| Colon_Sigmoid.transcript_expression | Colon_Sigmoid.expression_proportion |\n",
-       "+-------------------------------------+-------------------------------------+\n",
-       "|                             float64 |                             float64 |\n",
-       "+-------------------------------------+-------------------------------------+\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "+-------------------------------------+-------------------------------------+\n",
+       "+------------------------------------------------------+\n",
+       "| joint.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                int64 |\n",
+       "+------------------------------------------------------+\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "|                                                    0 |\n",
+       "+------------------------------------------------------+\n",
        "\n",
-       "+----------------------------------------+\n",
-       "| Colon_Transverse.transcript_expression |\n",
-       "+----------------------------------------+\n",
-       "|                                float64 |\n",
-       "+----------------------------------------+\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "+----------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+----------------------------------------+\n",
-       "| Colon_Transverse.expression_proportion |\n",
-       "+----------------------------------------+\n",
-       "|                                float64 |\n",
-       "+----------------------------------------+\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "+----------------------------------------+\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,1,0,0,1,0,0,0,0,0]                            |\n",
+       "| [0,0,1,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,1,0,1,0,0,0,0,0]                            |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| Esophagus_GastroesophagealJunction.transcript_expression |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                  float64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "+----------------------------------------------------------+\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------------+\n",
+       "|                                            int64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "|                                                0 |\n",
+       "+--------------------------------------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| Esophagus_GastroesophagealJunction.expression_proportion |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                  float64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "|                                                 0.00e+00 |\n",
-       "+----------------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.bin_edges                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+----------------------------------------+\n",
-       "| Esophagus_Mucosa.transcript_expression |\n",
-       "+----------------------------------------+\n",
-       "|                                float64 |\n",
-       "+----------------------------------------+\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "+----------------------------------------+\n",
+       "+--------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------------+\n",
+       "| array                                     |\n",
+       "+--------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
+       "+--------------------------------------------------+\n",
        "\n",
-       "+----------------------------------------+\n",
-       "| Esophagus_Mucosa.expression_proportion |\n",
-       "+----------------------------------------+\n",
-       "|                                float64 |\n",
-       "+----------------------------------------+\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "|                               0.00e+00 |\n",
-       "+----------------------------------------+\n",
+       "+---------------------------------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------------+\n",
+       "|                                             int64 |\n",
+       "+---------------------------------------------------+\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "|                                                 0 |\n",
+       "+---------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------+\n",
-       "| Esophagus_Muscularis.transcript_expression |\n",
-       "+--------------------------------------------+\n",
-       "|                                    float64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "+--------------------------------------------+\n",
+       "+--------------------------------------------------+--------------------------+\n",
+       "| joint.histograms.age_hists.age_hist_hom.n_larger | joint.flags              |\n",
+       "+--------------------------------------------------+--------------------------+\n",
+       "|                                            int64 | set                 |\n",
+       "+--------------------------------------------------+--------------------------+\n",
+       "|                                                0 | {\"not_called_in_exomes\"} |\n",
+       "|                                                0 | {\"not_called_in_exomes\"} |\n",
+       "|                                                0 | {\"not_called_in_exomes\"} |\n",
+       "|                                                0 | {\"not_called_in_exomes\"} |\n",
+       "|                                                0 | {\"not_called_in_exomes\"} |\n",
+       "+--------------------------------------------------+--------------------------+\n",
        "\n",
-       "+--------------------------------------------+\n",
-       "| Esophagus_Muscularis.expression_proportion |\n",
-       "+--------------------------------------------+\n",
-       "|                                    float64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "|                                   0.00e+00 |\n",
-       "+--------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| joint.freq_comparison_stats.contingency_table_test                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------+\n",
-       "| Heart_AtrialAppendage.transcript_expression |\n",
-       "+---------------------------------------------+\n",
-       "|                                     float64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "+---------------------------------------------+\n",
+       "+------------------------------------------------------------------+\n",
+       "| joint.freq_comparison_stats.cochran_mantel_haenszel_test.p_value |\n",
+       "+------------------------------------------------------------------+\n",
+       "|                                                          float64 |\n",
+       "+------------------------------------------------------------------+\n",
+       "|                                                               NA |\n",
+       "|                                                               NA |\n",
+       "|                                                               NA |\n",
+       "|                                                               NA |\n",
+       "|                                                               NA |\n",
+       "+------------------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------+\n",
-       "| Heart_AtrialAppendage.expression_proportion |\n",
-       "+---------------------------------------------+\n",
-       "|                                     float64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "|                                    0.00e+00 |\n",
-       "+---------------------------------------------+\n",
+       "+----------------------------------------------------------------+\n",
+       "| joint.freq_comparison_stats.cochran_mantel_haenszel_test.chisq |\n",
+       "+----------------------------------------------------------------+\n",
+       "|                                                        float64 |\n",
+       "+----------------------------------------------------------------+\n",
+       "|                                                             NA |\n",
+       "|                                                             NA |\n",
+       "|                                                             NA |\n",
+       "|                                                             NA |\n",
+       "|                                                             NA |\n",
+       "+----------------------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------+\n",
-       "| Heart_LeftVentricle.transcript_expression |\n",
-       "+-------------------------------------------+\n",
-       "|                                   float64 |\n",
-       "+-------------------------------------------+\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "+-------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "| joint.freq_comparison_stats.stat_union.p_value |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                             NA |\n",
+       "|                                             NA |\n",
+       "|                                             NA |\n",
+       "|                                             NA |\n",
+       "|                                             NA |\n",
+       "+------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------+\n",
-       "| Heart_LeftVentricle.expression_proportion |\n",
-       "+-------------------------------------------+\n",
-       "|                                   float64 |\n",
-       "+-------------------------------------------+\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "|                                  0.00e+00 |\n",
-       "+-------------------------------------------+\n",
+       "+-------------------------------------------------------+\n",
+       "| joint.freq_comparison_stats.stat_union.stat_test_name |\n",
+       "+-------------------------------------------------------+\n",
+       "| str                                                   |\n",
+       "+-------------------------------------------------------+\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "| NA                                                    |\n",
+       "+-------------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------+-------------------------------------+\n",
-       "| Kidney_Cortex.transcript_expression | Kidney_Cortex.expression_proportion |\n",
-       "+-------------------------------------+-------------------------------------+\n",
-       "|                             float64 |                             float64 |\n",
-       "+-------------------------------------+-------------------------------------+\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "|                            0.00e+00 |                            0.00e+00 |\n",
-       "+-------------------------------------+-------------------------------------+\n",
+       "+-------------------------------------------------+---------------------+\n",
+       "| joint.freq_comparison_stats.stat_union.gen_ancs | coverage.exome.mean |\n",
+       "+-------------------------------------------------+---------------------+\n",
+       "| array                                      |             float64 |\n",
+       "+-------------------------------------------------+---------------------+\n",
+       "| NA                                              |                  NA |\n",
+       "| NA                                              |                  NA |\n",
+       "| NA                                              |                  NA |\n",
+       "| NA                                              |                  NA |\n",
+       "| NA                                              |                  NA |\n",
+       "+-------------------------------------------------+---------------------+\n",
+       "\n",
+       "+------------------------------+-------------------------+\n",
+       "| coverage.exome.median_approx | coverage.exome.total_DP |\n",
+       "+------------------------------+-------------------------+\n",
+       "|                        int32 |                   int64 |\n",
+       "+------------------------------+-------------------------+\n",
+       "|                           NA |                      NA |\n",
+       "|                           NA |                      NA |\n",
+       "|                           NA |                      NA |\n",
+       "|                           NA |                      NA |\n",
+       "|                           NA |                      NA |\n",
+       "+------------------------------+-------------------------+\n",
        "\n",
-       "+-----------------------------+-----------------------------+\n",
-       "| Liver.transcript_expression | Liver.expression_proportion |\n",
-       "+-----------------------------+-----------------------------+\n",
-       "|                     float64 |                     float64 |\n",
-       "+-----------------------------+-----------------------------+\n",
-       "|                    0.00e+00 |                    0.00e+00 |\n",
-       "|                    0.00e+00 |                    0.00e+00 |\n",
-       "|                    0.00e+00 |                    0.00e+00 |\n",
-       "|                    0.00e+00 |                    0.00e+00 |\n",
-       "|                    0.00e+00 |                    0.00e+00 |\n",
-       "+-----------------------------+-----------------------------+\n",
+       "+-----------------------+-----------------------+------------------------+\n",
+       "| coverage.exome.over_1 | coverage.exome.over_5 | coverage.exome.over_10 |\n",
+       "+-----------------------+-----------------------+------------------------+\n",
+       "|               float64 |               float64 |                float64 |\n",
+       "+-----------------------+-----------------------+------------------------+\n",
+       "|                    NA |                    NA |                     NA |\n",
+       "|                    NA |                    NA |                     NA |\n",
+       "|                    NA |                    NA |                     NA |\n",
+       "|                    NA |                    NA |                     NA |\n",
+       "|                    NA |                    NA |                     NA |\n",
+       "+-----------------------+-----------------------+------------------------+\n",
        "\n",
-       "+----------------------------+----------------------------+\n",
-       "| Lung.transcript_expression | Lung.expression_proportion |\n",
-       "+----------------------------+----------------------------+\n",
-       "|                    float64 |                    float64 |\n",
-       "+----------------------------+----------------------------+\n",
-       "|                   0.00e+00 |                   0.00e+00 |\n",
-       "|                   0.00e+00 |                   0.00e+00 |\n",
-       "|                   0.00e+00 |                   0.00e+00 |\n",
-       "|                   0.00e+00 |                   0.00e+00 |\n",
-       "|                   0.00e+00 |                   0.00e+00 |\n",
-       "+----------------------------+----------------------------+\n",
+       "+------------------------+------------------------+------------------------+\n",
+       "| coverage.exome.over_15 | coverage.exome.over_20 | coverage.exome.over_25 |\n",
+       "+------------------------+------------------------+------------------------+\n",
+       "|                float64 |                float64 |                float64 |\n",
+       "+------------------------+------------------------+------------------------+\n",
+       "|                     NA |                     NA |                     NA |\n",
+       "|                     NA |                     NA |                     NA |\n",
+       "|                     NA |                     NA |                     NA |\n",
+       "|                     NA |                     NA |                     NA |\n",
+       "|                     NA |                     NA |                     NA |\n",
+       "+------------------------+------------------------+------------------------+\n",
        "\n",
-       "+------------------------------------------+\n",
-       "| MinorSalivaryGland.transcript_expression |\n",
-       "+------------------------------------------+\n",
-       "|                                  float64 |\n",
-       "+------------------------------------------+\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "+------------------------------------------+\n",
+       "+------------------------+------------------------+-------------------------+\n",
+       "| coverage.exome.over_30 | coverage.exome.over_50 | coverage.exome.over_100 |\n",
+       "+------------------------+------------------------+-------------------------+\n",
+       "|                float64 |                float64 |                 float64 |\n",
+       "+------------------------+------------------------+-------------------------+\n",
+       "|                     NA |                     NA |                      NA |\n",
+       "|                     NA |                     NA |                      NA |\n",
+       "|                     NA |                     NA |                      NA |\n",
+       "|                     NA |                     NA |                      NA |\n",
+       "|                     NA |                     NA |                      NA |\n",
+       "+------------------------+------------------------+-------------------------+\n",
        "\n",
-       "+------------------------------------------+\n",
-       "| MinorSalivaryGland.expression_proportion |\n",
-       "+------------------------------------------+\n",
-       "|                                  float64 |\n",
-       "+------------------------------------------+\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "|                                 0.00e+00 |\n",
-       "+------------------------------------------+\n",
+       "+----------------------+-------------------------------+\n",
+       "| coverage.genome.mean | coverage.genome.median_approx |\n",
+       "+----------------------+-------------------------------+\n",
+       "|              float64 |                         int32 |\n",
+       "+----------------------+-------------------------------+\n",
+       "|             3.74e+01 |                            38 |\n",
+       "|             4.29e+01 |                            44 |\n",
+       "|             4.42e+01 |                            47 |\n",
+       "|             4.54e+01 |                            51 |\n",
+       "|             5.43e+01 |                            58 |\n",
+       "+----------------------+-------------------------------+\n",
        "\n",
-       "+---------------------------------------+\n",
-       "| Muscle_Skeletal.transcript_expression |\n",
-       "+---------------------------------------+\n",
-       "|                               float64 |\n",
-       "+---------------------------------------+\n",
-       "|                              0.00e+00 |\n",
-       "|                              0.00e+00 |\n",
-       "|                              0.00e+00 |\n",
-       "|                              0.00e+00 |\n",
-       "|                              0.00e+00 |\n",
-       "+---------------------------------------+\n",
+       "+--------------------------+------------------------+------------------------+\n",
+       "| coverage.genome.total_DP | coverage.genome.over_1 | coverage.genome.over_5 |\n",
+       "+--------------------------+------------------------+------------------------+\n",
+       "|                    int64 |                float32 |                float32 |\n",
+       "+--------------------------+------------------------+------------------------+\n",
+       "|                  2684436 |               8.08e-01 |               8.08e-01 |\n",
+       "|                  3077955 |               8.43e-01 |               8.43e-01 |\n",
+       "|                  3165823 |               8.06e-01 |               8.06e-01 |\n",
+       "|                  3252282 |               7.16e-01 |               7.16e-01 |\n",
+       "|                  3891700 |               8.20e-01 |               8.20e-01 |\n",
+       "+--------------------------+------------------------+------------------------+\n",
        "\n",
-       "+---------------------------------------+------------------------------------+\n",
-       "| Muscle_Skeletal.expression_proportion | Nerve_Tibial.transcript_expression |\n",
-       "+---------------------------------------+------------------------------------+\n",
-       "|                               float64 |                            float64 |\n",
-       "+---------------------------------------+------------------------------------+\n",
-       "|                              0.00e+00 |                           0.00e+00 |\n",
-       "|                              0.00e+00 |                           0.00e+00 |\n",
-       "|                              0.00e+00 |                           0.00e+00 |\n",
-       "|                              0.00e+00 |                           0.00e+00 |\n",
-       "|                              0.00e+00 |                           0.00e+00 |\n",
-       "+---------------------------------------+------------------------------------+\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
+       "| coverage.genome.over_10 | coverage.genome.over_15 | coverage.genome.over_20 |\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
+       "|                 float32 |                 float32 |                 float32 |\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
+       "|                8.07e-01 |                8.01e-01 |                7.82e-01 |\n",
+       "|                8.42e-01 |                8.39e-01 |                8.27e-01 |\n",
+       "|                8.05e-01 |                8.03e-01 |                7.97e-01 |\n",
+       "|                7.15e-01 |                7.15e-01 |                7.13e-01 |\n",
+       "|                8.20e-01 |                8.20e-01 |                8.18e-01 |\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
        "\n",
-       "+------------------------------------+-----------------------------+\n",
-       "| Nerve_Tibial.expression_proportion | Ovary.transcript_expression |\n",
-       "+------------------------------------+-----------------------------+\n",
-       "|                            float64 |                     float64 |\n",
-       "+------------------------------------+-----------------------------+\n",
-       "|                           0.00e+00 |                    0.00e+00 |\n",
-       "|                           0.00e+00 |                    0.00e+00 |\n",
-       "|                           0.00e+00 |                    0.00e+00 |\n",
-       "|                           0.00e+00 |                    0.00e+00 |\n",
-       "|                           0.00e+00 |                    0.00e+00 |\n",
-       "+------------------------------------+-----------------------------+\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
+       "| coverage.genome.over_25 | coverage.genome.over_30 | coverage.genome.over_50 |\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
+       "|                 float32 |                 float32 |                 float32 |\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
+       "|                7.43e-01 |                6.67e-01 |                2.89e-01 |\n",
+       "|                8.02e-01 |                7.48e-01 |                3.93e-01 |\n",
+       "|                7.82e-01 |                7.48e-01 |                4.50e-01 |\n",
+       "|                7.10e-01 |                7.00e-01 |                5.30e-01 |\n",
+       "|                8.15e-01 |                8.07e-01 |                6.45e-01 |\n",
+       "+-------------------------+-------------------------+-------------------------+\n",
        "\n",
-       "+-----------------------------+--------------------------------+\n",
-       "| Ovary.expression_proportion | Pancreas.transcript_expression |\n",
-       "+-----------------------------+--------------------------------+\n",
-       "|                     float64 |                        float64 |\n",
-       "+-----------------------------+--------------------------------+\n",
-       "|                    0.00e+00 |                       0.00e+00 |\n",
-       "|                    0.00e+00 |                       0.00e+00 |\n",
-       "|                    0.00e+00 |                       0.00e+00 |\n",
-       "|                    0.00e+00 |                       0.00e+00 |\n",
-       "|                    0.00e+00 |                       0.00e+00 |\n",
-       "+-----------------------------+--------------------------------+\n",
+       "+--------------------------+\n",
+       "| coverage.genome.over_100 |\n",
+       "+--------------------------+\n",
+       "|                  float32 |\n",
+       "+--------------------------+\n",
+       "|                 1.13e-02 |\n",
+       "|                 1.66e-02 |\n",
+       "|                 2.14e-02 |\n",
+       "|                 3.76e-02 |\n",
+       "|                 5.38e-02 |\n",
+       "+--------------------------+\n",
        "\n",
-       "+--------------------------------+---------------------------------+\n",
-       "| Pancreas.expression_proportion | Pituitary.transcript_expression |\n",
-       "+--------------------------------+---------------------------------+\n",
-       "|                        float64 |                         float64 |\n",
-       "+--------------------------------+---------------------------------+\n",
-       "|                       0.00e+00 |                        0.00e+00 |\n",
-       "|                       0.00e+00 |                        0.00e+00 |\n",
-       "|                       0.00e+00 |                        0.00e+00 |\n",
-       "|                       0.00e+00 |                        0.00e+00 |\n",
-       "|                       0.00e+00 |                        0.00e+00 |\n",
-       "+--------------------------------+---------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| transcript_consequences                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, domains: set \n",
+      "    'mean': float64 \n",
+      "    'median_approx': int32 \n",
+      "    'total_DP': int64 \n",
+      "    'over_1': float64 \n",
+      "    'over_5': float64 \n",
+      "    'over_10': float64 \n",
+      "    'over_15': float64 \n",
+      "    'over_20': float64 \n",
+      "    'over_25': float64 \n",
+      "    'over_30': float64 \n",
+      "    'over_50': float64 \n",
+      "    'over_100': float64 \n",
+      "----------------------------------------\n",
+      "Key: ['locus']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5969ab0c-7cee-4061-8740-8b82366ae806",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "49fcf64b-1a91-4052-bb98-b329a0e8031b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
locus
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32int64float64float64float64float64float64float64float64float64float64
chr1:118191.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118201.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118211.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118221.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:118231.12e-010816952.79e-027.59e-030.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| locus         |     mean | median_approx | total_DP |   over_1 |   over_5 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| locus |  float64 |         int32 |    int64 |  float64 |  float64 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| chr1:11819    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "| chr1:11820    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "| chr1:11821    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "| chr1:11822    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "| chr1:11823    | 1.12e-01 |             0 |    81695 | 2.79e-02 | 7.59e-03 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
        "\n",
-       "+-----------------------------------------------------+\n",
-       "| Skin_NotSunExposed_Suprapubic.transcript_expression |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                             float64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "+-----------------------------------------------------+\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "|  over_10 |  over_15 |  over_20 |  over_25 |  over_30 |  over_50 | over_100 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "|  float64 |  float64 |  float64 |  float64 |  float64 |  float64 |  float64 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "| 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.00e+00 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19b2e9be-48fd-4859-af3d-a0775656d24c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Genomes coverage Hail Table\n",
+    "\n",
+    "Showing gnomAD **v3.0.1** coverage."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5918f8e0-a723-439a-a11f-558d5ed13be8",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a8d0be07-c35d-425a-b554-c86034e367fc",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='genomes', version='3.0.1', dataset=\"coverage\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d129b898-d642-44d1-8243-66a9cca8d1b1",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "18afb20f-7429-4fe2-a6c5-73a22dcbdb76",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    None\n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'mean': float64 \n",
+      "    'median_approx': int32 \n",
+      "    'total_DP': int64 \n",
+      "    'over_1': float32 \n",
+      "    'over_5': float32 \n",
+      "    'over_10': float32 \n",
+      "    'over_15': float32 \n",
+      "    'over_20': float32 \n",
+      "    'over_25': float32 \n",
+      "    'over_30': float32 \n",
+      "    'over_50': float32 \n",
+      "    'over_100': float32 \n",
+      "----------------------------------------\n",
+      "Key: ['locus']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05d8e22c-93d6-4ecf-b9e7-711945268c82",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b27cb655-3abb-4501-bcc9-3f634db64591",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
locus
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>float64int32int64float32float32float32float32float32float32float32float32float32
chr1:100012.41e+0001729261.25e-011.19e-019.12e-026.95e-025.15e-023.88e-022.62e-022.27e-030.00e+00
chr1:100024.61e+0003307602.20e-012.15e-011.79e-011.39e-011.02e-017.60e-025.06e-024.83e-032.79e-05
chr1:100036.38e+0004576112.62e-012.59e-012.41e-012.07e-011.59e-011.18e-017.77e-027.61e-035.58e-05
chr1:100041.04e+0107448404.27e-014.24e-013.99e-013.43e-012.60e-011.87e-011.21e-011.20e-021.67e-04
chr1:100051.18e+0108494414.83e-014.80e-014.56e-013.92e-012.96e-012.14e-011.38e-011.42e-022.37e-04
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| locus         |     mean | median_approx | total_DP |   over_1 |   over_5 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| locus |  float64 |         int32 |    int64 |  float32 |  float32 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
+       "| chr1:10001    | 2.41e+00 |             0 |   172926 | 1.25e-01 | 1.19e-01 |\n",
+       "| chr1:10002    | 4.61e+00 |             0 |   330760 | 2.20e-01 | 2.15e-01 |\n",
+       "| chr1:10003    | 6.38e+00 |             0 |   457611 | 2.62e-01 | 2.59e-01 |\n",
+       "| chr1:10004    | 1.04e+01 |             0 |   744840 | 4.27e-01 | 4.24e-01 |\n",
+       "| chr1:10005    | 1.18e+01 |             0 |   849441 | 4.83e-01 | 4.80e-01 |\n",
+       "+---------------+----------+---------------+----------+----------+----------+\n",
        "\n",
-       "+-----------------------------------------------------+\n",
-       "| Skin_NotSunExposed_Suprapubic.expression_proportion |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                             float64 |\n",
-       "+-----------------------------------------------------+\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "|                                            0.00e+00 |\n",
-       "+-----------------------------------------------------+\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "|  over_10 |  over_15 |  over_20 |  over_25 |  over_30 |  over_50 | over_100 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "|  float32 |  float32 |  float32 |  float32 |  float32 |  float32 |  float32 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "| 9.12e-02 | 6.95e-02 | 5.15e-02 | 3.88e-02 | 2.62e-02 | 2.27e-03 | 0.00e+00 |\n",
+       "| 1.79e-01 | 1.39e-01 | 1.02e-01 | 7.60e-02 | 5.06e-02 | 4.83e-03 | 2.79e-05 |\n",
+       "| 2.41e-01 | 2.07e-01 | 1.59e-01 | 1.18e-01 | 7.77e-02 | 7.61e-03 | 5.58e-05 |\n",
+       "| 3.99e-01 | 3.43e-01 | 2.60e-01 | 1.87e-01 | 1.21e-01 | 1.20e-02 | 1.67e-04 |\n",
+       "| 4.56e-01 | 3.92e-01 | 2.96e-01 | 2.14e-01 | 1.38e-01 | 1.42e-02 | 2.37e-04 |\n",
+       "+----------+----------+----------+----------+----------+----------+----------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4ae4713-f542-46e3-979f-0bcfb44ec56c",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Gene constraint\n",
+    "\n",
+    "For details on gene constraint see the [Help](https://gnomad.broadinstitute.org/help/constraint) and [FAQ](https://gnomad.broadinstitute.org/help#constraint)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed92af6-aacc-4cb2-890d-7ff76a00196d",
+   "metadata": {},
+   "source": [
+    "### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "3de343dc-37ae-4b17-a056-9d30e71e217c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(version='4.1', dataset=\"constraint\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b59b2f6-aadd-408d-8663-b676d41565bf",
+   "metadata": {},
+   "source": [
+    "### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c401dc94-0db0-4902-933d-e46c8aa447ce",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'version': str \n",
+      "    'apply_model_params': struct {\n",
+      "        max_af: float64, \n",
+      "        pops: tuple (\n",
+      "        ), \n",
+      "        plateau_models: struct {\n",
+      "            total: dict>\n",
+      "        }, \n",
+      "        coverage_model: str\n",
+      "    } \n",
+      "    'sd_raw_z': struct {\n",
+      "        lof: float64, \n",
+      "        mis: float64, \n",
+      "        syn: float64\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'gene': str \n",
+      "    'gene_id': str \n",
+      "    'transcript': str \n",
+      "    'canonical': bool \n",
+      "    'mane_select': bool \n",
+      "    'lof_hc_lc': struct {\n",
+      "        obs: int64, \n",
+      "        exp: float64, \n",
+      "        possible: int64, \n",
+      "        oe: float64, \n",
+      "        mu: float64, \n",
+      "        pLI: float64, \n",
+      "        pNull: float64, \n",
+      "        pRec: float64\n",
+      "    } \n",
+      "    'lof': struct {\n",
+      "        obs: int64, \n",
+      "        exp: float64, \n",
+      "        possible: int64, \n",
+      "        oe: float64, \n",
+      "        mu: float64, \n",
+      "        pLI: float64, \n",
+      "        pNull: float64, \n",
+      "        pRec: float64, \n",
+      "        oe_ci: struct {\n",
+      "            lower: float64, \n",
+      "            upper: float64, \n",
+      "            upper_rank: int64, \n",
+      "            upper_bin_decile: int32\n",
+      "        }, \n",
+      "        z_raw: float64, \n",
+      "        z_score: float64\n",
+      "    } \n",
+      "    'mis': struct {\n",
+      "        obs: int64, \n",
+      "        exp: float64, \n",
+      "        possible: int64, \n",
+      "        oe: float64, \n",
+      "        mu: float64, \n",
+      "        oe_ci: struct {\n",
+      "            lower: float64, \n",
+      "            upper: float64\n",
+      "        }, \n",
+      "        z_raw: float64, \n",
+      "        z_score: float64\n",
+      "    } \n",
+      "    'mis_pphen': struct {\n",
+      "        obs: int64, \n",
+      "        exp: float64, \n",
+      "        possible: int64, \n",
+      "        oe: float64\n",
+      "    } \n",
+      "    'syn': struct {\n",
+      "        obs: int64, \n",
+      "        exp: float64, \n",
+      "        possible: int64, \n",
+      "        oe: float64, \n",
+      "        mu: float64, \n",
+      "        oe_ci: struct {\n",
+      "            lower: float64, \n",
+      "            upper: float64\n",
+      "        }, \n",
+      "        z_raw: float64, \n",
+      "        z_score: float64\n",
+      "    } \n",
+      "    'constraint_flags': set \n",
+      "    'level': str \n",
+      "    'transcript_type': str \n",
+      "    'chromosome': str \n",
+      "    'cds_length': int64 \n",
+      "    'num_coding_exons': int64 \n",
+      "----------------------------------------\n",
+      "Key: ['gene', 'gene_id', 'transcript', 'canonical', 'mane_select']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b0e64c9-744c-4ed7-a832-9d68c548540b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "262cbbf1-9fc7-4819-9b2a-d1a1e27c10bf",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
lof
mis
syn
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oe_ci
oe_ci
mis_pphen
oe_ci
gene
gene_id
transcript
canonical
mane_select
obs
exp
possible
oe
mu
pLI
pNull
pRec
obs
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pNull
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lower
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constraint_flags
level
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strstrstrboolboolint64float64int64float64float64float64float64float64int64float64int64float64float64float64float64float64float64float64int64int32float64float64int64float64int64float64float64float64float64float64float64int64float64int64float64int64float64int64float64float64float64float64float64float64set<str>strstrstrint64int64
"A1BG""1""NM_130786.4"TrueTrue454.30e+011931.05e+007.06e-071.63e-168.49e-011.51e-01454.30e+011931.05e+007.06e-071.77e-168.43e-011.57e-018.22e-011.34e+00NANA-2.98e-01-2.52e-017076.47e+0228701.09e+007.67e-061.03e+001.16e+00-2.36e+00-8.61e-012201.91e+028901.15e+003162.96e+029941.07e+003.02e-069.73e-011.17e+00-1.17e+00-6.35e-01{}NANANANANA
"A1BG""ENSG00000121410""ENST00000263100"TrueTrue454.30e+011931.05e+007.06e-071.63e-168.49e-011.51e-01454.30e+011931.05e+007.06e-071.77e-168.43e-011.57e-018.22e-011.34e+00140577-2.98e-01-2.52e-017076.47e+0228701.09e+007.67e-061.03e+001.16e+00-2.36e+00-8.61e-012201.91e+028901.15e+003162.96e+029941.07e+003.02e-069.73e-011.17e+00-1.17e+00-6.35e-01{}"2""protein_coding""chr19"14858
"A1BG""ENSG00000121410""ENST00000600966"FalseFalse242.63e+011239.14e-014.20e-072.53e-083.43e-016.57e-01222.50e+011198.80e-013.96e-071.84e-072.65e-017.35e-016.28e-011.26e+00NANA6.01e-015.09e-013994.06e+0218589.84e-014.91e-069.06e-011.07e+003.24e-011.18e-011031.10e+025129.40e-011661.79e+026379.28e-011.91e-068.17e-011.06e+009.64e-015.26e-01{}"1""protein_coding""chr19"9175
"A1CF""29974""NM_001198818.2"FalseFalse457.00e+013526.43e-019.55e-077.32e-102.06e-039.98e-01457.00e+013526.43e-019.55e-077.66e-101.96e-039.98e-015.06e-018.25e-01NANA2.98e+002.53e+006537.46e+0237208.76e-014.38e-068.21e-019.34e-013.39e+001.24e+002362.77e+0213998.53e-012722.73e+0211579.98e-012.01e-069.03e-011.10e+003.27e-021.78e-02{}NANANANANA
"A1CF""29974""NM_001198819.2"FalseFalse487.28e+013676.59e-011.02e-067.64e-112.53e-039.97e-01477.03e+013556.69e-019.72e-077.70e-113.58e-039.96e-015.28e-018.53e-01NANA2.78e+002.35e+006707.62e+0238378.80e-014.43e-068.25e-019.38e-013.33e+001.21e+003093.65e+0218418.47e-012712.76e+0211889.82e-011.91e-068.88e-011.09e+003.00e-011.64e-01{}NANANANANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+--------+-------------------+-------------------+-----------+-------------+\n",
+       "| gene   | gene_id           | transcript        | canonical | mane_select |\n",
+       "+--------+-------------------+-------------------+-----------+-------------+\n",
+       "| str    | str               | str               |      bool |        bool |\n",
+       "+--------+-------------------+-------------------+-----------+-------------+\n",
+       "| \"A1BG\" | \"1\"               | \"NM_130786.4\"     |      True |        True |\n",
+       "| \"A1BG\" | \"ENSG00000121410\" | \"ENST00000263100\" |      True |        True |\n",
+       "| \"A1BG\" | \"ENSG00000121410\" | \"ENST00000600966\" |     False |       False |\n",
+       "| \"A1CF\" | \"29974\"           | \"NM_001198818.2\"  |     False |       False |\n",
+       "| \"A1CF\" | \"29974\"           | \"NM_001198819.2\"  |     False |       False |\n",
+       "+--------+-------------------+-------------------+-----------+-------------+\n",
        "\n",
-       "+------------------------------------------------+\n",
-       "| Skin_SunExposed_Lowerleg.transcript_expression |\n",
-       "+------------------------------------------------+\n",
-       "|                                        float64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "+------------------------------------------------+\n",
+       "+---------------+---------------+--------------------+--------------+\n",
+       "| lof_hc_lc.obs | lof_hc_lc.exp | lof_hc_lc.possible | lof_hc_lc.oe |\n",
+       "+---------------+---------------+--------------------+--------------+\n",
+       "|         int64 |       float64 |              int64 |      float64 |\n",
+       "+---------------+---------------+--------------------+--------------+\n",
+       "|            45 |      4.30e+01 |                193 |     1.05e+00 |\n",
+       "|            45 |      4.30e+01 |                193 |     1.05e+00 |\n",
+       "|            24 |      2.63e+01 |                123 |     9.14e-01 |\n",
+       "|            45 |      7.00e+01 |                352 |     6.43e-01 |\n",
+       "|            48 |      7.28e+01 |                367 |     6.59e-01 |\n",
+       "+---------------+---------------+--------------------+--------------+\n",
        "\n",
-       "+------------------------------------------------+\n",
-       "| Skin_SunExposed_Lowerleg.expression_proportion |\n",
-       "+------------------------------------------------+\n",
-       "|                                        float64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "|                                       0.00e+00 |\n",
-       "+------------------------------------------------+\n",
+       "+--------------+---------------+-----------------+----------------+---------+\n",
+       "| lof_hc_lc.mu | lof_hc_lc.pLI | lof_hc_lc.pNull | lof_hc_lc.pRec | lof.obs |\n",
+       "+--------------+---------------+-----------------+----------------+---------+\n",
+       "|      float64 |       float64 |         float64 |        float64 |   int64 |\n",
+       "+--------------+---------------+-----------------+----------------+---------+\n",
+       "|     7.06e-07 |      1.63e-16 |        8.49e-01 |       1.51e-01 |      45 |\n",
+       "|     7.06e-07 |      1.63e-16 |        8.49e-01 |       1.51e-01 |      45 |\n",
+       "|     4.20e-07 |      2.53e-08 |        3.43e-01 |       6.57e-01 |      22 |\n",
+       "|     9.55e-07 |      7.32e-10 |        2.06e-03 |       9.98e-01 |      45 |\n",
+       "|     1.02e-06 |      7.64e-11 |        2.53e-03 |       9.97e-01 |      47 |\n",
+       "+--------------+---------------+-----------------+----------------+---------+\n",
        "\n",
-       "+----------------------------------------------------+\n",
-       "| SmallIntestine_TerminalIleum.transcript_expression |\n",
-       "+----------------------------------------------------+\n",
-       "|                                            float64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "+----------------------------------------------------+\n",
+       "+----------+--------------+----------+----------+----------+-----------+\n",
+       "|  lof.exp | lof.possible |   lof.oe |   lof.mu |  lof.pLI | lof.pNull |\n",
+       "+----------+--------------+----------+----------+----------+-----------+\n",
+       "|  float64 |        int64 |  float64 |  float64 |  float64 |   float64 |\n",
+       "+----------+--------------+----------+----------+----------+-----------+\n",
+       "| 4.30e+01 |          193 | 1.05e+00 | 7.06e-07 | 1.77e-16 |  8.43e-01 |\n",
+       "| 4.30e+01 |          193 | 1.05e+00 | 7.06e-07 | 1.77e-16 |  8.43e-01 |\n",
+       "| 2.50e+01 |          119 | 8.80e-01 | 3.96e-07 | 1.84e-07 |  2.65e-01 |\n",
+       "| 7.00e+01 |          352 | 6.43e-01 | 9.55e-07 | 7.66e-10 |  1.96e-03 |\n",
+       "| 7.03e+01 |          355 | 6.69e-01 | 9.72e-07 | 7.70e-11 |  3.58e-03 |\n",
+       "+----------+--------------+----------+----------+----------+-----------+\n",
        "\n",
-       "+----------------------------------------------------+\n",
-       "| SmallIntestine_TerminalIleum.expression_proportion |\n",
-       "+----------------------------------------------------+\n",
-       "|                                            float64 |\n",
-       "+----------------------------------------------------+\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "|                                           0.00e+00 |\n",
-       "+----------------------------------------------------+\n",
+       "+----------+-----------------+-----------------+----------------------+\n",
+       "| lof.pRec | lof.oe_ci.lower | lof.oe_ci.upper | lof.oe_ci.upper_rank |\n",
+       "+----------+-----------------+-----------------+----------------------+\n",
+       "|  float64 |         float64 |         float64 |                int64 |\n",
+       "+----------+-----------------+-----------------+----------------------+\n",
+       "| 1.57e-01 |        8.22e-01 |        1.34e+00 |                   NA |\n",
+       "| 1.57e-01 |        8.22e-01 |        1.34e+00 |                14057 |\n",
+       "| 7.35e-01 |        6.28e-01 |        1.26e+00 |                   NA |\n",
+       "| 9.98e-01 |        5.06e-01 |        8.25e-01 |                   NA |\n",
+       "| 9.96e-01 |        5.28e-01 |        8.53e-01 |                   NA |\n",
+       "+----------+-----------------+-----------------+----------------------+\n",
        "\n",
-       "+------------------------------+------------------------------+\n",
-       "| Spleen.transcript_expression | Spleen.expression_proportion |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                      float64 |                      float64 |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "+------------------------------+------------------------------+\n",
+       "+----------------------------+-----------+-------------+---------+----------+\n",
+       "| lof.oe_ci.upper_bin_decile | lof.z_raw | lof.z_score | mis.obs |  mis.exp |\n",
+       "+----------------------------+-----------+-------------+---------+----------+\n",
+       "|                      int32 |   float64 |     float64 |   int64 |  float64 |\n",
+       "+----------------------------+-----------+-------------+---------+----------+\n",
+       "|                         NA | -2.98e-01 |   -2.52e-01 |     707 | 6.47e+02 |\n",
+       "|                          7 | -2.98e-01 |   -2.52e-01 |     707 | 6.47e+02 |\n",
+       "|                         NA |  6.01e-01 |    5.09e-01 |     399 | 4.06e+02 |\n",
+       "|                         NA |  2.98e+00 |    2.53e+00 |     653 | 7.46e+02 |\n",
+       "|                         NA |  2.78e+00 |    2.35e+00 |     670 | 7.62e+02 |\n",
+       "+----------------------------+-----------+-------------+---------+----------+\n",
        "\n",
-       "+-------------------------------+-------------------------------+\n",
-       "| Stomach.transcript_expression | Stomach.expression_proportion |\n",
-       "+-------------------------------+-------------------------------+\n",
-       "|                       float64 |                       float64 |\n",
-       "+-------------------------------+-------------------------------+\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "+-------------------------------+-------------------------------+\n",
+       "+--------------+----------+----------+-----------------+-----------------+\n",
+       "| mis.possible |   mis.oe |   mis.mu | mis.oe_ci.lower | mis.oe_ci.upper |\n",
+       "+--------------+----------+----------+-----------------+-----------------+\n",
+       "|        int64 |  float64 |  float64 |         float64 |         float64 |\n",
+       "+--------------+----------+----------+-----------------+-----------------+\n",
+       "|         2870 | 1.09e+00 | 7.67e-06 |        1.03e+00 |        1.16e+00 |\n",
+       "|         2870 | 1.09e+00 | 7.67e-06 |        1.03e+00 |        1.16e+00 |\n",
+       "|         1858 | 9.84e-01 | 4.91e-06 |        9.06e-01 |        1.07e+00 |\n",
+       "|         3720 | 8.76e-01 | 4.38e-06 |        8.21e-01 |        9.34e-01 |\n",
+       "|         3837 | 8.80e-01 | 4.43e-06 |        8.25e-01 |        9.38e-01 |\n",
+       "+--------------+----------+----------+-----------------+-----------------+\n",
        "\n",
-       "+------------------------------+------------------------------+\n",
-       "| Testis.transcript_expression | Testis.expression_proportion |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                      float64 |                      float64 |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "+------------------------------+------------------------------+\n",
+       "+-----------+-------------+---------------+---------------+--------------------+\n",
+       "| mis.z_raw | mis.z_score | mis_pphen.obs | mis_pphen.exp | mis_pphen.possible |\n",
+       "+-----------+-------------+---------------+---------------+--------------------+\n",
+       "|   float64 |     float64 |         int64 |       float64 |              int64 |\n",
+       "+-----------+-------------+---------------+---------------+--------------------+\n",
+       "| -2.36e+00 |   -8.61e-01 |           220 |      1.91e+02 |                890 |\n",
+       "| -2.36e+00 |   -8.61e-01 |           220 |      1.91e+02 |                890 |\n",
+       "|  3.24e-01 |    1.18e-01 |           103 |      1.10e+02 |                512 |\n",
+       "|  3.39e+00 |    1.24e+00 |           236 |      2.77e+02 |               1399 |\n",
+       "|  3.33e+00 |    1.21e+00 |           309 |      3.65e+02 |               1841 |\n",
+       "+-----------+-------------+---------------+---------------+--------------------+\n",
        "\n",
-       "+-------------------------------+-------------------------------+\n",
-       "| Thyroid.transcript_expression | Thyroid.expression_proportion |\n",
-       "+-------------------------------+-------------------------------+\n",
-       "|                       float64 |                       float64 |\n",
-       "+-------------------------------+-------------------------------+\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "|                      0.00e+00 |                      0.00e+00 |\n",
-       "+-------------------------------+-------------------------------+\n",
+       "+--------------+---------+----------+--------------+----------+----------+\n",
+       "| mis_pphen.oe | syn.obs |  syn.exp | syn.possible |   syn.oe |   syn.mu |\n",
+       "+--------------+---------+----------+--------------+----------+----------+\n",
+       "|      float64 |   int64 |  float64 |        int64 |  float64 |  float64 |\n",
+       "+--------------+---------+----------+--------------+----------+----------+\n",
+       "|     1.15e+00 |     316 | 2.96e+02 |          994 | 1.07e+00 | 3.02e-06 |\n",
+       "|     1.15e+00 |     316 | 2.96e+02 |          994 | 1.07e+00 | 3.02e-06 |\n",
+       "|     9.40e-01 |     166 | 1.79e+02 |          637 | 9.28e-01 | 1.91e-06 |\n",
+       "|     8.53e-01 |     272 | 2.73e+02 |         1157 | 9.98e-01 | 2.01e-06 |\n",
+       "|     8.47e-01 |     271 | 2.76e+02 |         1188 | 9.82e-01 | 1.91e-06 |\n",
+       "+--------------+---------+----------+--------------+----------+----------+\n",
        "\n",
-       "+------------------------------+------------------------------+\n",
-       "| Uterus.transcript_expression | Uterus.expression_proportion |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                      float64 |                      float64 |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "+------------------------------+------------------------------+\n",
+       "+-----------------+-----------------+-----------+-------------+\n",
+       "| syn.oe_ci.lower | syn.oe_ci.upper | syn.z_raw | syn.z_score |\n",
+       "+-----------------+-----------------+-----------+-------------+\n",
+       "|         float64 |         float64 |   float64 |     float64 |\n",
+       "+-----------------+-----------------+-----------+-------------+\n",
+       "|        9.73e-01 |        1.17e+00 | -1.17e+00 |   -6.35e-01 |\n",
+       "|        9.73e-01 |        1.17e+00 | -1.17e+00 |   -6.35e-01 |\n",
+       "|        8.17e-01 |        1.06e+00 |  9.64e-01 |    5.26e-01 |\n",
+       "|        9.03e-01 |        1.10e+00 |  3.27e-02 |    1.78e-02 |\n",
+       "|        8.88e-01 |        1.09e+00 |  3.00e-01 |    1.64e-01 |\n",
+       "+-----------------+-----------------+-----------+-------------+\n",
        "\n",
-       "+------------------------------+------------------------------+\n",
-       "| Vagina.transcript_expression | Vagina.expression_proportion |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                      float64 |                      float64 |\n",
-       "+------------------------------+------------------------------+\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "|                     0.00e+00 |                     0.00e+00 |\n",
-       "+------------------------------+------------------------------+\n",
+       "+------------------+-------+------------------+------------+------------+\n",
+       "| constraint_flags | level | transcript_type  | chromosome | cds_length |\n",
+       "+------------------+-------+------------------+------------+------------+\n",
+       "| set         | str   | str              | str        |      int64 |\n",
+       "+------------------+-------+------------------+------------+------------+\n",
+       "| {}               | NA    | NA               | NA         |         NA |\n",
+       "| {}               | \"2\"   | \"protein_coding\" | \"chr19\"    |       1485 |\n",
+       "| {}               | \"1\"   | \"protein_coding\" | \"chr19\"    |        917 |\n",
+       "| {}               | NA    | NA               | NA         |         NA |\n",
+       "| {}               | NA    | NA               | NA         |         NA |\n",
+       "+------------------+-------+------------------+------------+------------+\n",
        "\n",
-       "+----------------------------------+----------------------------------+\n",
-       "| WholeBlood.transcript_expression | WholeBlood.expression_proportion |\n",
-       "+----------------------------------+----------------------------------+\n",
-       "|                          float64 |                          float64 |\n",
-       "+----------------------------------+----------------------------------+\n",
-       "|                         0.00e+00 |                         0.00e+00 |\n",
-       "|                         0.00e+00 |                         0.00e+00 |\n",
-       "|                         0.00e+00 |                         0.00e+00 |\n",
-       "|                         0.00e+00 |                         0.00e+00 |\n",
-       "|                         0.00e+00 |                         0.00e+00 |\n",
-       "+----------------------------------+----------------------------------+\n",
+       "+------------------+\n",
+       "| num_coding_exons |\n",
+       "+------------------+\n",
+       "|            int64 |\n",
+       "+------------------+\n",
+       "|               NA |\n",
+       "|                8 |\n",
+       "|                5 |\n",
+       "|               NA |\n",
+       "|               NA |\n",
+       "+------------------+\n",
        "showing top 5 rows"
       ]
      },
@@ -9503,28 +10017,36 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae3e5ba8-075a-455c-8e5c-e244970d5217",
+   "id": "72ce8dac-994f-42ae-a6a0-948f5517e427",
    "metadata": {
     "tags": []
    },
    "source": [
-    "## Browser variant data\n",
+    "## Proportion expressed across transcripts (pext) score\n",
     "\n",
-    "For more information about these files, see the [changelog entry](https://gnomad.broadinstitute.org/new/2024-08-release-gnomad-browser-tables) on the browser tables, and the [Help](https://gnomad.broadinstitute.org/help/v4-browser-hts) page."
+    "For more information about the pext score see the [Help](https://gnomad.broadinstitute.org/help/pext) page."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d7c13865-814a-46e8-a4f3-c823a910cfa3",
+   "id": "ea178a83-1bb6-4f9b-8334-0ca1f6096021",
    "metadata": {},
    "source": [
-    "### Load the Hail Table\n"
+    "### Base level pext\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeb8bb32-d258-4b3a-ab5e-edc76502fa5b",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "329f08ef-6dbf-42cd-bbbc-d3f5396708ac",
+   "execution_count": 27,
+   "id": "1f448e05-7f99-4c63-b101-b33143d9d58b",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-12-06T20:08:59.197890Z",
@@ -9533,21 +10055,21 @@
    },
    "outputs": [],
    "source": [
-    "ht = get_gnomad_release(version='4.1', dataset=\"browser\")"
+    "ht = get_gnomad_release(version='v10', data_type=\"base_level\", dataset=\"pext\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6ec344e0-4951-44d8-8f7b-1283a427914d",
+   "id": "0773d5d3-21f1-4892-b16b-dc31eb2a7bf5",
    "metadata": {},
    "source": [
-    "### Print the schema of the Hail Table\n"
+    "#### Print the schema of the Hail Table\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "17efacb6-84c4-4345-9bf9-1dca259dd7fa",
+   "execution_count": 28,
+   "id": "b9f99311-91e6-454e-b940-3d036bddf3a5",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-12-06T20:09:00.032524Z",
@@ -9562,515 +10084,216 @@
      "text": [
       "----------------------------------------\n",
       "Global fields:\n",
-      "    'mane_select_version': str \n",
+      "    'tissues': array \n",
+      "    'exp_prop_mean_tissues': array \n",
       "----------------------------------------\n",
       "Row fields:\n",
       "    'locus': locus \n",
-      "    'alleles': array \n",
-      "    'exome': struct {\n",
-      "        colocated_variants: struct {\n",
-      "            all: array, \n",
-      "            non_ukb: array\n",
-      "        }, \n",
-      "        subsets: set, \n",
-      "        flags: set, \n",
-      "        freq: struct {\n",
-      "            all: struct {\n",
-      "                ac: int32, \n",
-      "                ac_raw: int32, \n",
-      "                an: int32, \n",
-      "                hemizygote_count: int32, \n",
-      "                homozygote_count: int64, \n",
-      "                ancestry_groups: array\n",
-      "            }, \n",
-      "            non_ukb: struct {\n",
-      "                ac: int32, \n",
-      "                ac_raw: int32, \n",
-      "                an: int32, \n",
-      "                hemizygote_count: int32, \n",
-      "                homozygote_count: int64, \n",
-      "                ancestry_groups: array\n",
-      "            }\n",
-      "        }, \n",
-      "        fafmax: struct {\n",
-      "            gnomad: struct {\n",
-      "                faf95_max: float64, \n",
-      "                faf95_max_gen_anc: str, \n",
-      "                faf99_max: float64, \n",
-      "                faf99_max_gen_anc: str\n",
-      "            }, \n",
-      "            non_ukb: struct {\n",
-      "                faf95_max: float64, \n",
-      "                faf95_max_gen_anc: str, \n",
-      "                faf99_max: float64, \n",
-      "                faf99_max_gen_anc: str\n",
-      "            }\n",
-      "        }, \n",
-      "        age_distribution: struct {\n",
-      "            het: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            hom: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }, \n",
-      "        filters: set, \n",
-      "        quality_metrics: struct {\n",
-      "            allele_balance: struct {\n",
-      "                alt_adj: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                alt_raw: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            genotype_depth: struct {\n",
-      "                all_adj: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                all_raw: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                alt_adj: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                alt_raw: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            genotype_quality: struct {\n",
-      "                all_adj: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                all_raw: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                alt_adj: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                alt_raw: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            site_quality_metrics: array\n",
-      "        }\n",
+      "    'gene_id': str \n",
+      "    'gene_symbol': str \n",
+      "    'exp_prop_mean': float64 \n",
+      "    'Adipose_Subcutaneous': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Adipose_Visceral_Omentum': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'AdrenalGland': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Artery_Aorta': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Artery_Coronary': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Artery_Tibial': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Bladder': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Amygdala': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Anteriorcingulatecortex_BA24': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Caudate_basalganglia': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_CerebellarHemisphere': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Cerebellum': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Cortex': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
       "    } \n",
-      "    'genome': struct {\n",
-      "        colocated_variants: struct {\n",
-      "            hgdp: array, \n",
-      "            tgp: array, \n",
-      "            all: array\n",
-      "        }, \n",
-      "        subsets: set, \n",
-      "        flags: set, \n",
-      "        freq: struct {\n",
-      "            hgdp: struct {\n",
-      "                ac: int32, \n",
-      "                ac_raw: int32, \n",
-      "                an: int32, \n",
-      "                hemizygote_count: int32, \n",
-      "                homozygote_count: int32, \n",
-      "                ancestry_groups: array\n",
-      "            }, \n",
-      "            tgp: struct {\n",
-      "                ac: int32, \n",
-      "                ac_raw: int32, \n",
-      "                an: int32, \n",
-      "                hemizygote_count: int32, \n",
-      "                homozygote_count: int32, \n",
-      "                ancestry_groups: array\n",
-      "            }, \n",
-      "            all: struct {\n",
-      "                ac: int32, \n",
-      "                ac_raw: int32, \n",
-      "                an: int32, \n",
-      "                hemizygote_count: int32, \n",
-      "                homozygote_count: int32, \n",
-      "                ancestry_groups: array\n",
-      "            }\n",
-      "        }, \n",
-      "        fafmax: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }, \n",
-      "        age_distribution: struct {\n",
-      "            het: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }, \n",
-      "            hom: struct {\n",
-      "                bin_edges: array, \n",
-      "                bin_freq: array, \n",
-      "                n_smaller: int64, \n",
-      "                n_larger: int64\n",
-      "            }\n",
-      "        }, \n",
-      "        filters: set, \n",
-      "        quality_metrics: struct {\n",
-      "            allele_balance: struct {\n",
-      "                alt_adj: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                alt_raw: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            genotype_depth: struct {\n",
-      "                all_adj: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                all_raw: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                alt_adj: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                alt_raw: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            genotype_quality: struct {\n",
-      "                all_adj: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                all_raw: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                alt_adj: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                alt_raw: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            site_quality_metrics: array\n",
-      "        }\n",
+      "    'Brain_FrontalCortex_BA9': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Hippocampus': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Hypothalamus': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Nucleusaccumbens_basalganglia': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Putamen_basalganglia': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Spinalcord_cervicalc_1': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Substantianigra': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Breast_MammaryTissue': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Cells_Culturedfibroblasts': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Cells_EBV_transformedlymphocytes': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Colon_Sigmoid': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Colon_Transverse': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Esophagus_GastroesophagealJunction': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Esophagus_Mucosa': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Esophagus_Muscularis': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Heart_AtrialAppendage': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Heart_LeftVentricle': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Kidney_Cortex': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Liver': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Lung': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'MinorSalivaryGland': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Muscle_Skeletal': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Nerve_Tibial': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Ovary': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Pancreas': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Pituitary': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Prostate': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Skin_NotSunExposed_Suprapubic': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
       "    } \n",
-      "    'rsids': set \n",
-      "    'in_silico_predictors': struct {\n",
-      "        cadd: struct {\n",
-      "            phred: float32, \n",
-      "            raw_score: float32\n",
-      "        }, \n",
-      "        revel_max: float64, \n",
-      "        spliceai_ds_max: float32, \n",
-      "        pangolin_largest_ds: float64, \n",
-      "        phylop: float64, \n",
-      "        sift_max: float64, \n",
-      "        polyphen_max: float64\n",
+      "    'Skin_SunExposed_Lowerleg': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
       "    } \n",
-      "    'variant_id': str \n",
-      "    'colocated_variants': struct {\n",
-      "        all: array, \n",
-      "        non_ukb: array, \n",
-      "        hgdp: array, \n",
-      "        tgp: array\n",
+      "    'SmallIntestine_TerminalIleum': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
       "    } \n",
-      "    'joint': struct {\n",
-      "        freq: struct {\n",
-      "            all: struct {\n",
-      "                ac: int32, \n",
-      "                ac_raw: int32, \n",
-      "                an: int32, \n",
-      "                hemizygote_count: int32, \n",
-      "                homozygote_count: int32, \n",
-      "                ancestry_groups: array\n",
-      "            }\n",
-      "        }, \n",
-      "        faf: array, \n",
-      "        fafmax: struct {\n",
-      "            faf95_max: float64, \n",
-      "            faf95_max_gen_anc: str, \n",
-      "            faf99_max: float64, \n",
-      "            faf99_max_gen_anc: str\n",
-      "        }, \n",
-      "        grpmax: struct {\n",
-      "            AC: int32, \n",
-      "            AF: float64, \n",
-      "            AN: int32, \n",
-      "            homozygote_count: int32, \n",
-      "            gen_anc: str\n",
-      "        }, \n",
-      "        histograms: struct {\n",
-      "            qual_hists: struct {\n",
-      "                gq_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                gq_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                ab_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            raw_qual_hists: struct {\n",
-      "                gq_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_all: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                gq_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                dp_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                ab_hist_alt: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }, \n",
-      "            age_hists: struct {\n",
-      "                age_hist_het: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }, \n",
-      "                age_hist_hom: struct {\n",
-      "                    bin_edges: array, \n",
-      "                    bin_freq: array, \n",
-      "                    n_smaller: int64, \n",
-      "                    n_larger: int64\n",
-      "                }\n",
-      "            }\n",
-      "        }, \n",
-      "        flags: set, \n",
-      "        freq_comparison_stats: struct {\n",
-      "            contingency_table_test: array, \n",
-      "            cochran_mantel_haenszel_test: struct {\n",
-      "                p_value: float64, \n",
-      "                chisq: float64\n",
-      "            }, \n",
-      "            stat_union: struct {\n",
-      "                p_value: float64, \n",
-      "                stat_test_name: str, \n",
-      "                gen_ancs: array\n",
-      "            }\n",
-      "        }\n",
+      "    'Spleen': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
       "    } \n",
-      "    'coverage': struct {\n",
-      "        exome: struct {\n",
-      "            mean: float64, \n",
-      "            median_approx: int32, \n",
-      "            total_DP: int64, \n",
-      "            over_1: float64, \n",
-      "            over_5: float64, \n",
-      "            over_10: float64, \n",
-      "            over_15: float64, \n",
-      "            over_20: float64, \n",
-      "            over_25: float64, \n",
-      "            over_30: float64, \n",
-      "            over_50: float64, \n",
-      "            over_100: float64\n",
-      "        }, \n",
-      "        genome: struct {\n",
-      "            mean: float64, \n",
-      "            median_approx: int32, \n",
-      "            total_DP: int64, \n",
-      "            over_1: float32, \n",
-      "            over_5: float32, \n",
-      "            over_10: float32, \n",
-      "            over_15: float32, \n",
-      "            over_20: float32, \n",
-      "            over_25: float32, \n",
-      "            over_30: float32, \n",
-      "            over_50: float32, \n",
-      "            over_100: float32\n",
-      "        }\n",
+      "    'Stomach': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
       "    } \n",
-      "    'transcript_consequences': array, \n",
-      "        domains: set, \n",
-      "        gene_id: str, \n",
-      "        gene_symbol: str, \n",
-      "        hgvsc: str, \n",
-      "        hgvsp: str, \n",
-      "        is_canonical: bool, \n",
-      "        lof_filter: str, \n",
-      "        lof_flags: str, \n",
-      "        lof: str, \n",
-      "        major_consequence: str, \n",
-      "        transcript_id: str, \n",
-      "        transcript_version: str, \n",
-      "        gene_version: str, \n",
-      "        is_mane_select: bool, \n",
-      "        is_mane_select_version: bool, \n",
-      "        refseq_id: str, \n",
-      "        refseq_version: str\n",
-      "    }> \n",
-      "    'caid': str \n",
-      "    'vrs': struct {\n",
-      "        ref: struct {\n",
-      "            allele_id: str, \n",
-      "            start: int32, \n",
-      "            end: int32, \n",
-      "            state: str\n",
-      "        }, \n",
-      "        alt: struct {\n",
-      "            allele_id: str, \n",
-      "            start: int32, \n",
-      "            end: int32, \n",
-      "            state: str\n",
-      "        }\n",
+      "    'Testis': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Thyroid': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Uterus': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Vagina': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'WholeBlood': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
       "    } \n",
       "----------------------------------------\n",
-      "Key: ['locus', 'alleles']\n",
+      "Key: ['locus']\n",
       "----------------------------------------\n"
      ]
     }
@@ -10081,18 +10304,18 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e113c4a2-25c2-4f55-bbb9-382c87cd770c",
+   "id": "a39f9dca-5db4-4ca3-8eac-e6c76db7c7bc",
    "metadata": {
     "tags": []
    },
    "source": [
-    "### Show the first 5 variants in the Hail Table\n"
+    "#### Show the first 5 variants in the Hail Table\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "id": "d03dd50b-37ac-49ec-b766-a86870ee6573",
+   "execution_count": 29,
+   "id": "a6ac695f-cfb2-4aa7-877c-b204351a77da",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-12-06T20:09:20.949958Z",
@@ -10104,1649 +10327,2874 @@
     {
      "data": {
       "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
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chr1:10031["T","C"]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA[][][]{}{"lcr","segdup"}0081200[]00107800[]025664200[("remaining",0,782,0,0),("remaining_XX",0,402,0,0),("remaining_XY",0,380,0,0),("amr",0,6420,0,0),("amr_XX",0,2770,0,0),("amr_XY",0,3650,0,0),("fin",0,4326,0,0),("fin_XX",0,1060,0,0),("fin_XY",0,3266,0,0),("ami",0,352,0,0),("ami_XX",0,154,0,0),("ami_XY",0,198,0,0),("eas",0,1712,0,0),("eas_XX",0,770,0,0),("eas_XY",0,942,0,0),("mid",0,192,0,0),("mid_XX",0,102,0,0),("mid_XY",0,90,0,0),("sas",0,1120,0,0),("sas_XX",0,260,0,0),("sas_XY",0,860,0,0),("asj",0,1550,0,0),("asj_XX",0,814,0,0),("asj_XY",0,736,0,0),("afr",0,14642,0,0),("afr_XX",0,7978,0,0),("afr_XY",0,6664,0,0),("nfe",0,25546,0,0),("nfe_XX",0,14998,0,0),("nfe_XY",0,10548,0,0),("XX",0,29308,0,0),("XY",0,27334,0,0)]NANANANA[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00{"AC0","AS_VQSR"}[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911,665,439,351]0527[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,1476,1023,741,500,418]0744[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[("SiteQuality",9.60e+01),("inbreeding_coeff",-1.65e-05),("AS_MQ",3.51e+01),("AS_FS",5.10e+00),("AS_MQRankSum",-5.72e-01),("AS_pab_max",6.87e-01),("AS_QUALapprox",7.70e+01),("AS_QD",2.96e+00),("AS_ReadPosRankSum",-1.38e+00),("AS_SOR",9.64e-02),("AS_VarDP",2.60e+01),("AS_VQSLOD",-4.57e+00)]{"rs1639542312"}8.97e+007.57e-01NANANANANANA"1-10031-T-C"[][][][]025664200[("remaining",0,782,0,0),("remaining_XX",0,402,0,0),("remaining_XY",0,380,0,0),("amr",0,6420,0,0),("amr_XX",0,2770,0,0),("amr_XY",0,3650,0,0),("fin",0,4326,0,0),("fin_XX",0,1060,0,0),("fin_XY",0,3266,0,0),("ami",0,352,0,0),("ami_XX",0,154,0,0),("ami_XY",0,198,0,0),("eas",0,1712,0,0),("eas_XX",0,770,0,0),("eas_XY",0,942,0,0),("mid",0,192,0,0),("mid_XX",0,102,0,0),("mid_XY",0,90,0,0),("sas",0,1120,0,0),("sas_XX",0,260,0,0),("sas_XY",0,860,0,0),("asj",0,1550,0,0),("asj_XX",0,814,0,0),("asj_XY",0,736,0,0),("afr",0,14642,0,0),("afr_XX",0,7978,0,0),("afr_XY",0,6664,0,0),("nfe",0,25546,0,0),("nfe_XX",0,14998,0,0),("nfe_XY",0,10548,0,0),("",0,56642,0,0),("XX",0,29308,0,0),("XY",0,27334,0,0),("XX",0,29308,0,0),("XY",0,27334,0,0)][(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANANANANANA[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3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chr1:10055["T","C"]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA[][][]{"hgdp"}{"lcr","segdup"}01131200[("japanese",0,50,0,0),("japanese_XX",0,12,0,0),("japanese_XY",0,38,0,0),("adygei",0,22,0,0),("adygei_XX",0,16,0,0),("adygei_XY",0,6,0,0),("orcadian",0,26,0,0),("orcadian_XX",0,14,0,0),("orcadian_XY",0,12,0,0),("bantusouthafrica",0,8,0,0),("bantusouthafrica_XX",0,0,0,0),("bantusouthafrica_XY",0,8,0,0),("yakut",0,36,0,0),("yakut_XX",0,14,0,0),("yakut_XY",0,22,0,0),("han",0,58,0,0),("han_XX",0,32,0,0),("han_XY",0,26,0,0),("uygur",0,10,0,0),("uygur_XX",0,4,0,0),("uygur_XY",0,6,0,0),("balochi",0,40,0,0),("balochi_XX",0,0,0,0),("balochi_XY",0,40,0,0),("bedouin",0,72,0,0),("bedouin_XX",0,28,0,0),("bedouin_XY",0,44,0,0),("russian",0,48,0,0),("russian_XX",0,18,0,0),("russian_XY",0,30,0,0),("daur",0,10,0,0),("daur_XX",0,2,0,0),("daur_XY",0,8,0,0),("pima",0,16,0,0),("pima_XX",0,6,0,0),("pima_XY",0,10,0,0),("hezhen",0,14,0,0),("hezhen_XX",0,4,0,0),("hezhen_XY",0,10,0,0),("biaka",0,20,0,0),("biaka_XX",0,0,0,0),("biaka_XY",0,20,0,0),("miao",0,8,0,0),("miao_XX",0,2,0,0),("miao_XY",0,6,0,0),("sindhi",0,40,0,0),("sindhi_XX",0,8,0,0),("sindhi_XY",0,32,0,0),("northernhan",0,14,0,0),("northernhan_XX",0,2,0,0),("northernhan_XY",0,12,0,0),("oroqen",0,14,0,0),("oroqen_XX",0,6,0,0),("oroqen_XY",0,8,0,0),("san",0,12,0,0),("san_XX",0,0,0,0),("san_XY",0,12,0,0),("tu",0,18,0,0),("tu_XX",0,4,0,0),("tu_XY",0,14,0,0),("tuscan",0,12,0,0),("tuscan_XX",0,4,0,0),("tuscan_XY",0,8,0,0),("mbuti",0,16,0,0),("mbuti_XX",0,4,0,0),("mbuti_XY",0,12,0,0),("palestinian",0,58,0,0),("palestinian_XX",0,38,0,0),("palestinian_XY",0,20,0,0),("tujia",0,16,0,0),("tujia_XX",0,2,0,0),("tujia_XY",0,14,0,0),("druze",0,58,0,0),("druze_XX",0,42,0,0),("druze_XY",0,16,0,0),("pathan",0,34,0,0),("pathan_XX",0,4,0,0),("pathan_XY",0,30,0,0),("basque",0,38,0,0),("basque_XX",0,12,0,0),("basque_XY",0,26,0,0),("makrani",0,38,0,0),("makrani_XX",0,6,0,0),("makrani_XY",0,32,0,0),("burusho",0,40,0,0),("burusho_XX",0,6,0,0),("burusho_XY",0,34,0,0),("mongolian",0,10,0,0),("mongolian_XX",0,4,0,0),("mongolian_XY",0,6,0,0),("bougainville",0,10,0,0),("bougainville_XX",0,10,0,0),("bougainville_XY",0,0,0,0),("papuansepik",0,2,0,0),("papuansepik_XX",0,2,0,0),("papuansepik_XY",0,0,0,0),("yi",0,18,0,0),("yi_XX",0,2,0,0),("yi_XY",0,16,0,0),("naxi",0,8,0,0),("naxi_XX",0,2,0,0),("naxi_XY",0,6,0,0),("lahu",0,8,0,0),("lahu_XX",0,0,0,0),("lahu_XY",0,8,0,0),("sardinian",0,52,0,0),("sardinian_XX",0,24,0,0),("sardinian_XY",0,28,0,0),("karitiana",0,14,0,0),("karitiana_XX",0,12,0,0),("karitiana_XY",0,2,0,0),("mozabite",0,36,0,0),("mozabite_XX",0,12,0,0),("mozabite_XY",0,24,0,0),("yoruba",0,20,0,0),("yoruba_XX",0,10,0,0),("yoruba_XY",0,10,0,0),("dai",0,12,0,0),("dai_XX",0,6,0,0),("dai_XY",0,6,0,0),("bergamoitalian",0,14,0,0),("bergamoitalian_XX",0,8,0,0),("bergamoitalian_XY",0,6,0,0),("cambodian",0,14,0,0),("cambodian_XX",0,8,0,0),("cambodian_XY",0,6,0,0),("french",0,48,0,0),("french_XX",0,26,0,0),("french_XY",0,22,0,0),("mandenka",0,26,0,0),("mandenka_XX",0,10,0,0),("mandenka_XY",0,16,0,0),("surui",0,12,0,0),("surui_XX",0,8,0,0),("surui_XY",0,4,0,0),("brahui",0,30,0,0),("brahui_XX",0,0,0,0),("brahui_XY",0,30,0,0),("hazara",0,22,0,0),("hazara_XX",0,0,0,0),("hazara_XY",0,22,0,0),("kalash",0,28,0,0),("kalash_XX",0,6,0,0),("kalash_XY",0,22,0,0),("papuanhighlands",0,4,0,0),("papuanhighlands_XX",0,2,0,0),("papuanhighlands_XY",0,2,0,0),("xibo",0,10,0,0),("xibo_XX",0,0,0,0),("xibo_XY",0,10,0,0),("colombian",0,6,0,0),("colombian_XX",0,4,0,0),("colombian_XY",0,2,0,0),("bantukenya",0,10,0,0),("bantukenya_XX",0,2,0,0),("bantukenya_XY",0,8,0,0),("she",0,18,0,0),("she_XX",0,6,0,0),("she_XY",0,12,0,0),("maya",0,34,0,0),("maya_XX",0,30,0,0),("maya_XY",0,4,0,0),("XX",0,484,0,0),("XY",0,828,0,0)]00147800[]159422400[("remaining",0,1228,0,0),("remaining_XX",0,624,0,0),("remaining_XY",0,604,0,0),("amr",0,8056,0,0),("amr_XX",0,3902,0,0),("amr_XY",0,4154,0,0),("fin",1,5570,0,0),("fin_XX",1,1034,0,0),("fin_XY",0,4536,0,0),("ami",0,682,0,0),("ami_XX",0,344,0,0),("ami_XY",0,338,0,0),("eas",0,3020,0,0),("eas_XX",0,1256,0,0),("eas_XY",0,1764,0,0),("mid",0,216,0,0),("mid_XX",0,110,0,0),("mid_XY",0,106,0,0),("sas",0,2668,0,0),("sas_XX",0,600,0,0),("sas_XY",0,2068,0,0),("asj",0,2370,0,0),("asj_XX",0,1246,0,0),("asj_XY",0,1124,0,0),("afr",0,26032,0,0),("afr_XX",0,14152,0,0),("afr_XY",0,11880,0,0),("nfe",0,44382,0,0),("nfe_XX",0,26312,0,0),("nfe_XY",0,18070,0,0),("XX",1,49580,0,0),("XY",0,44644,0,0)]NANANANA[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00{"AS_VQSR"}[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.5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chr1:10057["A","C"]NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA[][][]{}{"lcr","segdup"}00130000[]00281000[]3311353600[("remaining",0,1504,0,0),("remaining_XX",0,764,0,0),("remaining_XY",0,740,0,0),("amr",0,10448,0,0),("amr_XX",0,4944,0,0),("amr_XY",0,5504,0,0),("fin",0,6886,0,0),("fin_XX",0,1472,0,0),("fin_XY",0,5414,0,0),("ami",0,742,0,0),("ami_XX",0,378,0,0),("ami_XY",0,364,0,0),("eas",0,3850,0,0),("eas_XX",0,1670,0,0),("eas_XY",0,2180,0,0),("mid",0,266,0,0),("mid_XX",0,132,0,0),("mid_XY",0,134,0,0),("sas",0,3424,0,0),("sas_XX",0,784,0,0),("sas_XY",0,2640,0,0),("asj",0,2768,0,0),("asj_XX",0,1532,0,0),("asj_XY",0,1236,0,0),("afr",1,30736,0,0),("afr_XX",0,16598,0,0),("afr_XY",1,14138,0,0),("nfe",2,52912,0,0),("nfe_XX",2,31106,0,0),("nfe_XY",0,21806,0,0),("XX",2,59380,0,0),("XY",1,54156,0,0)]6.27e-06"nfe"2.35e-06"nfe"[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00{"AS_VQSR"}[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[("SiteQuality",2.92e+02),("inbreeding_coeff",-2.41e-05),("AS_MQ",3.61e+01),("AS_FS",3.79e+01),("AS_MQRankSum",7.88e-01),("AS_pab_max",1.00e+00),("AS_QUALapprox",2.64e+02),("AS_QD",2.06e+00),("AS_ReadPosRankSum",-6.84e-01),("AS_SOR",7.58e-01),("AS_VarDP",1.28e+02),("AS_VQSLOD",-3.31e+00)]{"rs1570391741"}8.53e+007.09e-01NANANANANANA"1-10057-A-C"[][][][]3311353600[("remaining",0,1504,0,0),("remaining_XX",0,764,0,0),("remaining_XY",0,740,0,0),("amr",0,10448,0,0),("amr_XX",0,4944,0,0),("amr_XY",0,5504,0,0),("fin",0,6886,0,0),("fin_XX",0,1472,0,0),("fin_XY",0,5414,0,0),("ami",0,742,0,0),("ami_XX",0,378,0,0),("ami_XY",0,364,0,0),("eas",0,3850,0,0),("eas_XX",0,1670,0,0),("eas_XY",0,2180,0,0),("mid",0,266,0,0),("mid_XX",0,132,0,0),("mid_XY",0,134,0,0),("sas",0,3424,0,0),("sas_XX",0,784,0,0),("sas_XY",0,2640,0,0),("asj",0,2768,0,0),("asj_XX",0,1532,0,0),("asj_XY",0,1236,0,0),("afr",1,30736,0,0),("afr_XX",0,16598,0,0),("afr_XY",1,14138,0,0),("nfe",2,52912,0,0),("nfe_XX",2,31106,0,0),("nfe_XY",0,21806,0,0),("",3,113536,0,0),("XX",2,59380,0,0),("XY",1,54156,0,0),("XX",2,59380,0,0),("XY",1,54156,0,0)][(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]6.27e-06"nfe"2.35e-06"nfe"23.78e-05529120"nfe"[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2503,1787,1521]03622[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90,117,231]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,3518,2603,1850,1572]03743[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00{"not_called_in_exomes"}[NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA]NANANANANANANANANANANANANANANANANA5.43e+015838917008.20e-018.20e-018.20e-018.20e-018.18e-018.15e-018.07e-016.45e-015.38e-02[]"CA997563815""ga4gh:VA.TV0UyS-jCvsVkEOMbipUnzTc4hYuVNTg"1005610057"A""ga4gh:VA.33zGTKcHyj5wWpTfc3OfD8-UlPQ-YOfx"1005610057"C"
\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
        "
Adipose_Subcutaneous
Adipose_Visceral_Omentum
AdrenalGland
Artery_Aorta
Artery_Coronary
Artery_Tibial
Bladder
Brain_Amygdala
Brain_Anteriorcingulatecortex_BA24
Brain_Caudate_basalganglia
Brain_CerebellarHemisphere
Brain_Cerebellum
Brain_Cortex
Brain_FrontalCortex_BA9
Brain_Hippocampus
Brain_Hypothalamus
Brain_Nucleusaccumbens_basalganglia
Brain_Putamen_basalganglia
Brain_Spinalcord_cervicalc_1
Brain_Substantianigra
Breast_MammaryTissue
Cells_Culturedfibroblasts
Cells_EBV_transformedlymphocytes
Colon_Sigmoid
Colon_Transverse
Esophagus_GastroesophagealJunction
Esophagus_Mucosa
Esophagus_Muscularis
Heart_AtrialAppendage
Heart_LeftVentricle
Kidney_Cortex
Liver
Lung
MinorSalivaryGland
Muscle_Skeletal
Nerve_Tibial
Ovary
Pancreas
Pituitary
Prostate
Skin_NotSunExposed_Suprapubic
Skin_SunExposed_Lowerleg
SmallIntestine_TerminalIleum
Spleen
Stomach
Testis
Thyroid
Uterus
Vagina
WholeBlood
locus
gene_id
gene_symbol
exp_prop_mean
transcript_expression
expression_proportion
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transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
locus<GRCh38>strstrfloat64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64
chr1:65565"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65566"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65567"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65568"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65569"ENSG00000186092""OR4F5"NaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
showing top 5 rows
\n"
       ],
       "text/plain": [
-       "+---------------+------------+------------------------------+\n",
-       "| locus         | alleles    | exome.colocated_variants.all |\n",
-       "+---------------+------------+------------------------------+\n",
-       "| locus | array | array                   |\n",
-       "+---------------+------------+------------------------------+\n",
-       "| chr1:10031    | [\"T\",\"C\"]  | NA                           |\n",
-       "| chr1:10037    | [\"T\",\"C\"]  | NA                           |\n",
-       "| chr1:10043    | [\"T\",\"C\"]  | NA                           |\n",
-       "| chr1:10055    | [\"T\",\"C\"]  | NA                           |\n",
-       "| chr1:10057    | [\"A\",\"C\"]  | NA                           |\n",
-       "+---------------+------------+------------------------------+\n",
+       "+---------------+-------------------+-------------+---------------+\n",
+       "| locus         | gene_id           | gene_symbol | exp_prop_mean |\n",
+       "+---------------+-------------------+-------------+---------------+\n",
+       "| locus | str               | str         |       float64 |\n",
+       "+---------------+-------------------+-------------+---------------+\n",
+       "| chr1:65565    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
+       "| chr1:65566    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
+       "| chr1:65567    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
+       "| chr1:65568    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
+       "| chr1:65569    | \"ENSG00000186092\" | \"OR4F5\"     |           NaN |\n",
+       "+---------------+-------------------+-------------+---------------+\n",
        "\n",
-       "+----------------------------------+---------------+-------------+\n",
-       "| exome.colocated_variants.non_ukb | exome.subsets | exome.flags |\n",
-       "+----------------------------------+---------------+-------------+\n",
-       "| array                       | set      | set    |\n",
-       "+----------------------------------+---------------+-------------+\n",
-       "| NA                               | NA            | NA          |\n",
-       "| NA                               | NA            | NA          |\n",
-       "| NA                               | NA            | NA          |\n",
-       "| NA                               | NA            | NA          |\n",
-       "| NA                               | NA            | NA          |\n",
-       "+----------------------------------+---------------+-------------+\n",
+       "+--------------------------------------------+\n",
+       "| Adipose_Subcutaneous.transcript_expression |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
        "\n",
-       "+-------------------+-----------------------+-------------------+\n",
-       "| exome.freq.all.ac | exome.freq.all.ac_raw | exome.freq.all.an |\n",
-       "+-------------------+-----------------------+-------------------+\n",
-       "|             int32 |                 int32 |             int32 |\n",
-       "+-------------------+-----------------------+-------------------+\n",
-       "|                NA |                    NA |                NA |\n",
-       "|                NA |                    NA |                NA |\n",
-       "|                NA |                    NA |                NA |\n",
-       "|                NA |                    NA |                NA |\n",
-       "|                NA |                    NA |                NA |\n",
-       "+-------------------+-----------------------+-------------------+\n",
+       "+--------------------------------------------+\n",
+       "| Adipose_Subcutaneous.expression_proportion |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
        "\n",
-       "+---------------------------------+---------------------------------+\n",
-       "| exome.freq.all.hemizygote_count | exome.freq.all.homozygote_count |\n",
-       "+---------------------------------+---------------------------------+\n",
-       "|                           int32 |                           int64 |\n",
-       "+---------------------------------+---------------------------------+\n",
-       "|                              NA |                              NA |\n",
-       "|                              NA |                              NA |\n",
-       "|                              NA |                              NA |\n",
-       "|                              NA |                              NA |\n",
-       "|                              NA |                              NA |\n",
-       "+---------------------------------+---------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "| Adipose_Visceral_Omentum.transcript_expression |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "+------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| exome.freq.all.ancestry_groups                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                       | array                        |\n",
-       "+--------------------------------------+-------------------------------------+\n",
-       "| NA                                   | NA                                  |\n",
-       "| NA                                   | NA                                  |\n",
-       "| NA                                   | NA                                  |\n",
-       "| NA                                   | NA                                  |\n",
-       "| NA                                   | NA                                  |\n",
-       "+--------------------------------------+-------------------------------------+\n",
+       "+----------------------------------------+\n",
+       "| Brain_Cerebellum.transcript_expression |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
        "\n",
-       "+--------------------------------------+-------------------------------------+\n",
-       "| exome.age_distribution.het.n_smaller | exome.age_distribution.het.n_larger |\n",
-       "+--------------------------------------+-------------------------------------+\n",
-       "|                                int64 |                               int64 |\n",
-       "+--------------------------------------+-------------------------------------+\n",
-       "|                                   NA |                                  NA |\n",
-       "|                                   NA |                                  NA |\n",
-       "|                                   NA |                                  NA |\n",
-       "|                                   NA |                                  NA |\n",
-       "|                                   NA |                                  NA |\n",
-       "+--------------------------------------+-------------------------------------+\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "| Brain_Cerebellum.expression_proportion | Brain_Cortex.transcript_expression |\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "|                                float64 |                            float64 |\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "+----------------------------------------+------------------------------------+\n",
        "\n",
-       "+--------------------------------------+-------------------------------------+\n",
-       "| exome.age_distribution.hom.bin_edges | exome.age_distribution.hom.bin_freq |\n",
-       "+--------------------------------------+-------------------------------------+\n",
-       "| array                       | array                        |\n",
-       "+--------------------------------------+-------------------------------------+\n",
-       "| NA                                   | NA                                  |\n",
-       "| NA                                   | NA                                  |\n",
-       "| NA                                   | NA                                  |\n",
-       "| NA                                   | NA                                  |\n",
-       "| NA                                   | NA                                  |\n",
-       "+--------------------------------------+-------------------------------------+\n",
+       "+------------------------------------+\n",
+       "| Brain_Cortex.expression_proportion |\n",
+       "+------------------------------------+\n",
+       "|                            float64 |\n",
+       "+------------------------------------+\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "+------------------------------------+\n",
        "\n",
-       "+--------------------------------------+-------------------------------------+\n",
-       "| exome.age_distribution.hom.n_smaller | exome.age_distribution.hom.n_larger |\n",
-       "+--------------------------------------+-------------------------------------+\n",
-       "|                                int64 |                               int64 |\n",
-       "+--------------------------------------+-------------------------------------+\n",
-       "|                                   NA |                                  NA |\n",
-       "|                                   NA |                                  NA |\n",
-       "|                                   NA |                                  NA |\n",
-       "|                                   NA |                                  NA |\n",
-       "|                                   NA |                                  NA |\n",
-       "+--------------------------------------+-------------------------------------+\n",
+       "+-----------------------------------------------+\n",
+       "| Brain_FrontalCortex_BA9.transcript_expression |\n",
+       "+-----------------------------------------------+\n",
+       "|                                       float64 |\n",
+       "+-----------------------------------------------+\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "+-----------------------------------------------+\n",
        "\n",
-       "+---------------+--------------------------------------------------------+\n",
-       "| exome.filters | exome.quality_metrics.allele_balance.alt_adj.bin_edges |\n",
-       "+---------------+--------------------------------------------------------+\n",
-       "| set      | array                                         |\n",
-       "+---------------+--------------------------------------------------------+\n",
-       "| NA            | NA                                                     |\n",
-       "| NA            | NA                                                     |\n",
-       "| NA            | NA                                                     |\n",
-       "| NA            | NA                                                     |\n",
-       "| NA            | NA                                                     |\n",
-       "+---------------+--------------------------------------------------------+\n",
+       "+-----------------------------------------------+\n",
+       "| Brain_FrontalCortex_BA9.expression_proportion |\n",
+       "+-----------------------------------------------+\n",
+       "|                                       float64 |\n",
+       "+-----------------------------------------------+\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "+-----------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| exome.quality_metrics.allele_balance.alt_adj.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
+       "+-----------------------------------------+\n",
+       "| Brain_Hippocampus.transcript_expression |\n",
+       "+-----------------------------------------+\n",
+       "|                                 float64 |\n",
+       "+-----------------------------------------+\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "+-----------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| exome.quality_metrics.allele_balance.alt_adj.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
+       "+-----------------------------------------+\n",
+       "| Brain_Hippocampus.expression_proportion |\n",
+       "+-----------------------------------------+\n",
+       "|                                 float64 |\n",
+       "+-----------------------------------------+\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "+-----------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+\n",
+       "| Brain_Hypothalamus.transcript_expression |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| exome.quality_metrics.allele_balance.alt_adj.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
+       "+------------------------------------------+\n",
+       "| Brain_Hypothalamus.expression_proportion |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| exome.quality_metrics.allele_balance.alt_raw.bin_edges |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "+--------------------------------------------------------+\n",
+       "+-----------------------------------------------------------+\n",
+       "| Brain_Nucleusaccumbens_basalganglia.transcript_expression |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                   float64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "+-----------------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| exome.quality_metrics.allele_balance.alt_raw.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
+       "+-----------------------------------------------------------+\n",
+       "| Brain_Nucleusaccumbens_basalganglia.expression_proportion |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                   float64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "+-----------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| exome.quality_metrics.allele_balance.alt_raw.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_Putamen_basalganglia.transcript_expression |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| exome.quality_metrics.allele_balance.alt_raw.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_Putamen_basalganglia.expression_proportion |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.all_adj.bin_edges |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "+--------------------------------------------------------+\n",
+       "+----------------------------------------------------+\n",
+       "| Brain_Spinalcord_cervicalc_1.transcript_expression |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.all_adj.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
+       "+----------------------------------------------------+\n",
+       "| Brain_Spinalcord_cervicalc_1.expression_proportion |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.all_adj.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "| Brain_Substantianigra.transcript_expression |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.all_adj.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "| Brain_Substantianigra.expression_proportion |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.all_raw.bin_edges |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "+--------------------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "| Breast_MammaryTissue.transcript_expression |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.all_raw.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "| Breast_MammaryTissue.expression_proportion |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| Cells_Culturedfibroblasts.transcript_expression |\n",
+       "+-------------------------------------------------+\n",
+       "|                                         float64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| Cells_Culturedfibroblasts.expression_proportion |\n",
+       "+-------------------------------------------------+\n",
+       "|                                         float64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "+-------------------------------------------------+\n",
        "\n",
        "+--------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.all_raw.n_smaller |\n",
+       "| Cells_EBV_transformedlymphocytes.transcript_expression |\n",
        "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
+       "|                                                float64 |\n",
        "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
        "+--------------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.all_raw.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
        "+--------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.alt_adj.bin_edges |\n",
+       "| Cells_EBV_transformedlymphocytes.expression_proportion |\n",
        "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
+       "|                                                float64 |\n",
        "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
        "+--------------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.alt_adj.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "| Colon_Sigmoid.transcript_expression | Colon_Sigmoid.expression_proportion |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                             float64 |                             float64 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.alt_adj.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
+       "+----------------------------------------+\n",
+       "| Colon_Transverse.transcript_expression |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.alt_adj.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
+       "+----------------------------------------+\n",
+       "| Colon_Transverse.expression_proportion |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.alt_raw.bin_edges |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+--------------------------------------------------------+\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "| NA                                                     |\n",
-       "+--------------------------------------------------------+\n",
+       "+----------------------------------------------------------+\n",
+       "| Esophagus_GastroesophagealJunction.transcript_expression |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                  float64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "+----------------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.alt_raw.bin_freq |\n",
-       "+-------------------------------------------------------+\n",
-       "| array                                          |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
+       "+----------------------------------------------------------+\n",
+       "| Esophagus_GastroesophagealJunction.expression_proportion |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                  float64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "+----------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Esophagus_Mucosa.transcript_expression |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Esophagus_Mucosa.expression_proportion |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Esophagus_Muscularis.transcript_expression |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Esophagus_Muscularis.expression_proportion |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| Heart_AtrialAppendage.transcript_expression |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| Heart_AtrialAppendage.expression_proportion |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------+\n",
+       "| Heart_LeftVentricle.transcript_expression |\n",
+       "+-------------------------------------------+\n",
+       "|                                   float64 |\n",
+       "+-------------------------------------------+\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "+-------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------+\n",
+       "| Heart_LeftVentricle.expression_proportion |\n",
+       "+-------------------------------------------+\n",
+       "|                                   float64 |\n",
+       "+-------------------------------------------+\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "+-------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.alt_raw.n_smaller |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "|                                                     NA |\n",
-       "+--------------------------------------------------------+\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "| Kidney_Cortex.transcript_expression | Kidney_Cortex.expression_proportion |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                             float64 |                             float64 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_depth.alt_raw.n_larger |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "|                                                    NA |\n",
-       "+-------------------------------------------------------+\n",
+       "+-----------------------------+-----------------------------+\n",
+       "| Liver.transcript_expression | Liver.expression_proportion |\n",
+       "+-----------------------------+-----------------------------+\n",
+       "|                     float64 |                     float64 |\n",
+       "+-----------------------------+-----------------------------+\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "+-----------------------------+-----------------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.all_adj.bin_edges |\n",
-       "+----------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+----------------------------------------------------------+\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "+----------------------------------------------------------+\n",
+       "+----------------------------+----------------------------+\n",
+       "| Lung.transcript_expression | Lung.expression_proportion |\n",
+       "+----------------------------+----------------------------+\n",
+       "|                    float64 |                    float64 |\n",
+       "+----------------------------+----------------------------+\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "+----------------------------+----------------------------+\n",
        "\n",
-       "+---------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.all_adj.bin_freq |\n",
-       "+---------------------------------------------------------+\n",
-       "| array                                            |\n",
-       "+---------------------------------------------------------+\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "+---------------------------------------------------------+\n",
+       "+------------------------------------------+\n",
+       "| MinorSalivaryGland.transcript_expression |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.all_adj.n_smaller |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                    int64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "+----------------------------------------------------------+\n",
+       "+------------------------------------------+\n",
+       "| MinorSalivaryGland.expression_proportion |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.all_adj.n_larger |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "+---------------------------------------------------------+\n",
+       "+---------------------------------------+\n",
+       "| Muscle_Skeletal.transcript_expression |\n",
+       "+---------------------------------------+\n",
+       "|                               float64 |\n",
+       "+---------------------------------------+\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "+---------------------------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.all_raw.bin_edges |\n",
-       "+----------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+----------------------------------------------------------+\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "+----------------------------------------------------------+\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "| Muscle_Skeletal.expression_proportion | Nerve_Tibial.transcript_expression |\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "|                               float64 |                            float64 |\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "+---------------------------------------+------------------------------------+\n",
        "\n",
-       "+---------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.all_raw.bin_freq |\n",
-       "+---------------------------------------------------------+\n",
-       "| array                                            |\n",
-       "+---------------------------------------------------------+\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "+---------------------------------------------------------+\n",
+       "+------------------------------------+-----------------------------+\n",
+       "| Nerve_Tibial.expression_proportion | Ovary.transcript_expression |\n",
+       "+------------------------------------+-----------------------------+\n",
+       "|                            float64 |                     float64 |\n",
+       "+------------------------------------+-----------------------------+\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "+------------------------------------+-----------------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.all_raw.n_smaller |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                    int64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "+----------------------------------------------------------+\n",
+       "+-----------------------------+--------------------------------+\n",
+       "| Ovary.expression_proportion | Pancreas.transcript_expression |\n",
+       "+-----------------------------+--------------------------------+\n",
+       "|                     float64 |                        float64 |\n",
+       "+-----------------------------+--------------------------------+\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "+-----------------------------+--------------------------------+\n",
        "\n",
-       "+---------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.all_raw.n_larger |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "+---------------------------------------------------------+\n",
+       "+--------------------------------+---------------------------------+\n",
+       "| Pancreas.expression_proportion | Pituitary.transcript_expression |\n",
+       "+--------------------------------+---------------------------------+\n",
+       "|                        float64 |                         float64 |\n",
+       "+--------------------------------+---------------------------------+\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "+--------------------------------+---------------------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.alt_adj.bin_edges |\n",
-       "+----------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+----------------------------------------------------------+\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "+----------------------------------------------------------+\n",
+       "+---------------------------------+--------------------------------+\n",
+       "| Pituitary.expression_proportion | Prostate.transcript_expression |\n",
+       "+---------------------------------+--------------------------------+\n",
+       "|                         float64 |                        float64 |\n",
+       "+---------------------------------+--------------------------------+\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "+---------------------------------+--------------------------------+\n",
        "\n",
-       "+---------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.alt_adj.bin_freq |\n",
-       "+---------------------------------------------------------+\n",
-       "| array                                            |\n",
-       "+---------------------------------------------------------+\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "+---------------------------------------------------------+\n",
+       "+--------------------------------+\n",
+       "| Prostate.expression_proportion |\n",
+       "+--------------------------------+\n",
+       "|                        float64 |\n",
+       "+--------------------------------+\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "+--------------------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.alt_adj.n_smaller |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                    int64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "+----------------------------------------------------------+\n",
+       "+-----------------------------------------------------+\n",
+       "| Skin_NotSunExposed_Suprapubic.transcript_expression |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                             float64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "+-----------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.alt_adj.n_larger |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "+---------------------------------------------------------+\n",
+       "+-----------------------------------------------------+\n",
+       "| Skin_NotSunExposed_Suprapubic.expression_proportion |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                             float64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "+-----------------------------------------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.alt_raw.bin_edges |\n",
-       "+----------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+----------------------------------------------------------+\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "| NA                                                       |\n",
-       "+----------------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "| Skin_SunExposed_Lowerleg.transcript_expression |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "+------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.alt_raw.bin_freq |\n",
-       "+---------------------------------------------------------+\n",
-       "| array                                            |\n",
-       "+---------------------------------------------------------+\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "| NA                                                      |\n",
-       "+---------------------------------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "| Skin_SunExposed_Lowerleg.expression_proportion |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "+------------------------------------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.alt_raw.n_smaller |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                    int64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "|                                                       NA |\n",
-       "+----------------------------------------------------------+\n",
+       "+----------------------------------------------------+\n",
+       "| SmallIntestine_TerminalIleum.transcript_expression |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------------------+\n",
-       "| exome.quality_metrics.genotype_quality.alt_raw.n_larger |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "|                                                      NA |\n",
-       "+---------------------------------------------------------+\n",
+       "+----------------------------------------------------+\n",
+       "| SmallIntestine_TerminalIleum.expression_proportion |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------+--------------------------------+\n",
-       "| exome.quality_metrics.site_quality_metrics | genome.colocated_variants.hgdp |\n",
-       "+--------------------------------------------+--------------------------------+\n",
-       "| array | array                     |\n",
-       "+--------------------------------------------+--------------------------------+\n",
-       "| NA                                         | []                             |\n",
-       "| NA                                         | []                             |\n",
-       "| NA                                         | []                             |\n",
-       "| NA                                         | []                             |\n",
-       "| NA                                         | []                             |\n",
-       "+--------------------------------------------+--------------------------------+\n",
+       "+------------------------------+------------------------------+\n",
+       "| Spleen.transcript_expression | Spleen.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
        "\n",
        "+-------------------------------+-------------------------------+\n",
-       "| genome.colocated_variants.tgp | genome.colocated_variants.all |\n",
+       "| Stomach.transcript_expression | Stomach.expression_proportion |\n",
        "+-------------------------------+-------------------------------+\n",
-       "| array                    | array                    |\n",
+       "|                       float64 |                       float64 |\n",
        "+-------------------------------+-------------------------------+\n",
-       "| []                            | []                            |\n",
-       "| []                            | []                            |\n",
-       "| []                            | []                            |\n",
-       "| []                            | []                            |\n",
-       "| []                            | []                            |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
        "+-------------------------------+-------------------------------+\n",
        "\n",
-       "+----------------+------------------+---------------------+\n",
-       "| genome.subsets | genome.flags     | genome.freq.hgdp.ac |\n",
-       "+----------------+------------------+---------------------+\n",
-       "| set       | set         |               int32 |\n",
-       "+----------------+------------------+---------------------+\n",
-       "| {}             | {\"lcr\",\"segdup\"} |                   0 |\n",
-       "| {}             | {\"lcr\",\"segdup\"} |                   0 |\n",
-       "| {}             | {\"lcr\",\"segdup\"} |                   0 |\n",
-       "| {\"hgdp\"}       | {\"lcr\",\"segdup\"} |                   0 |\n",
-       "| {}             | {\"lcr\",\"segdup\"} |                   0 |\n",
-       "+----------------+------------------+---------------------+\n",
+       "+------------------------------+------------------------------+\n",
+       "| Testis.transcript_expression | Testis.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
        "\n",
-       "+-------------------------+---------------------+\n",
-       "| genome.freq.hgdp.ac_raw | genome.freq.hgdp.an |\n",
-       "+-------------------------+---------------------+\n",
-       "|                   int32 |               int32 |\n",
-       "+-------------------------+---------------------+\n",
-       "|                       0 |                 812 |\n",
-       "|                       0 |                1028 |\n",
-       "|                       0 |                1154 |\n",
-       "|                       1 |                1312 |\n",
-       "|                       0 |                1300 |\n",
-       "+-------------------------+---------------------+\n",
+       "+-------------------------------+-------------------------------+\n",
+       "| Thyroid.transcript_expression | Thyroid.expression_proportion |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "|                       float64 |                       float64 |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "+-------------------------------+-------------------------------+\n",
        "\n",
-       "+-----------------------------------+-----------------------------------+\n",
-       "| genome.freq.hgdp.hemizygote_count | genome.freq.hgdp.homozygote_count |\n",
-       "+-----------------------------------+-----------------------------------+\n",
-       "|                             int32 |                             int32 |\n",
-       "+-----------------------------------+-----------------------------------+\n",
-       "|                                 0 |                                 0 |\n",
-       "|                                 0 |                                 0 |\n",
-       "|                                 0 |                                 0 |\n",
-       "|                                 0 |                                 0 |\n",
-       "|                                 0 |                                 0 |\n",
-       "+-----------------------------------+-----------------------------------+\n",
+       "+------------------------------+------------------------------+\n",
+       "| Uterus.transcript_expression | Uterus.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
+       "\n",
+       "+------------------------------+------------------------------+\n",
+       "| Vagina.transcript_expression | Vagina.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
+       "\n",
+       "+----------------------------------+----------------------------------+\n",
+       "| WholeBlood.transcript_expression | WholeBlood.expression_proportion |\n",
+       "+----------------------------------+----------------------------------+\n",
+       "|                          float64 |                          float64 |\n",
+       "+----------------------------------+----------------------------------+\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "+----------------------------------+----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32528515-1021-40e4-8f4c-3ca06792e0be",
+   "metadata": {},
+   "source": [
+    "### Annotation level pext\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afac2ca3-f3dc-4174-aa98-d9eb70bf343f",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "ce25b9e0-9118-4bf6-ba3c-eca75468b9f1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(version='v10', data_type=\"annotation_level\", dataset=\"pext\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9265ccc-0048-40f0-8f1c-d62e0d3f98ab",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "818f3fc2-09fa-479c-91e1-e19f5991a22d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'tissues': array \n",
+      "    'exp_prop_mean_tissues': array \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'gene_id': str \n",
+      "    'alleles': array \n",
+      "    'gene_symbol': str \n",
+      "    'most_severe_consequence': str \n",
+      "    'lof': str \n",
+      "    'lof_flags': str \n",
+      "    'exp_prop_mean': float64 \n",
+      "    'Adipose_Subcutaneous': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Adipose_Visceral_Omentum': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'AdrenalGland': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Artery_Aorta': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Artery_Coronary': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Artery_Tibial': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Bladder': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Amygdala': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Anteriorcingulatecortex_BA24': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Caudate_basalganglia': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_CerebellarHemisphere': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Cerebellum': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Cortex': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_FrontalCortex_BA9': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Hippocampus': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Hypothalamus': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Nucleusaccumbens_basalganglia': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Putamen_basalganglia': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Spinalcord_cervicalc_1': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Brain_Substantianigra': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Breast_MammaryTissue': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Cells_Culturedfibroblasts': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Cells_EBV_transformedlymphocytes': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Colon_Sigmoid': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Colon_Transverse': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Esophagus_GastroesophagealJunction': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Esophagus_Mucosa': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Esophagus_Muscularis': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Heart_AtrialAppendage': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Heart_LeftVentricle': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Kidney_Cortex': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Liver': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Lung': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'MinorSalivaryGland': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Muscle_Skeletal': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Nerve_Tibial': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Ovary': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Pancreas': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Pituitary': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Prostate': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Skin_NotSunExposed_Suprapubic': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Skin_SunExposed_Lowerleg': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'SmallIntestine_TerminalIleum': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Spleen': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Stomach': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Testis': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Thyroid': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Uterus': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'Vagina': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "    'WholeBlood': struct {\n",
+      "        transcript_expression: float64, \n",
+      "        expression_proportion: float64\n",
+      "    } \n",
+      "----------------------------------------\n",
+      "Key: ['locus', 'alleles']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77d8e1eb-d07d-4338-a401-ae9191ad2901",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "35371de7-9332-4aa6-a3b9-c573fb2e144f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
Adipose_Subcutaneous
Adipose_Visceral_Omentum
AdrenalGland
Artery_Aorta
Artery_Coronary
Artery_Tibial
Bladder
Brain_Amygdala
Brain_Anteriorcingulatecortex_BA24
Brain_Caudate_basalganglia
Brain_CerebellarHemisphere
Brain_Cerebellum
Brain_Cortex
Brain_FrontalCortex_BA9
Brain_Hippocampus
Brain_Hypothalamus
Brain_Nucleusaccumbens_basalganglia
Brain_Putamen_basalganglia
Brain_Spinalcord_cervicalc_1
Brain_Substantianigra
Breast_MammaryTissue
Cells_Culturedfibroblasts
Cells_EBV_transformedlymphocytes
Colon_Sigmoid
Colon_Transverse
Esophagus_GastroesophagealJunction
Esophagus_Mucosa
Esophagus_Muscularis
Heart_AtrialAppendage
Heart_LeftVentricle
Kidney_Cortex
Liver
Lung
MinorSalivaryGland
Muscle_Skeletal
Nerve_Tibial
Ovary
Pancreas
Pituitary
Prostate
Skin_NotSunExposed_Suprapubic
Skin_SunExposed_Lowerleg
SmallIntestine_TerminalIleum
Spleen
Stomach
Testis
Thyroid
Uterus
Vagina
WholeBlood
locus
gene_id
alleles
gene_symbol
most_severe_consequence
lof
lof_flags
exp_prop_mean
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
expression_proportion
transcript_expression
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transcript_expression
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transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
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transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
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transcript_expression
expression_proportion
transcript_expression
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transcript_expression
expression_proportion
transcript_expression
expression_proportion
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transcript_expression
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transcript_expression
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transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
transcript_expression
expression_proportion
locus<GRCh38>strarray<str>strstrstrstrfloat64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64
chr1:65565"ENSG00000186092"["A","C"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65565"ENSG00000186092"["A","G"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65565"ENSG00000186092"["A","T"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65566"ENSG00000186092"["T","A"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
chr1:65566"ENSG00000186092"["T","C"]"OR4F5""start_lost"NANANaN0.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+000.00e+00
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+-------------------+------------+-------------+\n",
+       "| locus         | gene_id           | alleles    | gene_symbol |\n",
+       "+---------------+-------------------+------------+-------------+\n",
+       "| locus | str               | array | str         |\n",
+       "+---------------+-------------------+------------+-------------+\n",
+       "| chr1:65565    | \"ENSG00000186092\" | [\"A\",\"C\"]  | \"OR4F5\"     |\n",
+       "| chr1:65565    | \"ENSG00000186092\" | [\"A\",\"G\"]  | \"OR4F5\"     |\n",
+       "| chr1:65565    | \"ENSG00000186092\" | [\"A\",\"T\"]  | \"OR4F5\"     |\n",
+       "| chr1:65566    | \"ENSG00000186092\" | [\"T\",\"A\"]  | \"OR4F5\"     |\n",
+       "| chr1:65566    | \"ENSG00000186092\" | [\"T\",\"C\"]  | \"OR4F5\"     |\n",
+       "+---------------+-------------------+------------+-------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.freq.hgdp.ancestry_groups                                             |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+-------------------------------------+-------------------------------+\n",
+       "| Artery_Tibial.expression_proportion | Bladder.transcript_expression |\n",
+       "+-------------------------------------+-------------------------------+\n",
+       "|                             float64 |                       float64 |\n",
+       "+-------------------------------------+-------------------------------+\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "|                            0.00e+00 |                      0.00e+00 |\n",
+       "+-------------------------------------+-------------------------------+\n",
        "\n",
-       "+--------------------------------------+---------------------------------------+\n",
-       "| genome.age_distribution.het.bin_freq | genome.age_distribution.het.n_smaller |\n",
-       "+--------------------------------------+---------------------------------------+\n",
-       "| array                         |                                 int64 |\n",
-       "+--------------------------------------+---------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
-       "| [0,1,0,0,1,0,0,0,0,0]                |                                     0 |\n",
-       "| [0,0,1,0,0,0,0,0,0,0]                |                                     0 |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
-       "| [0,0,1,0,1,0,0,0,0,0]                |                                     0 |\n",
-       "+--------------------------------------+---------------------------------------+\n",
+       "+-------------------------------+--------------------------------------+\n",
+       "| Bladder.expression_proportion | Brain_Amygdala.transcript_expression |\n",
+       "+-------------------------------+--------------------------------------+\n",
+       "|                       float64 |                              float64 |\n",
+       "+-------------------------------+--------------------------------------+\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "|                      0.00e+00 |                             0.00e+00 |\n",
+       "+-------------------------------+--------------------------------------+\n",
        "\n",
        "+--------------------------------------+\n",
-       "| genome.age_distribution.het.n_larger |\n",
+       "| Brain_Amygdala.expression_proportion |\n",
        "+--------------------------------------+\n",
-       "|                                int64 |\n",
+       "|                              float64 |\n",
        "+--------------------------------------+\n",
-       "|                                    0 |\n",
-       "|                                    0 |\n",
-       "|                                    0 |\n",
-       "|                                    0 |\n",
-       "|                                    0 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
        "+--------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.age_distribution.hom.bin_edges                                        |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+----------------------------------------------------------+\n",
+       "| Brain_Anteriorcingulatecortex_BA24.transcript_expression |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                  float64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "+----------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------+---------------------------------------+\n",
-       "| genome.age_distribution.hom.bin_freq | genome.age_distribution.hom.n_smaller |\n",
-       "+--------------------------------------+---------------------------------------+\n",
-       "| array                         |                                 int64 |\n",
-       "+--------------------------------------+---------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                |                                     0 |\n",
-       "+--------------------------------------+---------------------------------------+\n",
+       "+----------------------------------------------------------+\n",
+       "| Brain_Anteriorcingulatecortex_BA24.expression_proportion |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                  float64 |\n",
+       "+----------------------------------------------------------+\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "+----------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------+-------------------+\n",
-       "| genome.age_distribution.hom.n_larger | genome.filters    |\n",
-       "+--------------------------------------+-------------------+\n",
-       "|                                int64 | set          |\n",
-       "+--------------------------------------+-------------------+\n",
-       "|                                    0 | {\"AC0\",\"AS_VQSR\"} |\n",
-       "|                                    0 | {\"AS_VQSR\"}       |\n",
-       "|                                    0 | {\"AS_VQSR\"}       |\n",
-       "|                                    0 | {\"AS_VQSR\"}       |\n",
-       "|                                    0 | {\"AS_VQSR\"}       |\n",
-       "+--------------------------------------+-------------------+\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_Caudate_basalganglia.transcript_expression |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.allele_balance.alt_adj.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_Caudate_basalganglia.expression_proportion |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| genome.quality_metrics.allele_balance.alt_adj.bin_freq |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+--------------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "+--------------------------------------------------------+\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_CerebellarHemisphere.transcript_expression |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------------------+\n",
-       "| genome.quality_metrics.allele_balance.alt_adj.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_CerebellarHemisphere.expression_proportion |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Brain_Cerebellum.transcript_expression |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "| Brain_Cerebellum.expression_proportion | Brain_Cortex.transcript_expression |\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "|                                float64 |                            float64 |\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "|                               0.00e+00 |                           0.00e+00 |\n",
+       "+----------------------------------------+------------------------------------+\n",
+       "\n",
+       "+------------------------------------+\n",
+       "| Brain_Cortex.expression_proportion |\n",
+       "+------------------------------------+\n",
+       "|                            float64 |\n",
+       "+------------------------------------+\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "|                           0.00e+00 |\n",
+       "+------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| genome.quality_metrics.allele_balance.alt_adj.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "+--------------------------------------------------------+\n",
+       "+-----------------------------------------------+\n",
+       "| Brain_FrontalCortex_BA9.transcript_expression |\n",
+       "+-----------------------------------------------+\n",
+       "|                                       float64 |\n",
+       "+-----------------------------------------------+\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "+-----------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.allele_balance.alt_raw.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+-----------------------------------------------+\n",
+       "| Brain_FrontalCortex_BA9.expression_proportion |\n",
+       "+-----------------------------------------------+\n",
+       "|                                       float64 |\n",
+       "+-----------------------------------------------+\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "|                                      0.00e+00 |\n",
+       "+-----------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| genome.quality_metrics.allele_balance.alt_raw.bin_freq |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+--------------------------------------------------------+\n",
-       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "+--------------------------------------------------------+\n",
+       "+-----------------------------------------+\n",
+       "| Brain_Hippocampus.transcript_expression |\n",
+       "+-----------------------------------------+\n",
+       "|                                 float64 |\n",
+       "+-----------------------------------------+\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "+-----------------------------------------+\n",
        "\n",
-       "+---------------------------------------------------------+\n",
-       "| genome.quality_metrics.allele_balance.alt_raw.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
+       "+-----------------------------------------+\n",
+       "| Brain_Hippocampus.expression_proportion |\n",
+       "+-----------------------------------------+\n",
+       "|                                 float64 |\n",
+       "+-----------------------------------------+\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "|                                0.00e+00 |\n",
+       "+-----------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| genome.quality_metrics.allele_balance.alt_raw.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "+--------------------------------------------------------+\n",
+       "+------------------------------------------+\n",
+       "| Brain_Hypothalamus.transcript_expression |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.all_adj.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+------------------------------------------+\n",
+       "| Brain_Hypothalamus.expression_proportion |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.all_adj.bin_freq                       |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
-       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
-       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
-       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
-       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+-----------------------------------------------------------+\n",
+       "| Brain_Nucleusaccumbens_basalganglia.transcript_expression |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                   float64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "+-----------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.all_adj.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
+       "+-----------------------------------------------------------+\n",
+       "| Brain_Nucleusaccumbens_basalganglia.expression_proportion |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                   float64 |\n",
+       "+-----------------------------------------------------------+\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "|                                                  0.00e+00 |\n",
+       "+-----------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.all_adj.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                    527 |\n",
-       "|                                                    888 |\n",
-       "|                                                   1232 |\n",
-       "|                                                   2440 |\n",
-       "|                                                   3622 |\n",
-       "+--------------------------------------------------------+\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_Putamen_basalganglia.transcript_expression |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.all_raw.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+--------------------------------------------------+\n",
+       "| Brain_Putamen_basalganglia.expression_proportion |\n",
+       "+--------------------------------------------------+\n",
+       "|                                          float64 |\n",
+       "+--------------------------------------------------+\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "|                                         0.00e+00 |\n",
+       "+--------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.all_raw.bin_freq                       |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
-       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
-       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
-       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
-       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+----------------------------------------------------+\n",
+       "| Brain_Spinalcord_cervicalc_1.transcript_expression |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.all_raw.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
+       "+----------------------------------------------------+\n",
+       "| Brain_Spinalcord_cervicalc_1.expression_proportion |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.all_raw.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                    744 |\n",
-       "|                                                   1130 |\n",
-       "|                                                   1487 |\n",
-       "|                                                   2622 |\n",
-       "|                                                   3743 |\n",
-       "+--------------------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "| Brain_Substantianigra.transcript_expression |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.alt_adj.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+---------------------------------------------+\n",
+       "| Brain_Substantianigra.expression_proportion |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.alt_adj.bin_freq |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+--------------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "+--------------------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "| Breast_MammaryTissue.transcript_expression |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.alt_adj.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
+       "+--------------------------------------------+\n",
+       "| Breast_MammaryTissue.expression_proportion |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.alt_adj.n_larger |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
-       "+--------------------------------------------------------+\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "+--------------------------------------------------------+\n",
+       "+-------------------------------------------------+\n",
+       "| Cells_Culturedfibroblasts.transcript_expression |\n",
+       "+-------------------------------------------------+\n",
+       "|                                         float64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "+-------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.alt_raw.bin_edges                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+-------------------------------------------------+\n",
+       "| Cells_Culturedfibroblasts.expression_proportion |\n",
+       "+-------------------------------------------------+\n",
+       "|                                         float64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "|                                        0.00e+00 |\n",
+       "+-------------------------------------------------+\n",
        "\n",
        "+--------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.alt_raw.bin_freq |\n",
+       "| Cells_EBV_transformedlymphocytes.transcript_expression |\n",
        "+--------------------------------------------------------+\n",
-       "| array                                           |\n",
+       "|                                                float64 |\n",
        "+--------------------------------------------------------+\n",
-       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]              |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
        "+--------------------------------------------------------+\n",
        "\n",
-       "+---------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.alt_raw.n_smaller |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                   int64 |\n",
-       "+---------------------------------------------------------+\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "|                                                       0 |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
        "+--------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_depth.alt_raw.n_larger |\n",
+       "| Cells_EBV_transformedlymphocytes.expression_proportion |\n",
        "+--------------------------------------------------------+\n",
-       "|                                                  int64 |\n",
+       "|                                                float64 |\n",
        "+--------------------------------------------------------+\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
-       "|                                                      0 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
+       "|                                               0.00e+00 |\n",
        "+--------------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.all_adj.bin_edges                    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.all_adj.bin_freq                     |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,0,0,23215,2435,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]                |\n",
-       "| [0,0,0,0,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,4,5,15]          |\n",
-       "| [0,0,0,0,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5,3,9]           |\n",
-       "| [0,0,0,0,25677,6107,5433,3528,1753,1702,1130,485,518,282,119,125,77,35,29... |\n",
-       "| [0,0,0,0,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321,364,212,90... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.all_adj.n_smaller |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                     int64 |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "+-----------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.all_adj.n_larger |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                    int64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "+----------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.all_raw.bin_edges                    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.all_raw.bin_freq                     |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
-       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
-       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
-       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
-       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "| Colon_Sigmoid.transcript_expression | Colon_Sigmoid.expression_proportion |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                             float64 |                             float64 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
        "\n",
-       "+-----------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.all_raw.n_smaller |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                     int64 |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "+-----------------------------------------------------------+\n",
+       "+----------------------------------------+\n",
+       "| Colon_Transverse.transcript_expression |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Colon_Transverse.expression_proportion |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
        "\n",
        "+----------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.all_raw.n_larger |\n",
+       "| Esophagus_GastroesophagealJunction.transcript_expression |\n",
        "+----------------------------------------------------------+\n",
-       "|                                                    int64 |\n",
+       "|                                                  float64 |\n",
        "+----------------------------------------------------------+\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
        "+----------------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.alt_adj.bin_edges                    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
        "+----------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.alt_adj.bin_freq |\n",
+       "| Esophagus_GastroesophagealJunction.expression_proportion |\n",
        "+----------------------------------------------------------+\n",
-       "| array                                             |\n",
+       "|                                                  float64 |\n",
        "+----------------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]                |\n",
-       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]                |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
+       "|                                                 0.00e+00 |\n",
        "+----------------------------------------------------------+\n",
        "\n",
-       "+-----------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.alt_adj.n_smaller |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                     int64 |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "+-----------------------------------------------------------+\n",
+       "+----------------------------------------+\n",
+       "| Esophagus_Mucosa.transcript_expression |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+----------------------------------------+\n",
+       "| Esophagus_Mucosa.expression_proportion |\n",
+       "+----------------------------------------+\n",
+       "|                                float64 |\n",
+       "+----------------------------------------+\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "|                               0.00e+00 |\n",
+       "+----------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Esophagus_Muscularis.transcript_expression |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| Esophagus_Muscularis.expression_proportion |\n",
+       "+--------------------------------------------+\n",
+       "|                                    float64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "|                                   0.00e+00 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| Heart_AtrialAppendage.transcript_expression |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| Heart_AtrialAppendage.expression_proportion |\n",
+       "+---------------------------------------------+\n",
+       "|                                     float64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "|                                    0.00e+00 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------+\n",
+       "| Heart_LeftVentricle.transcript_expression |\n",
+       "+-------------------------------------------+\n",
+       "|                                   float64 |\n",
+       "+-------------------------------------------+\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "+-------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------+\n",
+       "| Heart_LeftVentricle.expression_proportion |\n",
+       "+-------------------------------------------+\n",
+       "|                                   float64 |\n",
+       "+-------------------------------------------+\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "|                                  0.00e+00 |\n",
+       "+-------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "| Kidney_Cortex.transcript_expression | Kidney_Cortex.expression_proportion |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                             float64 |                             float64 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "|                            0.00e+00 |                            0.00e+00 |\n",
+       "+-------------------------------------+-------------------------------------+\n",
+       "\n",
+       "+-----------------------------+-----------------------------+\n",
+       "| Liver.transcript_expression | Liver.expression_proportion |\n",
+       "+-----------------------------+-----------------------------+\n",
+       "|                     float64 |                     float64 |\n",
+       "+-----------------------------+-----------------------------+\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "|                    0.00e+00 |                    0.00e+00 |\n",
+       "+-----------------------------+-----------------------------+\n",
+       "\n",
+       "+----------------------------+----------------------------+\n",
+       "| Lung.transcript_expression | Lung.expression_proportion |\n",
+       "+----------------------------+----------------------------+\n",
+       "|                    float64 |                    float64 |\n",
+       "+----------------------------+----------------------------+\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "|                   0.00e+00 |                   0.00e+00 |\n",
+       "+----------------------------+----------------------------+\n",
+       "\n",
+       "+------------------------------------------+\n",
+       "| MinorSalivaryGland.transcript_expression |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+\n",
+       "| MinorSalivaryGland.expression_proportion |\n",
+       "+------------------------------------------+\n",
+       "|                                  float64 |\n",
+       "+------------------------------------------+\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "|                                 0.00e+00 |\n",
+       "+------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------+\n",
+       "| Muscle_Skeletal.transcript_expression |\n",
+       "+---------------------------------------+\n",
+       "|                               float64 |\n",
+       "+---------------------------------------+\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "|                              0.00e+00 |\n",
+       "+---------------------------------------+\n",
+       "\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "| Muscle_Skeletal.expression_proportion | Nerve_Tibial.transcript_expression |\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "|                               float64 |                            float64 |\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "|                              0.00e+00 |                           0.00e+00 |\n",
+       "+---------------------------------------+------------------------------------+\n",
+       "\n",
+       "+------------------------------------+-----------------------------+\n",
+       "| Nerve_Tibial.expression_proportion | Ovary.transcript_expression |\n",
+       "+------------------------------------+-----------------------------+\n",
+       "|                            float64 |                     float64 |\n",
+       "+------------------------------------+-----------------------------+\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "|                           0.00e+00 |                    0.00e+00 |\n",
+       "+------------------------------------+-----------------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.alt_adj.n_larger |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                    int64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "+----------------------------------------------------------+\n",
+       "+-----------------------------+--------------------------------+\n",
+       "| Ovary.expression_proportion | Pancreas.transcript_expression |\n",
+       "+-----------------------------+--------------------------------+\n",
+       "|                     float64 |                        float64 |\n",
+       "+-----------------------------+--------------------------------+\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "|                    0.00e+00 |                       0.00e+00 |\n",
+       "+-----------------------------+--------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.alt_raw.bin_edges                    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+--------------------------------+---------------------------------+\n",
+       "| Pancreas.expression_proportion | Pituitary.transcript_expression |\n",
+       "+--------------------------------+---------------------------------+\n",
+       "|                        float64 |                         float64 |\n",
+       "+--------------------------------+---------------------------------+\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "|                       0.00e+00 |                        0.00e+00 |\n",
+       "+--------------------------------+---------------------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.alt_raw.bin_freq |\n",
-       "+----------------------------------------------------------+\n",
-       "| array                                             |\n",
-       "+----------------------------------------------------------+\n",
-       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
-       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]                |\n",
-       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]                |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]                |\n",
-       "+----------------------------------------------------------+\n",
+       "+---------------------------------+--------------------------------+\n",
+       "| Pituitary.expression_proportion | Prostate.transcript_expression |\n",
+       "+---------------------------------+--------------------------------+\n",
+       "|                         float64 |                        float64 |\n",
+       "+---------------------------------+--------------------------------+\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "|                        0.00e+00 |                       0.00e+00 |\n",
+       "+---------------------------------+--------------------------------+\n",
        "\n",
-       "+-----------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.alt_raw.n_smaller |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                     int64 |\n",
-       "+-----------------------------------------------------------+\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "|                                                         0 |\n",
-       "+-----------------------------------------------------------+\n",
+       "+--------------------------------+\n",
+       "| Prostate.expression_proportion |\n",
+       "+--------------------------------+\n",
+       "|                        float64 |\n",
+       "+--------------------------------+\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "|                       0.00e+00 |\n",
+       "+--------------------------------+\n",
        "\n",
-       "+----------------------------------------------------------+\n",
-       "| genome.quality_metrics.genotype_quality.alt_raw.n_larger |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                    int64 |\n",
-       "+----------------------------------------------------------+\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "|                                                        0 |\n",
-       "+----------------------------------------------------------+\n",
+       "+-----------------------------------------------------+\n",
+       "| Skin_NotSunExposed_Suprapubic.transcript_expression |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                             float64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "+-----------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| genome.quality_metrics.site_quality_metrics                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(\"SiteQuality\",9.60e+01),(\"inbreeding_coeff\",-1.65e-05),(\"AS_MQ\",3.51e+0... |\n",
-       "| [(\"SiteQuality\",1.80e+02),(\"inbreeding_coeff\",-3.15e-05),(\"AS_MQ\",3.83e+0... |\n",
-       "| [(\"SiteQuality\",9.70e+01),(\"inbreeding_coeff\",-8.24e-06),(\"AS_MQ\",3.52e+0... |\n",
-       "| [(\"SiteQuality\",2.20e+02),(\"inbreeding_coeff\",-4.64e-05),(\"AS_MQ\",3.48e+0... |\n",
-       "| [(\"SiteQuality\",2.92e+02),(\"inbreeding_coeff\",-2.41e-05),(\"AS_MQ\",3.61e+0... |\n",
-       "+------------------------------------------------------------------------------+\n",
+       "+-----------------------------------------------------+\n",
+       "| Skin_NotSunExposed_Suprapubic.expression_proportion |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                             float64 |\n",
+       "+-----------------------------------------------------+\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "|                                            0.00e+00 |\n",
+       "+-----------------------------------------------------+\n",
        "\n",
-       "+------------------+---------------------------------+\n",
-       "| rsids            | in_silico_predictors.cadd.phred |\n",
-       "+------------------+---------------------------------+\n",
-       "| set         |                         float32 |\n",
-       "+------------------+---------------------------------+\n",
-       "| {\"rs1639542312\"} |                        8.97e+00 |\n",
-       "| {\"rs1639542418\"} |                        8.90e+00 |\n",
-       "| NA               |                        8.90e+00 |\n",
-       "| {\"rs892501864\"}  |                        8.88e+00 |\n",
-       "| {\"rs1570391741\"} |                        8.53e+00 |\n",
-       "+------------------+---------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "| Skin_SunExposed_Lowerleg.transcript_expression |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "+------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                            7.57e-01 |                             NA |\n",
-       "|                            7.49e-01 |                             NA |\n",
-       "|                            7.48e-01 |                             NA |\n",
-       "|                            7.46e-01 |                             NA |\n",
-       "|                            7.09e-01 |                             NA |\n",
-       "+-------------------------------------+--------------------------------+\n",
+       "+------------------------------------------------+\n",
+       "| Skin_SunExposed_Lowerleg.expression_proportion |\n",
+       "+------------------------------------------------+\n",
+       "|                                        float64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "|                                       0.00e+00 |\n",
+       "+------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "|                                   NA |\n",
-       "+--------------------------------------+\n",
+       "+----------------------------------------------------+\n",
+       "| SmallIntestine_TerminalIleum.transcript_expression |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                       NA |                          NA |\n",
-       "|                                       NA |                          NA |\n",
-       "|                                       NA |                          NA |\n",
-       "|                                       NA |                          NA |\n",
-       "|                                       NA |                          NA |\n",
-       "+------------------------------------------+-----------------------------+\n",
+       "+----------------------------------------------------+\n",
+       "| SmallIntestine_TerminalIleum.expression_proportion |\n",
+       "+----------------------------------------------------+\n",
+       "|                                            float64 |\n",
+       "+----------------------------------------------------+\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "|                                           0.00e+00 |\n",
+       "+----------------------------------------------------+\n",
+       "\n",
+       "+------------------------------+------------------------------+\n",
+       "| Spleen.transcript_expression | Spleen.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
        "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "+-------------------------------+-----------------------------------+\n",
+       "+-------------------------------+-------------------------------+\n",
+       "| Stomach.transcript_expression | Stomach.expression_proportion |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "|                       float64 |                       float64 |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "+-------------------------------+-------------------------------+\n",
        "\n",
-       "+---------------+------------------------+----------------------------+\n",
-       "| variant_id    | colocated_variants.all | colocated_variants.non_ukb |\n",
-       "+---------------+------------------------+----------------------------+\n",
-       "| str           | array             | array                 |\n",
-       "+---------------+------------------------+----------------------------+\n",
-       "| \"1-10031-T-C\" | []                     | []                         |\n",
-       "| \"1-10037-T-C\" | []                     | []                         |\n",
-       "| \"1-10043-T-C\" | []                     | []                         |\n",
-       "| \"1-10055-T-C\" | []                     | []                         |\n",
-       "| \"1-10057-A-C\" | []                     | []                         |\n",
-       "+---------------+------------------------+----------------------------+\n",
+       "+------------------------------+------------------------------+\n",
+       "| Testis.transcript_expression | Testis.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
        "\n",
-       "+-------------------------+------------------------+-------------------+\n",
-       "| colocated_variants.hgdp | colocated_variants.tgp | joint.freq.all.ac |\n",
-       "+-------------------------+------------------------+-------------------+\n",
-       "| array              | array             |             int32 |\n",
-       "+-------------------------+------------------------+-------------------+\n",
-       "| []                      | []                     |                 0 |\n",
-       "| []                      | []                     |                 2 |\n",
-       "| []                      | []                     |                 1 |\n",
-       "| []                      | []                     |                 1 |\n",
-       "| []                      | []                     |                 3 |\n",
-       "+-------------------------+------------------------+-------------------+\n",
+       "+-------------------------------+-------------------------------+\n",
+       "| Thyroid.transcript_expression | Thyroid.expression_proportion |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "|                       float64 |                       float64 |\n",
+       "+-------------------------------+-------------------------------+\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "|                      0.00e+00 |                      0.00e+00 |\n",
+       "+-------------------------------+-------------------------------+\n",
        "\n",
-       "+-----------------------+-------------------+---------------------------------+\n",
-       "| joint.freq.all.ac_raw | joint.freq.all.an | joint.freq.all.hemizygote_count |\n",
-       "+-----------------------+-------------------+---------------------------------+\n",
-       "|                 int32 |             int32 |                           int32 |\n",
-       "+-----------------------+-------------------+---------------------------------+\n",
-       "|                     2 |             56642 |                               0 |\n",
-       "|                     4 |             76882 |                               0 |\n",
-       "|                     1 |             85634 |                               0 |\n",
-       "|                     5 |             94224 |                               0 |\n",
-       "|                     3 |            113536 |                               0 |\n",
-       "+-----------------------+-------------------+---------------------------------+\n",
+       "+------------------------------+------------------------------+\n",
+       "| Uterus.transcript_expression | Uterus.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
        "\n",
-       "+---------------------------------+\n",
-       "| joint.freq.all.homozygote_count |\n",
-       "+---------------------------------+\n",
-       "|                           int32 |\n",
-       "+---------------------------------+\n",
-       "|                               0 |\n",
-       "|                               0 |\n",
-       "|                               0 |\n",
-       "|                               0 |\n",
-       "|                               0 |\n",
-       "+---------------------------------+\n",
+       "+------------------------------+------------------------------+\n",
+       "| Vagina.transcript_expression | Vagina.expression_proportion |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                      float64 |                      float64 |\n",
+       "+------------------------------+------------------------------+\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "|                     0.00e+00 |                     0.00e+00 |\n",
+       "+------------------------------+------------------------------+\n",
+       "\n",
+       "+----------------------------------+----------------------------------+\n",
+       "| WholeBlood.transcript_expression | WholeBlood.expression_proportion |\n",
+       "+----------------------------------+----------------------------------+\n",
+       "|                          float64 |                          float64 |\n",
+       "+----------------------------------+----------------------------------+\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "|                         0.00e+00 |                         0.00e+00 |\n",
+       "+----------------------------------+----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d1780f9",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Lifted over variant data (GRCh37 --> GRCh38)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93ba18a4",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Exomes liftover Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "207b8b4a",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "4a29c79d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='exomes', version='2.1.1', dataset=\"liftover\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d9872f9",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "dcd9ad6e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'rf': struct {\n",
+      "        variants_by_type: dict, \n",
+      "        feature_medians: dict, \n",
+      "        test_intervals: array>>, \n",
+      "        test_results: array, \n",
+      "        features_importance: dict, \n",
+      "        features: array, \n",
+      "        vqsr_training: bool, \n",
+      "        no_transmitted_singletons: bool, \n",
+      "        adj: bool, \n",
+      "        rf_hash: str, \n",
+      "        rf_snv_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }, \n",
+      "        rf_indel_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }\n",
+      "    } \n",
+      "    'freq_meta': array> \n",
+      "    'freq_index_dict': dict \n",
+      "    'popmax_index_dict': dict \n",
+      "    'age_index_dict': dict \n",
+      "    'faf_index_dict': dict \n",
+      "    'age_distribution': array \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'alleles': array \n",
+      "    'ReverseComplementedAlleles': bool \n",
+      "    'SwappedAlleles': bool \n",
+      "    'original_locus': locus \n",
+      "    'freq': array \n",
+      "    'age_hist_het': array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int64, \n",
+      "        n_larger: int64\n",
+      "    }> \n",
+      "    'age_hist_hom': array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int64, \n",
+      "        n_larger: int64\n",
+      "    }> \n",
+      "    'popmax': array \n",
+      "    'faf': array, \n",
+      "        faf95: float64, \n",
+      "        faf99: float64\n",
+      "    }> \n",
+      "    'lcr': bool \n",
+      "    'decoy': bool \n",
+      "    'segdup': bool \n",
+      "    'nonpar': bool \n",
+      "    'variant_type': str \n",
+      "    'allele_type': str \n",
+      "    'n_alt_alleles': int32 \n",
+      "    'was_mixed': bool \n",
+      "    'has_star': bool \n",
+      "    'qd': float64 \n",
+      "    'pab_max': float64 \n",
+      "    'info_MQRankSum': float64 \n",
+      "    'info_SOR': float64 \n",
+      "    'info_InbreedingCoeff': float64 \n",
+      "    'info_ReadPosRankSum': float64 \n",
+      "    'info_FS': float64 \n",
+      "    'info_QD': float64 \n",
+      "    'info_MQ': float64 \n",
+      "    'info_DP': int32 \n",
+      "    'transmitted_singleton': bool \n",
+      "    'fail_hard_filters': bool \n",
+      "    'info_POSITIVE_TRAIN_SITE': bool \n",
+      "    'info_NEGATIVE_TRAIN_SITE': bool \n",
+      "    'omni': bool \n",
+      "    'mills': bool \n",
+      "    'n_nonref': int32 \n",
+      "    'tp': bool \n",
+      "    'rf_train': bool \n",
+      "    'rf_label': str \n",
+      "    'rf_probability': float64 \n",
+      "    'singleton': bool \n",
+      "    'was_split': bool \n",
+      "    'score': float64 \n",
+      "    'rank': int64 \n",
+      "    'singleton_rank': int64 \n",
+      "    'biallelic_rank': int64 \n",
+      "    'adj_biallelic_singleton_rank': int64 \n",
+      "    'adj_rank': int64 \n",
+      "    'adj_biallelic_rank': int64 \n",
+      "    'adj_singleton_rank': int64 \n",
+      "    'biallelic_singleton_rank': int64 \n",
+      "    'filters': set \n",
+      "    'gq_hist_alt': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int64, \n",
+      "        n_larger: int64\n",
+      "    } \n",
+      "    'gq_hist_all': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int64, \n",
+      "        n_larger: int64\n",
+      "    } \n",
+      "    'dp_hist_alt': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int64, \n",
+      "        n_larger: int64\n",
+      "    } \n",
+      "    'dp_hist_all': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int64, \n",
+      "        n_larger: int64\n",
+      "    } \n",
+      "    'ab_hist_alt': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int64, \n",
+      "        n_larger: int64\n",
+      "    } \n",
+      "    'qual': float64 \n",
+      "    'vep': struct {\n",
+      "        assembly_name: str, \n",
+      "        allele_string: str, \n",
+      "        ancestral: str, \n",
+      "        colocated_variants: array, \n",
+      "            end: int32, \n",
+      "            eas_allele: str, \n",
+      "            eas_maf: float64, \n",
+      "            ea_allele: str, \n",
+      "            ea_maf: float64, \n",
+      "            eur_allele: str, \n",
+      "            eur_maf: float64, \n",
+      "            exac_adj_allele: str, \n",
+      "            exac_adj_maf: float64, \n",
+      "            exac_allele: str, \n",
+      "            exac_afr_allele: str, \n",
+      "            exac_afr_maf: float64, \n",
+      "            exac_amr_allele: str, \n",
+      "            exac_amr_maf: float64, \n",
+      "            exac_eas_allele: str, \n",
+      "            exac_eas_maf: float64, \n",
+      "            exac_fin_allele: str, \n",
+      "            exac_fin_maf: float64, \n",
+      "            exac_maf: float64, \n",
+      "            exac_nfe_allele: str, \n",
+      "            exac_nfe_maf: float64, \n",
+      "            exac_oth_allele: str, \n",
+      "            exac_oth_maf: float64, \n",
+      "            exac_sas_allele: str, \n",
+      "            exac_sas_maf: float64, \n",
+      "            id: str, \n",
+      "            minor_allele: str, \n",
+      "            minor_allele_freq: float64, \n",
+      "            phenotype_or_disease: int32, \n",
+      "            pubmed: array, \n",
+      "            sas_allele: str, \n",
+      "            sas_maf: float64, \n",
+      "            somatic: int32, \n",
+      "            start: int32, \n",
+      "            strand: int32\n",
+      "        }>, \n",
+      "        context: str, \n",
+      "        end: int32, \n",
+      "        id: str, \n",
+      "        input: str, \n",
+      "        intergenic_consequences: array, \n",
+      "            impact: str, \n",
+      "            minimised: int32, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        most_severe_consequence: str, \n",
+      "        motif_feature_consequences: array, \n",
+      "            high_inf_pos: str, \n",
+      "            impact: str, \n",
+      "            minimised: int32, \n",
+      "            motif_feature_id: str, \n",
+      "            motif_name: str, \n",
+      "            motif_pos: int32, \n",
+      "            motif_score_change: float64, \n",
+      "            strand: int32, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        regulatory_feature_consequences: array, \n",
+      "            impact: str, \n",
+      "            minimised: int32, \n",
+      "            regulatory_feature_id: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        seq_region_name: str, \n",
+      "        start: int32, \n",
+      "        strand: int32, \n",
+      "        transcript_consequences: array, \n",
+      "            distance: int32, \n",
+      "            domains: array, \n",
+      "            exon: str, \n",
+      "            gene_id: str, \n",
+      "            gene_pheno: int32, \n",
+      "            gene_symbol: str, \n",
+      "            gene_symbol_source: str, \n",
+      "            hgnc_id: str, \n",
+      "            hgvsc: str, \n",
+      "            hgvsp: str, \n",
+      "            hgvs_offset: int32, \n",
+      "            impact: str, \n",
+      "            intron: str, \n",
+      "            lof: str, \n",
+      "            lof_flags: str, \n",
+      "            lof_filter: str, \n",
+      "            lof_info: str, \n",
+      "            minimised: int32, \n",
+      "            polyphen_prediction: str, \n",
+      "            polyphen_score: float64, \n",
+      "            protein_end: int32, \n",
+      "            protein_start: int32, \n",
+      "            protein_id: str, \n",
+      "            sift_prediction: str, \n",
+      "            sift_score: float64, \n",
+      "            strand: int32, \n",
+      "            swissprot: str, \n",
+      "            transcript_id: str, \n",
+      "            trembl: str, \n",
+      "            uniparc: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        variant_class: str\n",
+      "    } \n",
+      "    'allele_info': struct {\n",
+      "        BaseQRankSum: float64, \n",
+      "        ClippingRankSum: float64, \n",
+      "        DB: bool, \n",
+      "        DP: int32, \n",
+      "        DS: bool, \n",
+      "        END: int32, \n",
+      "        FS: float64, \n",
+      "        HaplotypeScore: float64, \n",
+      "        InbreedingCoeff: float64, \n",
+      "        MQ: float64, \n",
+      "        MQRankSum: float64, \n",
+      "        NEGATIVE_TRAIN_SITE: bool, \n",
+      "        POSITIVE_TRAIN_SITE: bool, \n",
+      "        QD: float64, \n",
+      "        ReadPosRankSum: float64, \n",
+      "        SOR: float64, \n",
+      "        VQSLOD: float64, \n",
+      "        culprit: str\n",
+      "    } \n",
+      "    'rsid': str \n",
+      "    'original_alleles': array \n",
+      "----------------------------------------\n",
+      "Key: ['locus', 'alleles']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7305a806",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "04b6da53",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
gq_hist_alt
gq_hist_all
dp_hist_alt
dp_hist_all
ab_hist_alt
vep
allele_info
locus
alleles
ReverseComplementedAlleles
SwappedAlleles
original_locus
freq
age_hist_het
age_hist_hom
popmax
faf
lcr
decoy
segdup
nonpar
variant_type
allele_type
n_alt_alleles
was_mixed
has_star
qd
pab_max
info_MQRankSum
info_SOR
info_InbreedingCoeff
info_ReadPosRankSum
info_FS
info_QD
info_MQ
info_DP
transmitted_singleton
fail_hard_filters
info_POSITIVE_TRAIN_SITE
info_NEGATIVE_TRAIN_SITE
omni
mills
n_nonref
tp
rf_train
rf_label
rf_probability
singleton
was_split
score
rank
singleton_rank
biallelic_rank
adj_biallelic_singleton_rank
adj_rank
adj_biallelic_rank
adj_singleton_rank
biallelic_singleton_rank
filters
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
qual
assembly_name
allele_string
ancestral
colocated_variants
context
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
BaseQRankSum
ClippingRankSum
DB
DP
DS
END
FS
HaplotypeScore
InbreedingCoeff
MQ
MQRankSum
NEGATIVE_TRAIN_SITE
POSITIVE_TRAIN_SITE
QD
ReadPosRankSum
SOR
VQSLOD
culprit
rsid
original_alleles
locus<GRCh38>array<str>boolboollocus<GRCh37>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{bin_edges: array<float64>, bin_freq: array<int64>, n_smaller: int64, n_larger: int64}>array<struct{bin_edges: array<float64>, bin_freq: array<int64>, n_smaller: int64, n_larger: int64}>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32, pop: str}>array<struct{meta: dict<str, str>, faf95: float64, faf99: float64}>boolboolboolboolstrstrint32boolboolfloat64float64float64float64float64float64float64float64float64int32boolboolboolboolboolboolint32boolboolstrfloat64boolboolfloat64int64int64int64int64int64int64int64int64set<str>array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float64strstrstrarray<struct{aa_allele: str, aa_maf: float64, afr_allele: str, afr_maf: float64, allele_string: str, amr_allele: str, amr_maf: float64, clin_sig: array<str>, end: int32, eas_allele: str, eas_maf: float64, ea_allele: str, ea_maf: float64, eur_allele: str, eur_maf: float64, exac_adj_allele: str, exac_adj_maf: float64, exac_allele: str, exac_afr_allele: str, exac_afr_maf: float64, exac_amr_allele: str, exac_amr_maf: float64, exac_eas_allele: str, exac_eas_maf: float64, exac_fin_allele: str, exac_fin_maf: float64, exac_maf: float64, exac_nfe_allele: str, exac_nfe_maf: float64, exac_oth_allele: str, exac_oth_maf: float64, exac_sas_allele: str, exac_sas_maf: float64, id: str, minor_allele: str, minor_allele_freq: float64, phenotype_or_disease: int32, pubmed: array<int32>, sas_allele: str, sas_maf: float64, somatic: int32, start: int32, strand: int32}>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, minimised: int32, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, minimised: int32, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, minimised: int32, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, minimised: int32, polyphen_prediction: str, polyphen_score: float64, protein_end: int32, protein_start: int32, protein_id: str, sift_prediction: str, sift_score: float64, strand: int32, swissprot: str, transcript_id: str, trembl: str, uniparc: str, variant_allele: str}>strfloat64float64boolint32boolint32float64float64float64float64float64boolboolfloat64float64float64float64strstrarray<str>
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showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+------------+----------------------------+----------------+\n",
+       "| locus         | alleles    | ReverseComplementedAlleles | SwappedAlleles |\n",
+       "+---------------+------------+----------------------------+----------------+\n",
+       "| locus | array |                       bool |           bool |\n",
+       "+---------------+------------+----------------------------+----------------+\n",
+       "| chr1:12198    | [\"G\",\"C\"]  |                      False |          False |\n",
+       "| chr1:12237    | [\"G\",\"A\"]  |                      False |          False |\n",
+       "| chr1:12259    | [\"G\",\"C\"]  |                      False |          False |\n",
+       "| chr1:12266    | [\"G\",\"A\"]  |                      False |          False |\n",
+       "| chr1:12272    | [\"G\",\"A\"]  |                      False |          False |\n",
+       "+---------------+------------+----------------------------+----------------+\n",
+       "\n",
+       "+----------------+\n",
+       "| original_locus |\n",
+       "+----------------+\n",
+       "| locus  |\n",
+       "+----------------+\n",
+       "| 1:12198        |\n",
+       "| 1:12237        |\n",
+       "| 1:12259        |\n",
+       "| 1:12266        |\n",
+       "| 1:12272        |\n",
+       "+----------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.freq.all.ancestry_groups                                               |\n",
+       "| freq                                                                         |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [(\"remaining\",0,782,0,0),(\"remaining_XX\",0,402,0,0),(\"remaining_XY\",0,380... |\n",
-       "| [(\"remaining\",0,998,0,0),(\"remaining_XX\",0,528,0,0),(\"remaining_XY\",0,470... |\n",
-       "| [(\"remaining\",0,1148,0,0),(\"remaining_XX\",0,594,0,0),(\"remaining_XY\",0,55... |\n",
-       "| [(\"remaining\",0,1228,0,0),(\"remaining_XX\",0,624,0,0),(\"remaining_XY\",0,60... |\n",
-       "| [(\"remaining\",0,1504,0,0),(\"remaining_XX\",0,764,0,0),(\"remaining_XY\",0,74... |\n",
+       "| [(0,NA,0,0),(227,4.57e-02,4966,90),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA... |\n",
+       "| [(0,NA,0,0),(5,4.41e-04,11338,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA,0... |\n",
+       "| [(0,0.00e+00,2,0),(2,1.56e-04,12838,1),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(... |\n",
+       "| [(0,NA,0,0),(51,4.35e-03,11732,15),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0),(0,NA... |\n",
+       "| [(0,0.00e+00,2,0),(49,4.30e-03,11392,14),(0,NA,0,0),(0,NA,0,0),(0,NA,0,0)... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.faf                                                                    |\n",
+       "| age_hist_het                                                                 |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
+       "| array, bin_freq: array, n_smaller... |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(4.32e-06,1.62e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(7.02e-06,2.95e-06),(6.27e-06,2.35e-06),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------+--------------------------------+\n",
-       "| joint.fafmax.faf95_max | joint.fafmax.faf95_max_gen_anc |\n",
-       "+------------------------+--------------------------------+\n",
-       "|                float64 | str                            |\n",
-       "+------------------------+--------------------------------+\n",
-       "|                     NA | NA                             |\n",
-       "|                     NA | NA                             |\n",
-       "|                     NA | NA                             |\n",
-       "|                     NA | NA                             |\n",
-       "|               6.27e-06 | \"nfe\"                          |\n",
-       "+------------------------+--------------------------------+\n",
-       "\n",
-       "+------------------------+--------------------------------+-----------------+\n",
-       "| joint.fafmax.faf99_max | joint.fafmax.faf99_max_gen_anc | joint.grpmax.AC |\n",
-       "+------------------------+--------------------------------+-----------------+\n",
-       "|                float64 | str                            |           int32 |\n",
-       "+------------------------+--------------------------------+-----------------+\n",
-       "|                     NA | NA                             |              NA |\n",
-       "|                     NA | NA                             |               1 |\n",
-       "|                     NA | NA                             |               1 |\n",
-       "|                     NA | NA                             |              NA |\n",
-       "|               2.35e-06 | \"nfe\"                          |               2 |\n",
-       "+------------------------+--------------------------------+-----------------+\n",
-       "\n",
-       "+-----------------+-----------------+-------------------------------+\n",
-       "| joint.grpmax.AF | joint.grpmax.AN | joint.grpmax.homozygote_count |\n",
-       "+-----------------+-----------------+-------------------------------+\n",
-       "|         float64 |           int32 |                         int32 |\n",
-       "+-----------------+-----------------+-------------------------------+\n",
-       "|              NA |              NA |                            NA |\n",
-       "|        4.07e-04 |            2456 |                             0 |\n",
-       "|        4.39e-05 |           22760 |                             0 |\n",
-       "|              NA |              NA |                            NA |\n",
-       "|        3.78e-05 |           52912 |                             0 |\n",
-       "+-----------------+-----------------+-------------------------------+\n",
-       "\n",
-       "+----------------------+\n",
-       "| joint.grpmax.gen_anc |\n",
-       "+----------------------+\n",
-       "| str                  |\n",
-       "+----------------------+\n",
-       "| NA                   |\n",
-       "| \"eas\"                |\n",
-       "| \"afr\"                |\n",
-       "| NA                   |\n",
-       "| \"nfe\"                |\n",
-       "+----------------------+\n",
-       "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_all.bin_edges                            |\n",
+       "| age_hist_hom                                                                 |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
+       "| array, bin_freq: array, n_smaller... |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_all.bin_freq                             |\n",
+       "| popmax                                                                       |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
+       "| array, faf95: float64, faf99: float64}>          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n",
+       "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n",
+       "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n",
+       "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n",
+       "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------+-------+--------+--------+--------------+-------------+---------------+\n",
+       "|   lcr | decoy | segdup | nonpar | variant_type | allele_type | n_alt_alleles |\n",
+       "+-------+-------+--------+--------+--------------+-------------+---------------+\n",
+       "|  bool |  bool |   bool |   bool | str          | str         |         int32 |\n",
+       "+-------+-------+--------+--------+--------------+-------------+---------------+\n",
+       "| False | False |   True |  False | \"snv\"        | \"snv\"       |             1 |\n",
+       "| False | False |   True |  False | \"snv\"        | \"snv\"       |             1 |\n",
+       "| False | False |   True |  False | \"snv\"        | \"snv\"       |             1 |\n",
+       "| False | False |   True |  False | \"snv\"        | \"snv\"       |             1 |\n",
+       "| False | False |   True |  False | \"snv\"        | \"snv\"       |             1 |\n",
+       "+-------+-------+--------+--------+--------------+-------------+---------------+\n",
+       "\n",
+       "+-----------+----------+----------+----------+----------------+----------+\n",
+       "| was_mixed | has_star |       qd |  pab_max | info_MQRankSum | info_SOR |\n",
+       "+-----------+----------+----------+----------+----------------+----------+\n",
+       "|      bool |     bool |  float64 |  float64 |        float64 |  float64 |\n",
+       "+-----------+----------+----------+----------+----------------+----------+\n",
+       "|     False |    False | 2.03e+01 | 1.00e+00 |       7.36e-01 | 3.02e-01 |\n",
+       "|     False |    False | 1.55e+01 | 1.00e+00 |      -3.58e-01 | 1.29e+00 |\n",
+       "|     False |    False | 2.50e+01 |       NA |             NA | 6.93e-01 |\n",
+       "|     False |    False | 2.57e+01 | 1.00e+00 |      -7.27e-01 | 2.03e-01 |\n",
+       "|     False |    False | 2.65e+01 | 1.00e+00 |       0.00e+00 | 1.96e-01 |\n",
+       "+-----------+----------+----------+----------+----------------+----------+\n",
+       "\n",
+       "+----------------------+---------------------+----------+----------+----------+\n",
+       "| info_InbreedingCoeff | info_ReadPosRankSum |  info_FS |  info_QD |  info_MQ |\n",
+       "+----------------------+---------------------+----------+----------+----------+\n",
+       "|              float64 |             float64 |  float64 |  float64 |  float64 |\n",
+       "+----------------------+---------------------+----------+----------+----------+\n",
+       "|             9.80e-03 |            7.36e-01 | 0.00e+00 | 1.40e+01 | 2.30e+01 |\n",
+       "|            -1.44e-01 |            3.58e-01 | 0.00e+00 | 4.31e+00 | 2.20e+01 |\n",
+       "|            -1.42e-01 |                  NA | 0.00e+00 | 9.36e+00 | 2.00e+01 |\n",
+       "|            -1.38e-01 |            4.06e-01 | 0.00e+00 | 1.61e+01 | 2.23e+01 |\n",
+       "|            -1.34e-01 |            0.00e+00 | 0.00e+00 | 1.69e+01 | 2.23e+01 |\n",
+       "+----------------------+---------------------+----------+----------+----------+\n",
+       "\n",
+       "+---------+-----------------------+-------------------+\n",
+       "| info_DP | transmitted_singleton | fail_hard_filters |\n",
+       "+---------+-----------------------+-------------------+\n",
+       "|   int32 |                  bool |              bool |\n",
+       "+---------+-----------------------+-------------------+\n",
+       "|    9204 |                    NA |              True |\n",
+       "|   16096 |                    NA |              True |\n",
+       "|   18814 |                    NA |              True |\n",
+       "|   18372 |                    NA |              True |\n",
+       "|   17685 |                    NA |              True |\n",
+       "+---------+-----------------------+-------------------+\n",
+       "\n",
+       "+--------------------------+--------------------------+-------+-------+\n",
+       "| info_POSITIVE_TRAIN_SITE | info_NEGATIVE_TRAIN_SITE |  omni | mills |\n",
+       "+--------------------------+--------------------------+-------+-------+\n",
+       "|                     bool |                     bool |  bool |  bool |\n",
+       "+--------------------------+--------------------------+-------+-------+\n",
+       "|                       NA |                       NA | False | False |\n",
+       "|                       NA |                       NA | False | False |\n",
+       "|                       NA |                       NA | False | False |\n",
+       "|                       NA |                       NA | False | False |\n",
+       "|                       NA |                       NA | False | False |\n",
+       "+--------------------------+--------------------------+-------+-------+\n",
+       "\n",
+       "+----------+-------+----------+----------+----------------+-----------+\n",
+       "| n_nonref |    tp | rf_train | rf_label | rf_probability | singleton |\n",
+       "+----------+-------+----------+----------+----------------+-----------+\n",
+       "|    int32 |  bool |     bool | str      |        float64 |      bool |\n",
+       "+----------+-------+----------+----------+----------------+-----------+\n",
+       "|      231 | False |     True | \"FP\"     |       8.37e-01 |     False |\n",
+       "|        5 | False |     True | \"FP\"     |       4.33e-01 |     False |\n",
+       "|        2 | False |     True | \"FP\"     |       3.76e-01 |     False |\n",
+       "|       51 | False |     True | \"FP\"     |       3.75e-01 |     False |\n",
+       "|       49 | False |     True | \"FP\"     |       3.69e-01 |     False |\n",
+       "+----------+-------+----------+----------+----------------+-----------+\n",
+       "\n",
+       "+-----------+----------+----------+----------------+----------------+\n",
+       "| was_split |    score |     rank | singleton_rank | biallelic_rank |\n",
+       "+-----------+----------+----------+----------------+----------------+\n",
+       "|      bool |  float64 |    int64 |          int64 |          int64 |\n",
+       "+-----------+----------+----------+----------------+----------------+\n",
+       "|     False | 8.37e-01 |  6571357 |             NA |        4342152 |\n",
+       "|     False | 4.33e-01 | 13350857 |             NA |        9360383 |\n",
+       "|     False | 3.76e-01 | 13704393 |             NA |        9608904 |\n",
+       "|     False | 3.75e-01 | 13712337 |             NA |        9614461 |\n",
+       "|     False | 3.69e-01 | 13743987 |             NA |        9634223 |\n",
+       "+-----------+----------+----------+----------------+----------------+\n",
+       "\n",
+       "+------------------------------+----------+--------------------+\n",
+       "| adj_biallelic_singleton_rank | adj_rank | adj_biallelic_rank |\n",
+       "+------------------------------+----------+--------------------+\n",
+       "|                        int64 |    int64 |              int64 |\n",
+       "+------------------------------+----------+--------------------+\n",
+       "|                           NA |       NA |                 NA |\n",
+       "|                           NA |       NA |                 NA |\n",
+       "|                           NA |       NA |                 NA |\n",
+       "|                           NA |       NA |                 NA |\n",
+       "|                           NA |       NA |                 NA |\n",
+       "+------------------------------+----------+--------------------+\n",
+       "\n",
+       "+--------------------+--------------------------+----------+\n",
+       "| adj_singleton_rank | biallelic_singleton_rank | filters  |\n",
+       "+--------------------+--------------------------+----------+\n",
+       "|              int64 |                    int64 | set |\n",
+       "+--------------------+--------------------------+----------+\n",
+       "|                 NA |                       NA | {\"AC0\"}  |\n",
+       "|                 NA |                       NA | {\"AC0\"}  |\n",
+       "|                 NA |                       NA | {\"AC0\"}  |\n",
+       "|                 NA |                       NA | {\"AC0\"}  |\n",
+       "|                 NA |                       NA | {\"AC0\"}  |\n",
+       "+--------------------+--------------------------+----------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| gq_hist_alt.bin_edges                                                        |\n",
        "+------------------------------------------------------------------------------+\n",
        "| array                                                               |\n",
        "+------------------------------------------------------------------------------+\n",
@@ -11757,44 +13205,68 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
+       "+----------------------------------------------+-----------------------+\n",
+       "| gq_hist_alt.bin_freq                         | gq_hist_alt.n_smaller |\n",
+       "+----------------------------------------------+-----------------------+\n",
+       "| array                                 |                 int64 |\n",
+       "+----------------------------------------------+-----------------------+\n",
+       "| [14,78,1,25,7,4,0,5,2,0,0,0,1,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [0,1,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]    |                     0 |\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]    |                     0 |\n",
+       "| [0,15,0,0,0,2,0,2,5,0,1,1,1,3,0,6,0,0,0,0]   |                     0 |\n",
+       "| [0,14,0,0,0,2,0,2,5,0,1,1,1,3,0,6,0,0,0,0]   |                     0 |\n",
+       "+----------------------------------------------+-----------------------+\n",
+       "\n",
+       "+----------------------+\n",
+       "| gq_hist_alt.n_larger |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "+----------------------+\n",
+       "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_all.bin_freq                             |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
+       "| gq_hist_all.bin_edges                                                        |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [0,0,16,121,475,973,1736,2409,2950,3134,2995,2803,2514,2194,1773,1335,911... |\n",
-       "| [0,0,9,69,387,913,1737,2859,3749,4216,4302,4223,3745,3280,2661,1954,1380,... |\n",
-       "| [0,0,8,40,264,690,1525,2691,3754,4574,4813,4940,4490,3907,3242,2387,1704,... |\n",
-       "| [0,0,4,22,120,354,999,1950,3037,4168,4734,5196,5380,4766,4238,3206,2398,1... |\n",
-       "| [0,0,2,23,108,320,888,1844,3110,4447,5361,6012,6379,5879,5289,4273,3400,2... |\n",
+       "| array                                                               |\n",
        "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                              527 |\n",
-       "|                                              888 |\n",
-       "|                                             1232 |\n",
-       "|                                             2440 |\n",
-       "|                                             3622 |\n",
-       "+--------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------+-----------------------+\n",
+       "| gq_hist_all.bin_freq                                 | gq_hist_all.n_smaller |\n",
+       "+------------------------------------------------------+-----------------------+\n",
+       "| array                                         |                 int64 |\n",
+       "+------------------------------------------------------+-----------------------+\n",
+       "| [1898,511,26,28,8,4,0,5,2,0,0,0,1,0,0,0,0,0,0,0]     |                     0 |\n",
+       "| [3635,1739,178,101,14,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [3978,2020,252,143,22,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [3833,1709,188,102,11,4,0,2,5,0,1,1,1,3,0,6,0,0,0,0] |                     0 |\n",
+       "| [3792,1624,162,87,9,2,1,2,5,0,1,1,1,3,0,6,0,0,0,0]   |                     0 |\n",
+       "+------------------------------------------------------+-----------------------+\n",
+       "\n",
+       "+----------------------+\n",
+       "| gq_hist_all.n_larger |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "+----------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_alt.bin_edges                            |\n",
+       "| dp_hist_alt.bin_edges                                                        |\n",
        "+------------------------------------------------------------------------------+\n",
        "| array                                                               |\n",
        "+------------------------------------------------------------------------------+\n",
@@ -11805,44 +13277,32 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]        |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
+       "+---------------------------------------------+-----------------------+\n",
+       "| dp_hist_alt.bin_freq                        | dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+-----------------------+\n",
+       "| array                                |                 int64 |\n",
+       "+---------------------------------------------+-----------------------+\n",
+       "| [130,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |                     0 |\n",
+       "| [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |                     0 |\n",
+       "| [33,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |                     0 |\n",
+       "| [31,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |                     0 |\n",
+       "+---------------------------------------------+-----------------------+\n",
        "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
+       "+----------------------+\n",
+       "| dp_hist_alt.n_larger |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "+----------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_alt.bin_edges                            |\n",
+       "| dp_hist_all.bin_edges                                                        |\n",
        "+------------------------------------------------------------------------------+\n",
        "| array                                                               |\n",
        "+------------------------------------------------------------------------------+\n",
@@ -11853,44 +13313,32 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
+       "+--------------------------------------------------+-----------------------+\n",
+       "| dp_hist_all.bin_freq                             | dp_hist_all.n_smaller |\n",
+       "+--------------------------------------------------+-----------------------+\n",
+       "| array                                     |                 int64 |\n",
+       "+--------------------------------------------------+-----------------------+\n",
+       "| [125724,24,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |                     0 |\n",
+       "| [125622,126,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [125566,181,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [125584,163,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [125601,146,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "+--------------------------------------------------+-----------------------+\n",
        "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
+       "+----------------------+\n",
+       "| dp_hist_all.n_larger |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "+----------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.ab_hist_alt.bin_edges                            |\n",
+       "| ab_hist_alt.bin_edges                                                        |\n",
        "+------------------------------------------------------------------------------+\n",
        "| array                                                               |\n",
        "+------------------------------------------------------------------------------+\n",
@@ -11901,188 +13349,856 @@
        "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]        |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
+       "+---------------------------------------------+-----------------------+\n",
+       "| ab_hist_alt.bin_freq                        | ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+-----------------------+\n",
+       "| array                                |                 int64 |\n",
+       "+---------------------------------------------+-----------------------+\n",
+       "| [0,0,0,0,1,0,2,0,2,0,10,0,1,28,0,3,0,0,0,0] |                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0]   |                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]   |                     0 |\n",
+       "| [0,0,0,0,0,2,0,0,2,0,9,0,0,8,0,0,0,0,0,0]   |                     0 |\n",
+       "| [0,0,0,0,0,0,1,0,3,0,10,0,0,7,0,0,0,0,0,0]  |                     0 |\n",
+       "+---------------------------------------------+-----------------------+\n",
+       "\n",
+       "+----------------------+----------+-------------------+-------------------+\n",
+       "| ab_hist_alt.n_larger |     qual | vep.assembly_name | vep.allele_string |\n",
+       "+----------------------+----------+-------------------+-------------------+\n",
+       "|                int64 |  float64 | str               | str               |\n",
+       "+----------------------+----------+-------------------+-------------------+\n",
+       "|                    0 | 9.88e+03 | \"GRCh37\"          | \"G/C\"             |\n",
+       "|                    0 | 8.20e+01 | \"GRCh37\"          | \"G/A\"             |\n",
+       "|                    0 | 3.74e+01 | \"GRCh37\"          | \"G/C\"             |\n",
+       "|                    0 | 2.72e+03 | \"GRCh37\"          | \"G/A\"             |\n",
+       "|                    0 | 2.71e+03 | \"GRCh37\"          | \"G/A\"             |\n",
+       "+----------------------+----------+-------------------+-------------------+\n",
        "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
+       "+---------------+\n",
+       "| vep.ancestral |\n",
+       "+---------------+\n",
+       "| str           |\n",
+       "+---------------+\n",
+       "| NA            |\n",
+       "| NA            |\n",
+       "| NA            |\n",
+       "| NA            |\n",
+       "| NA            |\n",
+       "+---------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_all.bin_edges                        |\n",
+       "| vep.colocated_variants                                                       |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
+       "| array                                                                 |\n",
+       "| array, impact: st... |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [14149,7330,6840,3904,23217,2436,1647,594,176,118,49,29,19,9,11,6,5,2,4,2]   |\n",
-       "| [10121,5441,5610,3838,29394,3741,2868,1281,401,358,163,63,69,31,18,20,10,... |\n",
-       "| [6578,3782,4202,3336,31093,4532,3696,1752,595,585,261,88,82,59,17,26,14,5... |\n",
-       "| [1429,1382,1901,2026,25677,6108,5433,3528,1753,1702,1130,485,518,282,119,... |\n",
-       "| [995,1036,1539,1788,25460,7448,6933,4978,2881,2833,2048,1038,1129,685,321... |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
+       "+--------------------------------------+\n",
+       "| vep.most_severe_consequence          |\n",
+       "+--------------------------------------+\n",
+       "| str                                  |\n",
+       "+--------------------------------------+\n",
+       "| \"non_coding_transcript_exon_variant\" |\n",
+       "| \"intron_variant\"                     |\n",
+       "| \"intron_variant\"                     |\n",
+       "| \"intron_variant\"                     |\n",
+       "| \"intron_variant\"                     |\n",
+       "+--------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "+------------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_all.bin_edges                        |\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
+       "| array       |\n",
+       "+---------------------+----------------+------------------+\n",
+       "| \"MQ\"                | \"rs62635282\"   | [\"G\",\"C\"]        |\n",
+       "| \"QD\"                | \"rs1324090652\" | [\"G\",\"A\"]        |\n",
+       "| \"MQ\"                | \"rs1330604035\" | [\"G\",\"C\"]        |\n",
+       "| \"MQ\"                | \"rs1442951560\" | [\"G\",\"A\"]        |\n",
+       "| \"MQ\"                | \"rs1281272113\" | [\"G\",\"A\"]        |\n",
+       "+---------------------+----------------+------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ht.show(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d211d0f0",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Genomes liftover Hail Table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcb791b6",
+   "metadata": {},
+   "source": [
+    "#### Load the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "248e904e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:08:59.197890Z",
+     "start_time": "2024-12-06T20:08:50.859108Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ht = get_gnomad_release(data_type='genomes', version='2.1.1', dataset=\"liftover\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "620cca6f",
+   "metadata": {},
+   "source": [
+    "#### Print the schema of the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "f718adea",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:00.032524Z",
+     "start_time": "2024-12-06T20:09:00.029271Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------------------------\n",
+      "Global fields:\n",
+      "    'rf': struct {\n",
+      "        variants_by_type: dict, \n",
+      "        feature_medians: dict, \n",
+      "        test_intervals: array>>, \n",
+      "        test_results: array, \n",
+      "        features_importance: dict, \n",
+      "        features: array, \n",
+      "        vqsr_training: bool, \n",
+      "        no_transmitted_singletons: bool, \n",
+      "        adj: bool, \n",
+      "        rf_hash: str, \n",
+      "        rf_snv_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }, \n",
+      "        rf_indel_cutoff: struct {\n",
+      "            bin: int32, \n",
+      "            min_score: float64\n",
+      "        }\n",
+      "    } \n",
+      "    'freq_meta': array> \n",
+      "    'freq_index_dict': dict \n",
+      "    'popmax_index_dict': dict \n",
+      "    'age_index_dict': dict \n",
+      "    'faf_index_dict': dict \n",
+      "    'age_distribution': array \n",
+      "----------------------------------------\n",
+      "Row fields:\n",
+      "    'locus': locus \n",
+      "    'alleles': array \n",
+      "    'ReverseComplementedAlleles': bool \n",
+      "    'SwappedAlleles': bool \n",
+      "    'original_locus': locus \n",
+      "    'freq': array \n",
+      "    'age_hist_het': array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int64, \n",
+      "        n_larger: int64\n",
+      "    }> \n",
+      "    'age_hist_hom': array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int64, \n",
+      "        n_larger: int64\n",
+      "    }> \n",
+      "    'popmax': array \n",
+      "    'faf': array, \n",
+      "        faf95: float64, \n",
+      "        faf99: float64\n",
+      "    }> \n",
+      "    'lcr': bool \n",
+      "    'decoy': bool \n",
+      "    'segdup': bool \n",
+      "    'nonpar': bool \n",
+      "    'variant_type': str \n",
+      "    'allele_type': str \n",
+      "    'n_alt_alleles': int32 \n",
+      "    'was_mixed': bool \n",
+      "    'has_star': bool \n",
+      "    'qd': float64 \n",
+      "    'pab_max': float64 \n",
+      "    'info_MQRankSum': float64 \n",
+      "    'info_SOR': float64 \n",
+      "    'info_InbreedingCoeff': float64 \n",
+      "    'info_ReadPosRankSum': float64 \n",
+      "    'info_FS': float64 \n",
+      "    'info_QD': float64 \n",
+      "    'info_MQ': float64 \n",
+      "    'info_DP': int32 \n",
+      "    'transmitted_singleton': bool \n",
+      "    'fail_hard_filters': bool \n",
+      "    'info_POSITIVE_TRAIN_SITE': bool \n",
+      "    'info_NEGATIVE_TRAIN_SITE': bool \n",
+      "    'omni': bool \n",
+      "    'mills': bool \n",
+      "    'tp': bool \n",
+      "    'rf_train': bool \n",
+      "    'rf_label': str \n",
+      "    'rf_probability': float64 \n",
+      "    'rank': int64 \n",
+      "    'was_split': bool \n",
+      "    'singleton': bool \n",
+      "    '_score': float64 \n",
+      "    '_singleton': bool \n",
+      "    'biallelic_rank': int64 \n",
+      "    'singleton_rank': int64 \n",
+      "    'n_nonref': int32 \n",
+      "    'score': float64 \n",
+      "    'adj_biallelic_singleton_rank': int64 \n",
+      "    'adj_rank': int64 \n",
+      "    'adj_biallelic_rank': int64 \n",
+      "    'adj_singleton_rank': int64 \n",
+      "    'biallelic_singleton_rank': int64 \n",
+      "    'filters': set \n",
+      "    'gq_hist_alt': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int64, \n",
+      "        n_larger: int64\n",
+      "    } \n",
+      "    'gq_hist_all': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int64, \n",
+      "        n_larger: int64\n",
+      "    } \n",
+      "    'dp_hist_alt': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int64, \n",
+      "        n_larger: int64\n",
+      "    } \n",
+      "    'dp_hist_all': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int64, \n",
+      "        n_larger: int64\n",
+      "    } \n",
+      "    'ab_hist_alt': struct {\n",
+      "        bin_edges: array, \n",
+      "        bin_freq: array, \n",
+      "        n_smaller: int64, \n",
+      "        n_larger: int64\n",
+      "    } \n",
+      "    'qual': float64 \n",
+      "    'vep': struct {\n",
+      "        assembly_name: str, \n",
+      "        allele_string: str, \n",
+      "        ancestral: str, \n",
+      "        colocated_variants: array, \n",
+      "            end: int32, \n",
+      "            eas_allele: str, \n",
+      "            eas_maf: float64, \n",
+      "            ea_allele: str, \n",
+      "            ea_maf: float64, \n",
+      "            eur_allele: str, \n",
+      "            eur_maf: float64, \n",
+      "            exac_adj_allele: str, \n",
+      "            exac_adj_maf: float64, \n",
+      "            exac_allele: str, \n",
+      "            exac_afr_allele: str, \n",
+      "            exac_afr_maf: float64, \n",
+      "            exac_amr_allele: str, \n",
+      "            exac_amr_maf: float64, \n",
+      "            exac_eas_allele: str, \n",
+      "            exac_eas_maf: float64, \n",
+      "            exac_fin_allele: str, \n",
+      "            exac_fin_maf: float64, \n",
+      "            exac_maf: float64, \n",
+      "            exac_nfe_allele: str, \n",
+      "            exac_nfe_maf: float64, \n",
+      "            exac_oth_allele: str, \n",
+      "            exac_oth_maf: float64, \n",
+      "            exac_sas_allele: str, \n",
+      "            exac_sas_maf: float64, \n",
+      "            id: str, \n",
+      "            minor_allele: str, \n",
+      "            minor_allele_freq: float64, \n",
+      "            phenotype_or_disease: int32, \n",
+      "            pubmed: array, \n",
+      "            sas_allele: str, \n",
+      "            sas_maf: float64, \n",
+      "            somatic: int32, \n",
+      "            start: int32, \n",
+      "            strand: int32\n",
+      "        }>, \n",
+      "        context: str, \n",
+      "        end: int32, \n",
+      "        id: str, \n",
+      "        input: str, \n",
+      "        intergenic_consequences: array, \n",
+      "            impact: str, \n",
+      "            minimised: int32, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        most_severe_consequence: str, \n",
+      "        motif_feature_consequences: array, \n",
+      "            high_inf_pos: str, \n",
+      "            impact: str, \n",
+      "            minimised: int32, \n",
+      "            motif_feature_id: str, \n",
+      "            motif_name: str, \n",
+      "            motif_pos: int32, \n",
+      "            motif_score_change: float64, \n",
+      "            strand: int32, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        regulatory_feature_consequences: array, \n",
+      "            impact: str, \n",
+      "            minimised: int32, \n",
+      "            regulatory_feature_id: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        seq_region_name: str, \n",
+      "        start: int32, \n",
+      "        strand: int32, \n",
+      "        transcript_consequences: array, \n",
+      "            distance: int32, \n",
+      "            domains: array, \n",
+      "            exon: str, \n",
+      "            gene_id: str, \n",
+      "            gene_pheno: int32, \n",
+      "            gene_symbol: str, \n",
+      "            gene_symbol_source: str, \n",
+      "            hgnc_id: str, \n",
+      "            hgvsc: str, \n",
+      "            hgvsp: str, \n",
+      "            hgvs_offset: int32, \n",
+      "            impact: str, \n",
+      "            intron: str, \n",
+      "            lof: str, \n",
+      "            lof_flags: str, \n",
+      "            lof_filter: str, \n",
+      "            lof_info: str, \n",
+      "            minimised: int32, \n",
+      "            polyphen_prediction: str, \n",
+      "            polyphen_score: float64, \n",
+      "            protein_end: int32, \n",
+      "            protein_start: int32, \n",
+      "            protein_id: str, \n",
+      "            sift_prediction: str, \n",
+      "            sift_score: float64, \n",
+      "            strand: int32, \n",
+      "            swissprot: str, \n",
+      "            transcript_id: str, \n",
+      "            trembl: str, \n",
+      "            uniparc: str, \n",
+      "            variant_allele: str\n",
+      "        }>, \n",
+      "        variant_class: str\n",
+      "    } \n",
+      "    'allele_info': struct {\n",
+      "        BaseQRankSum: float64, \n",
+      "        ClippingRankSum: float64, \n",
+      "        DB: bool, \n",
+      "        DP: int32, \n",
+      "        DS: bool, \n",
+      "        END: int32, \n",
+      "        FS: float64, \n",
+      "        HaplotypeScore: float64, \n",
+      "        InbreedingCoeff: float64, \n",
+      "        MQ: float64, \n",
+      "        MQ0: int32, \n",
+      "        MQRankSum: float64, \n",
+      "        NEGATIVE_TRAIN_SITE: bool, \n",
+      "        POSITIVE_TRAIN_SITE: bool, \n",
+      "        QD: float64, \n",
+      "        RAW_MQ: float64, \n",
+      "        ReadPosRankSum: float64, \n",
+      "        SOR: float64, \n",
+      "        VQSLOD: float64, \n",
+      "        culprit: str\n",
+      "    } \n",
+      "    'rsid': str \n",
+      "    'original_alleles': array \n",
+      "----------------------------------------\n",
+      "Key: ['locus', 'alleles']\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "ht.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab2f106a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Show the first 5 variants in the Hail Table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "0a9ac872",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T20:09:20.949958Z",
+     "start_time": "2024-12-06T20:09:05.622171Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
gq_hist_alt
gq_hist_all
dp_hist_alt
dp_hist_all
ab_hist_alt
vep
allele_info
locus
alleles
ReverseComplementedAlleles
SwappedAlleles
original_locus
freq
age_hist_het
age_hist_hom
popmax
faf
lcr
decoy
segdup
nonpar
variant_type
allele_type
n_alt_alleles
was_mixed
has_star
qd
pab_max
info_MQRankSum
info_SOR
info_InbreedingCoeff
info_ReadPosRankSum
info_FS
info_QD
info_MQ
info_DP
transmitted_singleton
fail_hard_filters
info_POSITIVE_TRAIN_SITE
info_NEGATIVE_TRAIN_SITE
omni
mills
tp
rf_train
rf_label
rf_probability
rank
was_split
singleton
_score
_singleton
biallelic_rank
singleton_rank
n_nonref
score
adj_biallelic_singleton_rank
adj_rank
adj_biallelic_rank
adj_singleton_rank
biallelic_singleton_rank
filters
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
qual
assembly_name
allele_string
ancestral
colocated_variants
context
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
BaseQRankSum
ClippingRankSum
DB
DP
DS
END
FS
HaplotypeScore
InbreedingCoeff
MQ
MQ0
MQRankSum
NEGATIVE_TRAIN_SITE
POSITIVE_TRAIN_SITE
QD
RAW_MQ
ReadPosRankSum
SOR
VQSLOD
culprit
rsid
original_alleles
locus<GRCh38>array<str>boolboollocus<GRCh37>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32}>array<struct{bin_edges: array<float64>, bin_freq: array<int64>, n_smaller: int64, n_larger: int64}>array<struct{bin_edges: array<float64>, bin_freq: array<int64>, n_smaller: int64, n_larger: int64}>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int32, pop: str}>array<struct{meta: dict<str, str>, faf95: float64, faf99: float64}>boolboolboolboolstrstrint32boolboolfloat64float64float64float64float64float64float64float64float64int32boolboolboolboolboolboolboolboolstrfloat64int64boolboolfloat64boolint64int64int32float64int64int64int64int64int64set<str>array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float64strstrstrarray<struct{aa_allele: str, aa_maf: float64, afr_allele: str, afr_maf: float64, allele_string: str, amr_allele: str, amr_maf: float64, clin_sig: array<str>, end: int32, eas_allele: str, eas_maf: float64, ea_allele: str, ea_maf: float64, eur_allele: str, eur_maf: float64, exac_adj_allele: str, exac_adj_maf: float64, exac_allele: str, exac_afr_allele: str, exac_afr_maf: float64, exac_amr_allele: str, exac_amr_maf: float64, exac_eas_allele: str, exac_eas_maf: float64, exac_fin_allele: str, exac_fin_maf: float64, exac_maf: float64, exac_nfe_allele: str, exac_nfe_maf: float64, exac_oth_allele: str, exac_oth_maf: float64, exac_sas_allele: str, exac_sas_maf: float64, id: str, minor_allele: str, minor_allele_freq: float64, phenotype_or_disease: int32, pubmed: array<int32>, sas_allele: str, sas_maf: float64, somatic: int32, start: int32, strand: int32}>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, minimised: int32, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, minimised: int32, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, minimised: int32, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, minimised: int32, polyphen_prediction: str, polyphen_score: float64, protein_end: int32, protein_start: int32, protein_id: str, sift_prediction: str, sift_score: float64, strand: int32, swissprot: str, transcript_id: str, trembl: str, uniparc: str, variant_allele: str}>strfloat64float64boolint32boolint32float64float64float64float64int32float64boolboolfloat64float64float64float64float64strstrarray<str>
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showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+---------------+\n",
+       "| locus         |\n",
+       "+---------------+\n",
+       "| locus |\n",
+       "+---------------+\n",
+       "| chr1:10067    |\n",
+       "| chr1:10108    |\n",
+       "| chr1:10109    |\n",
+       "| chr1:10114    |\n",
+       "| chr1:10114    |\n",
+       "+---------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_all.bin_freq                         |\n",
+       "| alleles                                                                      |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
+       "| array                                                                   |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [10,66,471,1445,2990,5808,6940,7906,7262,6236,4943,3919,3129,2527,1993,14... |\n",
-       "| [7,37,269,903,1948,4127,5519,7075,7322,6972,6095,5249,4374,3624,2893,2106... |\n",
-       "| [2,24,149,466,1201,2614,4035,5568,6331,6725,6267,5855,5101,4268,3465,2554... |\n",
-       "| [1,9,42,122,294,787,1537,2717,3751,4972,5404,5762,5875,5161,4526,3415,252... |\n",
-       "| [2,6,39,104,247,638,1325,2417,3685,5047,5927,6484,6763,6192,5525,4439,351... |\n",
+       "| [\"T\",\"TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCC\"]                           |\n",
+       "| [\"CAACCCT\",\"C\"]                                                              |\n",
+       "| [\"AACCCT\",\"A\"]                                                               |\n",
+       "| [\"T\",\"C\"]                                                                    |\n",
+       "| [\"TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCTAACCCTAACCCTAACCCTAACCTAACC... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                  744 |\n",
-       "|                                                 1130 |\n",
-       "|                                                 1487 |\n",
-       "|                                                 2622 |\n",
-       "|                                                 3743 |\n",
-       "+------------------------------------------------------+\n",
+       "+----------------------------+----------------+----------------+\n",
+       "| ReverseComplementedAlleles | SwappedAlleles | original_locus |\n",
+       "+----------------------------+----------------+----------------+\n",
+       "|                       bool |           bool | locus  |\n",
+       "+----------------------------+----------------+----------------+\n",
+       "|                      False |          False | 1:10067        |\n",
+       "|                      False |          False | 1:10108        |\n",
+       "|                      False |          False | 1:10109        |\n",
+       "|                      False |          False | 1:10114        |\n",
+       "|                      False |          False | 1:10114        |\n",
+       "+----------------------------+----------------+----------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_alt.bin_edges                        |\n",
+       "| freq                                                                         |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
+       "| array    |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [(1,3.80e-05,26342,0),(3,9.55e-05,31414,0),(0,0.00e+00,13124,0),(1,3.75e-... |\n",
+       "| [(2,1.01e-03,1982,0),(2,6.49e-05,30832,0),(0,0.00e+00,1452,0),(1,2.27e-02... |\n",
+       "| [(19,6.42e-02,296,0),(31,1.01e-03,30766,0),(8,5.13e-02,156,0),(2,5.88e-02... |\n",
+       "| [(0,0.00e+00,8734,0),(8,2.56e-04,31192,0),(0,0.00e+00,4054,0),(0,0.00e+00... |\n",
+       "| [(1,1.14e-04,8734,0),(1,3.21e-05,31192,0),(0,0.00e+00,4054,0),(0,0.00e+00... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+------------------------------------------------------+\n",
-       "| [0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,1,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]            |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| age_hist_het                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, bin_freq: array, n_smaller... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "+------------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| age_hist_hom                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, bin_freq: array, n_smaller... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "| [([3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+0... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_alt.bin_edges                        |\n",
+       "| popmax                                                                       |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, faf95: float64, faf99: float64}>          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n",
+       "| [({\"group\":\"adj\"},1.79e-04,1.79e-04),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n",
+       "| [({\"group\":\"adj\"},4.20e-02,4.20e-02),({\"group\":\"adj\",\"pop\":\"nfe\"},2.55e-0... |\n",
+       "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n",
+       "| [({\"group\":\"adj\"},0.00e+00,0.00e+00),({\"group\":\"adj\",\"pop\":\"nfe\"},0.00e+0... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------+-------+--------+--------+--------------+-------------+---------------+\n",
+       "|  lcr | decoy | segdup | nonpar | variant_type | allele_type | n_alt_alleles |\n",
+       "+------+-------+--------+--------+--------------+-------------+---------------+\n",
+       "| bool |  bool |   bool |   bool | str          | str         |         int32 |\n",
+       "+------+-------+--------+--------+--------------+-------------+---------------+\n",
+       "| True | False |   True |  False | \"indel\"      | \"ins\"       |             1 |\n",
+       "| True | False |   True |  False | \"indel\"      | \"del\"       |             1 |\n",
+       "| True | False |   True |  False | \"indel\"      | \"del\"       |             1 |\n",
+       "| True | False |   True |  False | \"mixed\"      | \"snv\"       |             2 |\n",
+       "| True | False |   True |  False | \"mixed\"      | \"del\"       |             2 |\n",
+       "+------+-------+--------+--------+--------------+-------------+---------------+\n",
+       "\n",
+       "+-----------+----------+----------+----------+----------------+----------+\n",
+       "| was_mixed | has_star |       qd |  pab_max | info_MQRankSum | info_SOR |\n",
+       "+-----------+----------+----------+----------+----------------+----------+\n",
+       "|      bool |     bool |  float64 |  float64 |        float64 |  float64 |\n",
+       "+-----------+----------+----------+----------+----------------+----------+\n",
+       "|     False |    False | 6.43e+00 | 1.00e+00 |       7.36e-01 | 4.21e-01 |\n",
+       "|     False |    False | 4.66e+00 | 1.80e-01 |       2.08e-01 | 8.55e-01 |\n",
+       "|     False |     True | 1.83e+00 | 1.00e+00 |      -6.93e-01 | 4.88e+00 |\n",
+       "|      True |     True | 1.15e+00 | 2.49e-03 |      -1.23e+00 | 2.51e+00 |\n",
+       "|      True |     True | 5.60e+00 | 4.08e-03 |      -1.23e+00 | 2.51e+00 |\n",
+       "+-----------+----------+----------+----------+----------------+----------+\n",
+       "\n",
+       "+----------------------+---------------------+----------+----------+----------+\n",
+       "| info_InbreedingCoeff | info_ReadPosRankSum |  info_FS |  info_QD |  info_MQ |\n",
+       "+----------------------+---------------------+----------+----------+----------+\n",
+       "|              float64 |             float64 |  float64 |  float64 |  float64 |\n",
+       "+----------------------+---------------------+----------+----------+----------+\n",
+       "|            -3.52e-02 |           -7.27e-01 | 0.00e+00 | 1.38e+00 | 3.93e+01 |\n",
+       "|            -3.00e-01 |            1.27e+00 | 1.90e+00 | 3.42e+01 | 3.96e+01 |\n",
+       "|             1.09e-01 |           -9.31e-01 | 5.76e+01 | 3.06e+01 | 3.70e+01 |\n",
+       "|             2.08e-01 |           -9.11e-01 | 2.19e+01 | 2.01e+01 | 3.33e+01 |\n",
+       "|             2.08e-01 |           -9.11e-01 | 2.19e+01 | 2.01e+01 | 3.33e+01 |\n",
+       "+----------------------+---------------------+----------+----------+----------+\n",
+       "\n",
+       "+---------+-----------------------+-------------------+\n",
+       "| info_DP | transmitted_singleton | fail_hard_filters |\n",
+       "+---------+-----------------------+-------------------+\n",
+       "|   int32 |                  bool |              bool |\n",
+       "+---------+-----------------------+-------------------+\n",
+       "| 2040956 |                    NA |              True |\n",
+       "| 2147991 |                    NA |             False |\n",
+       "| 2134891 |                    NA |             False |\n",
+       "| 1908083 |                    NA |             False |\n",
+       "| 1908083 |                    NA |             False |\n",
+       "+---------+-----------------------+-------------------+\n",
+       "\n",
+       "+--------------------------+--------------------------+-------+-------+-------+\n",
+       "| info_POSITIVE_TRAIN_SITE | info_NEGATIVE_TRAIN_SITE |  omni | mills |    tp |\n",
+       "+--------------------------+--------------------------+-------+-------+-------+\n",
+       "|                     bool |                     bool |  bool |  bool |  bool |\n",
+       "+--------------------------+--------------------------+-------+-------+-------+\n",
+       "|                       NA |                       NA | False | False | False |\n",
+       "|                       NA |                       NA | False | False | False |\n",
+       "|                       NA |                       NA | False | False | False |\n",
+       "|                       NA |                       NA | False | False | False |\n",
+       "|                       NA |                       NA | False | False | False |\n",
+       "+--------------------------+--------------------------+-------+-------+-------+\n",
+       "\n",
+       "+----------+----------+----------------+-----------+-----------+-----------+\n",
+       "| rf_train | rf_label | rf_probability |      rank | was_split | singleton |\n",
+       "+----------+----------+----------------+-----------+-----------+-----------+\n",
+       "|     bool | str      |        float64 |     int64 |      bool |      bool |\n",
+       "+----------+----------+----------------+-----------+-----------+-----------+\n",
+       "|     True | \"FP\"     |       1.49e-01 |  29846037 |     False |     False |\n",
+       "|       NA | NA       |       5.10e-02 |  31936632 |     False |     False |\n",
+       "|       NA | NA       |       4.41e-03 |  34836964 |      True |     False |\n",
+       "|       NA | NA       |       5.81e-03 | 243227706 |      True |     False |\n",
+       "|       NA | NA       |       7.79e-03 |  34144024 |      True |      True |\n",
+       "+----------+----------+----------------+-----------+-----------+-----------+\n",
+       "\n",
+       "+----------+------------+----------------+----------------+----------+\n",
+       "|   _score | _singleton | biallelic_rank | singleton_rank | n_nonref |\n",
+       "+----------+------------+----------------+----------------+----------+\n",
+       "|  float64 |       bool |          int64 |          int64 |    int32 |\n",
+       "+----------+------------+----------------+----------------+----------+\n",
+       "| 8.51e-01 |      False |       15142389 |             NA |        3 |\n",
+       "| 9.49e-01 |      False |       15649487 |             NA |        5 |\n",
+       "| 9.96e-01 |      False |             NA |             NA |       44 |\n",
+       "| 9.94e-01 |      False |             NA |             NA |        9 |\n",
+       "| 9.92e-01 |       True |             NA |       10987969 |        1 |\n",
+       "+----------+------------+----------------+----------------+----------+\n",
+       "\n",
+       "+----------+------------------------------+----------+--------------------+\n",
+       "|    score | adj_biallelic_singleton_rank | adj_rank | adj_biallelic_rank |\n",
+       "+----------+------------------------------+----------+--------------------+\n",
+       "|  float64 |                        int64 |    int64 |              int64 |\n",
+       "+----------+------------------------------+----------+--------------------+\n",
+       "| 1.49e-01 |                           NA | 27460496 |           13856404 |\n",
+       "| 5.10e-02 |                           NA | 29036786 |           14252110 |\n",
+       "| 4.41e-03 |                           NA | 31102141 |                 NA |\n",
+       "| 5.81e-03 |                           NA |       NA |                 NA |\n",
+       "| 7.79e-03 |                           NA | 30615843 |                 NA |\n",
+       "+----------+------------------------------+----------+--------------------+\n",
+       "\n",
+       "+--------------------+--------------------------+--------------+\n",
+       "| adj_singleton_rank | biallelic_singleton_rank | filters      |\n",
+       "+--------------------+--------------------------+--------------+\n",
+       "|              int64 |                    int64 | set     |\n",
+       "+--------------------+--------------------------+--------------+\n",
+       "|                 NA |                       NA | {\"RF\"}       |\n",
+       "|                 NA |                       NA | {\"RF\"}       |\n",
+       "|                 NA |                       NA | {\"RF\"}       |\n",
+       "|                 NA |                       NA | {\"AC0\",\"RF\"} |\n",
+       "|            9632693 |                       NA | {\"RF\"}       |\n",
+       "+--------------------+--------------------------+--------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| gq_hist_alt.bin_edges                                                        |\n",
        "+------------------------------------------------------------------------------+\n",
        "| array                                                               |\n",
        "+------------------------------------------------------------------------------+\n",
@@ -12093,413 +14209,413 @@
        "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+------------------------------------------------------+\n",
-       "| [0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
+       "+-------------------------------------------+-----------------------+\n",
+       "| gq_hist_alt.bin_freq                      | gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------+-----------------------+\n",
+       "| array                              |                 int64 |\n",
+       "+-------------------------------------------+-----------------------+\n",
+       "| [0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1] |                     0 |\n",
+       "| [0,4,2,1,1,7,2,2,1,1,0,1,1,1,1,0,1,0,0,5] |                     0 |\n",
+       "| [1,2,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1] |                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "+-------------------------------------------+-----------------------+\n",
        "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "+------------------------------------------------------+\n",
+       "+----------------------+\n",
+       "| gq_hist_alt.n_larger |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "+----------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.ab_hist_alt.bin_edges                        |\n",
+       "| gq_hist_all.bin_edges                                                        |\n",
        "+------------------------------------------------------------------------------+\n",
        "| array                                                               |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------------+\n",
-       "| array                                         |\n",
-       "+------------------------------------------------------+\n",
-       "| [0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "| [0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]            |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                 int64 |\n",
-       "+-------------------------------------------------------+\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "|                                                     0 |\n",
-       "+-------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------+\n",
-       "| joint.histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                int64 |\n",
-       "+------------------------------------------------------+\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "|                                                    0 |\n",
-       "+------------------------------------------------------+\n",
-       "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_het.bin_edges                            |\n",
+       "| gq_hist_all.bin_freq                                                         |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
+       "| array                                                                 |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [1949,163,179,242,270,286,314,378,357,448,611,483,1069,368,826,491,1015,2... |\n",
+       "| [14116,107,108,94,86,90,88,83,56,64,76,43,58,32,33,35,51,27,38,131]          |\n",
+       "| [14814,196,99,120,70,27,18,7,4,6,3,2,1,2,2,0,1,0,0,11]                       |\n",
+       "| [8661,775,603,1162,1158,559,798,516,193,244,176,93,180,37,65,45,90,12,62,... |\n",
+       "| [8659,772,603,1160,1157,557,798,516,193,244,174,92,180,36,64,44,91,10,62,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,1,0,0,1,0,0,0,0,0]                            |\n",
-       "| [0,0,1,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,1,0,1,0,0,0,0,0]                            |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------------+\n",
-       "|                                            int64 |\n",
-       "+--------------------------------------------------+\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "|                                                0 |\n",
-       "+--------------------------------------------------+\n",
+       "+-----------------------+----------------------+\n",
+       "| gq_hist_all.n_smaller | gq_hist_all.n_larger |\n",
+       "+-----------------------+----------------------+\n",
+       "|                 int64 |                int64 |\n",
+       "+-----------------------+----------------------+\n",
+       "|                     0 |                    0 |\n",
+       "|                     0 |                    0 |\n",
+       "|                     0 |                    0 |\n",
+       "|                     0 |                    0 |\n",
+       "|                     0 |                    0 |\n",
+       "+-----------------------+----------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_hom.bin_edges                            |\n",
+       "| dp_hist_alt.bin_edges                                                        |\n",
        "+------------------------------------------------------------------------------+\n",
        "| array                                                               |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+--------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------------+\n",
-       "| array                                     |\n",
-       "+--------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                            |\n",
-       "+--------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------------+\n",
-       "|                                             int64 |\n",
-       "+---------------------------------------------------+\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "|                                                 0 |\n",
-       "+---------------------------------------------------+\n",
+       "+-------------------------------------------+-----------------------+\n",
+       "| dp_hist_alt.bin_freq                      | dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------+-----------------------+\n",
+       "| array                              |                 int64 |\n",
+       "+-------------------------------------------+-----------------------+\n",
+       "| [2,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [0,1,1,4,7,7,4,3,2,2,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [0,0,0,0,1,4,1,1,0,1,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "+-------------------------------------------+-----------------------+\n",
        "\n",
-       "+--------------------------------------------------+--------------------------+\n",
-       "| joint.histograms.age_hists.age_hist_hom.n_larger | joint.flags              |\n",
-       "+--------------------------------------------------+--------------------------+\n",
-       "|                                            int64 | set                 |\n",
-       "+--------------------------------------------------+--------------------------+\n",
-       "|                                                0 | {\"not_called_in_exomes\"} |\n",
-       "|                                                0 | {\"not_called_in_exomes\"} |\n",
-       "|                                                0 | {\"not_called_in_exomes\"} |\n",
-       "|                                                0 | {\"not_called_in_exomes\"} |\n",
-       "|                                                0 | {\"not_called_in_exomes\"} |\n",
-       "+--------------------------------------------------+--------------------------+\n",
+       "+----------------------+\n",
+       "| dp_hist_alt.n_larger |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "+----------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| joint.freq_comparison_stats.contingency_table_test                           |\n",
+       "| dp_hist_all.bin_edges                                                        |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| array                         |\n",
+       "| array                                                               |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
-       "| [NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------------------------------------------------+\n",
-       "| joint.freq_comparison_stats.cochran_mantel_haenszel_test.p_value |\n",
-       "+------------------------------------------------------------------+\n",
-       "|                                                          float64 |\n",
-       "+------------------------------------------------------------------+\n",
-       "|                                                               NA |\n",
-       "|                                                               NA |\n",
-       "|                                                               NA |\n",
-       "|                                                               NA |\n",
-       "|                                                               NA |\n",
-       "+------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------------------+\n",
-       "| joint.freq_comparison_stats.cochran_mantel_haenszel_test.chisq |\n",
-       "+----------------------------------------------------------------+\n",
-       "|                                                        float64 |\n",
-       "+----------------------------------------------------------------+\n",
-       "|                                                             NA |\n",
-       "|                                                             NA |\n",
-       "|                                                             NA |\n",
-       "|                                                             NA |\n",
-       "|                                                             NA |\n",
-       "+----------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| joint.freq_comparison_stats.stat_union.p_value |\n",
-       "+------------------------------------------------+\n",
-       "|                                        float64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                             NA |\n",
-       "|                                             NA |\n",
-       "|                                             NA |\n",
-       "|                                             NA |\n",
-       "|                                             NA |\n",
-       "+------------------------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| dp_hist_all.bin_freq                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3,3,8,18,30,55,95,190,324,554,834,1132,1343,1372,1367,1295,1125,925,766,... |\n",
+       "| [2,4,17,26,34,58,129,205,329,593,770,987,1155,1239,1324,1204,1051,941,816... |\n",
+       "| [2,6,18,34,45,72,136,232,356,629,774,1005,1168,1211,1307,1178,1036,922,80... |\n",
+       "| [2,9,23,48,90,140,242,440,710,949,1221,1301,1371,1371,1249,981,819,673,51... |\n",
+       "| [2,9,23,48,90,140,242,440,710,949,1221,1301,1371,1371,1249,981,819,673,51... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+-------------------------------------------------------+\n",
-       "| joint.freq_comparison_stats.stat_union.stat_test_name |\n",
-       "+-------------------------------------------------------+\n",
-       "| str                                                   |\n",
-       "+-------------------------------------------------------+\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "| NA                                                    |\n",
-       "+-------------------------------------------------------+\n",
+       "+-----------------------+----------------------+\n",
+       "| dp_hist_all.n_smaller | dp_hist_all.n_larger |\n",
+       "+-----------------------+----------------------+\n",
+       "|                 int64 |                int64 |\n",
+       "+-----------------------+----------------------+\n",
+       "|                     0 |                 3596 |\n",
+       "|                     0 |                 4104 |\n",
+       "|                     0 |                 4057 |\n",
+       "|                     0 |                 3065 |\n",
+       "|                     0 |                 3065 |\n",
+       "+-----------------------+----------------------+\n",
        "\n",
-       "+-------------------------------------------------+---------------------+\n",
-       "| joint.freq_comparison_stats.stat_union.gen_ancs | coverage.exome.mean |\n",
-       "+-------------------------------------------------+---------------------+\n",
-       "| array                                      |             float64 |\n",
-       "+-------------------------------------------------+---------------------+\n",
-       "| NA                                              |                  NA |\n",
-       "| NA                                              |                  NA |\n",
-       "| NA                                              |                  NA |\n",
-       "| NA                                              |                  NA |\n",
-       "| NA                                              |                  NA |\n",
-       "+-------------------------------------------------+---------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| ab_hist_alt.bin_edges                                                        |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------------+-------------------------+\n",
-       "| coverage.exome.median_approx | coverage.exome.total_DP |\n",
-       "+------------------------------+-------------------------+\n",
-       "|                        int32 |                   int64 |\n",
-       "+------------------------------+-------------------------+\n",
-       "|                           NA |                      NA |\n",
-       "|                           NA |                      NA |\n",
-       "|                           NA |                      NA |\n",
-       "|                           NA |                      NA |\n",
-       "|                           NA |                      NA |\n",
-       "+------------------------------+-------------------------+\n",
+       "+-------------------------------------------+-----------------------+\n",
+       "| ab_hist_alt.bin_freq                      | ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------+-----------------------+\n",
+       "| array                              |                 int64 |\n",
+       "+-------------------------------------------+-----------------------+\n",
+       "| [0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0] |                     0 |\n",
+       "| [0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [0,0,0,6,5,6,5,4,2,1,0,1,1,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [0,3,4,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] |                     0 |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0] |                     0 |\n",
+       "+-------------------------------------------+-----------------------+\n",
        "\n",
-       "+-----------------------+-----------------------+------------------------+\n",
-       "| coverage.exome.over_1 | coverage.exome.over_5 | coverage.exome.over_10 |\n",
-       "+-----------------------+-----------------------+------------------------+\n",
-       "|               float64 |               float64 |                float64 |\n",
-       "+-----------------------+-----------------------+------------------------+\n",
-       "|                    NA |                    NA |                     NA |\n",
-       "|                    NA |                    NA |                     NA |\n",
-       "|                    NA |                    NA |                     NA |\n",
-       "|                    NA |                    NA |                     NA |\n",
-       "|                    NA |                    NA |                     NA |\n",
-       "+-----------------------+-----------------------+------------------------+\n",
+       "+----------------------+----------+-------------------+\n",
+       "| ab_hist_alt.n_larger |     qual | vep.assembly_name |\n",
+       "+----------------------+----------+-------------------+\n",
+       "|                int64 |  float64 | str               |\n",
+       "+----------------------+----------+-------------------+\n",
+       "|                    0 | 3.04e+01 | \"GRCh37\"          |\n",
+       "|                    0 | 4.65e+04 | \"GRCh37\"          |\n",
+       "|                    0 | 8.98e+04 | \"GRCh37\"          |\n",
+       "|                    0 | 3.67e+04 | \"GRCh37\"          |\n",
+       "|                    0 | 3.67e+04 | \"GRCh37\"          |\n",
+       "+----------------------+----------+-------------------+\n",
        "\n",
-       "+------------------------+------------------------+------------------------+\n",
-       "| coverage.exome.over_15 | coverage.exome.over_20 | coverage.exome.over_25 |\n",
-       "+------------------------+------------------------+------------------------+\n",
-       "|                float64 |                float64 |                float64 |\n",
-       "+------------------------+------------------------+------------------------+\n",
-       "|                     NA |                     NA |                     NA |\n",
-       "|                     NA |                     NA |                     NA |\n",
-       "|                     NA |                     NA |                     NA |\n",
-       "|                     NA |                     NA |                     NA |\n",
-       "|                     NA |                     NA |                     NA |\n",
-       "+------------------------+------------------------+------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.allele_string                                                            |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| str                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| \"-/AACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCC\"                                |\n",
+       "| \"AACCCT/-\"                                                                   |\n",
+       "| \"ACCCT/-\"                                                                    |\n",
+       "| \"T/C\"                                                                        |\n",
+       "| \"AACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCTAACCCTAACCCTAACCCTAACCTAACCCT... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+------------------------+------------------------+-------------------------+\n",
-       "| coverage.exome.over_30 | coverage.exome.over_50 | coverage.exome.over_100 |\n",
-       "+------------------------+------------------------+-------------------------+\n",
-       "|                float64 |                float64 |                 float64 |\n",
-       "+------------------------+------------------------+-------------------------+\n",
-       "|                     NA |                     NA |                      NA |\n",
-       "|                     NA |                     NA |                      NA |\n",
-       "|                     NA |                     NA |                      NA |\n",
-       "|                     NA |                     NA |                      NA |\n",
-       "|                     NA |                     NA |                      NA |\n",
-       "+------------------------+------------------------+-------------------------+\n",
+       "+---------------+\n",
+       "| vep.ancestral |\n",
+       "+---------------+\n",
+       "| str           |\n",
+       "+---------------+\n",
+       "| NA            |\n",
+       "| NA            |\n",
+       "| NA            |\n",
+       "| NA            |\n",
+       "| NA            |\n",
+       "+---------------+\n",
        "\n",
-       "+----------------------+-------------------------------+\n",
-       "| coverage.genome.mean | coverage.genome.median_approx |\n",
-       "+----------------------+-------------------------------+\n",
-       "|              float64 |                         int32 |\n",
-       "+----------------------+-------------------------------+\n",
-       "|             3.74e+01 |                            38 |\n",
-       "|             4.29e+01 |                            44 |\n",
-       "|             4.42e+01 |                            47 |\n",
-       "|             4.54e+01 |                            51 |\n",
-       "|             5.43e+01 |                            58 |\n",
-       "+----------------------+-------------------------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.colocated_variants                                                       |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "| \"upstream_gene_variant\"     |\n",
+       "+-----------------------------+\n",
        "\n",
        "+------------------------------------------------------------------------------+\n",
-       "| transcript_consequences                                                      |\n",
+       "| vep.motif_feature_consequences                                               |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| array, domains: set, high_inf_p... |\n",
        "+------------------------------------------------------------------------------+\n",
-       "| []                                                                           |\n",
-       "| []                                                                           |\n",
-       "| []                                                                           |\n",
-       "| []                                                                           |\n",
-       "| []                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| [(1,[\"TF_binding_site_variant\",\"TFBS_ablation\"],\"N\",\"MODERATE\",1,\"MA0341.... |\n",
        "+------------------------------------------------------------------------------+\n",
        "\n",
-       "+---------------+---------------------------------------------+---------------+\n",
-       "| caid          | vrs.ref.allele_id                           | vrs.ref.start |\n",
-       "+---------------+---------------------------------------------+---------------+\n",
-       "| str           | str                                         |         int32 |\n",
-       "+---------------+---------------------------------------------+---------------+\n",
-       "| \"CA997563811\" | \"ga4gh:VA.oTAtTrgYxm81O9fu6Mrhfo1t3eHsgg4L\" |         10030 |\n",
-       "| \"CA997563812\" | \"ga4gh:VA.6pIoPAYDsphCprRcyrititenuEWlZaxV\" |         10036 |\n",
-       "| \"CA997563813\" | \"ga4gh:VA.HdrkFJS16zwJPcGSKX9___fJcdqSATlQ\" |         10042 |\n",
-       "| \"CA997563814\" | \"ga4gh:VA.xis4Nhtveh7q75mtkipQAUqZfWUfboWB\" |         10054 |\n",
-       "| \"CA997563815\" | \"ga4gh:VA.TV0UyS-jCvsVkEOMbipUnzTc4hYuVNTg\" |         10056 |\n",
-       "+---------------+---------------------------------------------+---------------+\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"T\",\"TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCTAACCC\"]                           |\n",
+       "| [\"CAACCCT\",\"C\"]                                                              |\n",
+       "| [\"AACCCT\",\"A\"]                                                               |\n",
+       "| [\"T\",\"C\"]                                                                    |\n",
+       "| [\"TAACCCTAACCCTAACCCTAACCCTAACCCTAACCCCTAACCCTAACCCTAACCCTAACCCTAACCTAACC... |\n",
+       "+------------------------------------------------------------------------------+\n",
        "showing top 5 rows"
       ]
      },
@@ -12546,7 +14662,7 @@
     "height": "calc(100% - 180px)",
     "left": "10px",
     "top": "150px",
-    "width": "418.428px"
+    "width": "220.42px"
    },
    "toc_section_display": true,
    "toc_window_display": true

From 397c14ff92da1954a53c3b236485af3c7ef382d6 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 15 Jan 2025 10:19:27 -0700
Subject: [PATCH 081/121] Apply suggestions from code review

Co-authored-by: Qin He <44242118+KoalaQin@users.noreply.github.com>
---
 gnomad_toolbox/filtering/variant.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index 410ba18..cb1842f 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -103,7 +103,7 @@ def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.
 
            This function is to match the number of variants that you will get in the
            gnomAD browser when you search for a gene symbol. The gnomAD browser
-           filters to only variants located in or within 75 base pairs of a coding exon.
+           filters to only variants located in or within 75 base pairs of CDS or non-coding exons of a gene.
 
     :param gene: Gencode gene symbol.
     :param exon_padding_bp: Number of base pairs to pad the intervals. Default is 75bp.

From b143d277b90d1ee9899f286faefa31c3506b6864 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 15 Jan 2025 10:41:01 -0700
Subject: [PATCH 082/121] Add comment explaining why we support a subset of the
 versions available

---
 gnomad_toolbox/load_data.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index 1a29ea2..488a177 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -13,6 +13,12 @@
     "GRCh37": grch37_res,
     "GRCh38": grch38_res,
 }
+# In the toolbox, for each dataset, we only support loading the most recent versions
+# for each reference genome build (GRCh37 and GRCh38) and data type (exomes, genomes,
+# joint). Older versions are not supported because they are less complete datasets, and
+# some may have errors that have been fixed in newer versions. If other versions are
+# actually needed, they can be loaded using `gnomad_methods` or directly loading the
+# Table with hl.read_table.
 VARIANT_DATA = {
     "2.1.1": {
         "reference_genome": "GRCh37",

From 5a71f8c26e3a3f2c27cf321607d022e31c63690e Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 15 Jan 2025 11:03:13 -0700
Subject: [PATCH 083/121] Apply suggestions from code review

Co-authored-by: Qin He <44242118+KoalaQin@users.noreply.github.com>
---
 gnomad_toolbox/load_data.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index 488a177..9f36c2a 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -154,7 +154,7 @@ def set_default_data(
 
         # Validate version.
         if version not in VARIANT_DATA:
-            raise ValueError(f"Version {version} is not a supported gnomAD version.")
+            raise ValueError(f"Version {version} is not a supported gnomAD version in the Toolbox.")
 
         # Validate data type for the version.
         version_info = VARIANT_DATA[version]
@@ -192,9 +192,7 @@ def _get_dataset(
         return ht
 
     # Validate dataset.
-    dataset_info = SUPPORTED_DATASETS.get(dataset)
-    if dataset_info is None:
-        dataset_info = SUPPORTED_REFERENCE_DATA.get(dataset)
+    dataset_info = SUPPORTED_DATASETS.get(dataset) or SUPPORTED_REFERENCE_DATA.get(dataset)
 
     if dataset_info is None:
         raise ValueError(

From 836994d1b902f4bc75246950695cef42065816f0 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 15 Jan 2025 11:04:08 -0700
Subject: [PATCH 084/121] Filter to only canonical in filter_to_plofs

---
 gnomad_toolbox/filtering/vep.py | 18 +++++++-----------
 1 file changed, 7 insertions(+), 11 deletions(-)

diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py
index 36cdbfa..dc78540 100644
--- a/gnomad_toolbox/filtering/vep.py
+++ b/gnomad_toolbox/filtering/vep.py
@@ -205,16 +205,13 @@ def filter_to_plofs(
     """
     Filter to observed pLoF variants used for gene constraint metrics.
 
-    .. note::
+    The pLOF variant count displayed on the browser meets the following requirements:
 
-        pLOF variants meets the following requirements:
-
-            - PASS variant QC
-            - SNV
-            - Allele frequency ≤ 0.1%
-            - High-confidence LOFTEE in the Canonical or MANE Select transcript (depends
-              on the version)
-            - ≥ a specified coverage threshold (depends on the version)
+        - PASS variant QC
+        - SNV
+        - Allele frequency ≤ 0.1%
+        - High-confidence LOFTEE in the MANE Select or Canonical transcript
+        - ≥ a specified coverage threshold (depends on the version)
 
     :param gene_symbol: Gene symbol.
     :param version: Optional gnomAD dataset version. If not provided, uses the gnomAD
@@ -280,8 +277,7 @@ def filter_to_plofs(
     variant_ht = filter_to_high_confidence_loftee(
         gene_symbol=gene_symbol,
         ht=variant_ht,
-        mane_select_only=constraint_info["mane_select"],
-        canonical_only=constraint_info["canonical"],
+        canonical_only=True,
     )
 
     return variant_ht

From e56464be7334e5beb9402ac459d5e6e421b088a5 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 15 Jan 2025 11:04:45 -0700
Subject: [PATCH 085/121] Don't need the MANE and canonical constraint config

---
 gnomad_toolbox/load_data.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index 488a177..a9ef582 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -69,16 +69,12 @@
         "exome_coverage_field": "median",
         "exome_coverage_cutoff": 30,
         "af_cutoff": 0.001,
-        "mane_select": False,
-        "canonical": True,
     },
     "4.1": {
         "reference_genome": "GRCh38",
         "exome_coverage_field": "median_approx",
         "exome_coverage_cutoff": 30,
         "af_cutoff": 0.001,
-        "mane_select": True,
-        "canonical": False,
     },
 }
 LIFTOVER_DATA = {

From de240b54f15cd09d85178e579df57c6d7077f617 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 15 Jan 2025 11:12:42 -0700
Subject: [PATCH 086/121] Fix table in `get_gnomad_release` docstring

---
 gnomad_toolbox/load_data.py | 44 +++++++++++++++++++------------------
 1 file changed, 23 insertions(+), 21 deletions(-)

diff --git a/gnomad_toolbox/load_data.py b/gnomad_toolbox/load_data.py
index 6fdac1b..d02af7b 100644
--- a/gnomad_toolbox/load_data.py
+++ b/gnomad_toolbox/load_data.py
@@ -150,7 +150,9 @@ def set_default_data(
 
         # Validate version.
         if version not in VARIANT_DATA:
-            raise ValueError(f"Version {version} is not a supported gnomAD version in the Toolbox.")
+            raise ValueError(
+                f"Version {version} is not a supported gnomAD version in the Toolbox."
+            )
 
         # Validate data type for the version.
         version_info = VARIANT_DATA[version]
@@ -188,7 +190,9 @@ def _get_dataset(
         return ht
 
     # Validate dataset.
-    dataset_info = SUPPORTED_DATASETS.get(dataset) or SUPPORTED_REFERENCE_DATA.get(dataset)
+    dataset_info = SUPPORTED_DATASETS.get(dataset) or SUPPORTED_REFERENCE_DATA.get(
+        dataset
+    )
 
     if dataset_info is None:
         raise ValueError(
@@ -257,32 +261,30 @@ def get_gnomad_release(
         :widths: auto
 
         +--------------+--------------+---------+------------------------------+
-        | Dataset      | Genome Build | Version | Data Types                   |
+        | Genome Build | Dataset      | Version | Data Types                   |
         +==============+==============+=========+==============================+
-        | variant      | GRCh37       | 2.1.1   | exomes, genomes              |
+        | GRCh37       | variant      | 2.1.1   | exomes, genomes              |
         |              +--------------+---------+------------------------------+
-        |              | GRCh38       | 4.1     | exomes, genomes, joint       |
-        +--------------+--------------+---------+------------------------------+
-        | coverage     | GRCh37       | 2.1     | exomes, genomes              |
+        |              | coverage     | 2.1     | exomes, genomes              |
         |              +--------------+---------+------------------------------+
-        |              | GRCh38       | 3.0.1   | genomes                      |
-        |              |              +---------+------------------------------+
-        |              |              | 4.0     | exomes                       |
-        +--------------+--------------+---------+------------------------------+
-        | all_sites_an | GRCh38       | 4.1     | exomes, genomes              |
-        +--------------+--------------+---------+------------------------------+
-        | constraint   | GRCh37       | 2.1.1   | N/A                          |
+        |              | constraint   | 2.1.1   | N/A                          |
         |              +--------------+---------+------------------------------+
-        |              | GRCh38       | 4.1     | N/A                          |
-        +--------------+--------------+---------+------------------------------+
-        | liftover     | GRCh37       | 2.1.1   | exomes, genomes              |
-        +--------------+--------------+---------+------------------------------+
-        | pext         | GRCh37       | v7      | base_level, annotation_level |
+        |              + pext         | v7      | base_level, annotation_level |
         |              +--------------+---------+------------------------------+
-        |              | GRCh38       | v10     | base_level, annotation_level |
+        |              | liftover     | 2.1.1   | exomes, genomes              |
         +--------------+--------------+---------+------------------------------+
-        | browser      | GRCh38       | 4.1     | N/A (joint, but doesn't need |
+        | GRCh38       | variant      | 4.1     | exomes, genomes, joint       |
+        |              +--------------+---------+------------------------------+
+        |              | all_sites_an | 4.1     | exomes, genomes              |
+        |              +--------------+---------+------------------------------+
+        |              | browser      | 4.1     | N/A (joint, but doesn't need |
         |              |              |         | to be specified)             |
+        |              +--------------+---------+------------------------------+
+        |              | coverage     | 3.0.1   | genomes                      |
+        |              +--------------+---------+------------------------------+
+        |              | constraint   | 4.1     | N/A                          |
+        |              +--------------+---------+------------------------------+
+        |              | pext         | v10     | base_level, annotation_level |
         +--------------+--------------+---------+------------------------------+
 
     :param dataset: Dataset type. One of "variant", "coverage", "all_sites_an",

From ffbe8e83c7cd482b0ea1f527ec7f862b0a040f56 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 15 Jan 2025 11:22:28 -0700
Subject: [PATCH 087/121] Run explore_release_data.ipynb all the way through

---
 .../notebooks/explore_release_data.ipynb      | 58 +++++++++----------
 1 file changed, 29 insertions(+), 29 deletions(-)

diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
index bc5c022..97e95af 100644
--- a/gnomad_toolbox/notebooks/explore_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -99,7 +99,7 @@
        "    \n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -271,7 +271,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"edebb21b-b3fc-4e55-b6bd-7218f358f8ee\");\n",
+       "    const el = document.getElementById(\"cd7063c9-b52f-4765-977f-209005fd6694\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -377,7 +377,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"edebb21b-b3fc-4e55-b6bd-7218f358f8ee\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"cd7063c9-b52f-4765-977f-209005fd6694\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -393,7 +393,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"edebb21b-b3fc-4e55-b6bd-7218f358f8ee\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"edebb21b-b3fc-4e55-b6bd-7218f358f8ee\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"cd7063c9-b52f-4765-977f-209005fd6694\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"cd7063c9-b52f-4765-977f-209005fd6694\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -419,7 +419,7 @@
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
       "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250114-1427-0.2.132-678e1f52b999.log\n"
+      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250115-1114-0.2.132-678e1f52b999.log\n"
      ]
     }
    ],
@@ -6356,7 +6356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 18,
    "id": "329f08ef-6dbf-42cd-bbbc-d3f5396708ac",
    "metadata": {
     "ExecuteTime": {
@@ -6379,7 +6379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 19,
    "id": "17efacb6-84c4-4345-9bf9-1dca259dd7fa",
    "metadata": {
     "ExecuteTime": {
@@ -6924,7 +6924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 20,
    "id": "d03dd50b-37ac-49ec-b766-a86870ee6573",
    "metadata": {
     "ExecuteTime": {
@@ -9378,7 +9378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "id": "7f005af0-df6d-4a0a-a2e0-9cb4ff6782c2",
    "metadata": {
     "ExecuteTime": {
@@ -9401,7 +9401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "id": "6058bf38-4ce9-4d37-89a7-d11af0e4d9f1",
    "metadata": {
     "ExecuteTime": {
@@ -9456,7 +9456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "id": "49fcf64b-1a91-4052-bb98-b329a0e8031b",
    "metadata": {
     "ExecuteTime": {
@@ -9534,7 +9534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "id": "a8d0be07-c35d-425a-b554-c86034e367fc",
    "metadata": {
     "ExecuteTime": {
@@ -9558,7 +9558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "id": "18afb20f-7429-4fe2-a6c5-73a22dcbdb76",
    "metadata": {
     "ExecuteTime": {
@@ -9612,7 +9612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "id": "b27cb655-3abb-4501-bcc9-3f634db64591",
    "metadata": {
     "ExecuteTime": {
@@ -9690,7 +9690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "id": "3de343dc-37ae-4b17-a056-9d30e71e217c",
    "metadata": {
     "ExecuteTime": {
@@ -9713,7 +9713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "id": "c401dc94-0db0-4902-933d-e46c8aa447ce",
    "metadata": {
     "ExecuteTime": {
@@ -9839,7 +9839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "id": "262cbbf1-9fc7-4819-9b2a-d1a1e27c10bf",
    "metadata": {
     "ExecuteTime": {
@@ -10045,7 +10045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "id": "1f448e05-7f99-4c63-b101-b33143d9d58b",
    "metadata": {
     "ExecuteTime": {
@@ -10068,7 +10068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "id": "b9f99311-91e6-454e-b940-3d036bddf3a5",
    "metadata": {
     "ExecuteTime": {
@@ -10314,7 +10314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 32,
    "id": "a6ac695f-cfb2-4aa7-877c-b204351a77da",
    "metadata": {
     "ExecuteTime": {
@@ -11312,7 +11312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 33,
    "id": "ce25b9e0-9118-4bf6-ba3c-eca75468b9f1",
    "metadata": {
     "ExecuteTime": {
@@ -11335,7 +11335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 34,
    "id": "818f3fc2-09fa-479c-91e1-e19f5991a22d",
    "metadata": {
     "ExecuteTime": {
@@ -11585,7 +11585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 35,
    "id": "35371de7-9332-4aa6-a3b9-c573fb2e144f",
    "metadata": {
     "ExecuteTime": {
@@ -12607,7 +12607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 36,
    "id": "4a29c79d",
    "metadata": {
     "ExecuteTime": {
@@ -12630,7 +12630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 37,
    "id": "dcd9ad6e",
    "metadata": {
     "ExecuteTime": {
@@ -12979,7 +12979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 38,
    "id": "04b6da53",
    "metadata": {
     "ExecuteTime": {
@@ -13595,7 +13595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 39,
    "id": "248e904e",
    "metadata": {
     "ExecuteTime": {
@@ -13619,7 +13619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 40,
    "id": "f718adea",
    "metadata": {
     "ExecuteTime": {
@@ -13971,7 +13971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 41,
    "id": "0a9ac872",
    "metadata": {
     "ExecuteTime": {

From 799e304f1f6b8a63087091d94533080dab9fbe8b Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 15 Jan 2025 11:24:25 -0700
Subject: [PATCH 088/121] Remove unneeded warning

---
 gnomad_toolbox/filtering/variant.py | 14 ++------------
 1 file changed, 2 insertions(+), 12 deletions(-)

diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index cb1842f..6705546 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -103,7 +103,8 @@ def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.
 
            This function is to match the number of variants that you will get in the
            gnomAD browser when you search for a gene symbol. The gnomAD browser
-           filters to only variants located in or within 75 base pairs of CDS or non-coding exons of a gene.
+           filters to only variants located in or within 75 base pairs of CDS or
+           non-coding exons of a gene.
 
     :param gene: Gencode gene symbol.
     :param exon_padding_bp: Number of base pairs to pad the intervals. Default is 75bp.
@@ -142,15 +143,4 @@ def filter_by_gene_symbol(gene: str, exon_padding_bp: int = 75, **kwargs) -> hl.
         filtered_gencode_ht.interval, ht=ht, padding_bp=exon_padding_bp
     )
 
-    contigs = filtered_gencode_ht.aggregate(
-        hl.agg.collect_as_set(filtered_gencode_ht.interval.start.contig)
-    )
-    if len(contigs) > 1:
-        hl.utils.warning(
-            "The gnomAD browser excludes genes on chrY that share the same gene symbol "
-            "as chrX. For example, if you use this function to filter 'ASMT' gene, you "
-            "may get more variants than shown in the gnomAD browser because it includes "
-            "both chrX and chrY variants."
-        )
-
     return ht

From 970b2dcaf9c5c549fffdf492719ad24c24f68d7c Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 15 Jan 2025 11:37:54 -0700
Subject: [PATCH 089/121] Fix browser change log link

---
 gnomad_toolbox/notebooks/explore_release_data.ipynb | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
index 97e95af..5454f80 100644
--- a/gnomad_toolbox/notebooks/explore_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -6343,7 +6343,9 @@
    "source": [
     "## Browser variant data\n",
     "\n",
-    "For more information about these files, see the [changelog entry](https://gnomad.broadinstitute.org/new/2024-08-release-gnomad-browser-tables) on the browser tables, and the [Help](https://gnomad.broadinstitute.org/help/v4-browser-hts) page."
+    "For more information about these files, see the [changelog entry](https://gnomad.broadinstitute.org/news/2024-08-release-gnomad-browser-tables) on the browser tables, and the [Help](https://gnomad.broadinstitute.org/help/v4-browser-hts) page.  \n",
+    "\n",
+    "Showing the gnomAD **v4.1** browser variant Hail Table."
    ]
   },
   {

From 4e7b02a65ef77b45b0989b329f177391f4ad4fd6 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Wed, 15 Jan 2025 15:13:56 -0500
Subject: [PATCH 090/121] Move pLOF function to contraint

---
 gnomad_toolbox/filtering/constraint.py | 102 +++++++++++++++++++++++++
 gnomad_toolbox/filtering/vep.py        |  95 +----------------------
 2 files changed, 103 insertions(+), 94 deletions(-)

diff --git a/gnomad_toolbox/filtering/constraint.py b/gnomad_toolbox/filtering/constraint.py
index edbb4ee..c9d3ec7 100644
--- a/gnomad_toolbox/filtering/constraint.py
+++ b/gnomad_toolbox/filtering/constraint.py
@@ -1 +1,103 @@
+"""Functions to filter gnomAD sites HT by constraint metrics."""
+
+import hail as hl
+
+from gnomad_toolbox.filtering.vep import (
+    filter_to_high_confidence_loftee,
+    get_gene_intervals,
+)
+from gnomad_toolbox.load_data import (
+    CONSTRAINT_DATA,
+    _get_dataset,
+    get_compatible_dataset_versions,
+)
+
+
+def get_observed_plofs_for_gene_constraint(
+    gene_symbol: str,
+    version: str = None,
+    variant_ht: hl.Table = None,
+    coverage_ht: hl.Table = None,
+) -> hl.Table:
+    """
+    Filter to observed pLoF variants used for gene constraint metrics.
+
+    The pLOF variant count displayed on the browser meets the following requirements:
+
+        - PASS variant QC
+        - SNV
+        - Allele frequency ≤ 0.1%
+        - High-confidence LOFTEE in the MANE Select or Canonical transcript
+        - ≥ a specified coverage threshold (depends on the version)
+
+    :param gene_symbol: Gene symbol.
+    :param version: Optional gnomAD dataset version. If not provided, uses the gnomAD
+        session version.
+    :param variant_ht: Optional Hail Table with variants. If not provided, uses the
+        exome variant Table for the gnomAD session version.
+    :param coverage_ht: Optional Hail Table with coverage data. If not provided, uses
+        the exome coverage Table for the gnomAD session version.
+    :return: Table with pLoF variants.
+    """
+    if variant_ht is not None and coverage_ht is None:
+        raise ValueError("Variant Hail Table provided without coverage Hail Table.")
+
+    if coverage_ht is not None and variant_ht is None:
+        raise ValueError("Coverage Hail Table provided without variant Hail Table.")
+
+    # Load the variant exomes Hail Table if not provided.
+    variant_ht = _get_dataset(
+        dataset="variant",
+        ht=variant_ht,
+        data_type="exomes",
+        version=version,
+    )
+
+    # Determine the coverage version compatible with the variant version.
+    coverage_version = get_compatible_dataset_versions("coverage", version, "exomes")
+
+    # Load the coverage Hail Table if not provided.
+    coverage_ht = _get_dataset(
+        dataset="coverage",
+        ht=coverage_ht,
+        data_type="exomes",
+        version=coverage_version,
+    )
+
+    # Get gene intervals and filter tables.
+    gencode_version = get_compatible_dataset_versions("gencode", version)
+    intervals = get_gene_intervals(gene_symbol, gencode_version=gencode_version)
+    variant_ht = hl.filter_intervals(variant_ht, intervals)
+    coverage_ht = hl.filter_intervals(coverage_ht, intervals)
+
+    # Determine constraint filters.
+    constraint_version = get_compatible_dataset_versions("constraint", version)
+    constraint_info = CONSTRAINT_DATA[constraint_version]
+    cov_field = constraint_info["exome_coverage_field"]
+    cov_cutoff = constraint_info["exome_coverage_cutoff"]
+    af_cutoff = constraint_info["af_cutoff"]
+
+    # Annotate the exome coverage.
+    variant_ht = variant_ht.annotate(
+        exome_coverage=coverage_ht[variant_ht.locus][cov_field]
+    )
+
+    # Apply constraint filters.
+    variant_ht = variant_ht.filter(
+        (hl.len(variant_ht.filters) == 0)
+        & (hl.is_snp(variant_ht.alleles[0], variant_ht.alleles[1]))
+        & (variant_ht.freq[0].AF <= af_cutoff)
+        & (variant_ht.exome_coverage >= cov_cutoff)
+    )
+
+    # Filter to high-confidence LOFTEE variants.
+    variant_ht = filter_to_high_confidence_loftee(
+        gene_symbol=gene_symbol,
+        ht=variant_ht,
+        canonical_only=True,
+    )
+
+    return variant_ht
+
+
 # noqa: D104, D100
diff --git a/gnomad_toolbox/filtering/vep.py b/gnomad_toolbox/filtering/vep.py
index dc78540..018c43b 100644
--- a/gnomad_toolbox/filtering/vep.py
+++ b/gnomad_toolbox/filtering/vep.py
@@ -10,11 +10,7 @@
     filter_vep_transcript_csqs_expr,
 )
 
-from gnomad_toolbox.load_data import (
-    CONSTRAINT_DATA,
-    _get_dataset,
-    get_compatible_dataset_versions,
-)
+from gnomad_toolbox.load_data import _get_dataset, get_compatible_dataset_versions
 
 
 # TODO: Check these csq sets, the ones in the code don't match what is listed on the
@@ -192,92 +188,3 @@ def filter_to_high_confidence_loftee(
         loftee_labels=["HC"],
         no_lof_flags=no_lof_flags,
     )
-
-
-# TODO: Let's move this function to constraint.py and change the name to something more
-#  descriptive, like maybe get_observed_plofs_for_gene_constraint.
-def filter_to_plofs(
-    gene_symbol: str,
-    version: str = None,
-    variant_ht: hl.Table = None,
-    coverage_ht: hl.Table = None,
-) -> hl.Table:
-    """
-    Filter to observed pLoF variants used for gene constraint metrics.
-
-    The pLOF variant count displayed on the browser meets the following requirements:
-
-        - PASS variant QC
-        - SNV
-        - Allele frequency ≤ 0.1%
-        - High-confidence LOFTEE in the MANE Select or Canonical transcript
-        - ≥ a specified coverage threshold (depends on the version)
-
-    :param gene_symbol: Gene symbol.
-    :param version: Optional gnomAD dataset version. If not provided, uses the gnomAD
-        session version.
-    :param variant_ht: Optional Hail Table with variants. If not provided, uses the
-        exome variant Table for the gnomAD session version.
-    :param coverage_ht: Optional Hail Table with coverage data. If not provided, uses
-        the exome coverage Table for the gnomAD session version.
-    :return: Table with pLoF variants.
-    """
-    if variant_ht is not None and coverage_ht is None:
-        raise ValueError("Variant Hail Table provided without coverage Hail Table.")
-
-    if coverage_ht is not None and variant_ht is None:
-        raise ValueError("Coverage Hail Table provided without variant Hail Table.")
-
-    # Load the variant exomes Hail Table if not provided.
-    variant_ht = _get_dataset(
-        dataset="variant",
-        ht=variant_ht,
-        data_type="exomes",
-        version=version,
-    )
-
-    # Determine the coverage version compatible with the variant version.
-    coverage_version = get_compatible_dataset_versions("coverage", version, "exomes")
-
-    # Load the coverage Hail Table if not provided.
-    coverage_ht = _get_dataset(
-        dataset="coverage",
-        ht=coverage_ht,
-        data_type="exomes",
-        version=coverage_version,
-    )
-
-    # Get gene intervals and filter tables.
-    gencode_version = get_compatible_dataset_versions("gencode", version)
-    intervals = get_gene_intervals(gene_symbol, gencode_version=gencode_version)
-    variant_ht = hl.filter_intervals(variant_ht, intervals)
-    coverage_ht = hl.filter_intervals(coverage_ht, intervals)
-
-    # Determine constraint filters.
-    constraint_version = get_compatible_dataset_versions("constraint", version)
-    constraint_info = CONSTRAINT_DATA[constraint_version]
-    cov_field = constraint_info["exome_coverage_field"]
-    cov_cutoff = constraint_info["exome_coverage_cutoff"]
-    af_cutoff = constraint_info["af_cutoff"]
-
-    # Annotate the exome coverage.
-    variant_ht = variant_ht.annotate(
-        exome_coverage=coverage_ht[variant_ht.locus][cov_field]
-    )
-
-    # Apply constraint filters.
-    variant_ht = variant_ht.filter(
-        (hl.len(variant_ht.filters) == 0)
-        & (hl.is_snp(variant_ht.alleles[0], variant_ht.alleles[1]))
-        & (variant_ht.freq[0].AF <= af_cutoff)
-        & (variant_ht.exome_coverage >= cov_cutoff)
-    )
-
-    # Filter to high-confidence LOFTEE variants.
-    variant_ht = filter_to_high_confidence_loftee(
-        gene_symbol=gene_symbol,
-        ht=variant_ht,
-        canonical_only=True,
-    )
-
-    return variant_ht

From e2f1d539d03a4f4897e8e071e350f43aedfa33db Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Wed, 15 Jan 2025 16:45:23 -0500
Subject: [PATCH 091/121] Update notebooks to use new functions

---
 .../notebooks/explore_release_data.ipynb      |    8 +-
 .../intro_to_filtering_variant_data.ipynb     | 5640 +----------------
 gnomad_toolbox/notebooks/needs_a_name.ipynb   |  746 ---
 3 files changed, 62 insertions(+), 6332 deletions(-)
 delete mode 100644 gnomad_toolbox/notebooks/needs_a_name.ipynb

diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
index 5454f80..1b7553e 100644
--- a/gnomad_toolbox/notebooks/explore_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -521,7 +521,9 @@
     "        v10\n",
     "        base_level, annotation_level\n",
     "    \n",
-    "\n"
+    "\n",
+    "\n",
+    "**NOTE:** In this notebook, we show examples to mainly explore our GRCh38 release datasets, click on the full name of the datasets will take you to the Downloads page on gnomAD Browser, and the abbreviations or the data_type to each section in this notebook (just as if you clink on the Table of Contents)."
    ]
   },
   {
@@ -14646,7 +14648,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.9"
   },
   "toc": {
    "base_numbering": 1,
@@ -14669,7 +14671,7 @@
    "toc_section_display": true,
    "toc_window_display": true
   },
-  "toc-autonumbering": false,
+  "toc-autonumbering": true,
   "toc-showcode": false,
   "toc-showmarkdowntxt": true,
   "toc-showtags": false,
diff --git a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
index ae240c0..8d2267c 100644
--- a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
+++ b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
@@ -41,7 +41,7 @@
     {
      "data": {
       "text/html": [
-       "\n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -90,14 +90,22 @@
        "   * Handle when an output is cleared or removed\n",
        "   */\n",
        "  function handleClearOutput(event, handle) {\n",
+       "    function drop(id) {\n",
+       "      const view = Bokeh.index.get_by_id(id)\n",
+       "      if (view != null) {\n",
+       "        view.model.document.clear()\n",
+       "        Bokeh.index.delete(view)\n",
+       "      }\n",
+       "    }\n",
+       "\n",
        "    const cell = handle.cell;\n",
        "\n",
        "    const id = cell.output_area._bokeh_element_id;\n",
        "    const server_id = cell.output_area._bokeh_server_id;\n",
+       "\n",
        "    // Clean up Bokeh references\n",
-       "    if (id != null && id in Bokeh.index) {\n",
-       "      Bokeh.index[id].model.document.clear();\n",
-       "      delete Bokeh.index[id];\n",
+       "    if (id != null) {\n",
+       "      drop(id)\n",
        "    }\n",
        "\n",
        "    if (server_id !== undefined) {\n",
@@ -106,11 +114,8 @@
        "      cell.notebook.kernel.execute(cmd_clean, {\n",
        "        iopub: {\n",
        "          output: function(msg) {\n",
-       "            const id = msg.content.text.trim();\n",
-       "            if (id in Bokeh.index) {\n",
-       "              Bokeh.index[id].model.document.clear();\n",
-       "              delete Bokeh.index[id];\n",
-       "            }\n",
+       "            const id = msg.content.text.trim()\n",
+       "            drop(id)\n",
        "          }\n",
        "        }\n",
        "      });\n",
@@ -218,7 +223,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"d5c4232b-0e66-4b61-983c-ba8423f8e445\");\n",
+       "    const el = document.getElementById(\"be1e6b75-a614-4af7-b778-f8e50b8b7664\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -301,7 +306,7 @@
        "    document.body.appendChild(element);\n",
        "  }\n",
        "\n",
-       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n",
+       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
        "  const css_urls = [];\n",
        "\n",
        "  const inline_js = [    function(Bokeh) {\n",
@@ -324,7 +329,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"d5c4232b-0e66-4b61-983c-ba8423f8e445\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"be1e6b75-a614-4af7-b778-f8e50b8b7664\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -340,7 +345,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"d5c4232b-0e66-4b61-983c-ba8423f8e445\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"d5c4232b-0e66-4b61-983c-ba8423f8e445\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"be1e6b75-a614-4af7-b778-f8e50b8b7664\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"be1e6b75-a614-4af7-b778-f8e50b8b7664\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -372,8 +377,8 @@
       "     __  __     <>__\n",
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
-      "  /_/ /_/\\_,_/_/_/   version 0.2.133-4c60fddb171a\n",
-      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241220-1230-0.2.133-4c60fddb171a.log\n"
+      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
+      "LOGGING: writing to /Users/heqin/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250115-1555-0.2.132-678e1f52b999.log\n"
      ]
     }
    ],
@@ -465,10 +470,24 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "The total number of variants in DRD2 is:  1764\n"
+      "01/15/2025 03:56:03 PM (gnomad.utils.filtering 456): No Gencode Table or version was supplied, using Gencode version v39\n",
+      "01/15/2025 03:56:16 PM (gnomad.utils.filtering 531): Since 1 is less than or equal to 'max_collect_intervals', collecting all intervals...\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "_get_dataset() got an unexpected keyword argument 'padding_bp'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m drd2_ht \u001b[38;5;241m=\u001b[39m \u001b[43mfilter_by_gene_symbol\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdrd2\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe total number of variants in DRD2 is: \u001b[39m\u001b[38;5;124m\"\u001b[39m, drd2_ht\u001b[38;5;241m.\u001b[39mcount())\n",
+      "File \u001b[0;32m~/miniconda3/envs/hail2132/lib/python3.11/site-packages/gnomad_toolbox/filtering/variant.py:139\u001b[0m, in \u001b[0;36mfilter_by_gene_symbol\u001b[0;34m(gene, exon_padding_bp, **kwargs)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filter_count \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    137\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo intervals match the gene symbol \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mgene\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 139\u001b[0m ht \u001b[38;5;241m=\u001b[39m \u001b[43mfilter_by_intervals\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_gencode_ht\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minterval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mht\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mht\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpadding_bp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexon_padding_bp\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ht\n",
+      "File \u001b[0;32m~/miniconda3/envs/hail2132/lib/python3.11/site-packages/gnomad_toolbox/filtering/variant.py:86\u001b[0m, in \u001b[0;36mfilter_by_intervals\u001b[0;34m(intervals, **kwargs)\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;124;03mFilter variants by interval(s).\u001b[39;00m\n\u001b[1;32m     78\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;124;03m:return: Table with variants in the interval(s).\u001b[39;00m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     85\u001b[0m \u001b[38;5;66;03m# Load the Hail Table if not provided\u001b[39;00m\n\u001b[0;32m---> 86\u001b[0m ht \u001b[38;5;241m=\u001b[39m \u001b[43m_get_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvariant\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m interval_filter(\n\u001b[1;32m     89\u001b[0m     ht,\n\u001b[1;32m     90\u001b[0m     intervals,\n\u001b[1;32m     91\u001b[0m     reference_genome\u001b[38;5;241m=\u001b[39mget_reference_genome(ht\u001b[38;5;241m.\u001b[39mlocus)\u001b[38;5;241m.\u001b[39mname,\n\u001b[1;32m     92\u001b[0m )\n",
+      "\u001b[0;31mTypeError\u001b[0m: _get_dataset() got an unexpected keyword argument 'padding_bp'"
      ]
     }
    ],
@@ -504,1079 +523,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "700582e4",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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,"1"),("ENSP_mappings","5aer"),("ENSP_mappings","5aer"),("ENSP_mappings","6cm4"),("ENSP_mappings","6luq"),("ENSP_mappings","6vms"),("ENSP_mappings","7dfp"),("ENSP_mappings","7jvr"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000542968.5:c.1323C>A","ENSP00000442172.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,441,441,"ENSP00000442172","Ensembl",-1,"ENST00000542968",1,["P14416-1"],"T"),(1,"L","A1","protein_coding",NA,NA,1353,1353,1320,1320,"ctC/ctA",["synonymous_variant"],NA,[("Gene3D","1"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000544518.5:c.1320C>A","ENSP00000441068.1:p.Leu440=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,440,440,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"T"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["intron_variant","non_coding_transcript_variant"],NA,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,"ENST00000546284.1:n.245-811G>T",NA,NA,"MODIFIER","2/3",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"T"),(1,"L",NA,"protein_coding",1,NA,1677,1677,1323,1323,"ctC/ctA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.1323C>A","NP_000786.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,441,441,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"T"),(1,"L",NA,"protein_coding",NA,NA,1590,1590,1236,1236,"ctC/ctA",["synonymous_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.1236C>A","NP_057658.2:p.Leu412=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,412,412,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"T"),(1,"L",NA,"protein_coding",NA,NA,1405,1405,1323,1323,"ctC/ctA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.1323C>A","XP_016872785.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,441,441,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"T")]"SNV"5.00e+00"AS_MQ"FalseFalseFalseFalseFalseTrueFalseFalse"multi-snv"2False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,2955,2914,2974,2923,2564,2680]07685[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,2955,2914,2974,2923,2564,2680]07685[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,1,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]002.96e+001.86e-01NA0.00e+003.00e-02-2.55e-01NANA
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+-----------------+------------+\n",
-       "| locus           | alleles    |\n",
-       "+-----------------+------------+\n",
-       "| locus   | array |\n",
-       "+-----------------+------------+\n",
-       "| chr11:113410731 | [\"C\",\"A\"]  |\n",
-       "| chr11:113410731 | [\"C\",\"T\"]  |\n",
-       "| chr11:113410735 | [\"G\",\"A\"]  |\n",
-       "| chr11:113410736 | [\"G\",\"A\"]  |\n",
-       "| chr11:113410736 | [\"G\",\"T\"]  |\n",
-       "+-----------------+------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq                                                                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(2,1.37e-06,1461866,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(2,4.... |\n",
-       "| [(1,6.84e-07,1461866,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
-       "| [(75,5.13e-05,1461876,0),(75,5.13e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
-       "| [(15,1.03e-05,1461882,0),(15,1.03e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
-       "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+\n",
-       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
-       "+------------------+------------------+------------------+\n",
-       "|            int32 |          float64 |            int32 |\n",
-       "+------------------+------------------+------------------+\n",
-       "|                2 |         4.47e-05 |            44724 |\n",
-       "|                1 |         8.99e-07 |          1112010 |\n",
-       "|                8 |         7.19e-06 |          1112004 |\n",
-       "|               15 |         1.35e-05 |          1112010 |\n",
-       "|                1 |         8.99e-07 |          1112010 |\n",
-       "+------------------+------------------+------------------+\n",
-       "\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                          int64 | str                   |             int32 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                              0 | \"amr\"                 |                 2 |\n",
-       "|                              0 | \"nfe\"                 |                NA |\n",
-       "|                              0 | \"nfe\"                 |                 2 |\n",
-       "|                              0 | \"nfe\"                 |                 1 |\n",
-       "|                              0 | \"nfe\"                 |                NA |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|           float64 |             int32 |                           int64 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|          4.57e-05 |             43740 |                               0 |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|          5.71e-06 |            350102 |                               0 |\n",
-       "|          2.86e-06 |            350106 |                               0 |\n",
-       "|                NA |                NA |                              NA |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "\n",
-       "+------------------------+\n",
-       "| grpmax.non_ukb.gen_anc |\n",
-       "+------------------------+\n",
-       "| str                    |\n",
-       "+------------------------+\n",
-       "| \"amr\"                  |\n",
-       "| NA                     |\n",
-       "| \"nfe\"                  |\n",
-       "| \"nfe\"                  |\n",
-       "| NA                     |\n",
-       "+------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| faf                                                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(7.41e-06,2.77e-06),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(4.19e-05,3.82e-05),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(6.16e-06,4.89e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                7.41e-06 | \"amr\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "|                3.09e-06 | \"nfe\"                           |\n",
-       "|                8.10e-06 | \"nfe\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                2.77e-06 | \"amr\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "|                2.24e-06 | \"nfe\"                           |\n",
-       "|                6.42e-06 | \"nfe\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                 7.58e-06 | \"amr\"                            |\n",
-       "|                       NA | NA                               |\n",
-       "|                 9.50e-07 | \"nfe\"                            |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                  float64 | str                              |   int32 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                 2.84e-06 | \"amr\"                            |       1 |\n",
-       "|                       NA | NA                               |       2 |\n",
-       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       2 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "|      bool | set         | set |  float64 |  float64 |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "|      True | {\"rs140938110\"}  | {}       | 5.56e+00 | 6.00e+01 |\n",
-       "|      True | NA               | {}       | 5.56e+00 | 6.00e+01 |\n",
-       "|      True | {\"rs201801648\"}  | {}       | 0.00e+00 | 6.00e+01 |\n",
-       "|      True | {\"rs1159504221\"} | {}       | 5.49e-01 | 6.00e+01 |\n",
-       "|      True | NA               | {}       | 5.49e-01 | 6.00e+01 |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|        float64 |           int64 |  float64 |             float64 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
-       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
-       "|       0.00e+00 |          127501 | 1.23e+01 |            3.70e-02 |\n",
-       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
-       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "| info.SB               | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "| array          |  float64 |      int32 |    float64 |    float64 |\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "| [134,83,109,52]       | 9.81e-01 |        378 |   1.31e+01 |   6.00e+01 |\n",
-       "| [134,83,109,52]       | 9.81e-01 |        378 |   0.00e+00 |   6.00e+01 |\n",
-       "| [3240,2211,2993,1962] | 7.34e-01 |      10404 |   0.00e+00 |   6.00e+01 |\n",
-       "| [386,195,316,166]     | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
-       "| [386,195,316,166]     | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|           float64 |         float64 |              int64 |    float64 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|          0.00e+00 |        2.94e-01 |               1895 |   1.06e+01 |\n",
-       "|          0.00e+00 |        9.04e-01 |               1967 |   9.88e+00 |\n",
-       "|          0.00e+00 |        1.00e+00 |             127480 |   1.23e+01 |\n",
-       "|          0.00e+00 |        9.11e-01 |              11899 |   1.17e+01 |\n",
-       "|          0.00e+00 |        3.28e-02 |                408 |   8.16e+00 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE      | info.AS_SOR | info.AS_VarDP |\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "|                float64 | array          |     float64 |         int32 |\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "|               2.52e-01 | [134,83,56,19]        |    1.42e+00 |           179 |\n",
-       "|              -5.01e-01 | [134,83,53,33]        |    6.81e-01 |           199 |\n",
-       "|               1.60e-02 | [3240,2211,2991,1961] |    7.34e-01 |         10383 |\n",
-       "|               9.70e-02 | [386,195,304,161]     |    6.47e-01 |          1013 |\n",
-       "|              -9.23e-01 | [386,195,12,5]        |    7.90e-01 |            50 |\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|           bool |                       bool |                   bool |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|          False |                         NA |                     NA |\n",
-       "|           True |                      False |                  False |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|           True |                      False |                  False |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|      bool |       bool |             bool |          bool |        float64 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|     False |      False |            False |         False |       5.27e+00 |\n",
-       "|     False |      False |            False |         False |       5.40e+00 |\n",
-       "|     False |      False |            False |         False |       8.85e+00 |\n",
-       "|     False |      False |            False |         False |       7.50e+00 |\n",
-       "|     False |      False |            False |         False |       5.00e+00 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "\n",
-       "+-----------------------+\n",
-       "| info.inbreeding_coeff |\n",
-       "+-----------------------+\n",
-       "|               float64 |\n",
-       "+-----------------------+\n",
-       "|             -1.37e-06 |\n",
-       "|             -6.84e-07 |\n",
-       "|             -5.13e-05 |\n",
-       "|             -1.03e-05 |\n",
-       "|             -6.84e-07 |\n",
-       "+-----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| info.vrs.VRS_Allele_IDs                                                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.ZonDIhdwyghm7SuWkX... |\n",
-       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.Hp20SlFHUdM7jlWmVV... |\n",
-       "| [\"ga4gh:VA.SBSoboxxiC0V-mhjjHg2geRlf3I0Ib2d\",\"ga4gh:VA.GzUD_86k_EDbtk-R58... |\n",
-       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.OXPDvbuulFZ9CSk4Xt... |\n",
-       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.4qhkDG1qrfqiD2TeOA... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| array          | array          | array          |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"A\"]           |\n",
-       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"T\"]           |\n",
-       "| [113410734,113410734] | [113410735,113410735] | [\"G\",\"A\"]           |\n",
-       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"A\"]           |\n",
-       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"T\"]           |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| str               |     int32 | str    | str                            |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| \"C/A\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tA\t.\t.\tGT\" |\n",
-       "| \"C/T\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tT\t.\t.\tGT\" |\n",
-       "| \"G/A\"             | 113410735 | \".\"    | \"chr11\t113410735\t.\tG\tA\t.\t.\tGT\" |\n",
-       "| \"G/A\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tA\t.\t.\tGT\" |\n",
-       "| \"G/T\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tT\t.\t.\tGT\" |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.intergenic_consequences                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, impact: st... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------+\n",
-       "| vep.most_severe_consequence |\n",
-       "+-----------------------------+\n",
-       "| str                         |\n",
-       "+-----------------------------+\n",
-       "| \"missense_variant\"          |\n",
-       "| \"missense_variant\"          |\n",
-       "| \"missense_variant\"          |\n",
-       "| \"synonymous_variant\"        |\n",
-       "| \"synonymous_variant\"        |\n",
-       "+-----------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.motif_feature_consequences                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, high_inf_p... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.regulatory_feature_consequences                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.bin_freq              |\n",
-       "+---------------------------------------------------------+\n",
-       "| array                                            |\n",
-       "+---------------------------------------------------------+\n",
-       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [0,0,0,0,12,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
-       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
-       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,0,65,332,11308,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
-       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                       7715 |\n",
-       "|                                       7715 |\n",
-       "|                                       7717 |\n",
-       "|                                       7685 |\n",
-       "|                                       7685 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75] |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15] |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]  |\n",
-       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          1 |\n",
-       "|                                          0 |\n",
-       "|                                         32 |\n",
-       "|                                          1 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq   |\n",
-       "+----------------------------------------------+\n",
-       "| array                                 |\n",
-       "+----------------------------------------------+\n",
-       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]    |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]    |\n",
-       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0] |\n",
-       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]    |\n",
-       "+----------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq          |\n",
-       "+---------------------------------------------------------+\n",
-       "| array                                            |\n",
-       "+---------------------------------------------------------+\n",
-       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [6,0,1,0,14,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
-       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
-       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,2,67,335,11310,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
-       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                           7715 |\n",
-       "|                                           7715 |\n",
-       "|                                           7717 |\n",
-       "|                                           7685 |\n",
-       "|                                           7685 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75]     |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15]     |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]      |\n",
-       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              1 |\n",
-       "|                                              0 |\n",
-       "|                                             32 |\n",
-       "|                                              1 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]     |\n",
-       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
-       "| [1,0,0,2,3,3,8,4,0,0]                      |\n",
-       "| [0,0,1,2,4,2,4,1,0,0]                      |\n",
-       "| [0,0,0,0,0,1,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           1 |\n",
-       "|                                           0 |\n",
-       "|                                           3 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          1 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                      int64 |                         float32 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        2.77e+01 |\n",
-       "|                                          0 |                        2.75e+01 |\n",
-       "|                                          0 |                        2.28e+01 |\n",
-       "|                                          0 |                        3.44e+00 |\n",
-       "|                                          0 |                        2.96e+00 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                            4.10e+00 |                       6.02e-01 |\n",
-       "|                            4.07e+00 |                       7.19e-01 |\n",
-       "|                            2.69e+00 |                       2.67e-01 |\n",
-       "|                            2.28e-01 |                             NA |\n",
-       "|                            1.86e-01 |                             NA |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                 0.00e+00 |                    8.78e+00 |\n",
-       "|                                 2.00e-02 |                    8.78e+00 |\n",
-       "|                                 1.00e-02 |                    8.67e+00 |\n",
-       "|                                 1.00e-02 |                   -2.55e-01 |\n",
-       "|                                 3.00e-02 |                   -2.55e-01 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                      0.00e+00 |                          9.89e-01 |\n",
-       "|                      0.00e+00 |                          9.89e-01 |\n",
-       "|                      1.30e-01 |                          1.18e-01 |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The total number of lof, missense, and synonymous variants passing filters in DRD2 is:  664\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "var_ht = filter_by_consequence_category(\n",
     "    plof=True, \n",
@@ -1605,1067 +555,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "9887fdb0",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
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AN
homozygote_count
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faf
faf95_max
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faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
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FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
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AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
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monoallelic
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AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
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n_smaller
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bin_edges
bin_freq
n_smaller
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revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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","HC","NAGNAG_SITE",NA,"INTRON_SIZE:1491",NA,NA,NA,NA,NA,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"C"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],768,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"C"),(1,NA,NA,"protein_coding",1,NA,NA,NA,NA,NA,NA,["splice_acceptor_variant"],NA,NA,NA,NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.811-2A>G",NA,NA,"HIGH","6/7","HC","NAGNAG_SITE",NA,"INTRON_SIZE:1491","ENST00000362072.8",NA,NA,NA,NA,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"C"),(1,NA,NA,"protein_coding",NA,NA,NA,NA,NA,NA,NA,["splice_acceptor_variant"],NA,NA,NA,NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.724-2A>G",NA,NA,"HIGH","5/6","HC","NAGNAG_SITE",NA,"INTRON_SIZE:2537",NA,NA,NA,NA,NA,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"C"),(1,NA,NA,"protein_coding",NA,NA,NA,NA,NA,NA,NA,["splice_acceptor_variant"],NA,NA,NA,NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.811-2A>G",NA,NA,"HIGH","6/7","HC","NAGNAG_SITE",NA,"INTRON_SIZE:1491",NA,NA,NA,NA,NA,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"C")]"SNV"5.84e+00"AS_MQ"FalseFalseFalseFalseFalseTrueTrueFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,929,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2699,3244,4728,36120,209411,224276,48878,7369,3147,3425,5116,7827,11506,15293,17615,18578,18759,20940]070149[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][1313,0,2,0,1481,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][618,1142,2737,3303,4730,36128,209411,224276,48878,7369,3147,3425,5116,7827,11506,15293,17615,18578,18759,20940]070149[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]003.50e+015.83e+00NA9.80e-01-7.90e-016.35e+00NANA
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+-----------------+-------------+\n",
-       "| locus           | alleles     |\n",
-       "+-----------------+-------------+\n",
-       "| locus   | array  |\n",
-       "+-----------------+-------------+\n",
-       "| chr11:113412554 | [\"A\",\"G\"]   |\n",
-       "| chr11:113412612 | [\"CT\",\"C\"]  |\n",
-       "| chr11:113412614 | [\"ACG\",\"A\"] |\n",
-       "| chr11:113412865 | [\"G\",\"A\"]   |\n",
-       "| chr11:113412885 | [\"T\",\"C\"]   |\n",
-       "+-----------------+-------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq                                                                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(1,6.85e-07,1459578,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
-       "| [(2,1.37e-06,1461892,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
-       "| [(2,1.37e-06,1461894,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
-       "| [(1,6.85e-07,1459642,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33422,0),(0,0.... |\n",
-       "| [(1,6.86e-07,1458160,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33408,0),(0,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+\n",
-       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
-       "+------------------+------------------+------------------+\n",
-       "|            int32 |          float64 |            int32 |\n",
-       "+------------------+------------------+------------------+\n",
-       "|                1 |         8.99e-07 |          1111998 |\n",
-       "|                2 |         1.80e-06 |          1112010 |\n",
-       "|                2 |         1.80e-06 |          1112012 |\n",
-       "|                1 |         8.99e-07 |          1111812 |\n",
-       "|                1 |         9.00e-07 |          1111636 |\n",
-       "+------------------+------------------+------------------+\n",
-       "\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                          int64 | str                   |             int32 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                              0 | \"nfe\"                 |                NA |\n",
-       "|                              0 | \"nfe\"                 |                 2 |\n",
-       "|                              0 | \"nfe\"                 |                 2 |\n",
-       "|                              0 | \"nfe\"                 |                NA |\n",
-       "|                              0 | \"nfe\"                 |                NA |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|           float64 |             int32 |                           int64 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|                NA |                NA |                              NA |\n",
-       "|          5.71e-06 |            350106 |                               0 |\n",
-       "|          5.71e-06 |            350108 |                               0 |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "\n",
-       "+------------------------+\n",
-       "| grpmax.non_ukb.gen_anc |\n",
-       "+------------------------+\n",
-       "| str                    |\n",
-       "+------------------------+\n",
-       "| NA                     |\n",
-       "| \"nfe\"                  |\n",
-       "| \"nfe\"                  |\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "+------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| faf                                                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                      NA | NA                              |\n",
-       "|                3.00e-07 | \"nfe\"                           |\n",
-       "|                3.00e-07 | \"nfe\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                      NA | NA                              |\n",
-       "|                1.10e-07 | \"nfe\"                           |\n",
-       "|                1.10e-07 | \"nfe\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                       NA | NA                               |\n",
-       "|                 9.50e-07 | \"nfe\"                            |\n",
-       "|                 9.50e-07 | \"nfe\"                            |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                  float64 | str                              |   int32 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                       NA | NA                               |       2 |\n",
-       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
-       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "\n",
-       "+-----------+----------+----------+----------+----------+----------------+\n",
-       "| was_split | rsid     | filters  |  info.FS |  info.MQ | info.MQRankSum |\n",
-       "+-----------+----------+----------+----------+----------+----------------+\n",
-       "|      bool | set | set |  float64 |  float64 |        float64 |\n",
-       "+-----------+----------+----------+----------+----------+----------------+\n",
-       "|      True | NA       | {}       | 2.83e+00 | 6.00e+01 |       0.00e+00 |\n",
-       "|     False | NA       | {}       | 5.48e-01 | 6.00e+01 |       0.00e+00 |\n",
-       "|      True | NA       | {}       | 2.32e+00 | 6.00e+01 |       0.00e+00 |\n",
-       "|      True | NA       | {}       | 1.02e+00 | 6.00e+01 |       0.00e+00 |\n",
-       "|     False | NA       | {}       | 2.45e+00 | 6.00e+01 |       0.00e+00 |\n",
-       "+-----------+----------+----------+----------+----------+----------------+\n",
-       "\n",
-       "+-----------------+----------+---------------------+---------------+----------+\n",
-       "| info.QUALapprox |  info.QD | info.ReadPosRankSum | info.SB       | info.SOR |\n",
-       "+-----------------+----------+---------------------+---------------+----------+\n",
-       "|           int64 |  float64 |             float64 | array  |  float64 |\n",
-       "+-----------------+----------+---------------------+---------------+----------+\n",
-       "|            1498 | 1.05e+01 |           -5.05e-01 | [63,17,52,10] | 1.04e+00 |\n",
-       "|            3458 | 1.79e+01 |            4.32e-01 | [65,38,58,32] | 7.50e-01 |\n",
-       "|            5336 | 1.56e+01 |            4.62e-01 | [99,81,94,68] | 8.22e-01 |\n",
-       "|             553 | 7.79e+00 |            2.96e-01 | [14,32,9,16]  | 4.68e-01 |\n",
-       "|             890 | 1.11e+01 |            1.80e-01 | [7,37,8,28]   | 3.79e-01 |\n",
-       "+-----------------+----------+---------------------+---------------+----------+\n",
-       "\n",
-       "+------------+------------+------------+-------------------+-----------------+\n",
-       "| info.VarDP | info.AS_FS | info.AS_MQ | info.AS_MQRankSum | info.AS_pab_max |\n",
-       "+------------+------------+------------+-------------------+-----------------+\n",
-       "|      int32 |    float64 |    float64 |           float64 |         float64 |\n",
-       "+------------+------------+------------+-------------------+-----------------+\n",
-       "|        142 |   1.10e+00 |   6.00e+01 |          0.00e+00 |        1.00e+00 |\n",
-       "|        193 |   5.48e-01 |   6.00e+01 |          0.00e+00 |        5.90e-01 |\n",
-       "|        342 |   8.20e+00 |   6.00e+01 |          0.00e+00 |        6.65e-01 |\n",
-       "|         71 |   1.04e+00 |   6.00e+01 |          0.00e+00 |        3.92e-01 |\n",
-       "|         80 |   2.45e+00 |   6.00e+01 |          0.00e+00 |        4.53e-01 |\n",
-       "+------------+------------+------------+-------------------+-----------------+\n",
-       "\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "| info.AS_QUALapprox | info.AS_QD | info.AS_ReadPosRankSum | info.AS_SB_TABLE |\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "|              int64 |    float64 |                float64 | array     |\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "|                661 |   1.35e+01 |              -1.83e+00 | [63,17,21,4]     |\n",
-       "|               3458 |   1.79e+01 |               4.32e-01 | [65,38,58,32]    |\n",
-       "|               3455 |   1.80e+01 |               4.62e-01 | [99,81,58,32]    |\n",
-       "|                531 |   8.56e+00 |               2.96e-01 | [14,32,8,15]     |\n",
-       "|                890 |   1.11e+01 |               1.80e-01 | [7,37,8,28]      |\n",
-       "+--------------------+------------+------------------------+------------------+\n",
-       "\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "| info.AS_SOR | info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "|     float64 |         int32 |           bool |                       bool |\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "|    9.29e-01 |            49 |           True |                      False |\n",
-       "|    7.50e-01 |           193 |          False |                         NA |\n",
-       "|    1.15e+00 |           192 |          False |                         NA |\n",
-       "|    5.03e-01 |            62 |           True |                      False |\n",
-       "|    3.79e-01 |            80 |          False |                      False |\n",
-       "+-------------+---------------+----------------+----------------------------+\n",
-       "\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "| info.sibling_singleton | info.omni | info.mills | info.monoallelic |\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "|                   bool |      bool |       bool |             bool |\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "|                  False |     False |      False |            False |\n",
-       "|                     NA |     False |      False |            False |\n",
-       "|                     NA |     False |      False |            False |\n",
-       "|                  False |     False |      False |            False |\n",
-       "|                  False |     False |      False |            False |\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "\n",
-       "+---------------+----------------+-----------------------+\n",
-       "| info.only_het | info.AS_VQSLOD | info.inbreeding_coeff |\n",
-       "+---------------+----------------+-----------------------+\n",
-       "|          bool |        float64 |               float64 |\n",
-       "+---------------+----------------+-----------------------+\n",
-       "|         False |       5.30e+00 |             -6.84e-07 |\n",
-       "|         False |       4.25e+00 |             -1.37e-06 |\n",
-       "|         False |       3.61e+00 |             -1.37e-06 |\n",
-       "|         False |       5.39e+00 |             -6.84e-07 |\n",
-       "|         False |       5.84e+00 |             -1.37e-06 |\n",
-       "+---------------+----------------+-----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| info.vrs.VRS_Allele_IDs                                                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.mqU9WPvGr3hduyybbeN-AKJ1zN-kclsI\",\"ga4gh:VA.uvlicmv1vZnrBBqm0U... |\n",
-       "| [\"ga4gh:VA.NztLKtEJA7H0h_Tz31YRmTUR4hT2PNxO\",\"ga4gh:VA.5xtydtW8O1beQhV-1c... |\n",
-       "| [\"ga4gh:VA.Lx5RcGLe0vnqmstRd134JgqaJUNZIzCF\",\"ga4gh:VA.N3JXRaksYTV6CSLvZ_... |\n",
-       "| [\"ga4gh:VA.uAq5BRYMw_lO7PZcOF5TpbGC5hA8JCCE\",\"ga4gh:VA.rN4HBsKxAnhIfQxMXe... |\n",
-       "| [\"ga4gh:VA.Hj6Aa_k4TOMxqWIs6lE_cgxiRrPFWp8C\",\"ga4gh:VA.NDTpZX0YUzM19pYVv6... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| array          | array          | array          |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| [113412553,113412553] | [113412554,113412554] | [\"A\",\"G\"]           |\n",
-       "| [113412611,113412612] | [113412613,113412613] | [\"CT\",\"\"]           |\n",
-       "| [113412613,113412614] | [113412616,113412616] | [\"ACG\",\"\"]          |\n",
-       "| [113412864,113412864] | [113412865,113412865] | [\"G\",\"A\"]           |\n",
-       "| [113412884,113412884] | [113412885,113412885] | [\"T\",\"C\"]           |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "\n",
-       "+-------------------+-----------+--------+----------------------------------+\n",
-       "| vep.allele_string |   vep.end | vep.id | vep.input                        |\n",
-       "+-------------------+-----------+--------+----------------------------------+\n",
-       "| str               |     int32 | str    | str                              |\n",
-       "+-------------------+-----------+--------+----------------------------------+\n",
-       "| \"A/G\"             | 113412554 | \".\"    | \"chr11\t113412554\t.\tA\tG\t.\t.\tGT\"   |\n",
-       "| \"T/-\"             | 113412613 | \".\"    | \"chr11\t113412612\t.\tCT\tC\t.\t.\tGT\"  |\n",
-       "| \"CG/-\"            | 113412616 | \".\"    | \"chr11\t113412614\t.\tACG\tA\t.\t.\tGT\" |\n",
-       "| \"G/A\"             | 113412865 | \".\"    | \"chr11\t113412865\t.\tG\tA\t.\t.\tGT\"   |\n",
-       "| \"T/C\"             | 113412885 | \".\"    | \"chr11\t113412885\t.\tT\tC\t.\t.\tGT\"   |\n",
-       "+-------------------+-----------+--------+----------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.intergenic_consequences                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, impact: st... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------+\n",
-       "| vep.most_severe_consequence |\n",
-       "+-----------------------------+\n",
-       "| str                         |\n",
-       "+-----------------------------+\n",
-       "| \"splice_donor_variant\"      |\n",
-       "| \"frameshift_variant\"        |\n",
-       "| \"frameshift_variant\"        |\n",
-       "| \"stop_gained\"               |\n",
-       "| \"splice_acceptor_variant\"   |\n",
-       "+-----------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.motif_feature_consequences                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, high_inf_p... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.regulatory_feature_consequences                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.bin_freq                 |\n",
-       "+------------------------------------------------------------+\n",
-       "| array                                               |\n",
-       "+------------------------------------------------------------+\n",
-       "| [0,0,0,0,134,0,1469,0,311660,0,82,0,416443,0,0,0,0,0,0,1]  |\n",
-       "| [0,0,0,0,1,0,2,0,314386,0,0,0,416555,0,0,0,0,0,0,2]        |\n",
-       "| [0,0,0,0,1,0,1,0,314388,0,0,0,416555,0,0,0,0,0,0,2]        |\n",
-       "| [0,0,0,0,547,0,1910,0,310815,0,33,0,416515,0,0,0,0,0,0,1]  |\n",
-       "| [0,0,0,0,929,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1] |\n",
-       "+------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,1555,817,16216,229270,272936,45138,9370,3566,3616,4059,4521,5017,549... |\n",
-       "| [0,0,1,279,16685,230939,273577,45298,9412,3597,3653,4094,4550,5033,5504,5... |\n",
-       "| [0,0,0,278,16685,230938,273578,45300,9412,3597,3653,4094,4550,5033,5504,5... |\n",
-       "| [0,0,1141,1461,3821,36585,211757,225880,49280,7441,3163,3441,5118,7834,11... |\n",
-       "| [0,0,2699,3244,4728,36120,209411,224276,48878,7369,3147,3425,5116,7827,11... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                     102137 |\n",
-       "|                                     102187 |\n",
-       "|                                     102188 |\n",
-       "|                                      70182 |\n",
-       "|                                      70149 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          1 |\n",
-       "|                                          1 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                 |\n",
-       "+----------------------------------------------------------------+\n",
-       "| array                                                   |\n",
-       "+----------------------------------------------------------------+\n",
-       "| [441,0,0,0,850,1,1469,0,311660,0,82,0,416443,0,0,0,0,0,0,1]    |\n",
-       "| [1,0,0,0,1,0,2,0,314386,0,0,0,416555,0,0,0,0,0,0,2]            |\n",
-       "| [0,0,0,0,1,0,1,0,314388,0,0,0,416555,0,0,0,0,0,0,2]            |\n",
-       "| [890,0,0,0,783,0,1910,0,310815,0,33,0,416515,0,0,0,0,0,0,1]    |\n",
-       "| [1313,0,2,0,1481,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1] |\n",
-       "+----------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [244,858,1593,833,16218,229270,272936,45138,9370,3566,3616,4059,4521,5017... |\n",
-       "| [0,0,1,279,16685,230939,273577,45298,9412,3597,3653,4094,4550,5034,5504,5... |\n",
-       "| [0,0,0,278,16685,230938,273578,45300,9412,3597,3653,4094,4550,5033,5504,5... |\n",
-       "| [431,622,1175,1493,3823,36589,211757,225880,49281,7441,3163,3441,5118,783... |\n",
-       "| [618,1142,2737,3303,4730,36128,209411,224276,48878,7369,3147,3425,5116,78... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                         102137 |\n",
-       "|                                         102187 |\n",
-       "|                                         102188 |\n",
-       "|                                          70182 |\n",
-       "|                                          70149 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              1 |\n",
-       "|                                              1 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,1,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           1 |\n",
-       "|                                           1 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                      int64 |                         float32 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        3.40e+01 |\n",
-       "|                                          0 |                        3.20e+01 |\n",
-       "|                                          0 |                        3.30e+01 |\n",
-       "|                                          0 |                        4.50e+01 |\n",
-       "|                                          0 |                        3.50e+01 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                            5.66e+00 |                             NA |\n",
-       "|                            4.56e+00 |                             NA |\n",
-       "|                            4.99e+00 |                             NA |\n",
-       "|                            8.76e+00 |                             NA |\n",
-       "|                            5.83e+00 |                             NA |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "|                             9.60e-01 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             9.80e-01 |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                -8.30e-01 |                    6.33e+00 |\n",
-       "|                                -4.00e-02 |                    8.89e+00 |\n",
-       "|                                -1.20e-01 |                    3.00e-01 |\n",
-       "|                                -1.10e-01 |                    8.79e+00 |\n",
-       "|                                -7.90e-01 |                    6.35e+00 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The total number of lof variants passing filters in DRD2 is:  17\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "var_ht = filter_by_consequence_category(plof=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
@@ -2689,1079 +582,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "86596aaf",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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A,[("Gene3D","1"),("ENSP_mappings","5aer"),("ENSP_mappings","5aer"),("ENSP_mappings","6cm4"),("ENSP_mappings","6luq"),("ENSP_mappings","6vms"),("ENSP_mappings","7dfp"),("ENSP_mappings","7jvr"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000542968.5:c.1319T>C","ENSP00000442172.1:p.Ile440Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,440,440,"ENSP00000442172","Ensembl",-1,"ENST00000542968",1,["P14416-1"],"G"),(1,"I/T","A1","protein_coding",NA,NA,1349,1349,1316,1316,"aTc/aCc",["missense_variant"],NA,[("Gene3D","1"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000544518.5:c.1316T>C","ENSP00000441068.1:p.Ile439Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,439,439,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"G"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["intron_variant","non_coding_transcript_variant"],NA,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,"ENST00000546284.1:n.245-807A>G",NA,NA,"MODIFIER","2/3",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"G"),(1,"I/T",NA,"protein_coding",1,NA,1673,1673,1319,1319,"aTc/aCc",["missense_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.1319T>C","NP_000786.1:p.Ile440Thr",NA,"MODERATE",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,440,440,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"G"),(1,"I/T",NA,"protein_coding",NA,NA,1586,1586,1232,1232,"aTc/aCc",["missense_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.1232T>C","NP_057658.2:p.Ile411Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,411,411,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"G"),(1,"I/T",NA,"protein_coding",NA,NA,1401,1401,1319,1319,"aTc/aCc",["missense_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.1319T>C","XP_016872785.1:p.Ile440Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,440,440,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"G")]"SNV"4.72e+00"AS_MQ"FalseFalseFalseFalseFalseTrueFalseFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,12,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,49,301,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,2954,2914,2973,2922,2564,2680]07683[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][4,0,0,0,13,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,50,304,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,2954,2914,2973,2922,2564,2680]07683[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]002.72e+014.02e+006.41e-010.00e+003.00e-026.25e+000.00e+005.08e-01
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+-----------------+------------+\n",
-       "| locus           | alleles    |\n",
-       "+-----------------+------------+\n",
-       "| locus   | array |\n",
-       "+-----------------+------------+\n",
-       "| chr11:113410731 | [\"C\",\"A\"]  |\n",
-       "| chr11:113410731 | [\"C\",\"T\"]  |\n",
-       "| chr11:113410735 | [\"G\",\"A\"]  |\n",
-       "| chr11:113410738 | [\"G\",\"T\"]  |\n",
-       "| chr11:113410740 | [\"A\",\"G\"]  |\n",
-       "+-----------------+------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq                                                                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(2,1.37e-06,1461866,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(2,4.... |\n",
-       "| [(1,6.84e-07,1461866,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
-       "| [(75,5.13e-05,1461876,0),(75,5.13e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
-       "| [(2,1.37e-06,1461886,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
-       "| [(1,6.84e-07,1461884,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(1,2.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+\n",
-       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
-       "+------------------+------------------+------------------+\n",
-       "|            int32 |          float64 |            int32 |\n",
-       "+------------------+------------------+------------------+\n",
-       "|                2 |         4.47e-05 |            44724 |\n",
-       "|                1 |         8.99e-07 |          1112010 |\n",
-       "|                8 |         7.19e-06 |          1112004 |\n",
-       "|                2 |         5.04e-05 |            39700 |\n",
-       "|                1 |         2.24e-05 |            44724 |\n",
-       "+------------------+------------------+------------------+\n",
-       "\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                          int64 | str                   |             int32 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                              0 | \"amr\"                 |                 2 |\n",
-       "|                              0 | \"nfe\"                 |                NA |\n",
-       "|                              0 | \"nfe\"                 |                 2 |\n",
-       "|                              0 | \"eas\"                 |                 1 |\n",
-       "|                              0 | \"amr\"                 |                 1 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|           float64 |             int32 |                           int64 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|          4.57e-05 |             43740 |                               0 |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|          5.71e-06 |            350102 |                               0 |\n",
-       "|          2.77e-05 |             36070 |                               0 |\n",
-       "|          2.29e-05 |             43740 |                               0 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "\n",
-       "+------------------------+\n",
-       "| grpmax.non_ukb.gen_anc |\n",
-       "+------------------------+\n",
-       "| str                    |\n",
-       "+------------------------+\n",
-       "| \"amr\"                  |\n",
-       "| NA                     |\n",
-       "| \"nfe\"                  |\n",
-       "| \"eas\"                  |\n",
-       "| \"amr\"                  |\n",
-       "+------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| faf                                                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(7.41e-06,2.77e-06),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(4.19e-05,3.82e-05),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(8.35e-06,3.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                7.41e-06 | \"amr\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "|                3.09e-06 | \"nfe\"                           |\n",
-       "|                8.35e-06 | \"eas\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                2.77e-06 | \"amr\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "|                2.24e-06 | \"nfe\"                           |\n",
-       "|                3.12e-06 | \"eas\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                 7.58e-06 | \"amr\"                            |\n",
-       "|                       NA | NA                               |\n",
-       "|                 9.50e-07 | \"nfe\"                            |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                  float64 | str                              |   int32 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                 2.84e-06 | \"amr\"                            |       1 |\n",
-       "|                       NA | NA                               |       2 |\n",
-       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "|      bool | set         | set |  float64 |  float64 |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "|      True | {\"rs140938110\"}  | {}       | 5.56e+00 | 6.00e+01 |\n",
-       "|      True | NA               | {}       | 5.56e+00 | 6.00e+01 |\n",
-       "|      True | {\"rs201801648\"}  | {}       | 0.00e+00 | 6.00e+01 |\n",
-       "|     False | {\"rs1223741945\"} | {}       | 1.38e+01 | 6.00e+01 |\n",
-       "|     False | NA               | {}       | 3.13e+00 | 6.00e+01 |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|        float64 |           int64 |  float64 |             float64 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
-       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
-       "|       0.00e+00 |          127501 | 1.23e+01 |            3.70e-02 |\n",
-       "|       0.00e+00 |            3253 | 1.30e+01 |           -1.33e+00 |\n",
-       "|       0.00e+00 |             402 | 1.01e+01 |            7.13e-01 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "| info.SB               | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "| array          |  float64 |      int32 |    float64 |    float64 |\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "| [134,83,109,52]       | 9.81e-01 |        378 |   1.31e+01 |   6.00e+01 |\n",
-       "| [134,83,109,52]       | 9.81e-01 |        378 |   0.00e+00 |   6.00e+01 |\n",
-       "| [3240,2211,2993,1962] | 7.34e-01 |      10404 |   0.00e+00 |   6.00e+01 |\n",
-       "| [63,66,76,46]         | 1.28e+00 |        251 |   1.38e+01 |   6.00e+01 |\n",
-       "| [17,8,12,3]           | 1.29e+00 |         40 |   3.13e+00 |   6.00e+01 |\n",
-       "+-----------------------+----------+------------+------------+------------+\n",
-       "\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|           float64 |         float64 |              int64 |    float64 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|          0.00e+00 |        2.94e-01 |               1895 |   1.06e+01 |\n",
-       "|          0.00e+00 |        9.04e-01 |               1967 |   9.88e+00 |\n",
-       "|          0.00e+00 |        1.00e+00 |             127480 |   1.23e+01 |\n",
-       "|          0.00e+00 |        1.00e+00 |               3253 |   1.30e+01 |\n",
-       "|          0.00e+00 |        1.54e-01 |                402 |   1.01e+01 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE      | info.AS_SOR | info.AS_VarDP |\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "|                float64 | array          |     float64 |         int32 |\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "|               2.52e-01 | [134,83,56,19]        |    1.42e+00 |           179 |\n",
-       "|              -5.01e-01 | [134,83,53,33]        |    6.81e-01 |           199 |\n",
-       "|               1.60e-02 | [3240,2211,2991,1961] |    7.34e-01 |         10383 |\n",
-       "|              -1.33e+00 | [63,66,76,46]         |    1.28e+00 |           251 |\n",
-       "|               7.13e-01 | [17,8,12,3]           |    1.29e+00 |            40 |\n",
-       "+------------------------+-----------------------+-------------+---------------+\n",
-       "\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|           bool |                       bool |                   bool |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|          False |                         NA |                     NA |\n",
-       "|           True |                      False |                  False |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|           True |                      False |                  False |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|      bool |       bool |             bool |          bool |        float64 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|     False |      False |            False |         False |       5.27e+00 |\n",
-       "|     False |      False |            False |         False |       5.40e+00 |\n",
-       "|     False |      False |            False |         False |       8.85e+00 |\n",
-       "|     False |      False |            False |         False |       3.73e+00 |\n",
-       "|     False |      False |            False |         False |       4.72e+00 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "\n",
-       "+-----------------------+\n",
-       "| info.inbreeding_coeff |\n",
-       "+-----------------------+\n",
-       "|               float64 |\n",
-       "+-----------------------+\n",
-       "|             -1.37e-06 |\n",
-       "|             -6.84e-07 |\n",
-       "|             -5.13e-05 |\n",
-       "|             -1.37e-06 |\n",
-       "|             -6.84e-07 |\n",
-       "+-----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| info.vrs.VRS_Allele_IDs                                                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.ZonDIhdwyghm7SuWkX... |\n",
-       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.Hp20SlFHUdM7jlWmVV... |\n",
-       "| [\"ga4gh:VA.SBSoboxxiC0V-mhjjHg2geRlf3I0Ib2d\",\"ga4gh:VA.GzUD_86k_EDbtk-R58... |\n",
-       "| [\"ga4gh:VA.cROnHWMnTSTuzMfVIhSezHexCPbFdF3z\",\"ga4gh:VA.WAkJ8ZSeVrsh6ThoH7... |\n",
-       "| [\"ga4gh:VA.gXv9EmAG-Lbw5gSNFxiBHWB_WT8twCnG\",\"ga4gh:VA.S-YUvVERFiE1cgkxu2... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| array          | array          | array          |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"A\"]           |\n",
-       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"T\"]           |\n",
-       "| [113410734,113410734] | [113410735,113410735] | [\"G\",\"A\"]           |\n",
-       "| [113410737,113410737] | [113410738,113410738] | [\"G\",\"T\"]           |\n",
-       "| [113410739,113410739] | [113410740,113410740] | [\"A\",\"G\"]           |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| str               |     int32 | str    | str                            |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| \"C/A\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tA\t.\t.\tGT\" |\n",
-       "| \"C/T\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tT\t.\t.\tGT\" |\n",
-       "| \"G/A\"             | 113410735 | \".\"    | \"chr11\t113410735\t.\tG\tA\t.\t.\tGT\" |\n",
-       "| \"G/T\"             | 113410738 | \".\"    | \"chr11\t113410738\t.\tG\tT\t.\t.\tGT\" |\n",
-       "| \"A/G\"             | 113410740 | \".\"    | \"chr11\t113410740\t.\tA\tG\t.\t.\tGT\" |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.intergenic_consequences                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, impact: st... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------+\n",
-       "| vep.most_severe_consequence |\n",
-       "+-----------------------------+\n",
-       "| str                         |\n",
-       "+-----------------------------+\n",
-       "| \"missense_variant\"          |\n",
-       "| \"missense_variant\"          |\n",
-       "| \"missense_variant\"          |\n",
-       "| \"missense_variant\"          |\n",
-       "| \"missense_variant\"          |\n",
-       "+-----------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.motif_feature_consequences                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, high_inf_p... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.regulatory_feature_consequences                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.bin_freq              |\n",
-       "+---------------------------------------------------------+\n",
-       "| array                                            |\n",
-       "+---------------------------------------------------------+\n",
-       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [0,0,0,0,12,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
-       "| [0,0,0,0,12,0,66,0,314321,0,31,0,416511,0,0,0,0,0,0,2]  |\n",
-       "| [0,0,0,0,12,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]  |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,0,65,332,11308,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
-       "| [0,0,57,319,11282,107355,345042,189122,36330,5605,2379,2756,3073,2932,295... |\n",
-       "| [0,0,49,301,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,295... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                       7715 |\n",
-       "|                                       7715 |\n",
-       "|                                       7717 |\n",
-       "|                                       7684 |\n",
-       "|                                       7683 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75] |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          1 |\n",
-       "|                                          0 |\n",
-       "|                                         32 |\n",
-       "|                                          1 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+----------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq   |\n",
-       "+----------------------------------------------+\n",
-       "| array                                 |\n",
-       "+----------------------------------------------+\n",
-       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]    |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]    |\n",
-       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0] |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0]    |\n",
-       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]    |\n",
-       "+----------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq          |\n",
-       "+---------------------------------------------------------+\n",
-       "| array                                            |\n",
-       "+---------------------------------------------------------+\n",
-       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
-       "| [6,0,1,0,14,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
-       "| [3,0,0,0,13,0,66,0,314321,0,31,0,416511,0,0,0,0,0,0,2]  |\n",
-       "| [4,0,0,0,13,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]  |\n",
-       "+---------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
-       "| [0,2,67,335,11310,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
-       "| [0,1,58,321,11282,107355,345042,189122,36330,5605,2379,2756,3073,2932,295... |\n",
-       "| [0,1,50,304,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,295... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                           7715 |\n",
-       "|                                           7715 |\n",
-       "|                                           7717 |\n",
-       "|                                           7684 |\n",
-       "|                                           7683 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75]     |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              1 |\n",
-       "|                                              0 |\n",
-       "|                                             32 |\n",
-       "|                                              1 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0]   |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
-       "| [1,0,0,2,3,3,8,4,0,0]                      |\n",
-       "| [0,0,0,1,0,0,0,1,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           1 |\n",
-       "|                                           0 |\n",
-       "|                                           3 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          1 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                      int64 |                         float32 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        2.77e+01 |\n",
-       "|                                          0 |                        2.75e+01 |\n",
-       "|                                          0 |                        2.28e+01 |\n",
-       "|                                          0 |                        2.53e+01 |\n",
-       "|                                          0 |                        2.72e+01 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                            4.10e+00 |                       6.02e-01 |\n",
-       "|                            4.07e+00 |                       7.19e-01 |\n",
-       "|                            2.69e+00 |                       2.67e-01 |\n",
-       "|                            3.67e+00 |                       2.65e-01 |\n",
-       "|                            4.02e+00 |                       6.41e-01 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                 0.00e+00 |                    8.78e+00 |\n",
-       "|                                 2.00e-02 |                    8.78e+00 |\n",
-       "|                                 1.00e-02 |                    8.67e+00 |\n",
-       "|                                 1.00e-02 |                    4.85e+00 |\n",
-       "|                                 3.00e-02 |                    6.25e+00 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                      0.00e+00 |                          9.89e-01 |\n",
-       "|                      0.00e+00 |                          9.89e-01 |\n",
-       "|                      1.30e-01 |                          1.18e-01 |\n",
-       "|                      3.00e-02 |                          9.36e-01 |\n",
-       "|                      0.00e+00 |                          5.08e-01 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The total number of missense variants passing filters in DRD2 is:  409\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "var_ht = filter_by_consequence_category(missense=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
@@ -3785,1079 +609,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "b7e4368b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
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negative_train_site
non_par
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fail_interval_qc
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has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
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n_smaller
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bin_edges
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bin_freq
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phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
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],3110,NA,NA,NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000540600",1,NA,"T"),(1,"K","P2","protein_coding",NA,"CCDS8361.1",1626,1626,1305,1305,"aaG/aaA",["synonymous_variant"],NA,[("Gene3D","1"),("ENSP_mappings","5aer"),("ENSP_mappings","5aer"),("ENSP_mappings","6cm4"),("ENSP_mappings","6luq"),("ENSP_mappings","6vms"),("ENSP_mappings","7dfp"),("ENSP_mappings","7jvr"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321"),("CDD","cd15309")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000542968.5:c.1305G>A","ENSP00000442172.1:p.Lys435=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,435,435,"ENSP00000442172","Ensembl",-1,"ENST00000542968",1,["P14416-1"],"T"),(1,"K","A1","protein_coding",NA,NA,1335,1335,1302,1302,"aaG/aaA",["synonymous_variant"],NA,[("Gene3D","1"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321"),("CDD","cd15309")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000544518.5:c.1302G>A","ENSP00000441068.1:p.Lys434=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,434,434,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"T"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["intron_variant","non_coding_transcript_variant"],NA,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,"ENST00000546284.1:n.245-793C>T",NA,NA,"MODIFIER","2/3",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"T"),(1,"K",NA,"protein_coding",1,NA,1659,1659,1305,1305,"aaG/aaA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.1305G>A","NP_000786.1:p.Lys435=",NA,"LOW",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,435,435,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"T"),(1,"K",NA,"protein_coding",NA,NA,1572,1572,1218,1218,"aaG/aaA",["synonymous_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.1218G>A","NP_057658.2:p.Lys406=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,406,406,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"T"),(1,"K",NA,"protein_coding",NA,NA,1387,1387,1305,1305,"aaG/aaA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.1305G>A","XP_016872785.1:p.Lys435=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,435,435,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"T")]"SNV"8.87e+00"AS_MQ"FalseFalseFalseFalseFalseTrueFalseFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,266,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,2971,2945,3008,2939,2587,2701]07737[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27]0207[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][1,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,267,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,2971,2945,3008,2939,2587,2701]07737[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27]0207[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][3,2,30,42,65,52,79,55,7,3]153[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,1,0,0,0,1,0]009.48e+008.09e-01NA0.00e+001.00e-025.82e+00NANA
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+-----------------+------------+\n",
-       "| locus           | alleles    |\n",
-       "+-----------------+------------+\n",
-       "| locus   | array |\n",
-       "+-----------------+------------+\n",
-       "| chr11:113410736 | [\"G\",\"A\"]  |\n",
-       "| chr11:113410736 | [\"G\",\"T\"]  |\n",
-       "| chr11:113410739 | [\"G\",\"A\"]  |\n",
-       "| chr11:113410751 | [\"G\",\"A\"]  |\n",
-       "| chr11:113410754 | [\"C\",\"T\"]  |\n",
-       "+-----------------+------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq                                                                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(15,1.03e-05,1461882,0),(15,1.03e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
-       "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
-       "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33478,0),(0,0.... |\n",
-       "| [(1,6.84e-07,1461894,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
-       "| [(576,3.94e-04,1461892,2),(576,3.94e-04,1461894,2),(12,3.58e-04,33480,1),... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+\n",
-       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
-       "+------------------+------------------+------------------+\n",
-       "|            int32 |          float64 |            int32 |\n",
-       "+------------------+------------------+------------------+\n",
-       "|               15 |         1.35e-05 |          1112010 |\n",
-       "|                1 |         8.99e-07 |          1112010 |\n",
-       "|               NA |               NA |               NA |\n",
-       "|                1 |         8.99e-07 |          1112012 |\n",
-       "|                8 |         1.39e-03 |             5768 |\n",
-       "+------------------+------------------+------------------+\n",
-       "\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                          int64 | str                   |             int32 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                              0 | \"nfe\"                 |                 1 |\n",
-       "|                              0 | \"nfe\"                 |                NA |\n",
-       "|                             NA | NA                    |                NA |\n",
-       "|                              0 | \"nfe\"                 |                 1 |\n",
-       "|                              0 | \"mid\"                 |                 4 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|           float64 |             int32 |                           int64 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|          2.86e-06 |            350106 |                               0 |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|                NA |                NA |                              NA |\n",
-       "|          2.86e-06 |            350108 |                               0 |\n",
-       "|          9.64e-04 |              4148 |                               0 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "\n",
-       "+------------------------+\n",
-       "| grpmax.non_ukb.gen_anc |\n",
-       "+------------------------+\n",
-       "| str                    |\n",
-       "+------------------------+\n",
-       "| \"nfe\"                  |\n",
-       "| NA                     |\n",
-       "| NA                     |\n",
-       "| \"nfe\"                  |\n",
-       "| \"mid\"                  |\n",
-       "+------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| faf                                                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(6.16e-06,4.89e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(3.67e-04,3.57e-04),(2.06e-04,1.62e-04),(7.33e-04,6.53e-04),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                8.10e-06 | \"nfe\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                7.33e-04 | \"amr\"                           |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                6.42e-06 | \"nfe\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "|                6.53e-04 | \"amr\"                           |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "|                 6.90e-04 | \"amr\"                            |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                  float64 | str                              |   int32 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       2 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "|                 6.12e-04 | \"amr\"                            |       1 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "|      bool | set         | set |  float64 |  float64 |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "|      True | {\"rs1159504221\"} | {}       | 5.49e-01 | 6.00e+01 |\n",
-       "|      True | NA               | {}       | 5.49e-01 | 6.00e+01 |\n",
-       "|     False | NA               | {}       | 4.26e+00 | 6.00e+01 |\n",
-       "|     False | NA               | {}       | 8.26e+00 | 6.00e+01 |\n",
-       "|     False | {\"rs77930100\"}   | {}       | 1.45e-15 | 6.00e+01 |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|        float64 |           int64 |  float64 |             float64 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
-       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
-       "|       0.00e+00 |             574 | 1.10e+01 |            5.32e-01 |\n",
-       "|       0.00e+00 |            1098 | 2.03e+01 |           -7.69e-01 |\n",
-       "|       0.00e+00 |          947678 | 1.23e+01 |           -7.00e-03 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "\n",
-       "+---------------------------+----------+------------+------------+------------+\n",
-       "| info.SB                   | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
-       "+---------------------------+----------+------------+------------+------------+\n",
-       "| array              |  float64 |      int32 |    float64 |    float64 |\n",
-       "+---------------------------+----------+------------+------------+------------+\n",
-       "| [386,195,316,166]         | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
-       "| [386,195,316,166]         | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
-       "| [23,8,13,8]               | 2.93e-01 |         52 |   4.26e+00 |   6.00e+01 |\n",
-       "| [5,11,21,18]              | 4.64e-01 |         54 |   8.26e+00 |   6.00e+01 |\n",
-       "| [20804,19237,19605,17401] | 7.35e-01 |      77027 |   1.45e-15 |   6.00e+01 |\n",
-       "+---------------------------+----------+------------+------------+------------+\n",
-       "\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|           float64 |         float64 |              int64 |    float64 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|          0.00e+00 |        9.11e-01 |              11899 |   1.17e+01 |\n",
-       "|          0.00e+00 |        3.28e-02 |                408 |   8.16e+00 |\n",
-       "|          0.00e+00 |        2.12e-01 |                574 |   1.10e+01 |\n",
-       "|          0.00e+00 |        3.84e-03 |               1098 |   2.03e+01 |\n",
-       "|          0.00e+00 |        1.00e+00 |             947678 |   1.23e+01 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "\n",
-       "+------------------------+---------------------------+-------------+\n",
-       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE          | info.AS_SOR |\n",
-       "+------------------------+---------------------------+-------------+\n",
-       "|                float64 | array              |     float64 |\n",
-       "+------------------------+---------------------------+-------------+\n",
-       "|               9.70e-02 | [386,195,304,161]         |    6.47e-01 |\n",
-       "|              -9.23e-01 | [386,195,12,5]            |    7.90e-01 |\n",
-       "|               5.32e-01 | [23,8,13,8]               |    2.93e-01 |\n",
-       "|              -7.69e-01 | [5,11,21,18]              |    4.64e-01 |\n",
-       "|               7.00e-03 | [20804,19237,19605,17401] |    7.35e-01 |\n",
-       "+------------------------+---------------------------+-------------+\n",
-       "\n",
-       "+---------------+----------------+----------------------------+\n",
-       "| info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
-       "+---------------+----------------+----------------------------+\n",
-       "|         int32 |           bool |                       bool |\n",
-       "+---------------+----------------+----------------------------+\n",
-       "|          1013 |          False |                         NA |\n",
-       "|            50 |           True |                      False |\n",
-       "|            52 |           True |                      False |\n",
-       "|            54 |           True |                      False |\n",
-       "|         77027 |          False |                         NA |\n",
-       "+---------------+----------------+----------------------------+\n",
-       "\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "| info.sibling_singleton | info.omni | info.mills | info.monoallelic |\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "|                   bool |      bool |       bool |             bool |\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "|                     NA |     False |      False |            False |\n",
-       "|                  False |     False |      False |            False |\n",
-       "|                  False |     False |      False |            False |\n",
-       "|                  False |     False |      False |            False |\n",
-       "|                     NA |     False |      False |            False |\n",
-       "+------------------------+-----------+------------+------------------+\n",
-       "\n",
-       "+---------------+----------------+-----------------------+\n",
-       "| info.only_het | info.AS_VQSLOD | info.inbreeding_coeff |\n",
-       "+---------------+----------------+-----------------------+\n",
-       "|          bool |        float64 |               float64 |\n",
-       "+---------------+----------------+-----------------------+\n",
-       "|         False |       7.50e+00 |             -1.03e-05 |\n",
-       "|         False |       5.00e+00 |             -6.84e-07 |\n",
-       "|         False |       5.73e+00 |             -6.84e-07 |\n",
-       "|         False |       2.35e+00 |             -6.84e-07 |\n",
-       "|         False |       8.87e+00 |              6.55e-03 |\n",
-       "+---------------+----------------+-----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| info.vrs.VRS_Allele_IDs                                                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.OXPDvbuulFZ9CSk4Xt... |\n",
-       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.4qhkDG1qrfqiD2TeOA... |\n",
-       "| [\"ga4gh:VA.uv4czuJ2nBK7lcR9wkK2XxHxMSui2-ME\",\"ga4gh:VA.bA0gzyCzudiyDon8QV... |\n",
-       "| [\"ga4gh:VA.euCgAx3O0dYWO86rUZqEKUoRwnhnhr-k\",\"ga4gh:VA.XQ_C7TKjlSKQw_ABwx... |\n",
-       "| [\"ga4gh:VA.8tDWBt70tPv5g5-MDqwnSl1-pElrIl1E\",\"ga4gh:VA.f3fUPUzazutlUq0Gul... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| array          | array          | array          |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"A\"]           |\n",
-       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"T\"]           |\n",
-       "| [113410738,113410738] | [113410739,113410739] | [\"G\",\"A\"]           |\n",
-       "| [113410750,113410750] | [113410751,113410751] | [\"G\",\"A\"]           |\n",
-       "| [113410753,113410753] | [113410754,113410754] | [\"C\",\"T\"]           |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| str               |     int32 | str    | str                            |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| \"G/A\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tA\t.\t.\tGT\" |\n",
-       "| \"G/T\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tT\t.\t.\tGT\" |\n",
-       "| \"G/A\"             | 113410739 | \".\"    | \"chr11\t113410739\t.\tG\tA\t.\t.\tGT\" |\n",
-       "| \"G/A\"             | 113410751 | \".\"    | \"chr11\t113410751\t.\tG\tA\t.\t.\tGT\" |\n",
-       "| \"C/T\"             | 113410754 | \".\"    | \"chr11\t113410754\t.\tC\tT\t.\t.\tGT\" |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.intergenic_consequences                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, impact: st... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------+\n",
-       "| vep.most_severe_consequence |\n",
-       "+-----------------------------+\n",
-       "| str                         |\n",
-       "+-----------------------------+\n",
-       "| \"synonymous_variant\"        |\n",
-       "| \"synonymous_variant\"        |\n",
-       "| \"synonymous_variant\"        |\n",
-       "| \"synonymous_variant\"        |\n",
-       "| \"synonymous_variant\"        |\n",
-       "+-----------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.motif_feature_consequences                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, high_inf_p... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.regulatory_feature_consequences                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.bin_freq             |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+--------------------------------------------------------+\n",
-       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
-       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
-       "| [0,0,0,0,9,0,68,0,314320,0,24,0,416519,0,0,0,0,0,0,1]  |\n",
-       "| [0,0,0,0,1,0,28,0,314363,0,2,0,416552,0,0,0,0,0,0,1]   |\n",
-       "| [0,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574] |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "| [0,0,55,309,11275,107353,345050,189133,36332,5605,2379,2755,3073,2932,295... |\n",
-       "| [0,0,18,272,11233,107401,345096,189157,36337,5604,2379,2755,3073,2932,295... |\n",
-       "| [0,0,16,266,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,297... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                       7685 |\n",
-       "|                                       7685 |\n",
-       "|                                       7683 |\n",
-       "|                                       7683 |\n",
-       "|                                       7737 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq  |\n",
-       "+---------------------------------------------+\n",
-       "| array                                |\n",
-       "+---------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574] |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq             |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+--------------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27] |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          1 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                        207 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq          |\n",
-       "+-----------------------------------------------------+\n",
-       "| array                                        |\n",
-       "+-----------------------------------------------------+\n",
-       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]           |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]           |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]           |\n",
-       "| [0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0] |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq         |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+--------------------------------------------------------+\n",
-       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
-       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
-       "| [5,0,0,0,10,0,68,0,314320,0,24,0,416519,0,0,0,0,0,0,1] |\n",
-       "| [0,0,0,0,1,0,28,0,314363,0,2,0,416552,0,0,0,0,0,0,1]   |\n",
-       "| [1,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574] |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
-       "| [0,1,56,312,11275,107353,345051,189133,36332,5605,2379,2755,3073,2932,295... |\n",
-       "| [0,0,18,272,11233,107401,345096,189157,36337,5604,2379,2755,3073,2932,295... |\n",
-       "| [0,0,16,267,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,297... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                           7685 |\n",
-       "|                                           7685 |\n",
-       "|                                           7683 |\n",
-       "|                                           7683 |\n",
-       "|                                           7737 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15]     |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574]    |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq         |\n",
-       "+--------------------------------------------------------+\n",
-       "| array                                           |\n",
-       "+--------------------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
-       "| [0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27] |\n",
-       "+--------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              1 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                            207 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq      |\n",
-       "+-----------------------------------------------------+\n",
-       "| array                                        |\n",
-       "+-----------------------------------------------------+\n",
-       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]          |\n",
-       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]           |\n",
-       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]           |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]           |\n",
-       "| [0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0] |\n",
-       "+-----------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,1,2,4,2,4,1,0,0]                      |\n",
-       "| [0,0,0,0,0,1,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [3,2,30,42,65,52,79,55,7,3]                |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                          15 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          3 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,1,0,0,0,1,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                      int64 |                         float32 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        3.44e+00 |\n",
-       "|                                          0 |                        2.96e+00 |\n",
-       "|                                          0 |                        1.03e+01 |\n",
-       "|                                          0 |                        1.20e+01 |\n",
-       "|                                          0 |                        9.48e+00 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                            2.28e-01 |                             NA |\n",
-       "|                            1.86e-01 |                             NA |\n",
-       "|                            8.89e-01 |                             NA |\n",
-       "|                            1.03e+00 |                             NA |\n",
-       "|                            8.09e-01 |                             NA |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                 1.00e-02 |                   -2.55e-01 |\n",
-       "|                                 3.00e-02 |                   -2.55e-01 |\n",
-       "|                                 0.00e+00 |                    3.26e+00 |\n",
-       "|                                 0.00e+00 |                    8.67e+00 |\n",
-       "|                                 1.00e-02 |                    5.82e+00 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The total number of synonymous variants passing filters in DRD2 is:  238\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "var_ht = filter_by_consequence_category(synonymous=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
@@ -4881,1079 +636,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "4141ccb3",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
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vrs
vep
vqsr_results
region_flags
allele_info
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dp_hist_all
gq_hist_alt
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ab_hist_alt
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AS_pab_max
AS_QUALapprox
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AS_SOR
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singleton
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omni
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allele_string
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id
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most_severe_consequence
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regulatory_feature_consequences
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start
strand
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AS_VQSLOD
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n_smaller
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revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
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A,"A"),(1,NA,NA,"protein_coding",1,NA,1751,1751,NA,NA,NA,["3_prime_UTR_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.*65C>T",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,NA,NA,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"A"),(1,NA,NA,"protein_coding",NA,NA,1664,1664,NA,NA,NA,["3_prime_UTR_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.*65C>T",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"A"),(1,NA,NA,"protein_coding",NA,NA,1479,1479,NA,NA,NA,["3_prime_UTR_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.*65C>T",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"A")]"SNV"5.24e+00"AS_MQ"FalseFalseFalseFalseFalseNATrueFalse"multi-snv"2False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,4139,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,22477,65025,118275,313361,178106,20388,3276,1054,693,645,582,508,355,259,173,113,79,64]0265[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]01[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][653,0,1690,2,6865,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][264,3602,22767,65834,118294,313419,178113,20399,3282,1056,696,645,582,508,355,259,173,113,79,64]0265[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]01[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]006.25e+004.76e-01NA0.00e+002.10e-011.62e+00NANA
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+-----------------+------------+\n",
-       "| locus           | alleles    |\n",
-       "+-----------------+------------+\n",
-       "| locus   | array |\n",
-       "+-----------------+------------+\n",
-       "| chr11:113410657 | [\"C\",\"T\"]  |\n",
-       "| chr11:113410658 | [\"G\",\"A\"]  |\n",
-       "| chr11:113410658 | [\"G\",\"T\"]  |\n",
-       "| chr11:113410660 | [\"A\",\"G\"]  |\n",
-       "| chr11:113410662 | [\"G\",\"A\"]  |\n",
-       "+-----------------+------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| freq                                                                         |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array    |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(18,1.25e-05,1445518,0),(24,1.64e-05,1461322,0),(1,3.02e-05,33096,0),(0,... |\n",
-       "| [(23,1.59e-05,1448166,0),(23,1.57e-05,1461456,0),(0,0.00e+00,33180,0),(0,... |\n",
-       "| [(94,6.49e-05,1448162,0),(105,7.18e-05,1461456,0),(24,7.23e-04,33180,0),(... |\n",
-       "| [(1,6.89e-07,1450438,0),(2,1.37e-06,1461588,0),(0,0.00e+00,33208,0),(0,0.... |\n",
-       "| [(2,1.38e-06,1451396,0),(2,1.37e-06,1461538,0),(0,0.00e+00,33252,0),(1,2.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+\n",
-       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
-       "+------------------+------------------+------------------+\n",
-       "|            int32 |          float64 |            int32 |\n",
-       "+------------------+------------------+------------------+\n",
-       "|                4 |         1.01e-04 |            39606 |\n",
-       "|                3 |         7.57e-05 |            39630 |\n",
-       "|               24 |         7.23e-04 |            33180 |\n",
-       "|                1 |         9.07e-07 |          1103066 |\n",
-       "|                1 |         2.24e-05 |            44696 |\n",
-       "+------------------+------------------+------------------+\n",
-       "\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                          int64 | str                   |             int32 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "|                              0 | \"eas\"                 |                 4 |\n",
-       "|                              0 | \"eas\"                 |                10 |\n",
-       "|                              0 | \"afr\"                 |                15 |\n",
-       "|                              0 | \"nfe\"                 |                 1 |\n",
-       "|                              0 | \"amr\"                 |                 1 |\n",
-       "+--------------------------------+-----------------------+-------------------+\n",
-       "\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|           float64 |             int32 |                           int64 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "|          1.11e-04 |             36050 |                               0 |\n",
-       "|          2.86e-05 |            349848 |                               0 |\n",
-       "|          8.50e-04 |             17650 |                               0 |\n",
-       "|          2.86e-06 |            349920 |                               0 |\n",
-       "|          2.29e-05 |             43728 |                               0 |\n",
-       "+-------------------+-------------------+---------------------------------+\n",
-       "\n",
-       "+------------------------+\n",
-       "| grpmax.non_ukb.gen_anc |\n",
-       "+------------------------+\n",
-       "| str                    |\n",
-       "+------------------------+\n",
-       "| \"eas\"                  |\n",
-       "| \"nfe\"                  |\n",
-       "| \"afr\"                  |\n",
-       "| \"nfe\"                  |\n",
-       "| \"amr\"                  |\n",
-       "+------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| faf                                                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [(7.78e-06,6.42e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(3.35e-05,1.... |\n",
-       "| [(1.06e-05,8.83e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(2.01e-05,1.... |\n",
-       "| [(5.41e-05,5.02e-05),(4.99e-04,4.24e-04),(2.99e-05,1.81e-05),(0.00e+00,0.... |\n",
-       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                3.35e-05 | \"eas\"                           |\n",
-       "|                2.01e-05 | \"eas\"                           |\n",
-       "|                4.99e-04 | \"afr\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------+---------------------------------+\n",
-       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                 float64 | str                             |\n",
-       "+-------------------------+---------------------------------+\n",
-       "|                1.99e-05 | \"eas\"                           |\n",
-       "|                1.06e-05 | \"eas\"                           |\n",
-       "|                4.24e-04 | \"afr\"                           |\n",
-       "|                      NA | NA                              |\n",
-       "|                      NA | NA                              |\n",
-       "+-------------------------+---------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+\n",
-       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                  float64 | str                              |\n",
-       "+--------------------------+----------------------------------+\n",
-       "|                 3.78e-05 | \"eas\"                            |\n",
-       "|                 1.54e-05 | \"nfe\"                            |\n",
-       "|                 5.23e-04 | \"afr\"                            |\n",
-       "|                       NA | NA                               |\n",
-       "|                       NA | NA                               |\n",
-       "+--------------------------+----------------------------------+\n",
-       "\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                  float64 | str                              |   int32 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "|                 2.24e-05 | \"eas\"                            |       3 |\n",
-       "|                 1.15e-05 | \"nfe\"                            |       1 |\n",
-       "|                 4.23e-04 | \"afr\"                            |       2 |\n",
-       "|                       NA | NA                               |       2 |\n",
-       "|                       NA | NA                               |       1 |\n",
-       "+--------------------------+----------------------------------+---------+\n",
-       "\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "|      bool | set         | set |  float64 |  float64 |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "|      True | {\"rs200559334\"}  | {}       | 0.00e+00 | 6.00e+01 |\n",
-       "|      True | {\"rs201475538\"}  | {}       | 8.65e-01 | 6.00e+01 |\n",
-       "|      True | {\"rs201475538\"}  | {}       | 8.65e-01 | 6.00e+01 |\n",
-       "|      True | {\"rs1950760213\"} | {}       | 0.00e+00 | 6.00e+01 |\n",
-       "|      True | NA               | {}       | 5.51e-01 | 6.00e+01 |\n",
-       "+-----------+------------------+----------+----------+----------+\n",
-       "\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|        float64 |           int64 |  float64 |             float64 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "|       0.00e+00 |           12992 | 1.27e+01 |            0.00e+00 |\n",
-       "|       0.00e+00 |          142805 | 1.36e+01 |           -1.80e-02 |\n",
-       "|       0.00e+00 |          142805 | 1.36e+01 |           -1.80e-02 |\n",
-       "|       0.00e+00 |            3619 | 1.78e+01 |            6.74e-01 |\n",
-       "|       0.00e+00 |            3070 | 9.27e+00 |            0.00e+00 |\n",
-       "+----------------+-----------------+----------+---------------------+\n",
-       "\n",
-       "+---------------------+----------+------------+------------+------------+\n",
-       "| info.SB             | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
-       "+---------------------+----------+------------+------------+------------+\n",
-       "| array        |  float64 |      int32 |    float64 |    float64 |\n",
-       "+---------------------+----------+------------+------------+------------+\n",
-       "| [543,53,386,37]     | 7.04e-01 |       1019 |   4.82e-16 |   6.00e+01 |\n",
-       "| [4798,567,4576,579] | 6.27e-01 |      10518 |   1.55e+00 |   6.00e+01 |\n",
-       "| [4798,567,4576,579] | 6.27e-01 |      10518 |   0.00e+00 |   6.00e+01 |\n",
-       "| [83,21,79,20]       | 6.91e-01 |        203 |   4.82e-16 |   6.00e+01 |\n",
-       "| [174,36,99,22]      | 6.12e-01 |        331 |   1.21e+00 |   6.00e+01 |\n",
-       "+---------------------+----------+------------+------------+------------+\n",
-       "\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|           float64 |         float64 |              int64 |    float64 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "|          0.00e+00 |        1.00e+00 |              12891 |   1.32e+01 |\n",
-       "|          0.00e+00 |        1.00e+00 |              19189 |   1.44e+01 |\n",
-       "|          0.00e+00 |        1.00e+00 |             123616 |   1.35e+01 |\n",
-       "|          0.00e+00 |        4.53e-01 |               3591 |   1.98e+01 |\n",
-       "|          0.00e+00 |        8.45e-01 |               2972 |   1.38e+01 |\n",
-       "+-------------------+-----------------+--------------------+------------+\n",
-       "\n",
-       "+------------------------+---------------------+-------------+---------------+\n",
-       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE    | info.AS_SOR | info.AS_VarDP |\n",
-       "+------------------------+---------------------+-------------+---------------+\n",
-       "|                float64 | array        |     float64 |         int32 |\n",
-       "+------------------------+---------------------+-------------+---------------+\n",
-       "|              -3.90e-02 | [543,53,379,36]     |    7.13e-01 |           976 |\n",
-       "|               1.34e-01 | [4798,567,588,57]   |    8.94e-01 |          1334 |\n",
-       "|              -2.10e-02 | [4798,567,3988,522] |    5.96e-01 |          9184 |\n",
-       "|               9.51e-01 | [83,21,77,19]       |    7.15e-01 |           181 |\n",
-       "|               1.49e+00 | [174,36,87,20]      |    5.79e-01 |           216 |\n",
-       "+------------------------+---------------------+-------------+---------------+\n",
-       "\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|           bool |                       bool |                   bool |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                         NA |                     NA |\n",
-       "|          False |                      False |                  False |\n",
-       "|          False |                         NA |                     NA |\n",
-       "+----------------+----------------------------+------------------------+\n",
-       "\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|      bool |       bool |             bool |          bool |        float64 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "|     False |      False |            False |         False |       9.14e+00 |\n",
-       "|     False |      False |            False |         False |       6.96e+00 |\n",
-       "|     False |      False |            False |         False |       9.35e+00 |\n",
-       "|     False |      False |            False |         False |       4.34e+00 |\n",
-       "|     False |      False |            False |         False |       5.24e+00 |\n",
-       "+-----------+------------+------------------+---------------+----------------+\n",
-       "\n",
-       "+-----------------------+\n",
-       "| info.inbreeding_coeff |\n",
-       "+-----------------------+\n",
-       "|               float64 |\n",
-       "+-----------------------+\n",
-       "|             -1.64e-05 |\n",
-       "|             -1.57e-05 |\n",
-       "|             -7.19e-05 |\n",
-       "|             -1.37e-06 |\n",
-       "|             -1.37e-06 |\n",
-       "+-----------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| info.vrs.VRS_Allele_IDs                                                      |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [\"ga4gh:VA.xwHxL6yO3I874HqhfGar1ZIaOp66ifsx\",\"ga4gh:VA.eglRJIN5-izMH1peMx... |\n",
-       "| [\"ga4gh:VA.3n-gYs3xsspnxwI91EMCEDsIcrj9bIqO\",\"ga4gh:VA.9Y9GDmOWCTqMks8dsN... |\n",
-       "| [\"ga4gh:VA.3n-gYs3xsspnxwI91EMCEDsIcrj9bIqO\",\"ga4gh:VA.R13f7bcCv-WGRkCM6W... |\n",
-       "| [\"ga4gh:VA.MHTiWiUO_hsQ_FAaIagdomN6DdnpjqRm\",\"ga4gh:VA.D8-MF1xUzSMIKncRc7... |\n",
-       "| [\"ga4gh:VA.oDr3967BFZ6uu2LJEc9_RVWIOnNqtSF-\",\"ga4gh:VA.N81rCvIHb-9PjhcHLL... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| array          | array          | array          |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "| [113410656,113410656] | [113410657,113410657] | [\"C\",\"T\"]           |\n",
-       "| [113410657,113410657] | [113410658,113410658] | [\"G\",\"A\"]           |\n",
-       "| [113410657,113410657] | [113410658,113410658] | [\"G\",\"T\"]           |\n",
-       "| [113410659,113410659] | [113410660,113410660] | [\"A\",\"G\"]           |\n",
-       "| [113410661,113410661] | [113410662,113410662] | [\"G\",\"A\"]           |\n",
-       "+-----------------------+-----------------------+---------------------+\n",
-       "\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| str               |     int32 | str    | str                            |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "| \"C/T\"             | 113410657 | \".\"    | \"chr11\t113410657\t.\tC\tT\t.\t.\tGT\" |\n",
-       "| \"G/A\"             | 113410658 | \".\"    | \"chr11\t113410658\t.\tG\tA\t.\t.\tGT\" |\n",
-       "| \"G/T\"             | 113410658 | \".\"    | \"chr11\t113410658\t.\tG\tT\t.\t.\tGT\" |\n",
-       "| \"A/G\"             | 113410660 | \".\"    | \"chr11\t113410660\t.\tA\tG\t.\t.\tGT\" |\n",
-       "| \"G/A\"             | 113410662 | \".\"    | \"chr11\t113410662\t.\tG\tA\t.\t.\tGT\" |\n",
-       "+-------------------+-----------+--------+--------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.intergenic_consequences                                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, impact: st... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------+\n",
-       "| vep.most_severe_consequence |\n",
-       "+-----------------------------+\n",
-       "| str                         |\n",
-       "+-----------------------------+\n",
-       "| \"3_prime_UTR_variant\"       |\n",
-       "| \"3_prime_UTR_variant\"       |\n",
-       "| \"3_prime_UTR_variant\"       |\n",
-       "| \"3_prime_UTR_variant\"       |\n",
-       "| \"3_prime_UTR_variant\"       |\n",
-       "+-----------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.motif_feature_consequences                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array, high_inf_p... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "| NA                                                                           |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| vep.regulatory_feature_consequences                                          |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.bin_freq                       |\n",
-       "+------------------------------------------------------------------+\n",
-       "| array                                                     |\n",
-       "+------------------------------------------------------------------+\n",
-       "| [0,0,0,0,4430,0,32721,0,357851,0,60423,0,267316,0,0,0,0,0,0,18]  |\n",
-       "| [0,0,0,0,4183,2,30033,1,354765,0,58262,0,276721,0,0,0,1,0,0,115] |\n",
-       "| [0,0,0,0,4183,0,30033,1,354765,0,58262,0,276721,0,0,0,1,0,0,115] |\n",
-       "| [0,0,0,0,3304,0,26636,0,351846,0,54948,0,288484,0,0,0,0,0,0,1]   |\n",
-       "| [0,0,0,0,4139,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]   |\n",
-       "+------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0,0,29987,74977,120278,300405,170342,18935,3117,1021,684,644,577,507,353... |\n",
-       "| [0,0,26988,70993,120130,305542,173013,19471,3177,1036,688,647,582,509,357... |\n",
-       "| [0,0,26986,70993,120130,305542,173013,19471,3177,1036,688,647,582,509,357... |\n",
-       "| [0,0,24253,67281,120581,309738,175490,19882,3222,1041,692,644,582,508,354... |\n",
-       "| [0,0,22477,65025,118275,313361,178106,20388,3276,1054,693,645,582,508,355... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                        252 |\n",
-       "|                                        265 |\n",
-       "|                                        265 |\n",
-       "|                                        264 |\n",
-       "|                                        265 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18] |\n",
-       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,21] |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,94] |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.bin_freq    |\n",
-       "+-----------------------------------------------+\n",
-       "| array                                  |\n",
-       "+-----------------------------------------------+\n",
-       "| [0,0,0,2,3,3,2,1,0,1,1,0,1,0,0,1,1,0,0,1]     |\n",
-       "| [0,0,1,2,4,3,4,1,1,0,0,1,0,1,0,0,0,2,0,1]     |\n",
-       "| [0,0,2,3,11,13,14,10,8,9,1,3,2,0,4,0,0,1,1,1] |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
-       "+-----------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          1 |\n",
-       "|                                          2 |\n",
-       "|                                         11 |\n",
-       "|                                          1 |\n",
-       "|                                          1 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.bin_freq    |\n",
-       "+-----------------------------------------------+\n",
-       "| array                                  |\n",
-       "+-----------------------------------------------+\n",
-       "| [0,0,0,0,0,0,2,0,4,4,4,3,1,0,0,0,0,0,0,0]     |\n",
-       "| [0,0,0,0,2,0,1,0,3,3,8,5,0,1,0,0,0,0,0,0]     |\n",
-       "| [0,0,0,0,0,0,6,5,19,14,25,17,6,1,1,0,0,0,0,0] |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]     |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]     |\n",
-       "+-----------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-----------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                        |\n",
-       "+-----------------------------------------------------------------------+\n",
-       "| array                                                          |\n",
-       "+-----------------------------------------------------------------------+\n",
-       "| [979,2,2487,1,8860,1,32722,0,357851,0,60423,0,267317,0,0,0,0,0,0,18]  |\n",
-       "| [886,4,2000,0,7936,2,30033,1,354765,1,58262,0,276721,0,0,0,1,0,0,116] |\n",
-       "| [886,4,2000,0,7936,2,30033,1,354765,1,58262,0,276721,0,0,0,1,0,0,116] |\n",
-       "| [684,0,1637,0,6556,2,26636,0,351846,0,54948,0,288484,0,0,0,0,0,0,1]   |\n",
-       "| [653,0,1690,2,6865,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]   |\n",
-       "+-----------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                                 |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [415,5663,30415,76214,120310,300505,170348,18947,3122,1023,686,644,577,50... |\n",
-       "| [362,4790,27388,71922,120182,305624,173020,19481,3184,1039,690,648,582,50... |\n",
-       "| [362,4790,27388,71922,120182,305624,173020,19481,3184,1039,690,648,582,50... |\n",
-       "| [296,4122,24622,67964,120609,309787,175498,19891,3228,1043,694,645,582,50... |\n",
-       "| [264,3602,22767,65834,118294,313419,178113,20399,3282,1056,696,645,582,50... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                            252 |\n",
-       "|                                            265 |\n",
-       "|                                            265 |\n",
-       "|                                            264 |\n",
-       "|                                            265 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [1,2,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,18]     |\n",
-       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,21]     |\n",
-       "| [2,4,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,95]     |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,0,1,3,6,4,2,1,0,1,1,0,1,0,0,1,1,0,0,1]      |\n",
-       "| [0,0,1,2,4,3,4,1,1,0,0,1,0,1,0,0,0,2,0,1]      |\n",
-       "| [0,2,4,4,17,13,14,10,8,9,1,3,2,0,4,0,0,1,1,1]  |\n",
-       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              1 |\n",
-       "|                                              2 |\n",
-       "|                                             11 |\n",
-       "|                                              1 |\n",
-       "|                                              1 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
-       "+------------------------------------------------+\n",
-       "| array                                   |\n",
-       "+------------------------------------------------+\n",
-       "| [0,1,3,2,0,0,2,0,4,4,4,3,1,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,2,0,1,0,3,3,8,5,0,1,0,0,0,0,0,0]      |\n",
-       "| [0,2,7,0,0,1,6,5,19,14,26,17,6,1,1,0,0,0,0,0]  |\n",
-       "| [0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]      |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+-------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
-       "+-------------------------------------------------+\n",
-       "|                                           int64 |\n",
-       "+-------------------------------------------------+\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "|                                               0 |\n",
-       "+-------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------+\n",
-       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
-       "+------------------------------------------------+\n",
-       "|                                          int64 |\n",
-       "+------------------------------------------------+\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "|                                              0 |\n",
-       "+------------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [1,1,0,2,5,2,1,0,0,0]                      |\n",
-       "| [0,0,1,1,5,3,2,0,0,0]                      |\n",
-       "| [0,1,4,4,2,5,0,1,4,1]                      |\n",
-       "| [1,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,1,0,1,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           2 |\n",
-       "|                                           0 |\n",
-       "|                                           1 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_het.n_larger |\n",
-       "+--------------------------------------------+\n",
-       "|                                      int64 |\n",
-       "+--------------------------------------------+\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "|                                          0 |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| array                                                               |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
-       "+------------------------------------------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
-       "+--------------------------------------------+\n",
-       "| array                               |\n",
-       "+--------------------------------------------+\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
-       "+--------------------------------------------+\n",
-       "\n",
-       "+---------------------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
-       "+---------------------------------------------+\n",
-       "|                                       int64 |\n",
-       "+---------------------------------------------+\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "|                                           0 |\n",
-       "+---------------------------------------------+\n",
-       "\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                      int64 |                         float32 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "|                                          0 |                        3.61e+00 |\n",
-       "|                                          0 |                        2.90e+00 |\n",
-       "|                                          0 |                        2.38e+00 |\n",
-       "|                                          0 |                        1.22e+00 |\n",
-       "|                                          0 |                        6.25e+00 |\n",
-       "+--------------------------------------------+---------------------------------+\n",
-       "\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                             float32 |                        float64 |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "|                            2.42e-01 |                             NA |\n",
-       "|                            1.80e-01 |                             NA |\n",
-       "|                            1.31e-01 |                             NA |\n",
-       "|                           -1.85e-02 |                             NA |\n",
-       "|                            4.76e-01 |                             NA |\n",
-       "+-------------------------------------+--------------------------------+\n",
-       "\n",
-       "+--------------------------------------+\n",
-       "| in_silico_predictors.spliceai_ds_max |\n",
-       "+--------------------------------------+\n",
-       "|                              float32 |\n",
-       "+--------------------------------------+\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "|                             0.00e+00 |\n",
-       "+--------------------------------------+\n",
-       "\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                  float64 |                     float64 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "|                                 1.20e-01 |                   -4.68e-01 |\n",
-       "|                                 5.00e-02 |                    7.10e-02 |\n",
-       "|                                 3.00e-02 |                    7.10e-02 |\n",
-       "|                                 4.00e-02 |                   -1.27e+00 |\n",
-       "|                                 2.10e-01 |                    1.62e+00 |\n",
-       "+------------------------------------------+-----------------------------+\n",
-       "\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                       float64 |                           float64 |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "|                            NA |                                NA |\n",
-       "+-------------------------------+-----------------------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The total number of other variants passing filters in DRD2 is:  783\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "var_ht = filter_by_consequence_category(other=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
@@ -5972,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "a25ddd1b",
    "metadata": {},
    "outputs": [],
@@ -5990,52 +676,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "4f78166f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
afr
locus
alleles
filters
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64
chr11:113410736["G","A"]{}00.00e+00334800
chr11:113410736["G","T"]{}00.00e+00334800
chr11:113410739["G","A"]{}00.00e+00334780
chr11:113410751["G","A"]{}00.00e+00334800
chr11:113410754["C","T"]{}123.58e-04334801
showing top 5 rows
\n"
-      ],
-      "text/plain": [
-       "+-----------------+------------+----------+--------+----------+--------+\n",
-       "| locus           | alleles    | filters  | afr.AC |   afr.AF | afr.AN |\n",
-       "+-----------------+------------+----------+--------+----------+--------+\n",
-       "| locus   | array | set |  int32 |  float64 |  int32 |\n",
-       "+-----------------+------------+----------+--------+----------+--------+\n",
-       "| chr11:113410736 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
-       "| chr11:113410736 | [\"G\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
-       "| chr11:113410739 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33478 |\n",
-       "| chr11:113410751 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
-       "| chr11:113410754 | [\"C\",\"T\"]  | {}       |     12 | 3.58e-04 |  33480 |\n",
-       "+-----------------+------------+----------+--------+----------+--------+\n",
-       "\n",
-       "+----------------------+\n",
-       "| afr.homozygote_count |\n",
-       "+----------------------+\n",
-       "|                int64 |\n",
-       "+----------------------+\n",
-       "|                    0 |\n",
-       "|                    0 |\n",
-       "|                    0 |\n",
-       "|                    0 |\n",
-       "|                    1 |\n",
-       "+----------------------+\n",
-       "showing top 5 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "var_ht = get_ancestry_callstats(gen_ancs='afr', ht=drd2_synonymous_ht)\n",
     "var_ht.show(5)"
@@ -6051,118 +695,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "e3a07848",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
afr
amr
eas
mid
nfe
sas
locus
alleles
filters
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64
chr11:113410736["G","A"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+0057620151.35e-051112010000.00e+00862540
chr11:113410736["G","T"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005762018.99e-071112010000.00e+00862540
chr11:113410739["G","A"]{}00.00e+0033478000.00e+0044724000.00e+0039700000.00e+005764000.00e+001112010000.00e+00862560
chr11:113410751["G","A"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005768018.99e-071112012000.00e+00862580
chr11:113410754["C","T"]{}123.58e-04334801439.61e-0444724000.00e+0039700081.39e-03576803282.95e-041112012000.00e+00862580
chr11:113410757["G","A"]{}00.00e+0033480000.00e+0044724012.52e-0539700000.00e+005766000.00e+001112012000.00e+00862580
chr11:113410757["G","T"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005766000.00e+001112012033.48e-05862580
chr11:113410763["C","T"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005768000.00e+001112010011.16e-05862580
chr11:113410769["G","A"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005768021.80e-061112012000.00e+00862580
chr11:113410775["G","A"]{}00.00e+0033480000.00e+0044724012.52e-0539700000.00e+005768018.99e-071112010000.00e+00862580
showing top 10 rows
\n"
-      ],
-      "text/plain": [
-       "+-----------------+------------+----------+--------+----------+--------+\n",
-       "| locus           | alleles    | filters  | afr.AC |   afr.AF | afr.AN |\n",
-       "+-----------------+------------+----------+--------+----------+--------+\n",
-       "| locus   | array | set |  int32 |  float64 |  int32 |\n",
-       "+-----------------+------------+----------+--------+----------+--------+\n",
-       "| chr11:113410736 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
-       "| chr11:113410736 | [\"G\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
-       "| chr11:113410739 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33478 |\n",
-       "| chr11:113410751 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
-       "| chr11:113410754 | [\"C\",\"T\"]  | {}       |     12 | 3.58e-04 |  33480 |\n",
-       "| chr11:113410757 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
-       "| chr11:113410757 | [\"G\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
-       "| chr11:113410763 | [\"C\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
-       "| chr11:113410769 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
-       "| chr11:113410775 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
-       "+-----------------+------------+----------+--------+----------+--------+\n",
-       "\n",
-       "+----------------------+--------+----------+--------+----------------------+\n",
-       "| afr.homozygote_count | amr.AC |   amr.AF | amr.AN | amr.homozygote_count |\n",
-       "+----------------------+--------+----------+--------+----------------------+\n",
-       "|                int64 |  int32 |  float64 |  int32 |                int64 |\n",
-       "+----------------------+--------+----------+--------+----------------------+\n",
-       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
-       "|                    1 |     43 | 9.61e-04 |  44724 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
-       "+----------------------+--------+----------+--------+----------------------+\n",
-       "\n",
-       "+--------+----------+--------+----------------------+--------+----------+\n",
-       "| eas.AC |   eas.AF | eas.AN | eas.homozygote_count | mid.AC |   mid.AF |\n",
-       "+--------+----------+--------+----------------------+--------+----------+\n",
-       "|  int32 |  float64 |  int32 |                int64 |  int32 |  float64 |\n",
-       "+--------+----------+--------+----------------------+--------+----------+\n",
-       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
-       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
-       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
-       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
-       "|      0 | 0.00e+00 |  39700 |                    0 |      8 | 1.39e-03 |\n",
-       "|      1 | 2.52e-05 |  39700 |                    0 |      0 | 0.00e+00 |\n",
-       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
-       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
-       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
-       "|      1 | 2.52e-05 |  39700 |                    0 |      0 | 0.00e+00 |\n",
-       "+--------+----------+--------+----------------------+--------+----------+\n",
-       "\n",
-       "+--------+----------------------+--------+----------+---------+\n",
-       "| mid.AN | mid.homozygote_count | nfe.AC |   nfe.AF |  nfe.AN |\n",
-       "+--------+----------------------+--------+----------+---------+\n",
-       "|  int32 |                int64 |  int32 |  float64 |   int32 |\n",
-       "+--------+----------------------+--------+----------+---------+\n",
-       "|   5762 |                    0 |     15 | 1.35e-05 | 1112010 |\n",
-       "|   5762 |                    0 |      1 | 8.99e-07 | 1112010 |\n",
-       "|   5764 |                    0 |      0 | 0.00e+00 | 1112010 |\n",
-       "|   5768 |                    0 |      1 | 8.99e-07 | 1112012 |\n",
-       "|   5768 |                    0 |    328 | 2.95e-04 | 1112012 |\n",
-       "|   5766 |                    0 |      0 | 0.00e+00 | 1112012 |\n",
-       "|   5766 |                    0 |      0 | 0.00e+00 | 1112012 |\n",
-       "|   5768 |                    0 |      0 | 0.00e+00 | 1112010 |\n",
-       "|   5768 |                    0 |      2 | 1.80e-06 | 1112012 |\n",
-       "|   5768 |                    0 |      1 | 8.99e-07 | 1112010 |\n",
-       "+--------+----------------------+--------+----------+---------+\n",
-       "\n",
-       "+----------------------+--------+----------+--------+----------------------+\n",
-       "| nfe.homozygote_count | sas.AC |   sas.AF | sas.AN | sas.homozygote_count |\n",
-       "+----------------------+--------+----------+--------+----------------------+\n",
-       "|                int64 |  int32 |  float64 |  int32 |                int64 |\n",
-       "+----------------------+--------+----------+--------+----------------------+\n",
-       "|                    0 |      0 | 0.00e+00 |  86254 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  86254 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  86256 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
-       "|                    0 |      3 | 3.48e-05 |  86258 |                    0 |\n",
-       "|                    0 |      1 | 1.16e-05 |  86258 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
-       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
-       "+----------------------+--------+----------+--------+----------------------+\n",
-       "showing top 10 rows"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "var_ht = get_ancestry_callstats(gen_ancs=['afr', 'amr', 'eas', 'mid', 'nfe', 'sas'], ht=drd2_synonymous_ht)\n",
     "var_ht.show()"
@@ -6186,39 +722,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "4845be1d-d4c0-4b83-9e92-bd72379b8a99",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "
afr
locus
alleles
filters
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64
chr22:15528692["C","G"]{}6351.90e-02333806
"
-      ],
-      "text/plain": [
-       "+----------------+------------+----------+--------+----------+--------+\n",
-       "| locus          | alleles    | filters  | afr.AC |   afr.AF | afr.AN |\n",
-       "+----------------+------------+----------+--------+----------+--------+\n",
-       "| locus  | array | set |  int32 |  float64 |  int32 |\n",
-       "+----------------+------------+----------+--------+----------+--------+\n",
-       "| chr22:15528692 | [\"C\",\"G\"]  | {}       |    635 | 1.90e-02 |  33380 |\n",
-       "+----------------+------------+----------+--------+----------+--------+\n",
-       "\n",
-       "+----------------------+\n",
-       "| afr.homozygote_count |\n",
-       "+----------------------+\n",
-       "|                int64 |\n",
-       "+----------------------+\n",
-       "|                    6 |\n",
-       "+----------------------+"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "var_ht = get_single_variant_ancestry_callstats(gen_ancs='AFR', contig='chr22', position=15528692, ref='C', alt='G')\n",
     "var_ht.show(5)"
@@ -6234,43 +741,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "bee28829",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-12-20 12:31:16.359 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "
afr
locus
alleles
filters
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64
"
-      ],
-      "text/plain": [
-       "+---------------+------------+----------+--------+---------+--------+\n",
-       "| locus         | alleles    | filters  | afr.AC |  afr.AF | afr.AN |\n",
-       "+---------------+------------+----------+--------+---------+--------+\n",
-       "| locus | array | set |  int32 | float64 |  int32 |\n",
-       "+---------------+------------+----------+--------+---------+--------+\n",
-       "+---------------+------------+----------+--------+---------+--------+\n",
-       "\n",
-       "+----------------------+\n",
-       "| afr.homozygote_count |\n",
-       "+----------------------+\n",
-       "|                int64 |\n",
-       "+----------------------+\n",
-       "+----------------------+"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "var_ht = get_single_variant_ancestry_callstats(gen_ancs='AFR', contig='chr22', position=15528692, ref='C', alt='A')\n",
     "var_ht.show(5)"
@@ -6293,7 +767,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.9"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/gnomad_toolbox/notebooks/needs_a_name.ipynb b/gnomad_toolbox/notebooks/needs_a_name.ipynb
deleted file mode 100644
index c63ea3a..0000000
--- a/gnomad_toolbox/notebooks/needs_a_name.ipynb
+++ /dev/null
@@ -1,746 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "8e609a46",
-   "metadata": {
-    "toc": true
-   },
-   "source": [
-    "
Table of Contents
\n",
-    "
"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e713032",
-   "metadata": {},
-   "source": [
-    "## Import modules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "e69953f7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-06T18:04:56.165634Z",
-     "start_time": "2024-12-06T18:04:55.603516Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "    \n",
-       "    
\n",
-       "        \n",
-       "        Loading BokehJS ...\n",
-       "    
\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "(function(root) {\n",
-       "  function now() {\n",
-       "    return new Date();\n",
-       "  }\n",
-       "\n",
-       "  const force = true;\n",
-       "\n",
-       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
-       "    root._bokeh_onload_callbacks = [];\n",
-       "    root._bokeh_is_loading = undefined;\n",
-       "  }\n",
-       "\n",
-       "const JS_MIME_TYPE = 'application/javascript';\n",
-       "  const HTML_MIME_TYPE = 'text/html';\n",
-       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
-       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
-       "\n",
-       "  /**\n",
-       "   * Render data to the DOM node\n",
-       "   */\n",
-       "  function render(props, node) {\n",
-       "    const script = document.createElement(\"script\");\n",
-       "    node.appendChild(script);\n",
-       "  }\n",
-       "\n",
-       "  /**\n",
-       "   * Handle when an output is cleared or removed\n",
-       "   */\n",
-       "  function handleClearOutput(event, handle) {\n",
-       "    function drop(id) {\n",
-       "      const view = Bokeh.index.get_by_id(id)\n",
-       "      if (view != null) {\n",
-       "        view.model.document.clear()\n",
-       "        Bokeh.index.delete(view)\n",
-       "      }\n",
-       "    }\n",
-       "\n",
-       "    const cell = handle.cell;\n",
-       "\n",
-       "    const id = cell.output_area._bokeh_element_id;\n",
-       "    const server_id = cell.output_area._bokeh_server_id;\n",
-       "\n",
-       "    // Clean up Bokeh references\n",
-       "    if (id != null) {\n",
-       "      drop(id)\n",
-       "    }\n",
-       "\n",
-       "    if (server_id !== undefined) {\n",
-       "      // Clean up Bokeh references\n",
-       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
-       "      cell.notebook.kernel.execute(cmd_clean, {\n",
-       "        iopub: {\n",
-       "          output: function(msg) {\n",
-       "            const id = msg.content.text.trim()\n",
-       "            drop(id)\n",
-       "          }\n",
-       "        }\n",
-       "      });\n",
-       "      // Destroy server and session\n",
-       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
-       "      cell.notebook.kernel.execute(cmd_destroy);\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  /**\n",
-       "   * Handle when a new output is added\n",
-       "   */\n",
-       "  function handleAddOutput(event, handle) {\n",
-       "    const output_area = handle.output_area;\n",
-       "    const output = handle.output;\n",
-       "\n",
-       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
-       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
-       "      return\n",
-       "    }\n",
-       "\n",
-       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
-       "\n",
-       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
-       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
-       "      // store reference to embed id on output_area\n",
-       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
-       "    }\n",
-       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
-       "      const bk_div = document.createElement(\"div\");\n",
-       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
-       "      const script_attrs = bk_div.children[0].attributes;\n",
-       "      for (let i = 0; i < script_attrs.length; i++) {\n",
-       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
-       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
-       "      }\n",
-       "      // store reference to server id on output_area\n",
-       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  function register_renderer(events, OutputArea) {\n",
-       "\n",
-       "    function append_mime(data, metadata, element) {\n",
-       "      // create a DOM node to render to\n",
-       "      const toinsert = this.create_output_subarea(\n",
-       "        metadata,\n",
-       "        CLASS_NAME,\n",
-       "        EXEC_MIME_TYPE\n",
-       "      );\n",
-       "      this.keyboard_manager.register_events(toinsert);\n",
-       "      // Render to node\n",
-       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
-       "      render(props, toinsert[toinsert.length - 1]);\n",
-       "      element.append(toinsert);\n",
-       "      return toinsert\n",
-       "    }\n",
-       "\n",
-       "    /* Handle when an output is cleared or removed */\n",
-       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
-       "    events.on('delete.Cell', handleClearOutput);\n",
-       "\n",
-       "    /* Handle when a new output is added */\n",
-       "    events.on('output_added.OutputArea', handleAddOutput);\n",
-       "\n",
-       "    /**\n",
-       "     * Register the mime type and append_mime function with output_area\n",
-       "     */\n",
-       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
-       "      /* Is output safe? */\n",
-       "      safe: true,\n",
-       "      /* Index of renderer in `output_area.display_order` */\n",
-       "      index: 0\n",
-       "    });\n",
-       "  }\n",
-       "\n",
-       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
-       "  if (root.Jupyter !== undefined) {\n",
-       "    const events = require('base/js/events');\n",
-       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
-       "\n",
-       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
-       "      register_renderer(events, OutputArea);\n",
-       "    }\n",
-       "  }\n",
-       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
-       "    root._bokeh_timeout = Date.now() + 5000;\n",
-       "    root._bokeh_failed_load = false;\n",
-       "  }\n",
-       "\n",
-       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
-       "     \"
\\n\"+\n",
-       "     \"
\\n\"+\n",
-       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
-       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
-       "     \"
\\n\"+\n",
-       "     \"
    \\n\"+\n",
-       "     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n",
-       "     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n",
-       "     \"
\\n\"+\n",
-       "     \"\\n\"+\n",
-       "     \"from bokeh.resources import INLINE\\n\"+\n",
-       "     \"output_notebook(resources=INLINE)\\n\"+\n",
-       "     \"\\n\"+\n",
-       "     \"
\"}};\n",
-       "\n",
-       "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"d7751d80-c376-4274-999c-135812562683\");\n",
-       "    if (el != null) {\n",
-       "      el.textContent = \"BokehJS is loading...\";\n",
-       "    }\n",
-       "    if (root.Bokeh !== undefined) {\n",
-       "      if (el != null) {\n",
-       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
-       "      }\n",
-       "    } else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(display_loaded, 100)\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  function run_callbacks() {\n",
-       "    try {\n",
-       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
-       "        if (callback != null)\n",
-       "          callback();\n",
-       "      });\n",
-       "    } finally {\n",
-       "      delete root._bokeh_onload_callbacks\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
-       "  }\n",
-       "\n",
-       "  function load_libs(css_urls, js_urls, callback) {\n",
-       "    if (css_urls == null) css_urls = [];\n",
-       "    if (js_urls == null) js_urls = [];\n",
-       "\n",
-       "    root._bokeh_onload_callbacks.push(callback);\n",
-       "    if (root._bokeh_is_loading > 0) {\n",
-       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
-       "      return null;\n",
-       "    }\n",
-       "    if (js_urls == null || js_urls.length === 0) {\n",
-       "      run_callbacks();\n",
-       "      return null;\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
-       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
-       "\n",
-       "    function on_load() {\n",
-       "      root._bokeh_is_loading--;\n",
-       "      if (root._bokeh_is_loading === 0) {\n",
-       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
-       "        run_callbacks()\n",
-       "      }\n",
-       "    }\n",
-       "\n",
-       "    function on_error(url) {\n",
-       "      console.error(\"failed to load \" + url);\n",
-       "    }\n",
-       "\n",
-       "    for (let i = 0; i < css_urls.length; i++) {\n",
-       "      const url = css_urls[i];\n",
-       "      const element = document.createElement(\"link\");\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error.bind(null, url);\n",
-       "      element.rel = \"stylesheet\";\n",
-       "      element.type = \"text/css\";\n",
-       "      element.href = url;\n",
-       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
-       "      document.body.appendChild(element);\n",
-       "    }\n",
-       "\n",
-       "    for (let i = 0; i < js_urls.length; i++) {\n",
-       "      const url = js_urls[i];\n",
-       "      const element = document.createElement('script');\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error.bind(null, url);\n",
-       "      element.async = false;\n",
-       "      element.src = url;\n",
-       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
-       "      document.head.appendChild(element);\n",
-       "    }\n",
-       "  };\n",
-       "\n",
-       "  function inject_raw_css(css) {\n",
-       "    const element = document.createElement(\"style\");\n",
-       "    element.appendChild(document.createTextNode(css));\n",
-       "    document.body.appendChild(element);\n",
-       "  }\n",
-       "\n",
-       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
-       "  const css_urls = [];\n",
-       "\n",
-       "  const inline_js = [    function(Bokeh) {\n",
-       "      Bokeh.set_log_level(\"info\");\n",
-       "    },\n",
-       "function(Bokeh) {\n",
-       "    }\n",
-       "  ];\n",
-       "\n",
-       "  function run_inline_js() {\n",
-       "    if (root.Bokeh !== undefined || force === true) {\n",
-       "          for (let i = 0; i < inline_js.length; i++) {\n",
-       "      inline_js[i].call(root, root.Bokeh);\n",
-       "    }\n",
-       "if (force === true) {\n",
-       "        display_loaded();\n",
-       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(run_inline_js, 100);\n",
-       "    } else if (!root._bokeh_failed_load) {\n",
-       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
-       "      root._bokeh_failed_load = true;\n",
-       "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"d7751d80-c376-4274-999c-135812562683\")).parents('.cell').data().cell;\n",
-       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  if (root._bokeh_is_loading === 0) {\n",
-       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
-       "    run_inline_js();\n",
-       "  } else {\n",
-       "    load_libs(css_urls, js_urls, function() {\n",
-       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
-       "      run_inline_js();\n",
-       "    });\n",
-       "  }\n",
-       "}(window));"
-      ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"d7751d80-c376-4274-999c-135812562683\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"d7751d80-c376-4274-999c-135812562683\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import hail as hl\n",
-    "\n",
-    "from gnomad_toolbox.analysis.general import get_variant_count_by_freq_bin\n",
-    "from gnomad_toolbox.filtering.variant import filter_by_intervals\n",
-    "from gnomad_toolbox.filtering.vep import filter_by_consequence_category, filter_to_plofs\n",
-    "from gnomad_toolbox.load_data import get_gnomad_release"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b3c44396-5ee1-4263-91f8-78bdb9417bab",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Welcome to\n",
-      "     __  __     <>__\n",
-      "    / /_/ /__  __/ /\n",
-      "   / __  / _ `/ / /\n",
-      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/jgoodric/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20241210-2229-0.2.132-678e1f52b999.log\n"
-     ]
-    }
-   ],
-   "source": [
-    "hl.init(backend=\"local\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d36c12aa-d395-4f6c-891c-77caa4779e47",
-   "metadata": {},
-   "source": [
-    "## Get variant count by allele frequency bin\n",
-    "\n",
-    "The examples below show variant counts using the Table filtered to DRD2."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dec1dc9c-f145-46cb-8d33-ca43d24dd06a",
-   "metadata": {},
-   "source": [
-    "### Counts for AF bins: *0.1% - 1%* and *>1.0%*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "261a3380-8dba-41cb-b51b-033b23c963d0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'0.1% - 1.0%': 49, '>1.0%': 28, 'AC0 - 0.1%': 2662}\n"
-     ]
-    }
-   ],
-   "source": [
-    "drd_interval_ht = filter_by_intervals(\"chr11:113409605-113475691\")\n",
-    "af_bin_ht = get_variant_count_by_freq_bin(ht=drd_interval_ht)\n",
-    "print(af_bin_ht)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d05fb1c6-91a9-42cf-87bf-eccb50f6e9b4",
-   "metadata": {},
-   "source": [
-    "### Counts for *singletons*, *doubletons*, and AF bins: *doubletons - 0.05%*, *0.05% - 0.1%*, *0.1% - 1%*, and *>1%*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0a07a84-584e-4b08-b356-9d38767a9c50",
-   "metadata": {},
-   "source": [
-    "#### All DRD2 variants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "aaa8dfac-f7b3-4ce2-b13d-b86a1648b7b7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'0.05% - 0.1%': 11, '0.1% - 1.0%': 34, '>1.0%': 26, 'doubletons': 384, 'doubletons - 0.05%': 894, 'singletons': 1390}\n"
-     ]
-    }
-   ],
-   "source": [
-    "af_bin_ht = get_variant_count_by_freq_bin(\n",
-    "    af_cutoffs=[0.0005, 0.001, 0.01], \n",
-    "    singletons=True, \n",
-    "    doubletons=True, \n",
-    "    ht=drd_interval_ht,\n",
-    ")\n",
-    "print(af_bin_ht)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5fcabb6e-12b5-4a51-aa06-da280ec8a654",
-   "metadata": {},
-   "source": [
-    "#### All DRD2 coding variants"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a0fd192a-b1f3-4726-96cd-e88c0d8baf08",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'0.05% - 0.1%': 4, '0.1% - 1.0%': 8, '>1.0%': 4, 'doubletons': 111, 'doubletons - 0.05%': 291, 'singletons': 365}\n"
-     ]
-    }
-   ],
-   "source": [
-    "af_bin_ht = get_variant_count_by_freq_bin(\n",
-    "    af_cutoffs=[0.0005, 0.001, 0.01], \n",
-    "    singletons=True, \n",
-    "    doubletons=True, \n",
-    "    ht=filter_by_csqs(['coding'], ht=drd_interval_ht),\n",
-    ")\n",
-    "print(af_bin_ht)"
-   ]
-  },
-  {
-   "attachments": {
-    "Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "id": "f9b2d921",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Filter to pLoF variants that we used to compute constraint metrics\n",
-    "pLOF variants meets the following requirements:\n",
-    "* High-confidence LOFTEE variants (without any flags),\n",
-    "* Only variants in the MANE Select transcript,\n",
-    "* PASS variants that are SNVs with MAF ≤ 0.1%, and\n",
-    "* Exome median depth ≥ 30 (**This is changing in v4 constraint?**)\n",
-    "\n",
-    "**Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**\n",
-    "\n",
-    "![Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png](attachment:Screenshot%202024-10-01%20at%2010.20.53%E2%80%AFAM.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "6ce87a77",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO (gnomad.utils.vep 947): Filtering to canonical transcripts\n",
-      "INFO (gnomad.utils.vep 950): Filtering to MANE Select transcripts...\n",
-      "INFO (gnomad.utils.vep 953): Filtering to Ensembl transcripts...\n",
-      "INFO (gnomad.utils.vep 959): Filtering to genes of interest...\n",
-      "INFO (gnomad.utils.vep 967): Filtering to variants with additional criteria...\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
freq
coverage
locus
alleles
AC
AF
AN
homozygote_count
csq
mean
median_approx
total_DP
over_1
over_5
over_10
over_15
over_20
over_25
over_30
over_50
over_100
locus<GRCh38>array<str>int32float64int32int64array<str>float64int32int64float64float64float64float64float64float64float64float64float64
chr1:155337668["G","A"]4593.14e-0414612700["stop_gained"]2.99e+0130218875281.00e+001.00e+001.00e+009.97e-019.86e-019.32e-016.07e-018.96e-046.02e-05
chr1:155337704["G","A"]64.10e-0614617140["stop_gained"]3.02e+0130220423721.00e+001.00e+001.00e+009.99e-019.94e-019.48e-016.21e-018.39e-044.93e-05
chr1:155337735["G","C"]16.84e-0714616940["stop_gained"]3.00e+0130219569141.00e+001.00e+001.00e+009.99e-019.92e-019.37e-016.12e-018.29e-044.51e-05
chr1:155338087["A","T"]16.85e-0714596480["splice_donor_variant"]3.16e+0131231113191.00e+001.00e+009.99e-019.95e-019.89e-019.59e-017.28e-011.35e-027.59e-04
chr1:155338161["G","A"]16.84e-0714618840["stop_gained"]3.19e+0131233084341.00e+001.00e+001.00e+001.00e+001.00e+009.76e-017.42e-011.35e-027.59e-04
chr1:155349380["T","A"]16.84e-0714617680["stop_gained"]3.21e+0132234449991.00e+001.00e+001.00e+001.00e+009.99e-019.76e-017.69e-015.01e-031.85e-04
chr1:155354631["C","T"]16.88e-0714534160["splice_acceptor_variant"]3.04e+0131222097141.00e+001.00e+009.95e-019.83e-019.70e-019.28e-016.53e-014.38e-044.93e-05
chr1:155357583["A","T"]16.84e-0714616800["splice_donor_variant"]3.13e+0131228588871.00e+001.00e+001.00e+009.99e-019.97e-019.79e-017.47e-018.14e-046.29e-05
chr1:155370984["C","T"]32.06e-0614557260["splice_acceptor_variant"]3.12e+0131228021811.00e+001.00e+001.00e+009.99e-019.97e-019.54e-016.97e-011.14e-033.69e-05
chr1:155415924["C","A"]21.48e-0613520320["splice_acceptor_variant"]2.73e+0130199323751.00e+009.84e-019.41e-018.72e-018.17e-017.64e-015.70e-011.35e-031.76e-04
chr1:155478020["G","C"]16.84e-0714618880["stop_gained"]3.26e+0133238300811.00e+001.00e+001.00e+001.00e+001.00e+009.72e-017.53e-013.11e-031.40e-03
chr1:155478203["C","T"]16.84e-0714618740["stop_gained"]3.27e+0133239248711.00e+001.00e+001.00e+001.00e+001.00e+009.72e-017.55e-014.07e-032.20e-03
chr1:155478439["C","T"]16.84e-0714618900["stop_gained"]3.28e+0133239948001.00e+001.00e+001.00e+001.00e+001.00e+009.73e-017.59e-014.64e-032.58e-03
chr1:155478528["G","A"]16.84e-0714618760["stop_gained"]3.28e+0133239977761.00e+001.00e+001.00e+001.00e+001.00e+009.73e-017.59e-014.67e-032.60e-03
chr1:155479767["G","A"]16.84e-0714618580["stop_gained"]3.30e+0133241434591.00e+001.00e+001.00e+001.00e+001.00e+009.74e-017.66e-016.55e-033.89e-03
chr1:155479862["G","T"]16.84e-0714611600["stop_gained"]3.24e+0132236512031.00e+001.00e+001.00e+009.98e-019.91e-019.42e-017.13e-016.42e-033.91e-03
chr1:155521291["C","A"]16.84e-0714618620["stop_gained"]3.30e+0132241144561.00e+001.00e+001.00e+001.00e+001.00e+009.81e-017.72e-011.94e-023.68e-03
chr1:155521474["C","A"]16.84e-0714617240["stop_gained"]3.29e+0132240775511.00e+001.00e+001.00e+009.99e-019.98e-019.79e-017.69e-011.94e-023.67e-03
"
-      ],
-      "text/plain": [
-       "+----------------+------------+---------+----------+---------+\n",
-       "| locus          | alleles    | freq.AC |  freq.AF | freq.AN |\n",
-       "+----------------+------------+---------+----------+---------+\n",
-       "| locus  | array |   int32 |  float64 |   int32 |\n",
-       "+----------------+------------+---------+----------+---------+\n",
-       "| chr1:155337668 | [\"G\",\"A\"]  |     459 | 3.14e-04 | 1461270 |\n",
-       "| chr1:155337704 | [\"G\",\"A\"]  |       6 | 4.10e-06 | 1461714 |\n",
-       "| chr1:155337735 | [\"G\",\"C\"]  |       1 | 6.84e-07 | 1461694 |\n",
-       "| chr1:155338087 | [\"A\",\"T\"]  |       1 | 6.85e-07 | 1459648 |\n",
-       "| chr1:155338161 | [\"G\",\"A\"]  |       1 | 6.84e-07 | 1461884 |\n",
-       "| chr1:155349380 | [\"T\",\"A\"]  |       1 | 6.84e-07 | 1461768 |\n",
-       "| chr1:155354631 | [\"C\",\"T\"]  |       1 | 6.88e-07 | 1453416 |\n",
-       "| chr1:155357583 | [\"A\",\"T\"]  |       1 | 6.84e-07 | 1461680 |\n",
-       "| chr1:155370984 | [\"C\",\"T\"]  |       3 | 2.06e-06 | 1455726 |\n",
-       "| chr1:155415924 | [\"C\",\"A\"]  |       2 | 1.48e-06 | 1352032 |\n",
-       "| chr1:155478020 | [\"G\",\"C\"]  |       1 | 6.84e-07 | 1461888 |\n",
-       "| chr1:155478203 | [\"C\",\"T\"]  |       1 | 6.84e-07 | 1461874 |\n",
-       "| chr1:155478439 | [\"C\",\"T\"]  |       1 | 6.84e-07 | 1461890 |\n",
-       "| chr1:155478528 | [\"G\",\"A\"]  |       1 | 6.84e-07 | 1461876 |\n",
-       "| chr1:155479767 | [\"G\",\"A\"]  |       1 | 6.84e-07 | 1461858 |\n",
-       "| chr1:155479862 | [\"G\",\"T\"]  |       1 | 6.84e-07 | 1461160 |\n",
-       "| chr1:155521291 | [\"C\",\"A\"]  |       1 | 6.84e-07 | 1461862 |\n",
-       "| chr1:155521474 | [\"C\",\"A\"]  |       1 | 6.84e-07 | 1461724 |\n",
-       "+----------------+------------+---------+----------+---------+\n",
-       "\n",
-       "+-----------------------+-----------------------------+---------------+\n",
-       "| freq.homozygote_count | csq                         | coverage.mean |\n",
-       "+-----------------------+-----------------------------+---------------+\n",
-       "|                 int64 | array                  |       float64 |\n",
-       "+-----------------------+-----------------------------+---------------+\n",
-       "|                     0 | [\"stop_gained\"]             |      2.99e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.02e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.00e+01 |\n",
-       "|                     0 | [\"splice_donor_variant\"]    |      3.16e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.19e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.21e+01 |\n",
-       "|                     0 | [\"splice_acceptor_variant\"] |      3.04e+01 |\n",
-       "|                     0 | [\"splice_donor_variant\"]    |      3.13e+01 |\n",
-       "|                     0 | [\"splice_acceptor_variant\"] |      3.12e+01 |\n",
-       "|                     0 | [\"splice_acceptor_variant\"] |      2.73e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.26e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.27e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.28e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.28e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.30e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.24e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.30e+01 |\n",
-       "|                     0 | [\"stop_gained\"]             |      3.29e+01 |\n",
-       "+-----------------------+-----------------------------+---------------+\n",
-       "\n",
-       "+------------------------+-------------------+-----------------+\n",
-       "| coverage.median_approx | coverage.total_DP | coverage.over_1 |\n",
-       "+------------------------+-------------------+-----------------+\n",
-       "|                  int32 |             int64 |         float64 |\n",
-       "+------------------------+-------------------+-----------------+\n",
-       "|                     30 |          21887528 |        1.00e+00 |\n",
-       "|                     30 |          22042372 |        1.00e+00 |\n",
-       "|                     30 |          21956914 |        1.00e+00 |\n",
-       "|                     31 |          23111319 |        1.00e+00 |\n",
-       "|                     31 |          23308434 |        1.00e+00 |\n",
-       "|                     32 |          23444999 |        1.00e+00 |\n",
-       "|                     31 |          22209714 |        1.00e+00 |\n",
-       "|                     31 |          22858887 |        1.00e+00 |\n",
-       "|                     31 |          22802181 |        1.00e+00 |\n",
-       "|                     30 |          19932375 |        1.00e+00 |\n",
-       "|                     33 |          23830081 |        1.00e+00 |\n",
-       "|                     33 |          23924871 |        1.00e+00 |\n",
-       "|                     33 |          23994800 |        1.00e+00 |\n",
-       "|                     33 |          23997776 |        1.00e+00 |\n",
-       "|                     33 |          24143459 |        1.00e+00 |\n",
-       "|                     32 |          23651203 |        1.00e+00 |\n",
-       "|                     32 |          24114456 |        1.00e+00 |\n",
-       "|                     32 |          24077551 |        1.00e+00 |\n",
-       "+------------------------+-------------------+-----------------+\n",
-       "\n",
-       "+-----------------+------------------+------------------+------------------+\n",
-       "| coverage.over_5 | coverage.over_10 | coverage.over_15 | coverage.over_20 |\n",
-       "+-----------------+------------------+------------------+------------------+\n",
-       "|         float64 |          float64 |          float64 |          float64 |\n",
-       "+-----------------+------------------+------------------+------------------+\n",
-       "|        1.00e+00 |         1.00e+00 |         9.97e-01 |         9.86e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.94e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.92e-01 |\n",
-       "|        1.00e+00 |         9.99e-01 |         9.95e-01 |         9.89e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         9.99e-01 |\n",
-       "|        1.00e+00 |         9.95e-01 |         9.83e-01 |         9.70e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.97e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.97e-01 |\n",
-       "|        9.84e-01 |         9.41e-01 |         8.72e-01 |         8.17e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.98e-01 |         9.91e-01 |\n",
-       "|        1.00e+00 |         1.00e+00 |         1.00e+00 |         1.00e+00 |\n",
-       "|        1.00e+00 |         1.00e+00 |         9.99e-01 |         9.98e-01 |\n",
-       "+-----------------+------------------+------------------+------------------+\n",
-       "\n",
-       "+------------------+------------------+------------------+-------------------+\n",
-       "| coverage.over_25 | coverage.over_30 | coverage.over_50 | coverage.over_100 |\n",
-       "+------------------+------------------+------------------+-------------------+\n",
-       "|          float64 |          float64 |          float64 |           float64 |\n",
-       "+------------------+------------------+------------------+-------------------+\n",
-       "|         9.32e-01 |         6.07e-01 |         8.96e-04 |          6.02e-05 |\n",
-       "|         9.48e-01 |         6.21e-01 |         8.39e-04 |          4.93e-05 |\n",
-       "|         9.37e-01 |         6.12e-01 |         8.29e-04 |          4.51e-05 |\n",
-       "|         9.59e-01 |         7.28e-01 |         1.35e-02 |          7.59e-04 |\n",
-       "|         9.76e-01 |         7.42e-01 |         1.35e-02 |          7.59e-04 |\n",
-       "|         9.76e-01 |         7.69e-01 |         5.01e-03 |          1.85e-04 |\n",
-       "|         9.28e-01 |         6.53e-01 |         4.38e-04 |          4.93e-05 |\n",
-       "|         9.79e-01 |         7.47e-01 |         8.14e-04 |          6.29e-05 |\n",
-       "|         9.54e-01 |         6.97e-01 |         1.14e-03 |          3.69e-05 |\n",
-       "|         7.64e-01 |         5.70e-01 |         1.35e-03 |          1.76e-04 |\n",
-       "|         9.72e-01 |         7.53e-01 |         3.11e-03 |          1.40e-03 |\n",
-       "|         9.72e-01 |         7.55e-01 |         4.07e-03 |          2.20e-03 |\n",
-       "|         9.73e-01 |         7.59e-01 |         4.64e-03 |          2.58e-03 |\n",
-       "|         9.73e-01 |         7.59e-01 |         4.67e-03 |          2.60e-03 |\n",
-       "|         9.74e-01 |         7.66e-01 |         6.55e-03 |          3.89e-03 |\n",
-       "|         9.42e-01 |         7.13e-01 |         6.42e-03 |          3.91e-03 |\n",
-       "|         9.81e-01 |         7.72e-01 |         1.94e-02 |          3.68e-03 |\n",
-       "|         9.79e-01 |         7.69e-01 |         1.94e-02 |          3.67e-03 |\n",
-       "+------------------+------------------+------------------+-------------------+"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "filter_to_plofs('ash1l', select_fields=True).show(-1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3c2212f4-a7cf-45bc-930d-aa18cebff7a3",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {
-    "height": "613.99px",
-    "width": "526.312px"
-   },
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": true,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "219.438px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 4455bd54019db9d70b024bf582e34e21ac3f8c69 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Wed, 15 Jan 2025 16:48:16 -0500
Subject: [PATCH 092/121] Rename the notebook

---
 .../dive_into_secondary_analyses.ipynb        | 1937 +++++++++++++++++
 1 file changed, 1937 insertions(+)
 create mode 100644 gnomad_toolbox/notebooks/dive_into_secondary_analyses.ipynb

diff --git a/gnomad_toolbox/notebooks/dive_into_secondary_analyses.ipynb b/gnomad_toolbox/notebooks/dive_into_secondary_analyses.ipynb
new file mode 100644
index 0000000..9f9fe61
--- /dev/null
+++ b/gnomad_toolbox/notebooks/dive_into_secondary_analyses.ipynb
@@ -0,0 +1,1937 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8e609a46",
+   "metadata": {
+    "toc": true
+   },
+   "source": [
+    "
Table of Contents
\n",
+    "
"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e713032",
+   "metadata": {},
+   "source": [
+    "## Import modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e69953f7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-06T18:04:56.165634Z",
+     "start_time": "2024-12-06T18:04:55.603516Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "    \n",
+       "    
\n",
+       "        \n",
+       "        Loading BokehJS ...\n",
+       "    
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  const force = true;\n",
+       "\n",
+       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+       "    root._bokeh_onload_callbacks = [];\n",
+       "    root._bokeh_is_loading = undefined;\n",
+       "  }\n",
+       "\n",
+       "const JS_MIME_TYPE = 'application/javascript';\n",
+       "  const HTML_MIME_TYPE = 'text/html';\n",
+       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
+       "\n",
+       "  /**\n",
+       "   * Render data to the DOM node\n",
+       "   */\n",
+       "  function render(props, node) {\n",
+       "    const script = document.createElement(\"script\");\n",
+       "    node.appendChild(script);\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when an output is cleared or removed\n",
+       "   */\n",
+       "  function handleClearOutput(event, handle) {\n",
+       "    function drop(id) {\n",
+       "      const view = Bokeh.index.get_by_id(id)\n",
+       "      if (view != null) {\n",
+       "        view.model.document.clear()\n",
+       "        Bokeh.index.delete(view)\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    const cell = handle.cell;\n",
+       "\n",
+       "    const id = cell.output_area._bokeh_element_id;\n",
+       "    const server_id = cell.output_area._bokeh_server_id;\n",
+       "\n",
+       "    // Clean up Bokeh references\n",
+       "    if (id != null) {\n",
+       "      drop(id)\n",
+       "    }\n",
+       "\n",
+       "    if (server_id !== undefined) {\n",
+       "      // Clean up Bokeh references\n",
+       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+       "      cell.notebook.kernel.execute(cmd_clean, {\n",
+       "        iopub: {\n",
+       "          output: function(msg) {\n",
+       "            const id = msg.content.text.trim()\n",
+       "            drop(id)\n",
+       "          }\n",
+       "        }\n",
+       "      });\n",
+       "      // Destroy server and session\n",
+       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+       "      cell.notebook.kernel.execute(cmd_destroy);\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when a new output is added\n",
+       "   */\n",
+       "  function handleAddOutput(event, handle) {\n",
+       "    const output_area = handle.output_area;\n",
+       "    const output = handle.output;\n",
+       "\n",
+       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
+       "      return\n",
+       "    }\n",
+       "\n",
+       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+       "      // store reference to embed id on output_area\n",
+       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "    }\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "      const bk_div = document.createElement(\"div\");\n",
+       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "      const script_attrs = bk_div.children[0].attributes;\n",
+       "      for (let i = 0; i < script_attrs.length; i++) {\n",
+       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
+       "      }\n",
+       "      // store reference to server id on output_area\n",
+       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function register_renderer(events, OutputArea) {\n",
+       "\n",
+       "    function append_mime(data, metadata, element) {\n",
+       "      // create a DOM node to render to\n",
+       "      const toinsert = this.create_output_subarea(\n",
+       "        metadata,\n",
+       "        CLASS_NAME,\n",
+       "        EXEC_MIME_TYPE\n",
+       "      );\n",
+       "      this.keyboard_manager.register_events(toinsert);\n",
+       "      // Render to node\n",
+       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "      render(props, toinsert[toinsert.length - 1]);\n",
+       "      element.append(toinsert);\n",
+       "      return toinsert\n",
+       "    }\n",
+       "\n",
+       "    /* Handle when an output is cleared or removed */\n",
+       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
+       "    events.on('delete.Cell', handleClearOutput);\n",
+       "\n",
+       "    /* Handle when a new output is added */\n",
+       "    events.on('output_added.OutputArea', handleAddOutput);\n",
+       "\n",
+       "    /**\n",
+       "     * Register the mime type and append_mime function with output_area\n",
+       "     */\n",
+       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "      /* Is output safe? */\n",
+       "      safe: true,\n",
+       "      /* Index of renderer in `output_area.display_order` */\n",
+       "      index: 0\n",
+       "    });\n",
+       "  }\n",
+       "\n",
+       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+       "  if (root.Jupyter !== undefined) {\n",
+       "    const events = require('base/js/events');\n",
+       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  }\n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
+       "     \"
\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"
    \\n\"+\n",
+       "     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n",
+       "     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n",
+       "     \"
\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"from bokeh.resources import INLINE\\n\"+\n",
+       "     \"output_notebook(resources=INLINE)\\n\"+\n",
+       "     \"\\n\"+\n",
+       "     \"
\"}};\n",
+       "\n",
+       "  function display_loaded() {\n",
+       "    const el = document.getElementById(\"fa775294-04ee-41da-8a2d-4ece189fbd0f\");\n",
+       "    if (el != null) {\n",
+       "      el.textContent = \"BokehJS is loading...\";\n",
+       "    }\n",
+       "    if (root.Bokeh !== undefined) {\n",
+       "      if (el != null) {\n",
+       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+       "      }\n",
+       "    } else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(display_loaded, 100)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls == null || js_urls.length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    function on_error(url) {\n",
+       "      console.error(\"failed to load \" + url);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < css_urls.length; i++) {\n",
+       "      const url = css_urls[i];\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < js_urls.length; i++) {\n",
+       "      const url = js_urls[i];\n",
+       "      const element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n",
+       "  const css_urls = [];\n",
+       "\n",
+       "  const inline_js = [    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
+       "    },\n",
+       "function(Bokeh) {\n",
+       "    }\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    if (root.Bokeh !== undefined || force === true) {\n",
+       "          for (let i = 0; i < inline_js.length; i++) {\n",
+       "      inline_js[i].call(root, root.Bokeh);\n",
+       "    }\n",
+       "if (force === true) {\n",
+       "        display_loaded();\n",
+       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    } else if (force !== true) {\n",
+       "      const cell = $(document.getElementById(\"fa775294-04ee-41da-8a2d-4ece189fbd0f\")).parents('.cell').data().cell;\n",
+       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  if (root._bokeh_is_loading === 0) {\n",
+       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+       "    run_inline_js();\n",
+       "  } else {\n",
+       "    load_libs(css_urls, js_urls, function() {\n",
+       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "      run_inline_js();\n",
+       "    });\n",
+       "  }\n",
+       "}(window));"
+      ],
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"fa775294-04ee-41da-8a2d-4ece189fbd0f\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"fa775294-04ee-41da-8a2d-4ece189fbd0f\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import hail as hl\n",
+    "\n",
+    "from gnomad_toolbox.analysis.general import get_variant_count_by_freq_bin\n",
+    "from gnomad_toolbox.filtering.variant import filter_by_intervals\n",
+    "from gnomad_toolbox.filtering.vep import filter_by_consequence_category\n",
+    "from gnomad_toolbox.filtering.constraint import get_observed_plofs_for_gene_constraint\n",
+    "from gnomad_toolbox.load_data import get_gnomad_release"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b3c44396-5ee1-4263-91f8-78bdb9417bab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Welcome to\n",
+      "     __  __     <>__\n",
+      "    / /_/ /__  __/ /\n",
+      "   / __  / _ `/ / /\n",
+      "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
+      "LOGGING: writing to /Users/heqin/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250115-1629-0.2.132-678e1f52b999.log\n"
+     ]
+    }
+   ],
+   "source": [
+    "hl.init(backend=\"local\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d36c12aa-d395-4f6c-891c-77caa4779e47",
+   "metadata": {},
+   "source": [
+    "## Get variant count by allele frequency bin\n",
+    "\n",
+    "The examples below show variant counts using the Table filtered to DRD2."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dec1dc9c-f145-46cb-8d33-ca43d24dd06a",
+   "metadata": {},
+   "source": [
+    "### Counts for AF bins: *0.1% - 1%* and *>1.0%*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "261a3380-8dba-41cb-b51b-033b23c963d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'0.1% - 1.0%': 49, '>1.0%': 28, 'AC0 - 0.1%': 2662}\n"
+     ]
+    }
+   ],
+   "source": [
+    "drd_interval_ht = filter_by_intervals(\"chr11:113409605-113475691\")\n",
+    "af_bin_ht = get_variant_count_by_freq_bin(ht=drd_interval_ht)\n",
+    "print(af_bin_ht)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d05fb1c6-91a9-42cf-87bf-eccb50f6e9b4",
+   "metadata": {},
+   "source": [
+    "### Counts for *singletons*, *doubletons*, and AF bins: *doubletons - 0.05%*, *0.05% - 0.1%*, *0.1% - 1%*, and *>1%*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0a07a84-584e-4b08-b356-9d38767a9c50",
+   "metadata": {},
+   "source": [
+    "#### All DRD2 variants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "aaa8dfac-f7b3-4ce2-b13d-b86a1648b7b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'0.05% - 0.1%': 11, '0.1% - 1.0%': 34, '>1.0%': 26, 'doubletons': 384, 'doubletons - 0.05%': 894, 'singletons': 1390}\n"
+     ]
+    }
+   ],
+   "source": [
+    "af_bin_ht = get_variant_count_by_freq_bin(\n",
+    "    af_cutoffs=[0.0005, 0.001, 0.01], \n",
+    "    singletons=True, \n",
+    "    doubletons=True, \n",
+    "    ht=drd_interval_ht,\n",
+    ")\n",
+    "print(af_bin_ht)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fcabb6e-12b5-4a51-aa06-da280ec8a654",
+   "metadata": {},
+   "source": [
+    "#### All DRD2 coding variants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a0fd192a-b1f3-4726-96cd-e88c0d8baf08",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'doubletons': 5, 'doubletons - 0.05%': 3, 'singletons': 9}\n"
+     ]
+    }
+   ],
+   "source": [
+    "af_bin_ht = get_variant_count_by_freq_bin(\n",
+    "    af_cutoffs=[0.0005, 0.001, 0.01], \n",
+    "    singletons=True, \n",
+    "    doubletons=True, \n",
+    "    ht=filter_by_consequence_category(['coding'], ht=drd_interval_ht),\n",
+    ")\n",
+    "print(af_bin_ht)"
+   ]
+  },
+  {
+   "attachments": {
+    "909c85bb-7920-49fc-9025-93d1bcc2d3ce.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "f9b2d921",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Get the pLoF variants that we used to compute constraint metrics\n",
+    "\n",
+    "The pLOF variant count displayed on the browser meets the following requirements:\n",
+    "\n",
+    "        - PASS variant QC\n",
+    "        - SNV\n",
+    "        - Allele frequency ≤ 0.1%\n",
+    "        - High-confidence LOFTEE in the MANE Select or Canonical transcript\n",
+    "        - ≥ a specified coverage threshold (depends on the version)\n",
+    "\n",
+    "**Note: this number should match the number of observed pLOF SNVs on the gene page of gnomAD Browser.**\n",
+    "\n",
+    "![Screenshot 2025-01-15 at 3.48.07 PM.png](attachment:909c85bb-7920-49fc-9025-93d1bcc2d3ce.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6ce87a77",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO (gnomad.utils.vep 953): Filtering to canonical transcripts\n",
+      "INFO (gnomad.utils.vep 959): Filtering to Ensembl transcripts...\n",
+      "INFO (gnomad.utils.vep 965): Filtering to consequences with LOFTEE labels: ['HC']...\n",
+      "INFO (gnomad.utils.vep 973): Filtering to genes of interest...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
exome_coverage
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64int32
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+      ],
+      "text/plain": [
+       "+-----------------+------------+\n",
+       "| locus           | alleles    |\n",
+       "+-----------------+------------+\n",
+       "| locus   | array |\n",
+       "+-----------------+------------+\n",
+       "| chr11:113412554 | [\"A\",\"G\"]  |\n",
+       "| chr11:113412865 | [\"G\",\"A\"]  |\n",
+       "| chr11:113412885 | [\"T\",\"C\"]  |\n",
+       "| chr11:113414463 | [\"T\",\"G\"]  |\n",
+       "| chr11:113415420 | [\"C\",\"G\"]  |\n",
+       "| chr11:113415480 | [\"G\",\"A\"]  |\n",
+       "| chr11:113416861 | [\"A\",\"G\"]  |\n",
+       "| chr11:113424628 | [\"C\",\"T\"]  |\n",
+       "+-----------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(1,6.85e-07,1459578,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(1,6.85e-07,1459642,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33422,0),(0,0.... |\n",
+       "| [(1,6.86e-07,1458160,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33408,0),(0,0.... |\n",
+       "| [(3,2.05e-06,1461878,0),(3,2.05e-06,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(1,6.85e-07,1459072,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33442,0),(0,0.... |\n",
+       "| [(2,1.37e-06,1461866,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(1,6.84e-07,1461444,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33468,0),(0,0.... |\n",
+       "| [(1,6.84e-07,1461858,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|                1 |         8.99e-07 |          1111998 |\n",
+       "|                1 |         8.99e-07 |          1111812 |\n",
+       "|                1 |         9.00e-07 |          1111636 |\n",
+       "|                3 |         2.70e-06 |          1111998 |\n",
+       "|                1 |         1.16e-05 |            85890 |\n",
+       "|                2 |         1.80e-06 |          1111994 |\n",
+       "|                1 |         8.99e-07 |          1111804 |\n",
+       "|                1 |         8.99e-07 |          1112012 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"sas\"                 |                 1 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          1.44e-05 |             69430 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          2.86e-06 |            349900 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| \"sas\"                  |\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| NA                     |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(5.50e-07,1.50e-07),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                7.20e-07 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                3.00e-07 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                2.00e-07 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                1.10e-07 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+-----------------+----------+----------+----------+\n",
+       "| was_split | rsid            | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+-----------------+----------+----------+----------+\n",
+       "|      bool | set        | set |  float64 |  float64 |\n",
+       "+-----------+-----------------+----------+----------+----------+\n",
+       "|      True | NA              | {}       | 2.83e+00 | 6.00e+01 |\n",
+       "|      True | NA              | {}       | 1.02e+00 | 6.00e+01 |\n",
+       "|     False | NA              | {}       | 2.45e+00 | 6.00e+01 |\n",
+       "|     False | NA              | {}       | 4.96e+00 | 6.00e+01 |\n",
+       "|     False | {\"rs770234535\"} | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|      True | NA              | {}       | 5.21e-01 | 6.00e+01 |\n",
+       "|     False | NA              | {}       | 1.62e+00 | 6.00e+01 |\n",
+       "|      True | NA              | {}       | 9.48e+00 | 6.00e+01 |\n",
+       "+-----------+-----------------+----------+----------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|       0.00e+00 |            1498 | 1.05e+01 |           -5.05e-01 |\n",
+       "|       0.00e+00 |             553 | 7.79e+00 |            2.96e-01 |\n",
+       "|       0.00e+00 |             890 | 1.11e+01 |            1.80e-01 |\n",
+       "|       0.00e+00 |            1567 | 1.20e+01 |            2.08e-01 |\n",
+       "|       0.00e+00 |             259 | 1.44e+01 |            1.41e+00 |\n",
+       "|       0.00e+00 |            8933 | 1.24e+01 |            1.75e-01 |\n",
+       "|       0.00e+00 |            1177 | 1.12e+01 |            3.00e-03 |\n",
+       "|       0.00e+00 |            1375 | 9.96e+00 |           -3.30e-02 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+-------------------+----------+------------+------------+------------+\n",
+       "| info.SB           | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+-------------------+----------+------------+------------+------------+\n",
+       "| array      |  float64 |      int32 |    float64 |    float64 |\n",
+       "+-------------------+----------+------------+------------+------------+\n",
+       "| [63,17,52,10]     | 1.04e+00 |        142 |   1.10e+00 |   6.00e+01 |\n",
+       "| [14,32,9,16]      | 4.68e-01 |         71 |   1.04e+00 |   6.00e+01 |\n",
+       "| [7,37,8,28]       | 3.79e-01 |         80 |   2.45e+00 |   6.00e+01 |\n",
+       "| [21,49,13,48]     | 1.22e+00 |        131 |   4.96e+00 |   6.00e+01 |\n",
+       "| [9,0,8,1]         | 1.84e-01 |         18 |   0.00e+00 |   6.00e+01 |\n",
+       "| [191,186,167,175] | 7.16e-01 |        719 |   2.44e+00 |   6.00e+01 |\n",
+       "| [34,22,32,17]     | 8.97e-01 |        105 |   1.62e+00 |   6.00e+01 |\n",
+       "| [27,53,28,30]     | 2.68e-01 |        138 |   8.73e+00 |   6.00e+01 |\n",
+       "+-------------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|          0.00e+00 |        1.00e+00 |                661 |   1.35e+01 |\n",
+       "|          0.00e+00 |        3.92e-01 |                531 |   8.56e+00 |\n",
+       "|          0.00e+00 |        4.53e-01 |                890 |   1.11e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |               1567 |   1.20e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |                259 |   1.44e+01 |\n",
+       "|          0.00e+00 |        1.84e-01 |               1886 |   1.17e+01 |\n",
+       "|          0.00e+00 |        5.58e-01 |               1177 |   1.12e+01 |\n",
+       "|          0.00e+00 |        5.98e-02 |                780 |   9.51e+00 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE | info.AS_SOR | info.AS_VarDP |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "|                float64 | array     |     float64 |         int32 |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "|              -1.83e+00 | [63,17,21,4]     |    9.29e-01 |            49 |\n",
+       "|               2.96e-01 | [14,32,8,15]     |    5.03e-01 |            62 |\n",
+       "|               1.80e-01 | [7,37,8,28]      |    3.79e-01 |            80 |\n",
+       "|               2.08e-01 | [21,49,13,48]    |    1.22e+00 |           131 |\n",
+       "|               1.41e+00 | [9,0,8,1]        |    1.84e-01 |            18 |\n",
+       "|               1.75e-01 | [191,186,43,33]  |    9.51e-01 |           161 |\n",
+       "|               3.00e-03 | [34,22,32,17]    |    8.97e-01 |           105 |\n",
+       "|              -8.52e-01 | [27,53,16,16]    |    2.38e-01 |            82 |\n",
+       "+------------------------+------------------+-------------+---------------+\n",
+       "\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           bool |                       bool |                   bool |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           True |                      False |                  False |\n",
+       "|           True |                      False |                  False |\n",
+       "|          False |                      False |                  False |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                      False |                  False |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                      False |                  False |\n",
+       "|           True |                      False |                  False |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|     False |      False |            False |         False |       5.30e+00 |\n",
+       "|     False |      False |            False |         False |       5.39e+00 |\n",
+       "|     False |      False |            False |         False |       5.84e+00 |\n",
+       "|     False |      False |            False |         False |       5.68e+00 |\n",
+       "|     False |      False |            False |         False |       5.88e+00 |\n",
+       "|     False |      False |            False |         False |       5.90e+00 |\n",
+       "|     False |      False |            False |         False |       6.28e+00 |\n",
+       "|     False |      False |            False |         False |       4.76e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|             -6.84e-07 |\n",
+       "|             -6.84e-07 |\n",
+       "|             -1.37e-06 |\n",
+       "|             -2.05e-06 |\n",
+       "|             -6.84e-07 |\n",
+       "|             -1.37e-06 |\n",
+       "|             -6.84e-07 |\n",
+       "|             -6.84e-07 |\n",
+       "+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.mqU9WPvGr3hduyybbeN-AKJ1zN-kclsI\",\"ga4gh:VA.uvlicmv1vZnrBBqm0U... |\n",
+       "| [\"ga4gh:VA.uAq5BRYMw_lO7PZcOF5TpbGC5hA8JCCE\",\"ga4gh:VA.rN4HBsKxAnhIfQxMXe... |\n",
+       "| [\"ga4gh:VA.Hj6Aa_k4TOMxqWIs6lE_cgxiRrPFWp8C\",\"ga4gh:VA.NDTpZX0YUzM19pYVv6... |\n",
+       "| [\"ga4gh:VA.YID7VSJVf3ww5ocPtHOFB_frOCeJZjbJ\",\"ga4gh:VA.l93VoU51AVnxcbBPtR... |\n",
+       "| [\"ga4gh:VA.wHqNt4w2xDKyEM7D_6m3p8RHFffZrM4Y\",\"ga4gh:VA.a3bz-IwYbVym7BJ1kJ... |\n",
+       "| [\"ga4gh:VA.gpSj8DCEFoC32K8p1WSplOzLfjobiNF2\",\"ga4gh:VA.-nqY9CAkzVrWBF_qv9... |\n",
+       "| [\"ga4gh:VA.rcxeySbGcgI3UI4PHmXE0-eOiGuPAbc7\",\"ga4gh:VA.aAET-0HX1tW01GWSMq... |\n",
+       "| [\"ga4gh:VA.8sGHUAS0hRa-H2y4rXi6-6Bsy4-qPPE-\",\"ga4gh:VA.mP-pOESIi9_OSLJHsW... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113412553,113412553] | [113412554,113412554] | [\"A\",\"G\"]           |\n",
+       "| [113412864,113412864] | [113412865,113412865] | [\"G\",\"A\"]           |\n",
+       "| [113412884,113412884] | [113412885,113412885] | [\"T\",\"C\"]           |\n",
+       "| [113414462,113414462] | [113414463,113414463] | [\"T\",\"G\"]           |\n",
+       "| [113415419,113415419] | [113415420,113415420] | [\"C\",\"G\"]           |\n",
+       "| [113415479,113415479] | [113415480,113415480] | [\"G\",\"A\"]           |\n",
+       "| [113416860,113416860] | [113416861,113416861] | [\"A\",\"G\"]           |\n",
+       "| [113424627,113424627] | [113424628,113424628] | [\"C\",\"T\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| str               |     int32 | str    | str                            |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| \"A/G\"             | 113412554 | \".\"    | \"chr11\t113412554\t.\tA\tG\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113412865 | \".\"    | \"chr11\t113412865\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"T/C\"             | 113412885 | \".\"    | \"chr11\t113412885\t.\tT\tC\t.\t.\tGT\" |\n",
+       "| \"T/G\"             | 113414463 | \".\"    | \"chr11\t113414463\t.\tT\tG\t.\t.\tGT\" |\n",
+       "| \"C/G\"             | 113415420 | \".\"    | \"chr11\t113415420\t.\tC\tG\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113415480 | \".\"    | \"chr11\t113415480\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"A/G\"             | 113416861 | \".\"    | \"chr11\t113416861\t.\tA\tG\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113424628 | \".\"    | \"chr11\t113424628\t.\tC\tT\t.\t.\tGT\" |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"splice_donor_variant\"      |\n",
+       "| \"stop_gained\"               |\n",
+       "| \"splice_acceptor_variant\"   |\n",
+       "| \"splice_acceptor_variant\"   |\n",
+       "| \"splice_donor_variant\"      |\n",
+       "| \"stop_gained\"               |\n",
+       "| \"splice_donor_variant\"      |\n",
+       "| \"stop_gained\"               |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq                 |\n",
+       "+------------------------------------------------------------+\n",
+       "| array                                               |\n",
+       "+------------------------------------------------------------+\n",
+       "| [0,0,0,0,134,0,1469,0,311660,0,82,0,416443,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,547,0,1910,0,310815,0,33,0,416515,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,929,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1] |\n",
+       "| [0,0,0,0,21,0,157,0,314583,0,1063,0,415112,0,0,0,0,0,0,3]  |\n",
+       "| [0,0,0,0,692,0,5013,0,307280,0,33,0,416517,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,29,0,103,0,314241,0,0,0,416552,0,0,0,0,0,0,8]     |\n",
+       "| [0,0,0,0,225,0,1535,0,312406,0,2,0,416553,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,19,0,104,0,314255,0,1,0,416548,0,0,0,0,0,0,2]     |\n",
+       "+------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,1555,817,16216,229270,272936,45138,9370,3566,3616,4059,4521,5017,549... |\n",
+       "| [0,0,1141,1461,3821,36585,211757,225880,49280,7441,3163,3441,5118,7834,11... |\n",
+       "| [0,0,2699,3244,4728,36120,209411,224276,48878,7369,3147,3425,5116,7827,11... |\n",
+       "| [0,0,87,765,26853,226717,412769,58256,3889,491,150,93,69,55,68,71,66,81,5... |\n",
+       "| [0,0,4589,5130,19240,219783,394259,72552,10630,1692,409,297,247,148,95,85... |\n",
+       "| [0,0,43,280,16751,227129,399638,73028,10691,1700,414,295,249,152,94,85,69... |\n",
+       "| [0,0,1267,2040,25889,200725,371864,92888,14543,2881,1075,788,744,786,1105... |\n",
+       "| [0,0,72,311,19701,184811,422719,92484,8754,911,187,104,83,87,80,57,58,52,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                     102137 |\n",
+       "|                                      70182 |\n",
+       "|                                      70149 |\n",
+       "|                                        333 |\n",
+       "|                                        176 |\n",
+       "|                                        179 |\n",
+       "|                                       5373 |\n",
+       "|                                        338 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                 |\n",
+       "+----------------------------------------------------------------+\n",
+       "| array                                                   |\n",
+       "+----------------------------------------------------------------+\n",
+       "| [441,0,0,0,850,1,1469,0,311660,0,82,0,416443,0,0,0,0,0,0,1]    |\n",
+       "| [890,0,0,0,783,0,1910,0,310815,0,33,0,416515,0,0,0,0,0,0,1]    |\n",
+       "| [1313,0,2,0,1481,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1] |\n",
+       "| [5,0,2,0,22,0,157,0,314583,0,1063,0,415112,0,0,0,0,0,0,3]      |\n",
+       "| [650,0,0,0,1453,0,5013,0,307280,0,33,0,416517,0,0,0,0,0,0,1]   |\n",
+       "| [10,0,0,0,33,0,103,0,314241,0,0,0,416552,0,0,0,0,0,0,8]        |\n",
+       "| [100,0,0,0,350,0,1535,0,312406,0,2,0,416553,0,0,0,0,0,0,1]     |\n",
+       "| [10,0,0,0,27,0,104,0,314255,0,1,0,416548,0,0,0,0,0,0,2]        |\n",
+       "+----------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [244,858,1593,833,16218,229270,272936,45138,9370,3566,3616,4059,4521,5017... |\n",
+       "| [431,622,1175,1493,3823,36589,211757,225880,49281,7441,3163,3441,5118,783... |\n",
+       "| [618,1142,2737,3303,4730,36128,209411,224276,48878,7369,3147,3425,5116,78... |\n",
+       "| [0,1,88,768,26854,226717,412771,58256,3889,491,150,93,69,55,68,71,66,81,5... |\n",
+       "| [157,923,4727,5278,19256,219808,394263,72552,10630,1692,409,297,247,148,9... |\n",
+       "| [0,5,44,284,16751,227131,399638,73029,10692,1700,414,295,249,152,94,85,69... |\n",
+       "| [7,158,1289,2075,25891,200726,371864,92888,14543,2881,1075,788,744,786,11... |\n",
+       "| [3,9,76,312,19702,184811,422719,92484,8754,911,187,104,83,87,80,57,58,52,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                         102137 |\n",
+       "|                                          70182 |\n",
+       "|                                          70149 |\n",
+       "|                                            333 |\n",
+       "|                                            176 |\n",
+       "|                                            179 |\n",
+       "|                                           5373 |\n",
+       "|                                            338 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
+       "| [0,0,0,1,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
+       "| [0,0,0,1,1,0,0,1,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,1,0,0,1,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,1,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        3.40e+01 |\n",
+       "|                                          0 |                        4.50e+01 |\n",
+       "|                                          0 |                        3.50e+01 |\n",
+       "|                                          0 |                        3.40e+01 |\n",
+       "|                                          0 |                        2.96e+01 |\n",
+       "|                                          0 |                        3.70e+01 |\n",
+       "|                                          0 |                        2.71e+01 |\n",
+       "|                                          0 |                        3.70e+01 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            5.66e+00 |                             NA |\n",
+       "|                            8.76e+00 |                             NA |\n",
+       "|                            5.83e+00 |                             NA |\n",
+       "|                            5.38e+00 |                             NA |\n",
+       "|                            4.30e+00 |                             NA |\n",
+       "|                            7.23e+00 |                             NA |\n",
+       "|                            4.02e+00 |                             NA |\n",
+       "|                            7.06e+00 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             9.60e-01 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             9.80e-01 |\n",
+       "|                             9.40e-01 |\n",
+       "|                             8.90e-01 |\n",
+       "|                             9.50e-01 |\n",
+       "|                             7.70e-01 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                -8.30e-01 |                    6.33e+00 |\n",
+       "|                                -1.10e-01 |                    8.79e+00 |\n",
+       "|                                -7.90e-01 |                    6.35e+00 |\n",
+       "|                                -8.10e-01 |                    6.34e+00 |\n",
+       "|                                -6.80e-01 |                    8.80e+00 |\n",
+       "|                                 8.10e-01 |                    1.80e+00 |\n",
+       "|                                -7.00e-01 |                    4.85e+00 |\n",
+       "|                                -5.00e-02 |                    8.90e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "\n",
+       "+----------------+\n",
+       "| exome_coverage |\n",
+       "+----------------+\n",
+       "|          int32 |\n",
+       "+----------------+\n",
+       "|             31 |\n",
+       "|             36 |\n",
+       "|             36 |\n",
+       "|             31 |\n",
+       "|             31 |\n",
+       "|             31 |\n",
+       "|             31 |\n",
+       "|             31 |\n",
+       "+----------------+"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "get_observed_plofs_for_gene_constraint('drd2').show(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c2212f4-a7cf-45bc-930d-aa18cebff7a3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {
+    "height": "613.99px",
+    "width": "526.312px"
+   },
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "219.438px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "toc-autonumbering": false,
+  "toc-showcode": false,
+  "toc-showmarkdowntxt": true,
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 119e4cd06196c36f482e3c62506a9aaa67745a5d Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Wed, 15 Jan 2025 17:41:03 -0500
Subject: [PATCH 093/121] Correct interval function in variant.py

---
 gnomad_toolbox/filtering/variant.py           |  5 ++++-
 .../dive_into_secondary_analyses.ipynb        | 17 ++++++--------
 .../notebooks/explore_release_data.ipynb      |  2 +-
 .../intro_to_filtering_variant_data.ipynb     | 22 +++++++++----------
 4 files changed, 23 insertions(+), 23 deletions(-)

diff --git a/gnomad_toolbox/filtering/variant.py b/gnomad_toolbox/filtering/variant.py
index db1f5e3..3b063d2 100644
--- a/gnomad_toolbox/filtering/variant.py
+++ b/gnomad_toolbox/filtering/variant.py
@@ -71,6 +71,7 @@ def get_single_variant(
 
 def filter_by_intervals(
     intervals: Union[str, list[str]],
+    padding_bp: int = 0,
     **kwargs,
 ) -> hl.Table:
     """
@@ -79,15 +80,17 @@ def filter_by_intervals(
     :param intervals: Interval string or list of interval strings. The interval string
         format has to be "contig:start-end", e.g.,"1:1000-2000" (GRCh37) or
         "chr1:1000-2000" (GRCh38).
+    :param padding_bp: Number of base pairs to pad the intervals. Default is 0bp.
     :param kwargs: Arguments to pass to `_get_dataset`.
     :return: Table with variants in the interval(s).
     """
-    # Load the Hail Table if not provided
+    # Load the Hail Table if not provided.
     ht = _get_dataset(dataset="variant", **kwargs)
 
     return interval_filter(
         ht,
         intervals,
+        padding_bp=padding_bp,
         reference_genome=get_reference_genome(ht.locus).name,
     )
 
diff --git a/gnomad_toolbox/notebooks/dive_into_secondary_analyses.ipynb b/gnomad_toolbox/notebooks/dive_into_secondary_analyses.ipynb
index 9f9fe61..2263683 100644
--- a/gnomad_toolbox/notebooks/dive_into_secondary_analyses.ipynb
+++ b/gnomad_toolbox/notebooks/dive_into_secondary_analyses.ipynb
@@ -8,7 +8,7 @@
    },
    "source": [
     "
Table of Contents
\n",
-    "
"
+    "
"
    ]
   },
   {
@@ -43,7 +43,7 @@
        "    \n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -215,7 +215,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"fa775294-04ee-41da-8a2d-4ece189fbd0f\");\n",
+       "    const el = document.getElementById(\"c4a512b6-e00b-4120-bf26-b79fc276c184\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -321,7 +321,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"fa775294-04ee-41da-8a2d-4ece189fbd0f\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"c4a512b6-e00b-4120-bf26-b79fc276c184\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -337,7 +337,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"fa775294-04ee-41da-8a2d-4ece189fbd0f\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"fa775294-04ee-41da-8a2d-4ece189fbd0f\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"c4a512b6-e00b-4120-bf26-b79fc276c184\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"c4a512b6-e00b-4120-bf26-b79fc276c184\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -368,7 +368,7 @@
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
       "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/heqin/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250115-1629-0.2.132-678e1f52b999.log\n"
+      "LOGGING: writing to /Users/heqin/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250115-1729-0.2.132-678e1f52b999.log\n"
      ]
     }
    ],
@@ -1898,7 +1898,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.9.6"
   },
   "toc": {
    "base_numbering": 1,
@@ -1921,9 +1921,6 @@
    "toc_section_display": true,
    "toc_window_display": true
   },
-  "toc-autonumbering": false,
-  "toc-showcode": false,
-  "toc-showmarkdowntxt": true,
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {},
diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
index 1b7553e..18cc006 100644
--- a/gnomad_toolbox/notebooks/explore_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -14648,7 +14648,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.9.6"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
index 8d2267c..3fdaf0e 100644
--- a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
+++ b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
@@ -16,7 +16,7 @@
    },
    "source": [
     "
Table of Contents
\n",
-    "
"
+    "
"
    ]
   },
   {
@@ -51,7 +51,7 @@
        "    \n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -223,7 +223,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"be1e6b75-a614-4af7-b778-f8e50b8b7664\");\n",
+       "    const el = document.getElementById(\"f22fcf70-de15-4a16-9b8f-9c5dcbf3db35\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -329,7 +329,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"be1e6b75-a614-4af7-b778-f8e50b8b7664\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"f22fcf70-de15-4a16-9b8f-9c5dcbf3db35\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -345,7 +345,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
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+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"f22fcf70-de15-4a16-9b8f-9c5dcbf3db35\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"f22fcf70-de15-4a16-9b8f-9c5dcbf3db35\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -378,7 +378,7 @@
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
       "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/heqin/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250115-1555-0.2.132-678e1f52b999.log\n"
+      "LOGGING: writing to /Users/heqin/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250115-1733-0.2.132-678e1f52b999.log\n"
      ]
     }
    ],
@@ -473,8 +473,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "01/15/2025 03:56:03 PM (gnomad.utils.filtering 456): No Gencode Table or version was supplied, using Gencode version v39\n",
-      "01/15/2025 03:56:16 PM (gnomad.utils.filtering 531): Since 1 is less than or equal to 'max_collect_intervals', collecting all intervals...\n"
+      "01/15/2025 05:33:59 PM (gnomad.utils.filtering 456): No Gencode Table or version was supplied, using Gencode version v39\n",
+      "01/15/2025 05:34:04 PM (gnomad.utils.filtering 531): Since 1 is less than or equal to 'max_collect_intervals', collecting all intervals...\n"
      ]
     },
     {
@@ -485,8 +485,8 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
       "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m drd2_ht \u001b[38;5;241m=\u001b[39m \u001b[43mfilter_by_gene_symbol\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdrd2\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe total number of variants in DRD2 is: \u001b[39m\u001b[38;5;124m\"\u001b[39m, drd2_ht\u001b[38;5;241m.\u001b[39mcount())\n",
-      "File \u001b[0;32m~/miniconda3/envs/hail2132/lib/python3.11/site-packages/gnomad_toolbox/filtering/variant.py:139\u001b[0m, in \u001b[0;36mfilter_by_gene_symbol\u001b[0;34m(gene, exon_padding_bp, **kwargs)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filter_count \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    137\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo intervals match the gene symbol \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mgene\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 139\u001b[0m ht \u001b[38;5;241m=\u001b[39m \u001b[43mfilter_by_intervals\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_gencode_ht\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minterval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mht\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mht\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpadding_bp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexon_padding_bp\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ht\n",
-      "File \u001b[0;32m~/miniconda3/envs/hail2132/lib/python3.11/site-packages/gnomad_toolbox/filtering/variant.py:86\u001b[0m, in \u001b[0;36mfilter_by_intervals\u001b[0;34m(intervals, **kwargs)\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;124;03mFilter variants by interval(s).\u001b[39;00m\n\u001b[1;32m     78\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;124;03m:return: Table with variants in the interval(s).\u001b[39;00m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     85\u001b[0m \u001b[38;5;66;03m# Load the Hail Table if not provided\u001b[39;00m\n\u001b[0;32m---> 86\u001b[0m ht \u001b[38;5;241m=\u001b[39m \u001b[43m_get_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvariant\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m interval_filter(\n\u001b[1;32m     89\u001b[0m     ht,\n\u001b[1;32m     90\u001b[0m     intervals,\n\u001b[1;32m     91\u001b[0m     reference_genome\u001b[38;5;241m=\u001b[39mget_reference_genome(ht\u001b[38;5;241m.\u001b[39mlocus)\u001b[38;5;241m.\u001b[39mname,\n\u001b[1;32m     92\u001b[0m )\n",
+      "File \u001b[0;32m~/miniconda3/envs/gnomad-toolbox/lib/python3.9/site-packages/gnomad_toolbox/filtering/variant.py:139\u001b[0m, in \u001b[0;36mfilter_by_gene_symbol\u001b[0;34m(gene, exon_padding_bp, **kwargs)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filter_count \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    137\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo intervals match the gene symbol \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mgene\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 139\u001b[0m ht \u001b[38;5;241m=\u001b[39m \u001b[43mfilter_by_intervals\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_gencode_ht\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minterval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mht\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mht\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpadding_bp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexon_padding_bp\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ht\n",
+      "File \u001b[0;32m~/miniconda3/envs/gnomad-toolbox/lib/python3.9/site-packages/gnomad_toolbox/filtering/variant.py:86\u001b[0m, in \u001b[0;36mfilter_by_intervals\u001b[0;34m(intervals, **kwargs)\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;124;03mFilter variants by interval(s).\u001b[39;00m\n\u001b[1;32m     78\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;124;03m:return: Table with variants in the interval(s).\u001b[39;00m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     85\u001b[0m \u001b[38;5;66;03m# Load the Hail Table if not provided\u001b[39;00m\n\u001b[0;32m---> 86\u001b[0m ht \u001b[38;5;241m=\u001b[39m \u001b[43m_get_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvariant\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m interval_filter(\n\u001b[1;32m     89\u001b[0m     ht,\n\u001b[1;32m     90\u001b[0m     intervals,\n\u001b[1;32m     91\u001b[0m     reference_genome\u001b[38;5;241m=\u001b[39mget_reference_genome(ht\u001b[38;5;241m.\u001b[39mlocus)\u001b[38;5;241m.\u001b[39mname,\n\u001b[1;32m     92\u001b[0m )\n",
       "\u001b[0;31mTypeError\u001b[0m: _get_dataset() got an unexpected keyword argument 'padding_bp'"
      ]
     }
@@ -767,7 +767,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.9.6"
   },
   "toc": {
    "base_numbering": 1,

From 6f41221efd3733d7773bd7f58e06605c24706cd8 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Wed, 15 Jan 2025 17:45:18 -0500
Subject: [PATCH 094/121] Rerun the filtering notebook with updated function

---
 .../intro_to_filtering_variant_data.ipynb     | 5634 ++++++++++++++++-
 1 file changed, 5584 insertions(+), 50 deletions(-)

diff --git a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
index 3fdaf0e..aefc979 100644
--- a/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
+++ b/gnomad_toolbox/notebooks/intro_to_filtering_variant_data.ipynb
@@ -51,7 +51,7 @@
        "    \n",
        "    
\n",
        "        \n",
-       "        Loading BokehJS ...\n",
+       "        Loading BokehJS ...\n",
        "    
\n"
       ]
      },
@@ -223,7 +223,7 @@
        "     \"\"}};\n",
        "\n",
        "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"f22fcf70-de15-4a16-9b8f-9c5dcbf3db35\");\n",
+       "    const el = document.getElementById(\"aa9e9329-0c6a-47d2-b48b-1f41e5353249\");\n",
        "    if (el != null) {\n",
        "      el.textContent = \"BokehJS is loading...\";\n",
        "    }\n",
@@ -329,7 +329,7 @@
        "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
        "      root._bokeh_failed_load = true;\n",
        "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"f22fcf70-de15-4a16-9b8f-9c5dcbf3db35\")).parents('.cell').data().cell;\n",
+       "      const cell = $(document.getElementById(\"aa9e9329-0c6a-47d2-b48b-1f41e5353249\")).parents('.cell').data().cell;\n",
        "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
        "    }\n",
        "  }\n",
@@ -345,7 +345,7 @@
        "  }\n",
        "}(window));"
       ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"f22fcf70-de15-4a16-9b8f-9c5dcbf3db35\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"f22fcf70-de15-4a16-9b8f-9c5dcbf3db35\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"
\\n\"+\n     \"
\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"
\\n\"+\n     \"
    \\n\"+\n     \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n     \"
  • use INLINE resources instead, as so:
    • \\n\"+\n     \"
\\n\"+\n     \"\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"\\n\"+\n     \"
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      },
      "metadata": {},
      "output_type": "display_data"
@@ -378,7 +378,8 @@
       "    / /_/ /__  __/ /\n",
       "   / __  / _ `/ / /\n",
       "  /_/ /_/\\_,_/_/_/   version 0.2.132-678e1f52b999\n",
-      "LOGGING: writing to /Users/heqin/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250115-1733-0.2.132-678e1f52b999.log\n"
+      "LOGGING: writing to /Users/heqin/PycharmProjects/gnomad-toolbox/gnomad_toolbox/notebooks/hail-20250115-1742-0.2.132-678e1f52b999.log\n",
+      "2025-01-15 17:44:17.708 Hail: WARN: No variant found at chr22:15528692 with alleles ['C', 'A']\n"
      ]
     }
    ],
@@ -473,21 +474,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "01/15/2025 05:33:59 PM (gnomad.utils.filtering 456): No Gencode Table or version was supplied, using Gencode version v39\n",
-      "01/15/2025 05:34:04 PM (gnomad.utils.filtering 531): Since 1 is less than or equal to 'max_collect_intervals', collecting all intervals...\n"
+      "01/15/2025 05:43:04 PM (gnomad.utils.filtering 456): No Gencode Table or version was supplied, using Gencode version v39\n",
+      "01/15/2025 05:43:12 PM (gnomad.utils.filtering 531): Since 1 is less than or equal to 'max_collect_intervals', collecting all intervals...\n",
+      "01/15/2025 05:43:25 PM (gnomad.utils.filtering 531): Since 39 is less than or equal to 'max_collect_intervals', collecting all intervals...\n"
      ]
     },
     {
-     "ename": "TypeError",
-     "evalue": "_get_dataset() got an unexpected keyword argument 'padding_bp'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m drd2_ht \u001b[38;5;241m=\u001b[39m \u001b[43mfilter_by_gene_symbol\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdrd2\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe total number of variants in DRD2 is: \u001b[39m\u001b[38;5;124m\"\u001b[39m, drd2_ht\u001b[38;5;241m.\u001b[39mcount())\n",
-      "File \u001b[0;32m~/miniconda3/envs/gnomad-toolbox/lib/python3.9/site-packages/gnomad_toolbox/filtering/variant.py:139\u001b[0m, in \u001b[0;36mfilter_by_gene_symbol\u001b[0;34m(gene, exon_padding_bp, **kwargs)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filter_count \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    137\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo intervals match the gene symbol \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mgene\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 139\u001b[0m ht \u001b[38;5;241m=\u001b[39m \u001b[43mfilter_by_intervals\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_gencode_ht\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minterval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mht\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mht\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpadding_bp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexon_padding_bp\u001b[49m\n\u001b[1;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ht\n",
-      "File \u001b[0;32m~/miniconda3/envs/gnomad-toolbox/lib/python3.9/site-packages/gnomad_toolbox/filtering/variant.py:86\u001b[0m, in \u001b[0;36mfilter_by_intervals\u001b[0;34m(intervals, **kwargs)\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;124;03mFilter variants by interval(s).\u001b[39;00m\n\u001b[1;32m     78\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[38;5;124;03m:return: Table with variants in the interval(s).\u001b[39;00m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     85\u001b[0m \u001b[38;5;66;03m# Load the Hail Table if not provided\u001b[39;00m\n\u001b[0;32m---> 86\u001b[0m ht \u001b[38;5;241m=\u001b[39m \u001b[43m_get_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvariant\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m interval_filter(\n\u001b[1;32m     89\u001b[0m     ht,\n\u001b[1;32m     90\u001b[0m     intervals,\n\u001b[1;32m     91\u001b[0m     reference_genome\u001b[38;5;241m=\u001b[39mget_reference_genome(ht\u001b[38;5;241m.\u001b[39mlocus)\u001b[38;5;241m.\u001b[39mname,\n\u001b[1;32m     92\u001b[0m )\n",
-      "\u001b[0;31mTypeError\u001b[0m: _get_dataset() got an unexpected keyword argument 'padding_bp'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of variants in DRD2 is:  1764\n"
      ]
     }
    ],
@@ -523,10 +519,1079 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "700582e4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
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polyphen_max
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,"1"),("ENSP_mappings","5aer"),("ENSP_mappings","5aer"),("ENSP_mappings","6cm4"),("ENSP_mappings","6luq"),("ENSP_mappings","6vms"),("ENSP_mappings","7dfp"),("ENSP_mappings","7jvr"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000542968.5:c.1323C>A","ENSP00000442172.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,441,441,"ENSP00000442172","Ensembl",-1,"ENST00000542968",1,["P14416-1"],"T"),(1,"L","A1","protein_coding",NA,NA,1353,1353,1320,1320,"ctC/ctA",["synonymous_variant"],NA,[("Gene3D","1"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000544518.5:c.1320C>A","ENSP00000441068.1:p.Leu440=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,440,440,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"T"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["intron_variant","non_coding_transcript_variant"],NA,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,"ENST00000546284.1:n.245-811G>T",NA,NA,"MODIFIER","2/3",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"T"),(1,"L",NA,"protein_coding",1,NA,1677,1677,1323,1323,"ctC/ctA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.1323C>A","NP_000786.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,441,441,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"T"),(1,"L",NA,"protein_coding",NA,NA,1590,1590,1236,1236,"ctC/ctA",["synonymous_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.1236C>A","NP_057658.2:p.Leu412=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,412,412,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"T"),(1,"L",NA,"protein_coding",NA,NA,1405,1405,1323,1323,"ctC/ctA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.1323C>A","XP_016872785.1:p.Leu441=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,441,441,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"T")]"SNV"5.00e+00"AS_MQ"FalseFalseFalseFalseFalseTrueFalseFalse"multi-snv"2False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,2955,2914,2974,2923,2564,2680]07685[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,2955,2914,2974,2923,2564,2680]07685[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,1,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]002.96e+001.86e-01NA0.00e+003.00e-02-2.55e-01NANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+\n",
+       "| locus           | alleles    |\n",
+       "+-----------------+------------+\n",
+       "| locus   | array |\n",
+       "+-----------------+------------+\n",
+       "| chr11:113410731 | [\"C\",\"A\"]  |\n",
+       "| chr11:113410731 | [\"C\",\"T\"]  |\n",
+       "| chr11:113410735 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410736 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410736 | [\"G\",\"T\"]  |\n",
+       "+-----------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(2,1.37e-06,1461866,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(2,4.... |\n",
+       "| [(1,6.84e-07,1461866,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(75,5.13e-05,1461876,0),(75,5.13e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
+       "| [(15,1.03e-05,1461882,0),(15,1.03e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
+       "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|                2 |         4.47e-05 |            44724 |\n",
+       "|                1 |         8.99e-07 |          1112010 |\n",
+       "|                8 |         7.19e-06 |          1112004 |\n",
+       "|               15 |         1.35e-05 |          1112010 |\n",
+       "|                1 |         8.99e-07 |          1112010 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                              0 | \"amr\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|          4.57e-05 |             43740 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          5.71e-06 |            350102 |                               0 |\n",
+       "|          2.86e-06 |            350106 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| \"amr\"                  |\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| \"nfe\"                  |\n",
+       "| NA                     |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(7.41e-06,2.77e-06),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(4.19e-05,3.82e-05),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(6.16e-06,4.89e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                7.41e-06 | \"amr\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                3.09e-06 | \"nfe\"                           |\n",
+       "|                8.10e-06 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                2.77e-06 | \"amr\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                2.24e-06 | \"nfe\"                           |\n",
+       "|                6.42e-06 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                 7.58e-06 | \"amr\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                 9.50e-07 | \"nfe\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                 2.84e-06 | \"amr\"                            |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      bool | set         | set |  float64 |  float64 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      True | {\"rs140938110\"}  | {}       | 5.56e+00 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.56e+00 | 6.00e+01 |\n",
+       "|      True | {\"rs201801648\"}  | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|      True | {\"rs1159504221\"} | {}       | 5.49e-01 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.49e-01 | 6.00e+01 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
+       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
+       "|       0.00e+00 |          127501 | 1.23e+01 |            3.70e-02 |\n",
+       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
+       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| info.SB               | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| array          |  float64 |      int32 |    float64 |    float64 |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| [134,83,109,52]       | 9.81e-01 |        378 |   1.31e+01 |   6.00e+01 |\n",
+       "| [134,83,109,52]       | 9.81e-01 |        378 |   0.00e+00 |   6.00e+01 |\n",
+       "| [3240,2211,2993,1962] | 7.34e-01 |      10404 |   0.00e+00 |   6.00e+01 |\n",
+       "| [386,195,316,166]     | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
+       "| [386,195,316,166]     | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|          0.00e+00 |        2.94e-01 |               1895 |   1.06e+01 |\n",
+       "|          0.00e+00 |        9.04e-01 |               1967 |   9.88e+00 |\n",
+       "|          0.00e+00 |        1.00e+00 |             127480 |   1.23e+01 |\n",
+       "|          0.00e+00 |        9.11e-01 |              11899 |   1.17e+01 |\n",
+       "|          0.00e+00 |        3.28e-02 |                408 |   8.16e+00 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE      | info.AS_SOR | info.AS_VarDP |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "|                float64 | array          |     float64 |         int32 |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "|               2.52e-01 | [134,83,56,19]        |    1.42e+00 |           179 |\n",
+       "|              -5.01e-01 | [134,83,53,33]        |    6.81e-01 |           199 |\n",
+       "|               1.60e-02 | [3240,2211,2991,1961] |    7.34e-01 |         10383 |\n",
+       "|               9.70e-02 | [386,195,304,161]     |    6.47e-01 |          1013 |\n",
+       "|              -9.23e-01 | [386,195,12,5]        |    7.90e-01 |            50 |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           bool |                       bool |                   bool |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                      False |                  False |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                      False |                  False |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|     False |      False |            False |         False |       5.27e+00 |\n",
+       "|     False |      False |            False |         False |       5.40e+00 |\n",
+       "|     False |      False |            False |         False |       8.85e+00 |\n",
+       "|     False |      False |            False |         False |       7.50e+00 |\n",
+       "|     False |      False |            False |         False |       5.00e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|             -1.37e-06 |\n",
+       "|             -6.84e-07 |\n",
+       "|             -5.13e-05 |\n",
+       "|             -1.03e-05 |\n",
+       "|             -6.84e-07 |\n",
+       "+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.ZonDIhdwyghm7SuWkX... |\n",
+       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.Hp20SlFHUdM7jlWmVV... |\n",
+       "| [\"ga4gh:VA.SBSoboxxiC0V-mhjjHg2geRlf3I0Ib2d\",\"ga4gh:VA.GzUD_86k_EDbtk-R58... |\n",
+       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.OXPDvbuulFZ9CSk4Xt... |\n",
+       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.4qhkDG1qrfqiD2TeOA... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"A\"]           |\n",
+       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"T\"]           |\n",
+       "| [113410734,113410734] | [113410735,113410735] | [\"G\",\"A\"]           |\n",
+       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"A\"]           |\n",
+       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"T\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| str               |     int32 | str    | str                            |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| \"C/A\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tA\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tT\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410735 | \".\"    | \"chr11\t113410735\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/T\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tT\t.\t.\tGT\" |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq              |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [0,0,0,0,12,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
+       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
+       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,0,65,332,11308,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
+       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                       7715 |\n",
+       "|                                       7715 |\n",
+       "|                                       7717 |\n",
+       "|                                       7685 |\n",
+       "|                                       7685 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                         32 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq   |\n",
+       "+----------------------------------------------+\n",
+       "| array                                 |\n",
+       "+----------------------------------------------+\n",
+       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0] |\n",
+       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]    |\n",
+       "+----------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq          |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [6,0,1,0,14,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
+       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
+       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16]  |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,2,67,335,11310,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
+       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                           7715 |\n",
+       "|                                           7715 |\n",
+       "|                                           7717 |\n",
+       "|                                           7685 |\n",
+       "|                                           7685 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "|                                             32 |\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
+       "| [1,0,0,2,3,3,8,4,0,0]                      |\n",
+       "| [0,0,1,2,4,2,4,1,0,0]                      |\n",
+       "| [0,0,0,0,0,1,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           1 |\n",
+       "|                                           0 |\n",
+       "|                                           3 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        2.77e+01 |\n",
+       "|                                          0 |                        2.75e+01 |\n",
+       "|                                          0 |                        2.28e+01 |\n",
+       "|                                          0 |                        3.44e+00 |\n",
+       "|                                          0 |                        2.96e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            4.10e+00 |                       6.02e-01 |\n",
+       "|                            4.07e+00 |                       7.19e-01 |\n",
+       "|                            2.69e+00 |                       2.67e-01 |\n",
+       "|                            2.28e-01 |                             NA |\n",
+       "|                            1.86e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                 0.00e+00 |                    8.78e+00 |\n",
+       "|                                 2.00e-02 |                    8.78e+00 |\n",
+       "|                                 1.00e-02 |                    8.67e+00 |\n",
+       "|                                 1.00e-02 |                   -2.55e-01 |\n",
+       "|                                 3.00e-02 |                   -2.55e-01 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                      0.00e+00 |                          9.89e-01 |\n",
+       "|                      0.00e+00 |                          9.89e-01 |\n",
+       "|                      1.30e-01 |                          1.18e-01 |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of lof, missense, and synonymous variants passing filters in DRD2 is:  664\n"
+     ]
+    }
+   ],
    "source": [
     "var_ht = filter_by_consequence_category(\n",
     "    plof=True, \n",
@@ -555,10 +1620,1067 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "9887fdb0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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","HC","NAGNAG_SITE",NA,"INTRON_SIZE:1491",NA,NA,NA,NA,NA,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"C"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["downstream_gene_variant"],768,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"C"),(1,NA,NA,"protein_coding",1,NA,NA,NA,NA,NA,NA,["splice_acceptor_variant"],NA,NA,NA,NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.811-2A>G",NA,NA,"HIGH","6/7","HC","NAGNAG_SITE",NA,"INTRON_SIZE:1491","ENST00000362072.8",NA,NA,NA,NA,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"C"),(1,NA,NA,"protein_coding",NA,NA,NA,NA,NA,NA,NA,["splice_acceptor_variant"],NA,NA,NA,NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.724-2A>G",NA,NA,"HIGH","5/6","HC","NAGNAG_SITE",NA,"INTRON_SIZE:2537",NA,NA,NA,NA,NA,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"C"),(1,NA,NA,"protein_coding",NA,NA,NA,NA,NA,NA,NA,["splice_acceptor_variant"],NA,NA,NA,NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.811-2A>G",NA,NA,"HIGH","6/7","HC","NAGNAG_SITE",NA,"INTRON_SIZE:1491",NA,NA,NA,NA,NA,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"C")]"SNV"5.84e+00"AS_MQ"FalseFalseFalseFalseFalseTrueTrueFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,929,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,2699,3244,4728,36120,209411,224276,48878,7369,3147,3425,5116,7827,11506,15293,17615,18578,18759,20940]070149[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][1313,0,2,0,1481,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][618,1142,2737,3303,4730,36128,209411,224276,48878,7369,3147,3425,5116,7827,11506,15293,17615,18578,18759,20940]070149[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]003.50e+015.83e+00NA9.80e-01-7.90e-016.35e+00NANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+-------------+\n",
+       "| locus           | alleles     |\n",
+       "+-----------------+-------------+\n",
+       "| locus   | array  |\n",
+       "+-----------------+-------------+\n",
+       "| chr11:113412554 | [\"A\",\"G\"]   |\n",
+       "| chr11:113412612 | [\"CT\",\"C\"]  |\n",
+       "| chr11:113412614 | [\"ACG\",\"A\"] |\n",
+       "| chr11:113412865 | [\"G\",\"A\"]   |\n",
+       "| chr11:113412885 | [\"T\",\"C\"]   |\n",
+       "+-----------------+-------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(1,6.85e-07,1459578,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(2,1.37e-06,1461892,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(2,1.37e-06,1461894,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(1,6.85e-07,1459642,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33422,0),(0,0.... |\n",
+       "| [(1,6.86e-07,1458160,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33408,0),(0,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|                1 |         8.99e-07 |          1111998 |\n",
+       "|                2 |         1.80e-06 |          1112010 |\n",
+       "|                2 |         1.80e-06 |          1112012 |\n",
+       "|                1 |         8.99e-07 |          1111812 |\n",
+       "|                1 |         9.00e-07 |          1111636 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          5.71e-06 |            350106 |                               0 |\n",
+       "|          5.71e-06 |            350108 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| \"nfe\"                  |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                3.00e-07 | \"nfe\"                           |\n",
+       "|                3.00e-07 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                      NA | NA                              |\n",
+       "|                1.10e-07 | \"nfe\"                           |\n",
+       "|                1.10e-07 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                 9.50e-07 | \"nfe\"                            |\n",
+       "|                 9.50e-07 | \"nfe\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
+       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+----------+----------+----------+----------+----------------+\n",
+       "| was_split | rsid     | filters  |  info.FS |  info.MQ | info.MQRankSum |\n",
+       "+-----------+----------+----------+----------+----------+----------------+\n",
+       "|      bool | set | set |  float64 |  float64 |        float64 |\n",
+       "+-----------+----------+----------+----------+----------+----------------+\n",
+       "|      True | NA       | {}       | 2.83e+00 | 6.00e+01 |       0.00e+00 |\n",
+       "|     False | NA       | {}       | 5.48e-01 | 6.00e+01 |       0.00e+00 |\n",
+       "|      True | NA       | {}       | 2.32e+00 | 6.00e+01 |       0.00e+00 |\n",
+       "|      True | NA       | {}       | 1.02e+00 | 6.00e+01 |       0.00e+00 |\n",
+       "|     False | NA       | {}       | 2.45e+00 | 6.00e+01 |       0.00e+00 |\n",
+       "+-----------+----------+----------+----------+----------+----------------+\n",
+       "\n",
+       "+-----------------+----------+---------------------+---------------+----------+\n",
+       "| info.QUALapprox |  info.QD | info.ReadPosRankSum | info.SB       | info.SOR |\n",
+       "+-----------------+----------+---------------------+---------------+----------+\n",
+       "|           int64 |  float64 |             float64 | array  |  float64 |\n",
+       "+-----------------+----------+---------------------+---------------+----------+\n",
+       "|            1498 | 1.05e+01 |           -5.05e-01 | [63,17,52,10] | 1.04e+00 |\n",
+       "|            3458 | 1.79e+01 |            4.32e-01 | [65,38,58,32] | 7.50e-01 |\n",
+       "|            5336 | 1.56e+01 |            4.62e-01 | [99,81,94,68] | 8.22e-01 |\n",
+       "|             553 | 7.79e+00 |            2.96e-01 | [14,32,9,16]  | 4.68e-01 |\n",
+       "|             890 | 1.11e+01 |            1.80e-01 | [7,37,8,28]   | 3.79e-01 |\n",
+       "+-----------------+----------+---------------------+---------------+----------+\n",
+       "\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "| info.VarDP | info.AS_FS | info.AS_MQ | info.AS_MQRankSum | info.AS_pab_max |\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "|      int32 |    float64 |    float64 |           float64 |         float64 |\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "|        142 |   1.10e+00 |   6.00e+01 |          0.00e+00 |        1.00e+00 |\n",
+       "|        193 |   5.48e-01 |   6.00e+01 |          0.00e+00 |        5.90e-01 |\n",
+       "|        342 |   8.20e+00 |   6.00e+01 |          0.00e+00 |        6.65e-01 |\n",
+       "|         71 |   1.04e+00 |   6.00e+01 |          0.00e+00 |        3.92e-01 |\n",
+       "|         80 |   2.45e+00 |   6.00e+01 |          0.00e+00 |        4.53e-01 |\n",
+       "+------------+------------+------------+-------------------+-----------------+\n",
+       "\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "| info.AS_QUALapprox | info.AS_QD | info.AS_ReadPosRankSum | info.AS_SB_TABLE |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "|              int64 |    float64 |                float64 | array     |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "|                661 |   1.35e+01 |              -1.83e+00 | [63,17,21,4]     |\n",
+       "|               3458 |   1.79e+01 |               4.32e-01 | [65,38,58,32]    |\n",
+       "|               3455 |   1.80e+01 |               4.62e-01 | [99,81,58,32]    |\n",
+       "|                531 |   8.56e+00 |               2.96e-01 | [14,32,8,15]     |\n",
+       "|                890 |   1.11e+01 |               1.80e-01 | [7,37,8,28]      |\n",
+       "+--------------------+------------+------------------------+------------------+\n",
+       "\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "| info.AS_SOR | info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "|     float64 |         int32 |           bool |                       bool |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "|    9.29e-01 |            49 |           True |                      False |\n",
+       "|    7.50e-01 |           193 |          False |                         NA |\n",
+       "|    1.15e+00 |           192 |          False |                         NA |\n",
+       "|    5.03e-01 |            62 |           True |                      False |\n",
+       "|    3.79e-01 |            80 |          False |                      False |\n",
+       "+-------------+---------------+----------------+----------------------------+\n",
+       "\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "| info.sibling_singleton | info.omni | info.mills | info.monoallelic |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                   bool |      bool |       bool |             bool |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                     NA |     False |      False |            False |\n",
+       "|                     NA |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "\n",
+       "+---------------+----------------+-----------------------+\n",
+       "| info.only_het | info.AS_VQSLOD | info.inbreeding_coeff |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|          bool |        float64 |               float64 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|         False |       5.30e+00 |             -6.84e-07 |\n",
+       "|         False |       4.25e+00 |             -1.37e-06 |\n",
+       "|         False |       3.61e+00 |             -1.37e-06 |\n",
+       "|         False |       5.39e+00 |             -6.84e-07 |\n",
+       "|         False |       5.84e+00 |             -1.37e-06 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.mqU9WPvGr3hduyybbeN-AKJ1zN-kclsI\",\"ga4gh:VA.uvlicmv1vZnrBBqm0U... |\n",
+       "| [\"ga4gh:VA.NztLKtEJA7H0h_Tz31YRmTUR4hT2PNxO\",\"ga4gh:VA.5xtydtW8O1beQhV-1c... |\n",
+       "| [\"ga4gh:VA.Lx5RcGLe0vnqmstRd134JgqaJUNZIzCF\",\"ga4gh:VA.N3JXRaksYTV6CSLvZ_... |\n",
+       "| [\"ga4gh:VA.uAq5BRYMw_lO7PZcOF5TpbGC5hA8JCCE\",\"ga4gh:VA.rN4HBsKxAnhIfQxMXe... |\n",
+       "| [\"ga4gh:VA.Hj6Aa_k4TOMxqWIs6lE_cgxiRrPFWp8C\",\"ga4gh:VA.NDTpZX0YUzM19pYVv6... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113412553,113412553] | [113412554,113412554] | [\"A\",\"G\"]           |\n",
+       "| [113412611,113412612] | [113412613,113412613] | [\"CT\",\"\"]           |\n",
+       "| [113412613,113412614] | [113412616,113412616] | [\"ACG\",\"\"]          |\n",
+       "| [113412864,113412864] | [113412865,113412865] | [\"G\",\"A\"]           |\n",
+       "| [113412884,113412884] | [113412885,113412885] | [\"T\",\"C\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+----------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                        |\n",
+       "+-------------------+-----------+--------+----------------------------------+\n",
+       "| str               |     int32 | str    | str                              |\n",
+       "+-------------------+-----------+--------+----------------------------------+\n",
+       "| \"A/G\"             | 113412554 | \".\"    | \"chr11\t113412554\t.\tA\tG\t.\t.\tGT\"   |\n",
+       "| \"T/-\"             | 113412613 | \".\"    | \"chr11\t113412612\t.\tCT\tC\t.\t.\tGT\"  |\n",
+       "| \"CG/-\"            | 113412616 | \".\"    | \"chr11\t113412614\t.\tACG\tA\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113412865 | \".\"    | \"chr11\t113412865\t.\tG\tA\t.\t.\tGT\"   |\n",
+       "| \"T/C\"             | 113412885 | \".\"    | \"chr11\t113412885\t.\tT\tC\t.\t.\tGT\"   |\n",
+       "+-------------------+-----------+--------+----------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"splice_donor_variant\"      |\n",
+       "| \"frameshift_variant\"        |\n",
+       "| \"frameshift_variant\"        |\n",
+       "| \"stop_gained\"               |\n",
+       "| \"splice_acceptor_variant\"   |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq                 |\n",
+       "+------------------------------------------------------------+\n",
+       "| array                                               |\n",
+       "+------------------------------------------------------------+\n",
+       "| [0,0,0,0,134,0,1469,0,311660,0,82,0,416443,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,1,0,2,0,314386,0,0,0,416555,0,0,0,0,0,0,2]        |\n",
+       "| [0,0,0,0,1,0,1,0,314388,0,0,0,416555,0,0,0,0,0,0,2]        |\n",
+       "| [0,0,0,0,547,0,1910,0,310815,0,33,0,416515,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,929,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1] |\n",
+       "+------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,1555,817,16216,229270,272936,45138,9370,3566,3616,4059,4521,5017,549... |\n",
+       "| [0,0,1,279,16685,230939,273577,45298,9412,3597,3653,4094,4550,5033,5504,5... |\n",
+       "| [0,0,0,278,16685,230938,273578,45300,9412,3597,3653,4094,4550,5033,5504,5... |\n",
+       "| [0,0,1141,1461,3821,36585,211757,225880,49280,7441,3163,3441,5118,7834,11... |\n",
+       "| [0,0,2699,3244,4728,36120,209411,224276,48878,7369,3147,3425,5116,7827,11... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                     102137 |\n",
+       "|                                     102187 |\n",
+       "|                                     102188 |\n",
+       "|                                      70182 |\n",
+       "|                                      70149 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          1 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                 |\n",
+       "+----------------------------------------------------------------+\n",
+       "| array                                                   |\n",
+       "+----------------------------------------------------------------+\n",
+       "| [441,0,0,0,850,1,1469,0,311660,0,82,0,416443,0,0,0,0,0,0,1]    |\n",
+       "| [1,0,0,0,1,0,2,0,314386,0,0,0,416555,0,0,0,0,0,0,2]            |\n",
+       "| [0,0,0,0,1,0,1,0,314388,0,0,0,416555,0,0,0,0,0,0,2]            |\n",
+       "| [890,0,0,0,783,0,1910,0,310815,0,33,0,416515,0,0,0,0,0,0,1]    |\n",
+       "| [1313,0,2,0,1481,0,3800,0,308036,0,642,0,415672,0,0,0,0,0,0,1] |\n",
+       "+----------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [244,858,1593,833,16218,229270,272936,45138,9370,3566,3616,4059,4521,5017... |\n",
+       "| [0,0,1,279,16685,230939,273577,45298,9412,3597,3653,4094,4550,5034,5504,5... |\n",
+       "| [0,0,0,278,16685,230938,273578,45300,9412,3597,3653,4094,4550,5033,5504,5... |\n",
+       "| [431,622,1175,1493,3823,36589,211757,225880,49281,7441,3163,3441,5118,783... |\n",
+       "| [618,1142,2737,3303,4730,36128,209411,224276,48878,7369,3147,3425,5116,78... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                         102137 |\n",
+       "|                                         102187 |\n",
+       "|                                         102188 |\n",
+       "|                                          70182 |\n",
+       "|                                          70149 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              1 |\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,1,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           1 |\n",
+       "|                                           1 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        3.40e+01 |\n",
+       "|                                          0 |                        3.20e+01 |\n",
+       "|                                          0 |                        3.30e+01 |\n",
+       "|                                          0 |                        4.50e+01 |\n",
+       "|                                          0 |                        3.50e+01 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            5.66e+00 |                             NA |\n",
+       "|                            4.56e+00 |                             NA |\n",
+       "|                            4.99e+00 |                             NA |\n",
+       "|                            8.76e+00 |                             NA |\n",
+       "|                            5.83e+00 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             9.60e-01 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             9.80e-01 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                -8.30e-01 |                    6.33e+00 |\n",
+       "|                                -4.00e-02 |                    8.89e+00 |\n",
+       "|                                -1.20e-01 |                    3.00e-01 |\n",
+       "|                                -1.10e-01 |                    8.79e+00 |\n",
+       "|                                -7.90e-01 |                    6.35e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of lof variants passing filters in DRD2 is:  17\n"
+     ]
+    }
+   ],
    "source": [
     "var_ht = filter_by_consequence_category(plof=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
@@ -582,10 +2704,1079 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "86596aaf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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A,[("Gene3D","1"),("ENSP_mappings","5aer"),("ENSP_mappings","5aer"),("ENSP_mappings","6cm4"),("ENSP_mappings","6luq"),("ENSP_mappings","6vms"),("ENSP_mappings","7dfp"),("ENSP_mappings","7jvr"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000542968.5:c.1319T>C","ENSP00000442172.1:p.Ile440Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,440,440,"ENSP00000442172","Ensembl",-1,"ENST00000542968",1,["P14416-1"],"G"),(1,"I/T","A1","protein_coding",NA,NA,1349,1349,1316,1316,"aTc/aCc",["missense_variant"],NA,[("Gene3D","1"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000544518.5:c.1316T>C","ENSP00000441068.1:p.Ile439Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,439,439,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"G"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["intron_variant","non_coding_transcript_variant"],NA,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,"ENST00000546284.1:n.245-807A>G",NA,NA,"MODIFIER","2/3",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"G"),(1,"I/T",NA,"protein_coding",1,NA,1673,1673,1319,1319,"aTc/aCc",["missense_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.1319T>C","NP_000786.1:p.Ile440Thr",NA,"MODERATE",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,440,440,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"G"),(1,"I/T",NA,"protein_coding",NA,NA,1586,1586,1232,1232,"aTc/aCc",["missense_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.1232T>C","NP_057658.2:p.Ile411Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,411,411,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"G"),(1,"I/T",NA,"protein_coding",NA,NA,1401,1401,1319,1319,"aTc/aCc",["missense_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.1319T>C","XP_016872785.1:p.Ile440Thr",NA,"MODERATE",NA,NA,NA,NA,NA,NA,NA,NA,440,440,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"G")]"SNV"4.72e+00"AS_MQ"FalseFalseFalseFalseFalseTrueFalseFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,12,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,49,301,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,2954,2914,2973,2922,2564,2680]07683[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][4,0,0,0,13,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,1,50,304,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,2954,2914,2973,2922,2564,2680]07683[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]002.72e+014.02e+006.41e-010.00e+003.00e-026.25e+000.00e+005.08e-01
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+\n",
+       "| locus           | alleles    |\n",
+       "+-----------------+------------+\n",
+       "| locus   | array |\n",
+       "+-----------------+------------+\n",
+       "| chr11:113410731 | [\"C\",\"A\"]  |\n",
+       "| chr11:113410731 | [\"C\",\"T\"]  |\n",
+       "| chr11:113410735 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410738 | [\"G\",\"T\"]  |\n",
+       "| chr11:113410740 | [\"A\",\"G\"]  |\n",
+       "+-----------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(2,1.37e-06,1461866,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(2,4.... |\n",
+       "| [(1,6.84e-07,1461866,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(75,5.13e-05,1461876,0),(75,5.13e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
+       "| [(2,1.37e-06,1461886,0),(2,1.37e-06,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(1,6.84e-07,1461884,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(1,2.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|                2 |         4.47e-05 |            44724 |\n",
+       "|                1 |         8.99e-07 |          1112010 |\n",
+       "|                8 |         7.19e-06 |          1112004 |\n",
+       "|                2 |         5.04e-05 |            39700 |\n",
+       "|                1 |         2.24e-05 |            44724 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                              0 | \"amr\"                 |                 2 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 2 |\n",
+       "|                              0 | \"eas\"                 |                 1 |\n",
+       "|                              0 | \"amr\"                 |                 1 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|          4.57e-05 |             43740 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          5.71e-06 |            350102 |                               0 |\n",
+       "|          2.77e-05 |             36070 |                               0 |\n",
+       "|          2.29e-05 |             43740 |                               0 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| \"amr\"                  |\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| \"eas\"                  |\n",
+       "| \"amr\"                  |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(7.41e-06,2.77e-06),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(4.19e-05,3.82e-05),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(8.35e-06,3.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                7.41e-06 | \"amr\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                3.09e-06 | \"nfe\"                           |\n",
+       "|                8.35e-06 | \"eas\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                2.77e-06 | \"amr\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                2.24e-06 | \"nfe\"                           |\n",
+       "|                3.12e-06 | \"eas\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                 7.58e-06 | \"amr\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                 9.50e-07 | \"nfe\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                 2.84e-06 | \"amr\"                            |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                 3.60e-07 | \"nfe\"                            |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      bool | set         | set |  float64 |  float64 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      True | {\"rs140938110\"}  | {}       | 5.56e+00 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.56e+00 | 6.00e+01 |\n",
+       "|      True | {\"rs201801648\"}  | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|     False | {\"rs1223741945\"} | {}       | 1.38e+01 | 6.00e+01 |\n",
+       "|     False | NA               | {}       | 3.13e+00 | 6.00e+01 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
+       "|       0.00e+00 |            3862 | 1.02e+01 |           -1.98e-01 |\n",
+       "|       0.00e+00 |          127501 | 1.23e+01 |            3.70e-02 |\n",
+       "|       0.00e+00 |            3253 | 1.30e+01 |           -1.33e+00 |\n",
+       "|       0.00e+00 |             402 | 1.01e+01 |            7.13e-01 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| info.SB               | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| array          |  float64 |      int32 |    float64 |    float64 |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "| [134,83,109,52]       | 9.81e-01 |        378 |   1.31e+01 |   6.00e+01 |\n",
+       "| [134,83,109,52]       | 9.81e-01 |        378 |   0.00e+00 |   6.00e+01 |\n",
+       "| [3240,2211,2993,1962] | 7.34e-01 |      10404 |   0.00e+00 |   6.00e+01 |\n",
+       "| [63,66,76,46]         | 1.28e+00 |        251 |   1.38e+01 |   6.00e+01 |\n",
+       "| [17,8,12,3]           | 1.29e+00 |         40 |   3.13e+00 |   6.00e+01 |\n",
+       "+-----------------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|          0.00e+00 |        2.94e-01 |               1895 |   1.06e+01 |\n",
+       "|          0.00e+00 |        9.04e-01 |               1967 |   9.88e+00 |\n",
+       "|          0.00e+00 |        1.00e+00 |             127480 |   1.23e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |               3253 |   1.30e+01 |\n",
+       "|          0.00e+00 |        1.54e-01 |                402 |   1.01e+01 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE      | info.AS_SOR | info.AS_VarDP |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "|                float64 | array          |     float64 |         int32 |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "|               2.52e-01 | [134,83,56,19]        |    1.42e+00 |           179 |\n",
+       "|              -5.01e-01 | [134,83,53,33]        |    6.81e-01 |           199 |\n",
+       "|               1.60e-02 | [3240,2211,2991,1961] |    7.34e-01 |         10383 |\n",
+       "|              -1.33e+00 | [63,66,76,46]         |    1.28e+00 |           251 |\n",
+       "|               7.13e-01 | [17,8,12,3]           |    1.29e+00 |            40 |\n",
+       "+------------------------+-----------------------+-------------+---------------+\n",
+       "\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           bool |                       bool |                   bool |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                      False |                  False |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|           True |                      False |                  False |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|     False |      False |            False |         False |       5.27e+00 |\n",
+       "|     False |      False |            False |         False |       5.40e+00 |\n",
+       "|     False |      False |            False |         False |       8.85e+00 |\n",
+       "|     False |      False |            False |         False |       3.73e+00 |\n",
+       "|     False |      False |            False |         False |       4.72e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|             -1.37e-06 |\n",
+       "|             -6.84e-07 |\n",
+       "|             -5.13e-05 |\n",
+       "|             -1.37e-06 |\n",
+       "|             -6.84e-07 |\n",
+       "+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.ZonDIhdwyghm7SuWkX... |\n",
+       "| [\"ga4gh:VA.RY2FNHeS0QpJNM9lQpQ_pzX7Q27-MXmI\",\"ga4gh:VA.Hp20SlFHUdM7jlWmVV... |\n",
+       "| [\"ga4gh:VA.SBSoboxxiC0V-mhjjHg2geRlf3I0Ib2d\",\"ga4gh:VA.GzUD_86k_EDbtk-R58... |\n",
+       "| [\"ga4gh:VA.cROnHWMnTSTuzMfVIhSezHexCPbFdF3z\",\"ga4gh:VA.WAkJ8ZSeVrsh6ThoH7... |\n",
+       "| [\"ga4gh:VA.gXv9EmAG-Lbw5gSNFxiBHWB_WT8twCnG\",\"ga4gh:VA.S-YUvVERFiE1cgkxu2... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"A\"]           |\n",
+       "| [113410730,113410730] | [113410731,113410731] | [\"C\",\"T\"]           |\n",
+       "| [113410734,113410734] | [113410735,113410735] | [\"G\",\"A\"]           |\n",
+       "| [113410737,113410737] | [113410738,113410738] | [\"G\",\"T\"]           |\n",
+       "| [113410739,113410739] | [113410740,113410740] | [\"A\",\"G\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| str               |     int32 | str    | str                            |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| \"C/A\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tA\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113410731 | \".\"    | \"chr11\t113410731\t.\tC\tT\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410735 | \".\"    | \"chr11\t113410735\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/T\"             | 113410738 | \".\"    | \"chr11\t113410738\t.\tG\tT\t.\t.\tGT\" |\n",
+       "| \"A/G\"             | 113410740 | \".\"    | \"chr11\t113410740\t.\tA\tG\t.\t.\tGT\" |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "| \"missense_variant\"          |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq              |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [0,0,0,0,21,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [0,0,0,0,12,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
+       "| [0,0,0,0,12,0,66,0,314321,0,31,0,416511,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,12,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]  |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,0,98,374,11344,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,0,65,332,11308,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
+       "| [0,0,57,319,11282,107355,345042,189122,36330,5605,2379,2756,3073,2932,295... |\n",
+       "| [0,0,49,301,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,295... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                       7715 |\n",
+       "|                                       7715 |\n",
+       "|                                       7717 |\n",
+       "|                                       7684 |\n",
+       "|                                       7683 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                         32 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+----------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq   |\n",
+       "+----------------------------------------------+\n",
+       "| array                                 |\n",
+       "+----------------------------------------------+\n",
+       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0] |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0]    |\n",
+       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]    |\n",
+       "+----------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq          |\n",
+       "+---------------------------------------------------------+\n",
+       "| array                                            |\n",
+       "+---------------------------------------------------------+\n",
+       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [7,0,0,0,28,0,124,0,314262,0,99,0,416424,0,0,0,0,0,0,3] |\n",
+       "| [6,0,1,0,14,0,76,0,314251,0,32,0,416492,0,0,0,0,0,0,75] |\n",
+       "| [3,0,0,0,13,0,66,0,314321,0,31,0,416511,0,0,0,0,0,0,2]  |\n",
+       "| [4,0,0,0,13,0,49,0,314328,0,18,0,416534,0,0,0,0,0,0,1]  |\n",
+       "+---------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,7,98,380,11345,107362,344923,189028,36319,5607,2388,2759,3074,2931,295... |\n",
+       "| [0,2,67,335,11310,107353,344982,189073,36327,5607,2387,2756,3080,2935,295... |\n",
+       "| [0,1,58,321,11282,107355,345042,189122,36330,5605,2379,2756,3073,2932,295... |\n",
+       "| [0,1,50,304,11269,107358,345061,189138,36333,5605,2378,2755,3073,2932,295... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                           7715 |\n",
+       "|                                           7715 |\n",
+       "|                                           7717 |\n",
+       "|                                           7684 |\n",
+       "|                                           7683 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,75]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,1,2,3,4,7,3,7,5,1,4,1,3,0,2]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "|                                             32 |\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,1,1,6,12,25,24,5,1,0,0,0,0,0,0,0]   |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,0,0,0,0,0]                      |\n",
+       "| [1,0,0,2,3,3,8,4,0,0]                      |\n",
+       "| [0,0,0,1,0,0,0,1,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           1 |\n",
+       "|                                           0 |\n",
+       "|                                           3 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        2.77e+01 |\n",
+       "|                                          0 |                        2.75e+01 |\n",
+       "|                                          0 |                        2.28e+01 |\n",
+       "|                                          0 |                        2.53e+01 |\n",
+       "|                                          0 |                        2.72e+01 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            4.10e+00 |                       6.02e-01 |\n",
+       "|                            4.07e+00 |                       7.19e-01 |\n",
+       "|                            2.69e+00 |                       2.67e-01 |\n",
+       "|                            3.67e+00 |                       2.65e-01 |\n",
+       "|                            4.02e+00 |                       6.41e-01 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                 0.00e+00 |                    8.78e+00 |\n",
+       "|                                 2.00e-02 |                    8.78e+00 |\n",
+       "|                                 1.00e-02 |                    8.67e+00 |\n",
+       "|                                 1.00e-02 |                    4.85e+00 |\n",
+       "|                                 3.00e-02 |                    6.25e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                      0.00e+00 |                          9.89e-01 |\n",
+       "|                      0.00e+00 |                          9.89e-01 |\n",
+       "|                      1.30e-01 |                          1.18e-01 |\n",
+       "|                      3.00e-02 |                          9.36e-01 |\n",
+       "|                      0.00e+00 |                          5.08e-01 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of missense variants passing filters in DRD2 is:  409\n"
+     ]
+    }
+   ],
    "source": [
     "var_ht = filter_by_consequence_category(missense=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
@@ -597,22 +3788,1091 @@
     "f42fc1f8-afe3-4684-9fee-66582eba9080.png": {
      "image/png": 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"
     }
-   },
-   "cell_type": "markdown",
-   "id": "5b980b4f-13bd-4bb4-bdbc-5584a6a59ca5",
-   "metadata": {},
-   "source": [
-    "### Filter to `synonymous` variants passing filters\n",
-    "\n",
-    "![Screenshot 2024-12-20 at 11.12.57 AM.png](attachment:f42fc1f8-afe3-4684-9fee-66582eba9080.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b7e4368b",
-   "metadata": {},
-   "outputs": [],
+   },
+   "cell_type": "markdown",
+   "id": "5b980b4f-13bd-4bb4-bdbc-5584a6a59ca5",
+   "metadata": {},
+   "source": [
+    "### Filter to `synonymous` variants passing filters\n",
+    "\n",
+    "![Screenshot 2024-12-20 at 11.12.57 AM.png](attachment:f42fc1f8-afe3-4684-9fee-66582eba9080.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b7e4368b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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],3110,NA,NA,NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023",NA,NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",-1,"ENST00000540600",1,NA,"T"),(1,"K","P2","protein_coding",NA,"CCDS8361.1",1626,1626,1305,1305,"aaG/aaA",["synonymous_variant"],NA,[("Gene3D","1"),("ENSP_mappings","5aer"),("ENSP_mappings","5aer"),("ENSP_mappings","6cm4"),("ENSP_mappings","6luq"),("ENSP_mappings","6vms"),("ENSP_mappings","7dfp"),("ENSP_mappings","7jvr"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321"),("CDD","cd15309")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000542968.5:c.1305G>A","ENSP00000442172.1:p.Lys435=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,435,435,"ENSP00000442172","Ensembl",-1,"ENST00000542968",1,["P14416-1"],"T"),(1,"K","A1","protein_coding",NA,NA,1335,1335,1302,1302,"aaG/aaA",["synonymous_variant"],NA,[("Gene3D","1"),("Prints","PR00242"),("PANTHER","PTHR24248"),("PANTHER","PTHR24248"),("SMART","SM01381"),("Superfamily","SSF81321"),("CDD","cd15309")],"7/7",NA,"ENSG00000149295",1,"DRD2","HGNC","HGNC:3023","ENST00000544518.5:c.1302G>A","ENSP00000441068.1:p.Lys434=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,434,434,"ENSP00000441068","Ensembl",-1,"ENST00000544518",1,NA,"T"),(1,NA,NA,"lncRNA",1,NA,NA,NA,NA,NA,NA,["intron_variant","non_coding_transcript_variant"],NA,NA,NA,NA,"ENSG00000256757",NA,NA,NA,NA,"ENST00000546284.1:n.245-793C>T",NA,NA,"MODIFIER","2/3",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"Ensembl",1,"ENST00000546284",3,NA,"T"),(1,"K",NA,"protein_coding",1,NA,1659,1659,1305,1305,"aaG/aaA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.1305G>A","NP_000786.1:p.Lys435=",NA,"LOW",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,435,435,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"T"),(1,"K",NA,"protein_coding",NA,NA,1572,1572,1218,1218,"aaG/aaA",["synonymous_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.1218G>A","NP_057658.2:p.Lys406=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,406,406,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"T"),(1,"K",NA,"protein_coding",NA,NA,1387,1387,1305,1305,"aaG/aaA",["synonymous_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.1305G>A","XP_016872785.1:p.Lys435=",NA,"LOW",NA,NA,NA,NA,NA,NA,NA,NA,435,435,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"T")]"SNV"8.87e+00"AS_MQ"FalseFalseFalseFalseFalseTrueFalseFalse"snv"1False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,266,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,2971,2945,3008,2939,2587,2701]07737[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27]0207[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][1,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,16,267,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,2971,2945,3008,2939,2587,2701]07737[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27]0207[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][3,2,30,42,65,52,79,55,7,3]153[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,1,0,0,0,1,0]009.48e+008.09e-01NA0.00e+001.00e-025.82e+00NANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+\n",
+       "| locus           | alleles    |\n",
+       "+-----------------+------------+\n",
+       "| locus   | array |\n",
+       "+-----------------+------------+\n",
+       "| chr11:113410736 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410736 | [\"G\",\"T\"]  |\n",
+       "| chr11:113410739 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410751 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410754 | [\"C\",\"T\"]  |\n",
+       "+-----------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(15,1.03e-05,1461882,0),(15,1.03e-05,1461894,0),(0,0.00e+00,33480,0),(0,... |\n",
+       "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(1,6.84e-07,1461882,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33478,0),(0,0.... |\n",
+       "| [(1,6.84e-07,1461894,0),(1,6.84e-07,1461894,0),(0,0.00e+00,33480,0),(0,0.... |\n",
+       "| [(576,3.94e-04,1461892,2),(576,3.94e-04,1461894,2),(12,3.58e-04,33480,1),... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|               15 |         1.35e-05 |          1112010 |\n",
+       "|                1 |         8.99e-07 |          1112010 |\n",
+       "|               NA |               NA |               NA |\n",
+       "|                1 |         8.99e-07 |          1112012 |\n",
+       "|                8 |         1.39e-03 |             5768 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                              0 | \"nfe\"                 |                NA |\n",
+       "|                             NA | NA                    |                NA |\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                              0 | \"mid\"                 |                 4 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|          2.86e-06 |            350106 |                               0 |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|                NA |                NA |                              NA |\n",
+       "|          2.86e-06 |            350108 |                               0 |\n",
+       "|          9.64e-04 |              4148 |                               0 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| \"nfe\"                  |\n",
+       "| NA                     |\n",
+       "| NA                     |\n",
+       "| \"nfe\"                  |\n",
+       "| \"mid\"                  |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(6.16e-06,4.89e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(3.67e-04,3.57e-04),(2.06e-04,1.62e-04),(7.33e-04,6.53e-04),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                8.10e-06 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                7.33e-04 | \"amr\"                           |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                6.42e-06 | \"nfe\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "|                6.53e-04 | \"amr\"                           |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "|                 6.90e-04 | \"amr\"                            |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "|                 6.12e-04 | \"amr\"                            |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      bool | set         | set |  float64 |  float64 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      True | {\"rs1159504221\"} | {}       | 5.49e-01 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.49e-01 | 6.00e+01 |\n",
+       "|     False | NA               | {}       | 4.26e+00 | 6.00e+01 |\n",
+       "|     False | NA               | {}       | 8.26e+00 | 6.00e+01 |\n",
+       "|     False | {\"rs77930100\"}   | {}       | 1.45e-15 | 6.00e+01 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
+       "|       0.00e+00 |           12307 | 1.16e+01 |            7.40e-02 |\n",
+       "|       0.00e+00 |             574 | 1.10e+01 |            5.32e-01 |\n",
+       "|       0.00e+00 |            1098 | 2.03e+01 |           -7.69e-01 |\n",
+       "|       0.00e+00 |          947678 | 1.23e+01 |           -7.00e-03 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| info.SB                   | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| array              |  float64 |      int32 |    float64 |    float64 |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "| [386,195,316,166]         | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
+       "| [386,195,316,166]         | 6.55e-01 |       1063 |   9.64e-16 |   6.00e+01 |\n",
+       "| [23,8,13,8]               | 2.93e-01 |         52 |   4.26e+00 |   6.00e+01 |\n",
+       "| [5,11,21,18]              | 4.64e-01 |         54 |   8.26e+00 |   6.00e+01 |\n",
+       "| [20804,19237,19605,17401] | 7.35e-01 |      77027 |   1.45e-15 |   6.00e+01 |\n",
+       "+---------------------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|          0.00e+00 |        9.11e-01 |              11899 |   1.17e+01 |\n",
+       "|          0.00e+00 |        3.28e-02 |                408 |   8.16e+00 |\n",
+       "|          0.00e+00 |        2.12e-01 |                574 |   1.10e+01 |\n",
+       "|          0.00e+00 |        3.84e-03 |               1098 |   2.03e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |             947678 |   1.23e+01 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE          | info.AS_SOR |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "|                float64 | array              |     float64 |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "|               9.70e-02 | [386,195,304,161]         |    6.47e-01 |\n",
+       "|              -9.23e-01 | [386,195,12,5]            |    7.90e-01 |\n",
+       "|               5.32e-01 | [23,8,13,8]               |    2.93e-01 |\n",
+       "|              -7.69e-01 | [5,11,21,18]              |    4.64e-01 |\n",
+       "|               7.00e-03 | [20804,19237,19605,17401] |    7.35e-01 |\n",
+       "+------------------------+---------------------------+-------------+\n",
+       "\n",
+       "+---------------+----------------+----------------------------+\n",
+       "| info.AS_VarDP | info.singleton | info.transmitted_singleton |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "|         int32 |           bool |                       bool |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "|          1013 |          False |                         NA |\n",
+       "|            50 |           True |                      False |\n",
+       "|            52 |           True |                      False |\n",
+       "|            54 |           True |                      False |\n",
+       "|         77027 |          False |                         NA |\n",
+       "+---------------+----------------+----------------------------+\n",
+       "\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "| info.sibling_singleton | info.omni | info.mills | info.monoallelic |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                   bool |      bool |       bool |             bool |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "|                     NA |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                  False |     False |      False |            False |\n",
+       "|                     NA |     False |      False |            False |\n",
+       "+------------------------+-----------+------------+------------------+\n",
+       "\n",
+       "+---------------+----------------+-----------------------+\n",
+       "| info.only_het | info.AS_VQSLOD | info.inbreeding_coeff |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|          bool |        float64 |               float64 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "|         False |       7.50e+00 |             -1.03e-05 |\n",
+       "|         False |       5.00e+00 |             -6.84e-07 |\n",
+       "|         False |       5.73e+00 |             -6.84e-07 |\n",
+       "|         False |       2.35e+00 |             -6.84e-07 |\n",
+       "|         False |       8.87e+00 |              6.55e-03 |\n",
+       "+---------------+----------------+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.OXPDvbuulFZ9CSk4Xt... |\n",
+       "| [\"ga4gh:VA.QVbOetW6APM1_3jfGLLwArt4GfwgT6jE\",\"ga4gh:VA.4qhkDG1qrfqiD2TeOA... |\n",
+       "| [\"ga4gh:VA.uv4czuJ2nBK7lcR9wkK2XxHxMSui2-ME\",\"ga4gh:VA.bA0gzyCzudiyDon8QV... |\n",
+       "| [\"ga4gh:VA.euCgAx3O0dYWO86rUZqEKUoRwnhnhr-k\",\"ga4gh:VA.XQ_C7TKjlSKQw_ABwx... |\n",
+       "| [\"ga4gh:VA.8tDWBt70tPv5g5-MDqwnSl1-pElrIl1E\",\"ga4gh:VA.f3fUPUzazutlUq0Gul... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"A\"]           |\n",
+       "| [113410735,113410735] | [113410736,113410736] | [\"G\",\"T\"]           |\n",
+       "| [113410738,113410738] | [113410739,113410739] | [\"G\",\"A\"]           |\n",
+       "| [113410750,113410750] | [113410751,113410751] | [\"G\",\"A\"]           |\n",
+       "| [113410753,113410753] | [113410754,113410754] | [\"C\",\"T\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| str               |     int32 | str    | str                            |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| \"G/A\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/T\"             | 113410736 | \".\"    | \"chr11\t113410736\t.\tG\tT\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410739 | \".\"    | \"chr11\t113410739\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410751 | \".\"    | \"chr11\t113410751\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"C/T\"             | 113410754 | \".\"    | \"chr11\t113410754\t.\tC\tT\t.\t.\tGT\" |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "| \"synonymous_variant\"        |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq             |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
+       "| [0,0,0,0,8,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
+       "| [0,0,0,0,9,0,68,0,314320,0,24,0,416519,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,1,0,28,0,314363,0,2,0,416552,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574] |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,0,61,328,11304,107355,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,0,55,309,11275,107353,345050,189133,36332,5605,2379,2755,3073,2932,295... |\n",
+       "| [0,0,18,272,11233,107401,345096,189157,36337,5604,2379,2755,3073,2932,295... |\n",
+       "| [0,0,16,266,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,297... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                       7685 |\n",
+       "|                                       7685 |\n",
+       "|                                       7683 |\n",
+       "|                                       7683 |\n",
+       "|                                       7737 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq  |\n",
+       "+---------------------------------------------+\n",
+       "| array                                |\n",
+       "+---------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574] |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq             |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27] |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          1 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                        207 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq          |\n",
+       "+-----------------------------------------------------+\n",
+       "| array                                        |\n",
+       "+-----------------------------------------------------+\n",
+       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0] |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq         |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
+       "| [5,0,0,0,9,0,71,0,314313,0,37,0,416496,0,0,0,0,0,0,16] |\n",
+       "| [5,0,0,0,10,0,68,0,314320,0,24,0,416519,0,0,0,0,0,0,1] |\n",
+       "| [0,0,0,0,1,0,28,0,314363,0,2,0,416552,0,0,0,0,0,0,1]   |\n",
+       "| [1,0,0,0,3,0,24,0,314081,0,0,0,416264,0,0,0,0,0,0,574] |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,1,62,330,11305,107356,345020,189098,36329,5607,2382,2757,3074,2931,295... |\n",
+       "| [0,1,56,312,11275,107353,345051,189133,36332,5605,2379,2755,3073,2932,295... |\n",
+       "| [0,0,18,272,11233,107401,345096,189157,36337,5604,2379,2755,3073,2932,295... |\n",
+       "| [0,0,16,267,11219,107342,344994,189134,36335,5603,2386,2754,3071,2938,297... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                           7685 |\n",
+       "|                                           7685 |\n",
+       "|                                           7683 |\n",
+       "|                                           7683 |\n",
+       "|                                           7737 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,15]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,574]    |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq         |\n",
+       "+--------------------------------------------------------+\n",
+       "| array                                           |\n",
+       "+--------------------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,1,3,1,2,3,1,0,1,0,1,1,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]              |\n",
+       "| [0,0,0,0,1,1,4,13,22,21,26,25,30,25,35,40,41,25,31,27] |\n",
+       "+--------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              1 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                            207 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq      |\n",
+       "+-----------------------------------------------------+\n",
+       "| array                                        |\n",
+       "+-----------------------------------------------------+\n",
+       "| [0,0,0,0,1,0,0,1,1,11,1,0,0,0,0,0,0,0,0,0]          |\n",
+       "| [0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0]           |\n",
+       "| [0,0,0,0,3,1,13,43,101,196,147,52,11,5,0,0,0,0,0,0] |\n",
+       "+-----------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,1,2,4,2,4,1,0,0]                      |\n",
+       "| [0,0,0,0,0,1,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [3,2,30,42,65,52,79,55,7,3]                |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                          15 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          3 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,1,0,0,0,1,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        3.44e+00 |\n",
+       "|                                          0 |                        2.96e+00 |\n",
+       "|                                          0 |                        1.03e+01 |\n",
+       "|                                          0 |                        1.20e+01 |\n",
+       "|                                          0 |                        9.48e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            2.28e-01 |                             NA |\n",
+       "|                            1.86e-01 |                             NA |\n",
+       "|                            8.89e-01 |                             NA |\n",
+       "|                            1.03e+00 |                             NA |\n",
+       "|                            8.09e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                 1.00e-02 |                   -2.55e-01 |\n",
+       "|                                 3.00e-02 |                   -2.55e-01 |\n",
+       "|                                 0.00e+00 |                    3.26e+00 |\n",
+       "|                                 0.00e+00 |                    8.67e+00 |\n",
+       "|                                 1.00e-02 |                    5.82e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of synonymous variants passing filters in DRD2 is:  238\n"
+     ]
+    }
+   ],
    "source": [
     "var_ht = filter_by_consequence_category(synonymous=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
@@ -636,10 +4896,1079 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "4141ccb3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
histograms
grpmax
fafmax
info
qual_hists
raw_qual_hists
age_hists
in_silico_predictors
gnomad
non_ukb
gnomad
non_ukb
vrs
vep
vqsr_results
region_flags
allele_info
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
gq_hist_all
dp_hist_all
gq_hist_alt
dp_hist_alt
ab_hist_alt
age_hist_het
age_hist_hom
cadd
locus
alleles
freq
AC
AF
AN
homozygote_count
gen_anc
AC
AF
AN
homozygote_count
gen_anc
faf
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
faf95_max
faf95_max_gen_anc
faf99_max
faf99_max_gen_anc
a_index
was_split
rsid
filters
FS
MQ
MQRankSum
QUALapprox
QD
ReadPosRankSum
SB
SOR
VarDP
AS_FS
AS_MQ
AS_MQRankSum
AS_pab_max
AS_QUALapprox
AS_QD
AS_ReadPosRankSum
AS_SB_TABLE
AS_SOR
AS_VarDP
singleton
transmitted_singleton
sibling_singleton
omni
mills
monoallelic
only_het
AS_VQSLOD
inbreeding_coeff
VRS_Allele_IDs
VRS_Starts
VRS_Ends
VRS_States
allele_string
end
id
input
intergenic_consequences
most_severe_consequence
motif_feature_consequences
regulatory_feature_consequences
seq_region_name
start
strand
transcript_consequences
variant_class
AS_VQSLOD
AS_culprit
positive_train_site
negative_train_site
non_par
lcr
segdup
fail_interval_qc
outside_ukb_capture_region
outside_broad_capture_region
variant_type
n_alt_alleles
has_star
allele_type
was_mixed
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
bin_edges
bin_freq
n_smaller
n_larger
phred
raw_score
revel_max
spliceai_ds_max
pangolin_largest_ds
phylop
sift_max
polyphen_max
locus<GRCh38>array<str>array<struct{AC: int32, AF: float64, AN: int32, homozygote_count: int64}>int32float64int32int64strint32float64int32int64strarray<struct{faf95: float64, faf99: float64}>float64strfloat64strfloat64strfloat64strint32boolset<str>set<str>float64float64float64int64float64float64array<int32>float64int32float64float64float64float64int64float64float64array<int32>float64int32boolboolboolboolboolboolboolfloat64float64array<str>array<int32>array<int32>array<str>strint32strstrarray<struct{allele_num: int32, consequence_terms: array<str>, impact: str, variant_allele: str}>strarray<struct{allele_num: int32, consequence_terms: array<str>, high_inf_pos: str, impact: str, motif_feature_id: str, motif_name: str, motif_pos: int32, motif_score_change: float64, transcription_factors: array<str>, strand: int32, variant_allele: str}>array<struct{allele_num: int32, biotype: str, consequence_terms: array<str>, impact: str, regulatory_feature_id: str, variant_allele: str}>strint32int32array<struct{allele_num: int32, amino_acids: str, appris: str, biotype: str, canonical: int32, ccds: str, cdna_start: int32, cdna_end: int32, cds_end: int32, cds_start: int32, codons: str, consequence_terms: array<str>, distance: int32, domains: array<struct{db: str, name: str}>, exon: str, flags: str, gene_id: str, gene_pheno: int32, gene_symbol: str, gene_symbol_source: str, hgnc_id: str, hgvsc: str, hgvsp: str, hgvs_offset: int32, impact: str, intron: str, lof: str, lof_flags: str, lof_filter: str, lof_info: str, mane_select: str, mane_plus_clinical: str, mirna: array<str>, protein_end: int32, protein_start: int32, protein_id: str, source: str, strand: int32, transcript_id: str, tsl: int32, uniprot_isoform: array<str>, variant_allele: str}>strfloat64strboolboolboolboolboolboolboolboolstrint32boolstrboolarray<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64array<float64>array<int64>int64int64float32float32float64float32float64float64float64float64
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A,"A"),(1,NA,NA,"protein_coding",1,NA,1751,1751,NA,NA,NA,["3_prime_UTR_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_000795.4:c.*65C>T",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,"ENST00000362072.8",NA,NA,NA,NA,"NP_000786.1","RefSeq",-1,"NM_000795.4",NA,NA,"A"),(1,NA,NA,"protein_coding",NA,NA,1664,1664,NA,NA,NA,["3_prime_UTR_variant"],NA,NA,"7/7",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","NM_016574.4:c.*65C>T",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"NP_057658.2","RefSeq",-1,"NM_016574.4",NA,NA,"A"),(1,NA,NA,"protein_coding",NA,NA,1479,1479,NA,NA,NA,["3_prime_UTR_variant"],NA,NA,"8/8",NA,"1813",NA,"DRD2","EntrezGene","HGNC:3023","XM_017017296.2:c.*65C>T",NA,NA,"MODIFIER",NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,"XP_016872785.1","RefSeq",-1,"XM_017017296.2",NA,NA,"A")]"SNV"5.24e+00"AS_MQ"FalseFalseFalseFalseFalseNATrueFalse"multi-snv"2False"snv"False[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,4139,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,22477,65025,118275,313361,178106,20388,3276,1054,693,645,582,508,355,259,173,113,79,64]0265[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]01[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][653,0,1690,2,6865,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][264,3602,22767,65834,118294,313419,178113,20399,3282,1056,696,645,582,508,355,259,173,113,79,64]0265[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]00[0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01,8.50e+01,9.00e+01,9.50e+01,1.00e+02][0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]01[0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,4.00e-01,4.50e-01,5.00e-01,5.50e-01,6.00e-01,6.50e-01,7.00e-01,7.50e-01,8.00e-01,8.50e-01,9.00e-01,9.50e-01,1.00e+00][0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,1,0,1,0,0,0,0,0]00[3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,7.00e+01,7.50e+01,8.00e+01][0,0,0,0,0,0,0,0,0,0]006.25e+004.76e-01NA0.00e+002.10e-011.62e+00NANA
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+\n",
+       "| locus           | alleles    |\n",
+       "+-----------------+------------+\n",
+       "| locus   | array |\n",
+       "+-----------------+------------+\n",
+       "| chr11:113410657 | [\"C\",\"T\"]  |\n",
+       "| chr11:113410658 | [\"G\",\"A\"]  |\n",
+       "| chr11:113410658 | [\"G\",\"T\"]  |\n",
+       "| chr11:113410660 | [\"A\",\"G\"]  |\n",
+       "| chr11:113410662 | [\"G\",\"A\"]  |\n",
+       "+-----------------+------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| freq                                                                         |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array    |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(18,1.25e-05,1445518,0),(24,1.64e-05,1461322,0),(1,3.02e-05,33096,0),(0,... |\n",
+       "| [(23,1.59e-05,1448166,0),(23,1.57e-05,1461456,0),(0,0.00e+00,33180,0),(0,... |\n",
+       "| [(94,6.49e-05,1448162,0),(105,7.18e-05,1461456,0),(24,7.23e-04,33180,0),(... |\n",
+       "| [(1,6.89e-07,1450438,0),(2,1.37e-06,1461588,0),(0,0.00e+00,33208,0),(0,0.... |\n",
+       "| [(2,1.38e-06,1451396,0),(2,1.37e-06,1461538,0),(0,0.00e+00,33252,0),(1,2.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------+------------------+------------------+\n",
+       "| grpmax.gnomad.AC | grpmax.gnomad.AF | grpmax.gnomad.AN |\n",
+       "+------------------+------------------+------------------+\n",
+       "|            int32 |          float64 |            int32 |\n",
+       "+------------------+------------------+------------------+\n",
+       "|                4 |         1.01e-04 |            39606 |\n",
+       "|                3 |         7.57e-05 |            39630 |\n",
+       "|               24 |         7.23e-04 |            33180 |\n",
+       "|                1 |         9.07e-07 |          1103066 |\n",
+       "|                1 |         2.24e-05 |            44696 |\n",
+       "+------------------+------------------+------------------+\n",
+       "\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "| grpmax.gnomad.homozygote_count | grpmax.gnomad.gen_anc | grpmax.non_ukb.AC |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                          int64 | str                   |             int32 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "|                              0 | \"eas\"                 |                 4 |\n",
+       "|                              0 | \"eas\"                 |                10 |\n",
+       "|                              0 | \"afr\"                 |                15 |\n",
+       "|                              0 | \"nfe\"                 |                 1 |\n",
+       "|                              0 | \"amr\"                 |                 1 |\n",
+       "+--------------------------------+-----------------------+-------------------+\n",
+       "\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "| grpmax.non_ukb.AF | grpmax.non_ukb.AN | grpmax.non_ukb.homozygote_count |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|           float64 |             int32 |                           int64 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "|          1.11e-04 |             36050 |                               0 |\n",
+       "|          2.86e-05 |            349848 |                               0 |\n",
+       "|          8.50e-04 |             17650 |                               0 |\n",
+       "|          2.86e-06 |            349920 |                               0 |\n",
+       "|          2.29e-05 |             43728 |                               0 |\n",
+       "+-------------------+-------------------+---------------------------------+\n",
+       "\n",
+       "+------------------------+\n",
+       "| grpmax.non_ukb.gen_anc |\n",
+       "+------------------------+\n",
+       "| str                    |\n",
+       "+------------------------+\n",
+       "| \"eas\"                  |\n",
+       "| \"nfe\"                  |\n",
+       "| \"afr\"                  |\n",
+       "| \"nfe\"                  |\n",
+       "| \"amr\"                  |\n",
+       "+------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| faf                                                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [(7.78e-06,6.42e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(3.35e-05,1.... |\n",
+       "| [(1.06e-05,8.83e-06),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(2.01e-05,1.... |\n",
+       "| [(5.41e-05,5.02e-05),(4.99e-04,4.24e-04),(2.99e-05,1.81e-05),(0.00e+00,0.... |\n",
+       "| [(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "| [(2.30e-07,9.00e-08),(0.00e+00,0.00e+00),(0.00e+00,0.00e+00),(0.00e+00,0.... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf95_max | fafmax.gnomad.faf95_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                3.35e-05 | \"eas\"                           |\n",
+       "|                2.01e-05 | \"eas\"                           |\n",
+       "|                4.99e-04 | \"afr\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------+---------------------------------+\n",
+       "| fafmax.gnomad.faf99_max | fafmax.gnomad.faf99_max_gen_anc |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                 float64 | str                             |\n",
+       "+-------------------------+---------------------------------+\n",
+       "|                1.99e-05 | \"eas\"                           |\n",
+       "|                1.06e-05 | \"eas\"                           |\n",
+       "|                4.24e-04 | \"afr\"                           |\n",
+       "|                      NA | NA                              |\n",
+       "|                      NA | NA                              |\n",
+       "+-------------------------+---------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+\n",
+       "| fafmax.non_ukb.faf95_max | fafmax.non_ukb.faf95_max_gen_anc |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                  float64 | str                              |\n",
+       "+--------------------------+----------------------------------+\n",
+       "|                 3.78e-05 | \"eas\"                            |\n",
+       "|                 1.54e-05 | \"nfe\"                            |\n",
+       "|                 5.23e-04 | \"afr\"                            |\n",
+       "|                       NA | NA                               |\n",
+       "|                       NA | NA                               |\n",
+       "+--------------------------+----------------------------------+\n",
+       "\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "| fafmax.non_ukb.faf99_max | fafmax.non_ukb.faf99_max_gen_anc | a_index |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                  float64 | str                              |   int32 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "|                 2.24e-05 | \"eas\"                            |       3 |\n",
+       "|                 1.15e-05 | \"nfe\"                            |       1 |\n",
+       "|                 4.23e-04 | \"afr\"                            |       2 |\n",
+       "|                       NA | NA                               |       2 |\n",
+       "|                       NA | NA                               |       1 |\n",
+       "+--------------------------+----------------------------------+---------+\n",
+       "\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "| was_split | rsid             | filters  |  info.FS |  info.MQ |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      bool | set         | set |  float64 |  float64 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "|      True | {\"rs200559334\"}  | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|      True | {\"rs201475538\"}  | {}       | 8.65e-01 | 6.00e+01 |\n",
+       "|      True | {\"rs201475538\"}  | {}       | 8.65e-01 | 6.00e+01 |\n",
+       "|      True | {\"rs1950760213\"} | {}       | 0.00e+00 | 6.00e+01 |\n",
+       "|      True | NA               | {}       | 5.51e-01 | 6.00e+01 |\n",
+       "+-----------+------------------+----------+----------+----------+\n",
+       "\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "| info.MQRankSum | info.QUALapprox |  info.QD | info.ReadPosRankSum |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|        float64 |           int64 |  float64 |             float64 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "|       0.00e+00 |           12992 | 1.27e+01 |            0.00e+00 |\n",
+       "|       0.00e+00 |          142805 | 1.36e+01 |           -1.80e-02 |\n",
+       "|       0.00e+00 |          142805 | 1.36e+01 |           -1.80e-02 |\n",
+       "|       0.00e+00 |            3619 | 1.78e+01 |            6.74e-01 |\n",
+       "|       0.00e+00 |            3070 | 9.27e+00 |            0.00e+00 |\n",
+       "+----------------+-----------------+----------+---------------------+\n",
+       "\n",
+       "+---------------------+----------+------------+------------+------------+\n",
+       "| info.SB             | info.SOR | info.VarDP | info.AS_FS | info.AS_MQ |\n",
+       "+---------------------+----------+------------+------------+------------+\n",
+       "| array        |  float64 |      int32 |    float64 |    float64 |\n",
+       "+---------------------+----------+------------+------------+------------+\n",
+       "| [543,53,386,37]     | 7.04e-01 |       1019 |   4.82e-16 |   6.00e+01 |\n",
+       "| [4798,567,4576,579] | 6.27e-01 |      10518 |   1.55e+00 |   6.00e+01 |\n",
+       "| [4798,567,4576,579] | 6.27e-01 |      10518 |   0.00e+00 |   6.00e+01 |\n",
+       "| [83,21,79,20]       | 6.91e-01 |        203 |   4.82e-16 |   6.00e+01 |\n",
+       "| [174,36,99,22]      | 6.12e-01 |        331 |   1.21e+00 |   6.00e+01 |\n",
+       "+---------------------+----------+------------+------------+------------+\n",
+       "\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "| info.AS_MQRankSum | info.AS_pab_max | info.AS_QUALapprox | info.AS_QD |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|           float64 |         float64 |              int64 |    float64 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "|          0.00e+00 |        1.00e+00 |              12891 |   1.32e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |              19189 |   1.44e+01 |\n",
+       "|          0.00e+00 |        1.00e+00 |             123616 |   1.35e+01 |\n",
+       "|          0.00e+00 |        4.53e-01 |               3591 |   1.98e+01 |\n",
+       "|          0.00e+00 |        8.45e-01 |               2972 |   1.38e+01 |\n",
+       "+-------------------+-----------------+--------------------+------------+\n",
+       "\n",
+       "+------------------------+---------------------+-------------+---------------+\n",
+       "| info.AS_ReadPosRankSum | info.AS_SB_TABLE    | info.AS_SOR | info.AS_VarDP |\n",
+       "+------------------------+---------------------+-------------+---------------+\n",
+       "|                float64 | array        |     float64 |         int32 |\n",
+       "+------------------------+---------------------+-------------+---------------+\n",
+       "|              -3.90e-02 | [543,53,379,36]     |    7.13e-01 |           976 |\n",
+       "|               1.34e-01 | [4798,567,588,57]   |    8.94e-01 |          1334 |\n",
+       "|              -2.10e-02 | [4798,567,3988,522] |    5.96e-01 |          9184 |\n",
+       "|               9.51e-01 | [83,21,77,19]       |    7.15e-01 |           181 |\n",
+       "|               1.49e+00 | [174,36,87,20]      |    5.79e-01 |           216 |\n",
+       "+------------------------+---------------------+-------------+---------------+\n",
+       "\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "| info.singleton | info.transmitted_singleton | info.sibling_singleton |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|           bool |                       bool |                   bool |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                         NA |                     NA |\n",
+       "|          False |                      False |                  False |\n",
+       "|          False |                         NA |                     NA |\n",
+       "+----------------+----------------------------+------------------------+\n",
+       "\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "| info.omni | info.mills | info.monoallelic | info.only_het | info.AS_VQSLOD |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|      bool |       bool |             bool |          bool |        float64 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "|     False |      False |            False |         False |       9.14e+00 |\n",
+       "|     False |      False |            False |         False |       6.96e+00 |\n",
+       "|     False |      False |            False |         False |       9.35e+00 |\n",
+       "|     False |      False |            False |         False |       4.34e+00 |\n",
+       "|     False |      False |            False |         False |       5.24e+00 |\n",
+       "+-----------+------------+------------------+---------------+----------------+\n",
+       "\n",
+       "+-----------------------+\n",
+       "| info.inbreeding_coeff |\n",
+       "+-----------------------+\n",
+       "|               float64 |\n",
+       "+-----------------------+\n",
+       "|             -1.64e-05 |\n",
+       "|             -1.57e-05 |\n",
+       "|             -7.19e-05 |\n",
+       "|             -1.37e-06 |\n",
+       "|             -1.37e-06 |\n",
+       "+-----------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| info.vrs.VRS_Allele_IDs                                                      |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [\"ga4gh:VA.xwHxL6yO3I874HqhfGar1ZIaOp66ifsx\",\"ga4gh:VA.eglRJIN5-izMH1peMx... |\n",
+       "| [\"ga4gh:VA.3n-gYs3xsspnxwI91EMCEDsIcrj9bIqO\",\"ga4gh:VA.9Y9GDmOWCTqMks8dsN... |\n",
+       "| [\"ga4gh:VA.3n-gYs3xsspnxwI91EMCEDsIcrj9bIqO\",\"ga4gh:VA.R13f7bcCv-WGRkCM6W... |\n",
+       "| [\"ga4gh:VA.MHTiWiUO_hsQ_FAaIagdomN6DdnpjqRm\",\"ga4gh:VA.D8-MF1xUzSMIKncRc7... |\n",
+       "| [\"ga4gh:VA.oDr3967BFZ6uu2LJEc9_RVWIOnNqtSF-\",\"ga4gh:VA.N81rCvIHb-9PjhcHLL... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| info.vrs.VRS_Starts   | info.vrs.VRS_Ends     | info.vrs.VRS_States |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| array          | array          | array          |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "| [113410656,113410656] | [113410657,113410657] | [\"C\",\"T\"]           |\n",
+       "| [113410657,113410657] | [113410658,113410658] | [\"G\",\"A\"]           |\n",
+       "| [113410657,113410657] | [113410658,113410658] | [\"G\",\"T\"]           |\n",
+       "| [113410659,113410659] | [113410660,113410660] | [\"A\",\"G\"]           |\n",
+       "| [113410661,113410661] | [113410662,113410662] | [\"G\",\"A\"]           |\n",
+       "+-----------------------+-----------------------+---------------------+\n",
+       "\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| vep.allele_string |   vep.end | vep.id | vep.input                      |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| str               |     int32 | str    | str                            |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "| \"C/T\"             | 113410657 | \".\"    | \"chr11\t113410657\t.\tC\tT\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410658 | \".\"    | \"chr11\t113410658\t.\tG\tA\t.\t.\tGT\" |\n",
+       "| \"G/T\"             | 113410658 | \".\"    | \"chr11\t113410658\t.\tG\tT\t.\t.\tGT\" |\n",
+       "| \"A/G\"             | 113410660 | \".\"    | \"chr11\t113410660\t.\tA\tG\t.\t.\tGT\" |\n",
+       "| \"G/A\"             | 113410662 | \".\"    | \"chr11\t113410662\t.\tG\tA\t.\t.\tGT\" |\n",
+       "+-------------------+-----------+--------+--------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.intergenic_consequences                                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, impact: st... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------+\n",
+       "| vep.most_severe_consequence |\n",
+       "+-----------------------------+\n",
+       "| str                         |\n",
+       "+-----------------------------+\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "| \"3_prime_UTR_variant\"       |\n",
+       "+-----------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.motif_feature_consequences                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array, high_inf_p... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "| NA                                                                           |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| vep.regulatory_feature_consequences                                          |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.bin_freq                       |\n",
+       "+------------------------------------------------------------------+\n",
+       "| array                                                     |\n",
+       "+------------------------------------------------------------------+\n",
+       "| [0,0,0,0,4430,0,32721,0,357851,0,60423,0,267316,0,0,0,0,0,0,18]  |\n",
+       "| [0,0,0,0,4183,2,30033,1,354765,0,58262,0,276721,0,0,0,1,0,0,115] |\n",
+       "| [0,0,0,0,4183,0,30033,1,354765,0,58262,0,276721,0,0,0,1,0,0,115] |\n",
+       "| [0,0,0,0,3304,0,26636,0,351846,0,54948,0,288484,0,0,0,0,0,0,1]   |\n",
+       "| [0,0,0,0,4139,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]   |\n",
+       "+------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.bin_freq                                   |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0,0,29987,74977,120278,300405,170342,18935,3117,1021,684,644,577,507,353... |\n",
+       "| [0,0,26988,70993,120130,305542,173013,19471,3177,1036,688,647,582,509,357... |\n",
+       "| [0,0,26986,70993,120130,305542,173013,19471,3177,1036,688,647,582,509,357... |\n",
+       "| [0,0,24253,67281,120581,309738,175490,19882,3222,1041,692,644,582,508,354... |\n",
+       "| [0,0,22477,65025,118275,313361,178106,20388,3276,1054,693,645,582,508,355... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_all.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                        252 |\n",
+       "|                                        265 |\n",
+       "|                                        265 |\n",
+       "|                                        264 |\n",
+       "|                                        265 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18] |\n",
+       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,21] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,94] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1]  |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]  |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.gq_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.bin_freq    |\n",
+       "+-----------------------------------------------+\n",
+       "| array                                  |\n",
+       "+-----------------------------------------------+\n",
+       "| [0,0,0,2,3,3,2,1,0,1,1,0,1,0,0,1,1,0,0,1]     |\n",
+       "| [0,0,1,2,4,3,4,1,1,0,0,1,0,1,0,0,0,2,0,1]     |\n",
+       "| [0,0,2,3,11,13,14,10,8,9,1,3,2,0,4,0,0,1,1,1] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]     |\n",
+       "+-----------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.dp_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          1 |\n",
+       "|                                          2 |\n",
+       "|                                         11 |\n",
+       "|                                          1 |\n",
+       "|                                          1 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.bin_freq    |\n",
+       "+-----------------------------------------------+\n",
+       "| array                                  |\n",
+       "+-----------------------------------------------+\n",
+       "| [0,0,0,0,0,0,2,0,4,4,4,3,1,0,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,2,0,1,0,3,3,8,5,0,1,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,0,0,6,5,19,14,25,17,6,1,1,0,0,0,0,0] |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]     |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]     |\n",
+       "+-----------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.qual_hists.ab_hist_alt.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-----------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.bin_freq                        |\n",
+       "+-----------------------------------------------------------------------+\n",
+       "| array                                                          |\n",
+       "+-----------------------------------------------------------------------+\n",
+       "| [979,2,2487,1,8860,1,32722,0,357851,0,60423,0,267317,0,0,0,0,0,0,18]  |\n",
+       "| [886,4,2000,0,7936,2,30033,1,354765,1,58262,0,276721,0,0,0,1,0,0,116] |\n",
+       "| [886,4,2000,0,7936,2,30033,1,354765,1,58262,0,276721,0,0,0,1,0,0,116] |\n",
+       "| [684,0,1637,0,6556,2,26636,0,351846,0,54948,0,288484,0,0,0,0,0,0,1]   |\n",
+       "| [653,0,1690,2,6865,0,27770,1,353590,0,56800,0,283396,0,0,0,0,0,0,2]   |\n",
+       "+-----------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.bin_freq                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                                 |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [415,5663,30415,76214,120310,300505,170348,18947,3122,1023,686,644,577,50... |\n",
+       "| [362,4790,27388,71922,120182,305624,173020,19481,3184,1039,690,648,582,50... |\n",
+       "| [362,4790,27388,71922,120182,305624,173020,19481,3184,1039,690,648,582,50... |\n",
+       "| [296,4122,24622,67964,120609,309787,175498,19891,3228,1043,694,645,582,50... |\n",
+       "| [264,3602,22767,65834,118294,313419,178113,20399,3282,1056,696,645,582,50... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_all.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                            252 |\n",
+       "|                                            265 |\n",
+       "|                                            265 |\n",
+       "|                                            264 |\n",
+       "|                                            265 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [1,2,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,18]     |\n",
+       "| [0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,21]     |\n",
+       "| [2,4,1,0,0,2,0,0,0,1,0,0,0,0,0,0,0,0,0,95]     |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.gq_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "| [0.00e+00,5.00e+00,1.00e+01,1.50e+01,2.00e+01,2.50e+01,3.00e+01,3.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,0,1,3,6,4,2,1,0,1,1,0,1,0,0,1,1,0,0,1]      |\n",
+       "| [0,0,1,2,4,3,4,1,1,0,0,1,0,1,0,0,0,2,0,1]      |\n",
+       "| [0,2,4,4,17,13,14,10,8,9,1,3,2,0,4,0,0,1,1,1]  |\n",
+       "| [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.dp_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              1 |\n",
+       "|                                              2 |\n",
+       "|                                             11 |\n",
+       "|                                              1 |\n",
+       "|                                              1 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_edges                              |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "| [0.00e+00,5.00e-02,1.00e-01,1.50e-01,2.00e-01,2.50e-01,3.00e-01,3.50e-01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.bin_freq |\n",
+       "+------------------------------------------------+\n",
+       "| array                                   |\n",
+       "+------------------------------------------------+\n",
+       "| [0,1,3,2,0,0,2,0,4,4,4,3,1,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,2,0,1,0,3,3,8,5,0,1,0,0,0,0,0,0]      |\n",
+       "| [0,2,7,0,0,1,6,5,19,14,26,17,6,1,1,0,0,0,0,0]  |\n",
+       "| [0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0]      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0]      |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+-------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_smaller |\n",
+       "+-------------------------------------------------+\n",
+       "|                                           int64 |\n",
+       "+-------------------------------------------------+\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "|                                               0 |\n",
+       "+-------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------+\n",
+       "| histograms.raw_qual_hists.ab_hist_alt.n_larger |\n",
+       "+------------------------------------------------+\n",
+       "|                                          int64 |\n",
+       "+------------------------------------------------+\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "|                                              0 |\n",
+       "+------------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [1,1,0,2,5,2,1,0,0,0]                      |\n",
+       "| [0,0,1,1,5,3,2,0,0,0]                      |\n",
+       "| [0,1,4,4,2,5,0,1,4,1]                      |\n",
+       "| [1,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,1,0,1,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           2 |\n",
+       "|                                           0 |\n",
+       "|                                           1 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_het.n_larger |\n",
+       "+--------------------------------------------+\n",
+       "|                                      int64 |\n",
+       "+--------------------------------------------+\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "|                                          0 |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_edges                                  |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| array                                                               |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "| [3.00e+01,3.50e+01,4.00e+01,4.50e+01,5.00e+01,5.50e+01,6.00e+01,6.50e+01,... |\n",
+       "+------------------------------------------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.bin_freq |\n",
+       "+--------------------------------------------+\n",
+       "| array                               |\n",
+       "+--------------------------------------------+\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "| [0,0,0,0,0,0,0,0,0,0]                      |\n",
+       "+--------------------------------------------+\n",
+       "\n",
+       "+---------------------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_smaller |\n",
+       "+---------------------------------------------+\n",
+       "|                                       int64 |\n",
+       "+---------------------------------------------+\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "|                                           0 |\n",
+       "+---------------------------------------------+\n",
+       "\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "| histograms.age_hists.age_hist_hom.n_larger | in_silico_predictors.cadd.phred |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                      int64 |                         float32 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "|                                          0 |                        3.61e+00 |\n",
+       "|                                          0 |                        2.90e+00 |\n",
+       "|                                          0 |                        2.38e+00 |\n",
+       "|                                          0 |                        1.22e+00 |\n",
+       "|                                          0 |                        6.25e+00 |\n",
+       "+--------------------------------------------+---------------------------------+\n",
+       "\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "| in_silico_predictors.cadd.raw_score | in_silico_predictors.revel_max |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                             float32 |                        float64 |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "|                            2.42e-01 |                             NA |\n",
+       "|                            1.80e-01 |                             NA |\n",
+       "|                            1.31e-01 |                             NA |\n",
+       "|                           -1.85e-02 |                             NA |\n",
+       "|                            4.76e-01 |                             NA |\n",
+       "+-------------------------------------+--------------------------------+\n",
+       "\n",
+       "+--------------------------------------+\n",
+       "| in_silico_predictors.spliceai_ds_max |\n",
+       "+--------------------------------------+\n",
+       "|                              float32 |\n",
+       "+--------------------------------------+\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "|                             0.00e+00 |\n",
+       "+--------------------------------------+\n",
+       "\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "| in_silico_predictors.pangolin_largest_ds | in_silico_predictors.phylop |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                  float64 |                     float64 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "|                                 1.20e-01 |                   -4.68e-01 |\n",
+       "|                                 5.00e-02 |                    7.10e-02 |\n",
+       "|                                 3.00e-02 |                    7.10e-02 |\n",
+       "|                                 4.00e-02 |                   -1.27e+00 |\n",
+       "|                                 2.10e-01 |                    1.62e+00 |\n",
+       "+------------------------------------------+-----------------------------+\n",
+       "\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "| in_silico_predictors.sift_max | in_silico_predictors.polyphen_max |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                       float64 |                           float64 |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "|                            NA |                                NA |\n",
+       "+-------------------------------+-----------------------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The total number of other variants passing filters in DRD2 is:  783\n"
+     ]
+    }
+   ],
    "source": [
     "var_ht = filter_by_consequence_category(other=True, ht=drd2_ht)\n",
     "var_ht.show(5)\n",
@@ -658,7 +5987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "a25ddd1b",
    "metadata": {},
    "outputs": [],
@@ -676,10 +6005,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "4f78166f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
afr
locus
alleles
filters
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64
chr11:113410736["G","A"]{}00.00e+00334800
chr11:113410736["G","T"]{}00.00e+00334800
chr11:113410739["G","A"]{}00.00e+00334780
chr11:113410751["G","A"]{}00.00e+00334800
chr11:113410754["C","T"]{}123.58e-04334801
showing top 5 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| locus           | alleles    | filters  | afr.AC |   afr.AF | afr.AN |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| locus   | array | set |  int32 |  float64 |  int32 |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| chr11:113410736 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410736 | [\"G\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410739 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33478 |\n",
+       "| chr11:113410751 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410754 | [\"C\",\"T\"]  | {}       |     12 | 3.58e-04 |  33480 |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "\n",
+       "+----------------------+\n",
+       "| afr.homozygote_count |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    0 |\n",
+       "|                    1 |\n",
+       "+----------------------+\n",
+       "showing top 5 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "var_ht = get_ancestry_callstats(gen_ancs='afr', ht=drd2_synonymous_ht)\n",
     "var_ht.show(5)"
@@ -695,10 +6066,118 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "e3a07848",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
afr
amr
eas
mid
nfe
sas
locus
alleles
filters
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64int32float64int32int64
chr11:113410736["G","A"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+0057620151.35e-051112010000.00e+00862540
chr11:113410736["G","T"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005762018.99e-071112010000.00e+00862540
chr11:113410739["G","A"]{}00.00e+0033478000.00e+0044724000.00e+0039700000.00e+005764000.00e+001112010000.00e+00862560
chr11:113410751["G","A"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005768018.99e-071112012000.00e+00862580
chr11:113410754["C","T"]{}123.58e-04334801439.61e-0444724000.00e+0039700081.39e-03576803282.95e-041112012000.00e+00862580
chr11:113410757["G","A"]{}00.00e+0033480000.00e+0044724012.52e-0539700000.00e+005766000.00e+001112012000.00e+00862580
chr11:113410757["G","T"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005766000.00e+001112012033.48e-05862580
chr11:113410763["C","T"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005768000.00e+001112010011.16e-05862580
chr11:113410769["G","A"]{}00.00e+0033480000.00e+0044724000.00e+0039700000.00e+005768021.80e-061112012000.00e+00862580
chr11:113410775["G","A"]{}00.00e+0033480000.00e+0044724012.52e-0539700000.00e+005768018.99e-071112010000.00e+00862580
showing top 10 rows
\n"
+      ],
+      "text/plain": [
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| locus           | alleles    | filters  | afr.AC |   afr.AF | afr.AN |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| locus   | array | set |  int32 |  float64 |  int32 |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "| chr11:113410736 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410736 | [\"G\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410739 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33478 |\n",
+       "| chr11:113410751 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410754 | [\"C\",\"T\"]  | {}       |     12 | 3.58e-04 |  33480 |\n",
+       "| chr11:113410757 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410757 | [\"G\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410763 | [\"C\",\"T\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410769 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "| chr11:113410775 | [\"G\",\"A\"]  | {}       |      0 | 0.00e+00 |  33480 |\n",
+       "+-----------------+------------+----------+--------+----------+--------+\n",
+       "\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "| afr.homozygote_count | amr.AC |   amr.AF | amr.AN | amr.homozygote_count |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "|                int64 |  int32 |  float64 |  int32 |                int64 |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    1 |     43 | 9.61e-04 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  44724 |                    0 |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "\n",
+       "+--------+----------+--------+----------------------+--------+----------+\n",
+       "| eas.AC |   eas.AF | eas.AN | eas.homozygote_count | mid.AC |   mid.AF |\n",
+       "+--------+----------+--------+----------------------+--------+----------+\n",
+       "|  int32 |  float64 |  int32 |                int64 |  int32 |  float64 |\n",
+       "+--------+----------+--------+----------------------+--------+----------+\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      8 | 1.39e-03 |\n",
+       "|      1 | 2.52e-05 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      0 | 0.00e+00 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "|      1 | 2.52e-05 |  39700 |                    0 |      0 | 0.00e+00 |\n",
+       "+--------+----------+--------+----------------------+--------+----------+\n",
+       "\n",
+       "+--------+----------------------+--------+----------+---------+\n",
+       "| mid.AN | mid.homozygote_count | nfe.AC |   nfe.AF |  nfe.AN |\n",
+       "+--------+----------------------+--------+----------+---------+\n",
+       "|  int32 |                int64 |  int32 |  float64 |   int32 |\n",
+       "+--------+----------------------+--------+----------+---------+\n",
+       "|   5762 |                    0 |     15 | 1.35e-05 | 1112010 |\n",
+       "|   5762 |                    0 |      1 | 8.99e-07 | 1112010 |\n",
+       "|   5764 |                    0 |      0 | 0.00e+00 | 1112010 |\n",
+       "|   5768 |                    0 |      1 | 8.99e-07 | 1112012 |\n",
+       "|   5768 |                    0 |    328 | 2.95e-04 | 1112012 |\n",
+       "|   5766 |                    0 |      0 | 0.00e+00 | 1112012 |\n",
+       "|   5766 |                    0 |      0 | 0.00e+00 | 1112012 |\n",
+       "|   5768 |                    0 |      0 | 0.00e+00 | 1112010 |\n",
+       "|   5768 |                    0 |      2 | 1.80e-06 | 1112012 |\n",
+       "|   5768 |                    0 |      1 | 8.99e-07 | 1112010 |\n",
+       "+--------+----------------------+--------+----------+---------+\n",
+       "\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "| nfe.homozygote_count | sas.AC |   sas.AF | sas.AN | sas.homozygote_count |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "|                int64 |  int32 |  float64 |  int32 |                int64 |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "|                    0 |      0 | 0.00e+00 |  86254 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86254 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86256 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "|                    0 |      3 | 3.48e-05 |  86258 |                    0 |\n",
+       "|                    0 |      1 | 1.16e-05 |  86258 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "|                    0 |      0 | 0.00e+00 |  86258 |                    0 |\n",
+       "+----------------------+--------+----------+--------+----------------------+\n",
+       "showing top 10 rows"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "var_ht = get_ancestry_callstats(gen_ancs=['afr', 'amr', 'eas', 'mid', 'nfe', 'sas'], ht=drd2_synonymous_ht)\n",
     "var_ht.show()"
@@ -722,10 +6201,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "4845be1d-d4c0-4b83-9e92-bd72379b8a99",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "\n",
+       "
afr
locus
alleles
filters
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64
chr22:15528692["C","G"]{}6351.90e-02333806
"
+      ],
+      "text/plain": [
+       "+----------------+------------+----------+--------+----------+--------+\n",
+       "| locus          | alleles    | filters  | afr.AC |   afr.AF | afr.AN |\n",
+       "+----------------+------------+----------+--------+----------+--------+\n",
+       "| locus  | array | set |  int32 |  float64 |  int32 |\n",
+       "+----------------+------------+----------+--------+----------+--------+\n",
+       "| chr22:15528692 | [\"C\",\"G\"]  | {}       |    635 | 1.90e-02 |  33380 |\n",
+       "+----------------+------------+----------+--------+----------+--------+\n",
+       "\n",
+       "+----------------------+\n",
+       "| afr.homozygote_count |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "|                    6 |\n",
+       "+----------------------+"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "var_ht = get_single_variant_ancestry_callstats(gen_ancs='AFR', contig='chr22', position=15528692, ref='C', alt='G')\n",
     "var_ht.show(5)"
@@ -741,10 +6249,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "bee28829",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "
afr
locus
alleles
filters
AC
AF
AN
homozygote_count
locus<GRCh38>array<str>set<str>int32float64int32int64
"
+      ],
+      "text/plain": [
+       "+---------------+------------+----------+--------+---------+--------+\n",
+       "| locus         | alleles    | filters  | afr.AC |  afr.AF | afr.AN |\n",
+       "+---------------+------------+----------+--------+---------+--------+\n",
+       "| locus | array | set |  int32 | float64 |  int32 |\n",
+       "+---------------+------------+----------+--------+---------+--------+\n",
+       "+---------------+------------+----------+--------+---------+--------+\n",
+       "\n",
+       "+----------------------+\n",
+       "| afr.homozygote_count |\n",
+       "+----------------------+\n",
+       "|                int64 |\n",
+       "+----------------------+\n",
+       "+----------------------+"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "var_ht = get_single_variant_ancestry_callstats(gen_ancs='AFR', contig='chr22', position=15528692, ref='C', alt='A')\n",
     "var_ht.show(5)"

From 233704c5571abb781b7710d2c194bacbe2432ed0 Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Wed, 15 Jan 2025 18:04:44 -0500
Subject: [PATCH 095/121] Apply suggestions

---
 gnomad_toolbox/notebooks/explore_release_data.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
index 18cc006..5070564 100644
--- a/gnomad_toolbox/notebooks/explore_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -523,7 +523,7 @@
     "    \n",
     "\n",
     "\n",
-    "**NOTE:** In this notebook, we show examples to mainly explore our GRCh38 release datasets, click on the full name of the datasets will take you to the Downloads page on gnomAD Browser, and the abbreviations or the data_type to each section in this notebook (just as if you clink on the Table of Contents)."
+    "**NOTE**: In this notebook, most of the examples show the GRCh38 release datasets. Clicking on the full name under the Datasets column in the above table links to the relevant section of the Downloads page on gnomAD Browser. Clicking on the abbreviation under the data_type column links to the relevant section in this notebook that includes an example of loading and exploring the Hail Table."
    ]
   },
   {

From 306daf751691baad043522cc4720dce7e1b39b0a Mon Sep 17 00:00:00 2001
From: Qin He <44242118+KoalaQin@users.noreply.github.com>
Date: Wed, 15 Jan 2025 18:11:43 -0500
Subject: [PATCH 096/121] Add jupyter server version limit to avoid jupyter
 notebook 403 error

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index b0183e0..6604853 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,3 +7,4 @@ jupyterlab
 nodejs
 npm
 jupyter_bokeh
+jupyter-server<2.0.0

From aabf57448fb31964d5ac4b82d79443f04e201a90 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 21 Jan 2025 16:08:55 -0700
Subject: [PATCH 097/121] Add changes to the README.md and support for jupyter
 configs

---
 MANIFEST.in               |   2 +
 README.md                 | 166 +++++++++++++++++++++-------------
 gnomad_toolbox/scripts.py | 184 ++++++++++++++++++++++++++++++++++++++
 setup.py                  |   8 ++
 4 files changed, 296 insertions(+), 64 deletions(-)
 create mode 100644 MANIFEST.in
 create mode 100644 gnomad_toolbox/scripts.py

diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000..2151586
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,2 @@
+include gnomad_toolbox/notebooks/*.ipynb
+include gnomad_toolbox/configs/*
diff --git a/README.md b/README.md
index ae3d9fb..fab60eb 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,17 @@
 # gnomad-toolbox
-This repository provides a set of Python functions to simplify working with gnomAD Hail Tables. It includes tools for data access, filtering, and analysis.
+This repository provides a set of Python functions to simplify working with gnomAD Hail Tables. It includes tools for
+data access, filtering, and analysis.
+
+**Disclaimer: This package is in its early stages of development, and we are actively working on improving it. There may
+be bugs, and the API may change. We welcome feedback and contributions.**
 
 ## Repository structure
 ```
-ggnomad_toolbox/
+gnomad_toolbox/
 │
 ├── load_data.py         # Functions to load gnomAD release Hail Tables.
 │
 ├── filtering/
-│   ├── __init__.py
 │   ├── constraint.py    # Functions to filter constraint metrics (e.g., observed/expected ratios).
 │   ├── coverage.py      # Functions to filter variants or regions based on coverage thresholds.
 │   ├── frequency.py     # Functions to filter variants by allele frequency thresholds.
@@ -17,40 +20,56 @@ ggnomad_toolbox/
 │   ├── vep.py           # Functions to filter variants based on VEP (Variant Effect Predictor) annotations.
 │
 ├── analysis/
-│   ├── __init__.py
 │   ├── general.py       # General analysis functions, such as summarizing variant statistics.
 │
 ├── notebooks/
-│   ├── intro_to_release_data.ipynb    # Jupyter notebook introducing the loading of gnomAD release data.
+│   ├── explore_release_data.ipynb    # Jupyter notebook introducing the loading of gnomAD release data.
+|   |── intro_to_filtering_variant_data.ipynb    # Jupyter notebook introducing the filtering of gnomAD variant data.
+|   |── dive_into_secondary_analyses.ipynb    # Jupyter notebook introducing secondary analyses using gnomAD data.
 ```
 
+---
+
 ## Setting Up Your Environment for Hail and gnomAD Toolbox
 
 This guide provides step-by-step instructions to set up a working environment for
-using Hail and the gnomad_toolbox.
+using Hail and the gnomAD Toolbox.
+
+**Disclaimer: We provide this guide to help you set up your environment, but we cannot
+guarantee that it will work on all systems. If you encounter any issues, you can
+reach out to us on the [gnomAD Forum](https://discuss.gnomad.broadinstitute.org), and
+if it is something that we have come across before, we will try to help you out.**
 
-Prerequisites
+### Prerequisites
 
 Before starting, ensure you have the following:
 * Administrator access to your system to install software.
 * Internet connection for downloading dependencies.
-
-
-## Part 1: Setting Up Your Environment
+* **Note: Hail 0.2.127+ requires Java 8 or Java 11 and jupyter labs requires Java
+11+, and if you have an Apple M1 or M2, you must have arm64 Java installed, you
+can first check your Java version by running:**
+  ```commandline
+  java -version
+  ```
+  and check if you have the arm64 Java by running:
+  ```commandline
+  file $JAVA_HOME/bin/java
+  ```
+  If you don't have the arm64 Java, you can find it [here](https://www.azul.com/downloads/?os=macos&architecture=arm-64-bit&package=jre#zulu)
 
 ### Install Miniconda
 Miniconda is a lightweight distribution of Conda that includes just Conda and its dependencies.
 1. Download Miniconda for your system from the [official website](https://docs.anaconda.com/miniconda/install/).
 2. Follow the installation instructions for your operating system:
-
-	•	Linux/macOS: Run the installer script in your terminal:
-    ```
-    bash Miniconda3-latest-Linux-x86_64.sh
-    ```
-    •	Windows: Run the installer executable and follow the installation wizard.
-        (# TODO: Will we encourage users to use Windows?)
+   * Linux/macOS: Run the installer script in your terminal:
+      ```commandline
+      bash Miniconda3-latest-Linux-x86_64.sh
+      ```
+   * Windows: Run the installer executable and follow the installation wizard.
+   **Note: Our team has not tested the gnomad-toolbox on Windows, so we cannot guarantee that it will
+   work as expected or support any issues that may arise.**
 3. Confirm the installation by running:
-   ```
+   ```commandline
    conda --version
    ```
 
@@ -63,67 +82,51 @@ Miniconda is a lightweight distribution of Conda that includes just Conda and it
    ```commandline
    conda activate gnomad-toolbox
    ```
-3. Clone the gnomad-toolbox repository and install the dependencies:
+3. Install the gnomad-toolbox package and its dependencies:
+   * To install the latest version from PyPI:
+      ```commandline
+      pip install gnomad-toolbox
+      ```
+   * To install the most up-to-date version from GitHub:
+      ```commandline
+      pip install git+https://github.com/broadinstitute/gnomad-toolbox@main
+      ```
+   Note: If you encounter an error like: `pg_config executable not found`, you may need to install the `postgresql` package:
    ```commandline
-   cd /path/to/your/directory
-   git clone https://github.com/broadinstitute/gnomad-toolbox.git
-   cd gnomad-toolbox
-   pip install -r requirements.txt
-   ```
-   **Note:** Hail 0.2.127+ requires Java 8 or Java 11 and jupyter labs requires Java
-   11+, and if you have an Apple M1 or M2, you must have arm64 Java installed, you
-   can first check your Java version by running:
-   ```commandline
-    java -version
-   ```
-   and check if you have the arm64 Java by running:
-   ```commandline
-    file $JAVA_HOME/bin/java
-   ```
-   If you don't have the arm64 Java, you can find it [here](https://www.azul.com/downloads/?os=macos&architecture=arm-64-bit&package=jre#zulu)
-
-   You might encounter errors when installing the dependencies, such as `pg_config
-   executable not found`. If so, you may need to install additional system packages.
-   For example, on Ubuntu, you can install the `libpq-dev` package:
-   ```commandline
-    sudo apt-get install libpq-dev
-   ```
-   or on macOS, you can install the `postgresql` package:
-   ```commandline
-    brew install postgresql
+   conda install postgresql
    ```
 
 ### Verify the Setup
-   Start a Python shell and test if Hail and gnomad_toolbox are working:
-   ```commandline
-   import hail as hl
-   from gnomad_toolbox.analysis.general import get_variant_count_by_freq_bin
-   hl.init()
-   print("Hail and gnomad_toolbox setup is complete!")
-   ```
-   Or open the notebooks:
-   ```commandline
-   jupyter lab
-   ```
+Start a Python shell and test if Hail and gnomad_toolbox are working:
+```python
+import hail as hl
+import gnomad_toolbox
+hl.init()
+print("Hail and gnomad_toolbox setup is complete!")
+```
 
-## Part2: Accessing gnomAD Data Locally with example notebooks
+## Accessing gnomAD Data Locally with example notebooks
 If you already have experience with gcloud and have no problem running these notebooks,
 you can skip this section.
 
+### Prerequisites
+You must have a Google Cloud account and a project set up.
+   * If you don't have a Google Cloud account, you can sign up for a free trial [here](https://cloud.google.com/free).
+
 ### Install Google Cloud SDK (gcloud)
 
 The Google Cloud SDK is required to interact with Google Cloud services and access gnomAD public data locally.
 1. Follow the official Google Cloud SDK installation [guide](https://cloud.google.com/sdk/docs/install) for your operating system.
 2. After installation, initialize gcloud to log in and set up your default project:
-   ```
+   ```commandline
    gcloud init
    ```
 3. You can check your gcloud config by:
-   ```
+   ```commandline
    gcloud config list
    ```
    or set the default project:
-   ```
+   ```commandline
    gcloud config set project {YOUR_PROJECT_ID}
    ```
 
@@ -131,10 +134,45 @@ The Google Cloud SDK is required to interact with Google Cloud services and acce
 You will need to create a service account in gcloud console IAM & Admin or using
 cloud CLI. Then you can create a key for service account and set the key.
 
+1. Create a service account:
+   ```commandline
+   gcloud iam service-accounts create hail-local-sa --display-name "Hail Local Service Account"
+   ```
+2. Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of the service account key:
    ```commandline
-   gcloud iam service-accounts keys create hail-local-sa-key.json --iam-account {YOUR_SERVICE_ACCOUNT}
    export GOOGLE_APPLICATION_CREDENTIALS=./hail-local-sa-key.json
    ```
-Now, you can access gnomAD data locally using the gnomad_toolbox functions, however,
-you should avoid running queries on the full dataset as it may take a long time and
-consume a lot of resources, and most importantly, it may generate costs.
+
+### Using the Example Notebooks
+The gnomAD tool-box package includes example notebooks to help you get started with
+loading and filtering gnomAD data.
+
+1. Run the `copy-gnomad-toolbox-notebooks` command to copy the example notebooks to a new directory:
+   ```commandline
+   copy-gnomad-toolbox-notebooks /path/to/copy/notebooks
+   ```
+   Note: If the specified directory already exists, you will need to provide a different path, or if you want to
+   overwrite the existing directory, you will need to add the `--overwrite` flag:
+   ```commandline
+   copy-gnomad-toolbox-notebooks /path/to/copy/notebooks --overwrite
+   ```
+
+2. Use the `gnomad-toolbox-jupyter` command to start a Jupyter server:
+   * To start jupyter Notebook:
+      ```commandline
+      gnomad-toolbox-jupyter notebook
+      ```
+   * To start jupyter Lab:
+      ```commandline
+      gnomad-toolbox-jupyter lab
+      ```
+
+   These commands will start a Jupyter notebook/lab server and open a new tab in your default web browser. The notebook
+   directory containing the example notebooks will be displayed.
+
+3. Open the `explore_release_data.ipynb` notebook to learn how to load gnomAD release data.
+
+4. Explore the other notebooks to learn about additional functionalities and analyses you can perform with gnomAD data.
+
+5. Try adding your own queries to the notebooks to explore the data further.
+**WARNING: you should avoid running queries on the full dataset as it may take a long time.**
diff --git a/gnomad_toolbox/scripts.py b/gnomad_toolbox/scripts.py
new file mode 100644
index 0000000..8b4c589
--- /dev/null
+++ b/gnomad_toolbox/scripts.py
@@ -0,0 +1,184 @@
+"""Script to copy Jupyter notebooks and configurations to a user-specified directory."""
+
+import json
+import logging
+import os
+import shutil
+import subprocess
+import sys
+from typing import Union
+
+import click
+
+# Constants
+CONFIG_FILE = os.path.expanduser("~/.gnomad_toolbox_config.json")
+CONFIGS_DIR = "configs"
+NOTEBOOKS_DIR = "notebooks"
+
+# Logging configuration
+logging.basicConfig(format="%(levelname)s (%(name)s %(lineno)s): %(message)s")
+logger = logging.getLogger("gnomad_toolbox")
+logger.setLevel(logging.INFO)
+
+
+def load_config(config_file: str = CONFIG_FILE) -> dict:
+    """
+    Load configuration from a JSON file.
+
+    :param config_file: Path to the configuration file.
+    :return: The configuration dictionary.
+    """
+    if os.path.exists(config_file):
+        with open(config_file, "r") as f:
+            return json.load(f)
+    return {}
+
+
+def save_config(config: dict, config_file: str = CONFIG_FILE) -> None:
+    """
+    Save configuration to a JSON file.
+
+    :param config: The configuration dictionary.
+    :param config_file: Path to the configuration file.
+    :return: None.
+    """
+    with open(config_file, "w") as f:
+        json.dump(config, f, indent=4)
+
+
+def set_config(
+    key: str, value: Union[str, dict], config_file: str = CONFIG_FILE
+) -> None:
+    """
+    Add a key-value pair to the configuration file, supporting nested keys.
+
+    :param key: The key to save, with nesting indicated by periods.
+    :param value: The value to save.
+    :param config_file: Path to the configuration file.
+    :return: None.
+    """
+    config = load_config(config_file)
+    keys = key.split(".")
+    current = config
+
+    # Traverse or create nested dictionaries
+    for k in keys[:-1]:
+        current = current.setdefault(k, {})
+
+    current[keys[-1]] = value
+    save_config(config, config_file)
+
+
+def get_config(key: str, config_file: str = CONFIG_FILE) -> Union[str, None]:
+    """
+    Retrieve a value from the configuration file by key, supporting nested keys.
+
+    :param key: The key to retrieve, with nesting indicated by periods.
+    :param config_file: Path to the configuration file.
+    :return: The value associated with the key, or None if the key doesn't exist.
+    """
+    config = load_config(config_file)
+    keys = key.split(".")
+    current = config
+
+    for k in keys:
+        if k in current:
+            current = current[k]
+        else:
+            return None
+
+    return current
+
+
+def copy_directory(src: str, dest: str, overwrite: bool = False) -> None:
+    """
+    Copy a directory to a destination.
+
+    :param src: Source directory.
+    :param dest: Destination directory.
+    :param overwrite: Whether to overwrite if the destination exists.
+    :return: None.
+    """
+    if os.path.exists(dest):
+        if overwrite:
+            shutil.rmtree(dest)
+            logger.info("Overwriting existing directory: %s", dest)
+        else:
+            raise FileExistsError(f"Directory '{dest}' already exists.")
+    shutil.copytree(src, dest)
+    logger.info("Copied %s to %s", src, dest)
+
+
+def copy_notebooks(destination: str, overwrite: bool = False) -> None:
+    """
+    Copy Jupyter notebooks and configurations to a user-specified directory.
+
+    :param destination: The target directory.
+    :param overwrite: Whether to overwrite existing files/directories.
+    :return: None.
+    """
+    pkg_dir = os.path.dirname(__file__)
+    notebook_dir = os.path.join(pkg_dir, NOTEBOOKS_DIR)
+    config_dir = os.path.join(pkg_dir, CONFIGS_DIR)
+
+    # Validate source directories
+    if not os.path.exists(notebook_dir):
+        raise FileNotFoundError(f"No notebooks directory found at {notebook_dir}")
+    if not os.path.exists(config_dir):
+        raise FileNotFoundError(f"No configs directory found at {config_dir}")
+
+    # Copy notebooks
+    copy_directory(notebook_dir, destination, overwrite)
+
+    # Copy configs
+    config_dest = os.path.join(destination, "jupyter_configs")
+    copy_directory(config_dir, config_dest, overwrite)
+
+    # Update configuration
+    set_config("notebook_dir", destination)
+    logger.info("Default notebook directory set to: %s", destination)
+
+
+@click.command()
+@click.argument("destination", type=click.Path())
+@click.option(
+    "--overwrite", is_flag=True, help="Overwrite existing files if necessary."
+)
+def copy_notebooks_cli(destination: str, overwrite: bool) -> None:
+    """
+    CLI command to copy Jupyter notebooks and configurations.
+
+    :param destination: Target directory for the notebooks and configs.
+    :param overwrite: Whether to overwrite existing files.
+    :return: None.
+    """
+    try:
+        copy_notebooks(destination, overwrite)
+        logger.info("Notebooks successfully copied to %s.", destination)
+    except Exception as e:
+        logger.error("Error during notebook copy: %s", e)
+
+
+def run_jupyter_cli() -> None:
+    """
+    Launch Jupyter Lab or Notebook using the configured directory.
+
+    :return: None.
+    """
+    notebook_dir = get_config("notebook_dir")
+    if not notebook_dir:
+        logger.error("No notebook directory configured. Run `copy-notebooks` first.")
+        return
+
+    if not os.path.exists(notebook_dir):
+        logger.error("Configured notebook directory does not exist: %s", notebook_dir)
+        return
+
+    # Set Jupyter configuration directory
+    jupyter_config_dir = os.path.join(notebook_dir, "jupyter_configs")
+    os.environ["JUPYTER_CONFIG_DIR"] = jupyter_config_dir
+    logger.info("Launching Jupyter with config directory: %s", jupyter_config_dir)
+
+    # Launch Jupyter
+    command = sys.argv[1] if len(sys.argv) > 1 else "lab"
+    subprocess.run(["jupyter", command])
diff --git a/setup.py b/setup.py
index 78ab1cb..c3cf301 100644
--- a/setup.py
+++ b/setup.py
@@ -22,6 +22,8 @@
     long_description_content_type="text/markdown",
     url="https://github.com/broadinstitute/gnomad-toolbox",
     packages=setuptools.find_namespace_packages(include=["gnomad_toolbox*"]),
+    include_package_data=True,
+    package_data={"gnomad_toolbox": ["notebooks/*.ipynb", "configs/*"]},
     project_urls={
         "Documentation": "https://broadinstitute.github.io/gnomad-toolbox/",
         "Source Code": "https://github.com/broadinstitute/gnomad-toolbox",
@@ -36,5 +38,11 @@
         "Development Status :: 4 - Beta",
     ],
     python_requires=">=3.9",
+    entry_points={
+        "console_scripts": [
+            "copy-gnomad-toolbox-notebooks=gnomad_toolbox.scripts:copy_notebooks_cli",
+            "gnomad-toolbox-jupyter=gnomad_toolbox.scripts:run_jupyter_cli",
+        ],
+    },
     install_requires=install_requires,
 )

From b9b5095f95b96ce00b74201c80088006e176f4c1 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 21 Jan 2025 16:10:14 -0700
Subject: [PATCH 098/121] Add jupyter configs

---
 configs/jupyter_notebook_config.json                       | 7 +++++++
 .../@jupyterlab/toc-extension/plugin.jupyterlab-settings   | 5 +++++
 2 files changed, 12 insertions(+)
 create mode 100644 configs/jupyter_notebook_config.json
 create mode 100644 configs/lab/user-settings/@jupyterlab/toc-extension/plugin.jupyterlab-settings

diff --git a/configs/jupyter_notebook_config.json b/configs/jupyter_notebook_config.json
new file mode 100644
index 0000000..d2d2d3e
--- /dev/null
+++ b/configs/jupyter_notebook_config.json
@@ -0,0 +1,7 @@
+{
+  "NotebookApp": {
+    "nbserver_extensions": {
+      "jupyter_nbextensions_configurator": true
+    }
+  }
+}
diff --git a/configs/lab/user-settings/@jupyterlab/toc-extension/plugin.jupyterlab-settings b/configs/lab/user-settings/@jupyterlab/toc-extension/plugin.jupyterlab-settings
new file mode 100644
index 0000000..86e72da
--- /dev/null
+++ b/configs/lab/user-settings/@jupyterlab/toc-extension/plugin.jupyterlab-settings
@@ -0,0 +1,5 @@
+{
+    "numberingH1": false,
+    "includeOutput": false,
+    "syncCollapseState": false
+}

From a1eece825709f0168e9e8ed0162df33b5c798aed Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 21 Jan 2025 16:36:43 -0700
Subject: [PATCH 099/121] Move jupyter configs

---
 {configs => gnomad_toolbox/configs}/jupyter_notebook_config.json  | 0
 .../@jupyterlab/toc-extension/plugin.jupyterlab-settings          | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename {configs => gnomad_toolbox/configs}/jupyter_notebook_config.json (100%)
 rename {configs => gnomad_toolbox/configs}/lab/user-settings/@jupyterlab/toc-extension/plugin.jupyterlab-settings (100%)

diff --git a/configs/jupyter_notebook_config.json b/gnomad_toolbox/configs/jupyter_notebook_config.json
similarity index 100%
rename from configs/jupyter_notebook_config.json
rename to gnomad_toolbox/configs/jupyter_notebook_config.json
diff --git a/configs/lab/user-settings/@jupyterlab/toc-extension/plugin.jupyterlab-settings b/gnomad_toolbox/configs/lab/user-settings/@jupyterlab/toc-extension/plugin.jupyterlab-settings
similarity index 100%
rename from configs/lab/user-settings/@jupyterlab/toc-extension/plugin.jupyterlab-settings
rename to gnomad_toolbox/configs/lab/user-settings/@jupyterlab/toc-extension/plugin.jupyterlab-settings

From 18b1a9683512ece9b58ebfba4155d007532d4f97 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 21 Jan 2025 16:47:39 -0700
Subject: [PATCH 100/121] Modify the Jupyter config file to set the notebook
 directory.

---
 gnomad_toolbox/scripts.py | 22 +++++++++++++---------
 1 file changed, 13 insertions(+), 9 deletions(-)

diff --git a/gnomad_toolbox/scripts.py b/gnomad_toolbox/scripts.py
index 8b4c589..a184170 100644
--- a/gnomad_toolbox/scripts.py
+++ b/gnomad_toolbox/scripts.py
@@ -10,12 +10,10 @@
 
 import click
 
-# Constants
 CONFIG_FILE = os.path.expanduser("~/.gnomad_toolbox_config.json")
 CONFIGS_DIR = "configs"
 NOTEBOOKS_DIR = "notebooks"
 
-# Logging configuration
 logging.basicConfig(format="%(levelname)s (%(name)s %(lineno)s): %(message)s")
 logger = logging.getLogger("gnomad_toolbox")
 logger.setLevel(logging.INFO)
@@ -61,7 +59,7 @@ def set_config(
     keys = key.split(".")
     current = config
 
-    # Traverse or create nested dictionaries
+    # Traverse or create nested dictionaries.
     for k in keys[:-1]:
         current = current.setdefault(k, {})
 
@@ -121,23 +119,29 @@ def copy_notebooks(destination: str, overwrite: bool = False) -> None:
     notebook_dir = os.path.join(pkg_dir, NOTEBOOKS_DIR)
     config_dir = os.path.join(pkg_dir, CONFIGS_DIR)
 
-    # Validate source directories
+    # Validate source directories.
     if not os.path.exists(notebook_dir):
         raise FileNotFoundError(f"No notebooks directory found at {notebook_dir}")
     if not os.path.exists(config_dir):
         raise FileNotFoundError(f"No configs directory found at {config_dir}")
 
-    # Copy notebooks
+    # Copy example jupyter notebooks.
     copy_directory(notebook_dir, destination, overwrite)
 
-    # Copy configs
+    # Copy jupyter configs.
     config_dest = os.path.join(destination, "jupyter_configs")
     copy_directory(config_dir, config_dest, overwrite)
 
-    # Update configuration
+    # Update gnomAD Toolbox configuration.
     set_config("notebook_dir", destination)
     logger.info("Default notebook directory set to: %s", destination)
 
+    # Modify the Jupyter config file to set the notebook directory.
+    jupyter_config = os.path.join(
+        destination, "jupyter_configs/jupyter_notebook_config.json"
+    )
+    set_config("NotebookApp.notebook_dir", destination, jupyter_config)
+
 
 @click.command()
 @click.argument("destination", type=click.Path())
@@ -174,11 +178,11 @@ def run_jupyter_cli() -> None:
         logger.error("Configured notebook directory does not exist: %s", notebook_dir)
         return
 
-    # Set Jupyter configuration directory
+    # Set Jupyter configuration directory.
     jupyter_config_dir = os.path.join(notebook_dir, "jupyter_configs")
     os.environ["JUPYTER_CONFIG_DIR"] = jupyter_config_dir
     logger.info("Launching Jupyter with config directory: %s", jupyter_config_dir)
 
-    # Launch Jupyter
+    # Launch Jupyter.
     command = sys.argv[1] if len(sys.argv) > 1 else "lab"
     subprocess.run(["jupyter", command])

From d9f10396c36334194e1324527ba8ce6f97afc8a7 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 21 Jan 2025 16:59:59 -0700
Subject: [PATCH 101/121] Add nbconfig

---
 gnomad_toolbox/configs/nbconfig/notebook.json | 8 ++++++++
 gnomad_toolbox/configs/nbconfig/tree.json     | 5 +++++
 2 files changed, 13 insertions(+)
 create mode 100644 gnomad_toolbox/configs/nbconfig/notebook.json
 create mode 100644 gnomad_toolbox/configs/nbconfig/tree.json

diff --git a/gnomad_toolbox/configs/nbconfig/notebook.json b/gnomad_toolbox/configs/nbconfig/notebook.json
new file mode 100644
index 0000000..a53aa71
--- /dev/null
+++ b/gnomad_toolbox/configs/nbconfig/notebook.json
@@ -0,0 +1,8 @@
+{
+  "load_extensions": {
+    "nbextensions_configurator/config_menu/main": true,
+    "contrib_nbextensions_help_item/main": true,
+    "toc": true,
+    "toc2/main": true
+  }
+}
diff --git a/gnomad_toolbox/configs/nbconfig/tree.json b/gnomad_toolbox/configs/nbconfig/tree.json
new file mode 100644
index 0000000..8592283
--- /dev/null
+++ b/gnomad_toolbox/configs/nbconfig/tree.json
@@ -0,0 +1,5 @@
+{
+  "load_extensions": {
+    "nbextensions_configurator/tree_tab/main": true
+  }
+}

From 9ea6b535948b0d888d986bcebb20e6665e6769a0 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 21 Jan 2025 17:21:35 -0700
Subject: [PATCH 102/121] Use recursive glob

---
 MANIFEST.in | 2 +-
 setup.py    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/MANIFEST.in b/MANIFEST.in
index 2151586..5bc0296 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,2 +1,2 @@
 include gnomad_toolbox/notebooks/*.ipynb
-include gnomad_toolbox/configs/*
+include gnomad_toolbox/configs/**
diff --git a/setup.py b/setup.py
index c3cf301..60757d3 100644
--- a/setup.py
+++ b/setup.py
@@ -23,7 +23,7 @@
     url="https://github.com/broadinstitute/gnomad-toolbox",
     packages=setuptools.find_namespace_packages(include=["gnomad_toolbox*"]),
     include_package_data=True,
-    package_data={"gnomad_toolbox": ["notebooks/*.ipynb", "configs/*"]},
+    package_data={"gnomad_toolbox": ["notebooks/*.ipynb", "configs/**"]},
     project_urls={
         "Documentation": "https://broadinstitute.github.io/gnomad-toolbox/",
         "Source Code": "https://github.com/broadinstitute/gnomad-toolbox",

From c22aac0f9acc0bbe951c5f0ace6d439525b937db Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 21 Jan 2025 17:24:47 -0700
Subject: [PATCH 103/121] I don't think the MANIFEST.in is needed

---
 MANIFEST.in | 2 --
 1 file changed, 2 deletions(-)
 delete mode 100644 MANIFEST.in

diff --git a/MANIFEST.in b/MANIFEST.in
deleted file mode 100644
index 5bc0296..0000000
--- a/MANIFEST.in
+++ /dev/null
@@ -1,2 +0,0 @@
-include gnomad_toolbox/notebooks/*.ipynb
-include gnomad_toolbox/configs/**

From 612342917176ba4a0ccbde0d8a78b07642df7ffa Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 21 Jan 2025 17:25:11 -0700
Subject: [PATCH 104/121] Small changes in README.md

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index fab60eb..107e8ca 100644
--- a/README.md
+++ b/README.md
@@ -91,7 +91,7 @@ Miniconda is a lightweight distribution of Conda that includes just Conda and it
       ```commandline
       pip install git+https://github.com/broadinstitute/gnomad-toolbox@main
       ```
-   Note: If you encounter an error like: `pg_config executable not found`, you may need to install the `postgresql` package:
+   Note: If you encounter an error like: `Error: pg_config executable not found.`, you may need to install the `postgresql` package:
    ```commandline
    conda install postgresql
    ```
@@ -110,8 +110,8 @@ If you already have experience with gcloud and have no problem running these not
 you can skip this section.
 
 ### Prerequisites
-You must have a Google Cloud account and a project set up.
-   * If you don't have a Google Cloud account, you can sign up for a free trial [here](https://cloud.google.com/free).
+You must have a Google Cloud account and a project set up. If you don't have a Google Cloud account, you can sign up
+for a free trial [here](https://cloud.google.com/free).
 
 ### Install Google Cloud SDK (gcloud)
 

From 25516a3b53e793cda0c3d6c78b2169ca0e3aaee9 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 21 Jan 2025 17:52:40 -0700
Subject: [PATCH 105/121] Make sure to install hail

---
 setup.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 60757d3..dc613a4 100644
--- a/setup.py
+++ b/setup.py
@@ -8,8 +8,7 @@
 install_requires = []
 with open("requirements.txt", "r") as requirements_file:
     for req in (line.strip() for line in requirements_file):
-        if req != "hail":
-            install_requires.append(req)
+        install_requires.append(req)
 
 
 setuptools.setup(

From 957c1490f2ba07557ffc22c8589106022bdaa7a4 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 21 Jan 2025 19:21:17 -0700
Subject: [PATCH 106/121] Add resources to README.md

---
 README.md                                     | 32 +++++++++++++++++--
 .../notebooks/explore_release_data.ipynb      |  2 +-
 requirements.txt                              |  1 -
 3 files changed, 30 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 107e8ca..5aa9762 100644
--- a/README.md
+++ b/README.md
@@ -138,9 +138,13 @@ cloud CLI. Then you can create a key for service account and set the key.
    ```commandline
    gcloud iam service-accounts create hail-local-sa --display-name "Hail Local Service Account"
    ```
-2. Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of the service account key:
+2. Create service account key:
    ```commandline
-   export GOOGLE_APPLICATION_CREDENTIALS=./hail-local-sa-key.json
+   gcloud iam service-accounts keys create hail-local-sa-key.json --iam-account hail-local-sa@{YOUR_PROJECT_ID}.iam.gserviceaccount.com
+   ```
+3. Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of the service account key:
+   ```commandline
+   export GOOGLE_APPLICATION_CREDENTIALS=/full/path/to/hail-local-sa-key.json
    ```
 
 ### Using the Example Notebooks
@@ -170,9 +174,31 @@ loading and filtering gnomAD data.
    These commands will start a Jupyter notebook/lab server and open a new tab in your default web browser. The notebook
    directory containing the example notebooks will be displayed.
 
-3. Open the `explore_release_data.ipynb` notebook to learn how to load gnomAD release data.
+3. Open the `explore_release_data.ipynb` notebook to learn how to load gnomAD release data. You can run all cells by
+clicking on the >> button in the toolbar (shown in the image below) or by selecting "Run All" from the "Cell" menu.
+   ![jupyter notebook -- run all cells](images/run_all_cells.png)
 
 4. Explore the other notebooks to learn about additional functionalities and analyses you can perform with gnomAD data.
 
 5. Try adding your own queries to the notebooks to explore the data further.
 **WARNING: you should avoid running queries on the full dataset as it may take a long time.**
+
+---
+
+## Resources
+
+### gnomAD:
+   * [gnomAD Toolbox Documentation](https://broadinstitute.github.io/gnomad-toolbox/)
+   * [gnomAD Browser](https://gnomad.broadinstitute.org/)
+   * [gnomAD Download Page](https://gnomad.broadinstitute.org/downloads)
+   * [gnomAD Forum](https://discuss.gnomad.broadinstitute.org)
+
+### Hail:
+   * [Hail Documentation](https://hail.is/docs/0.2/index.html)
+   * [Hail Discussion Forum](https://discuss.hail.is/)
+
+### Google Cloud:
+   * [SDK Documentation](https://cloud.google.com/sdk/docs)
+   * [Free Trial](https://cloud.google.com/free)
+   * [Service Account Creation](https://cloud.google.com/iam/docs/service-accounts-create#creating)
+   * [Service Account Key Creation](https://cloud.google.com/iam/docs/keys-create-delete#creating)
diff --git a/gnomad_toolbox/notebooks/explore_release_data.ipynb b/gnomad_toolbox/notebooks/explore_release_data.ipynb
index 5070564..8a1f28e 100644
--- a/gnomad_toolbox/notebooks/explore_release_data.ipynb
+++ b/gnomad_toolbox/notebooks/explore_release_data.ipynb
@@ -14648,7 +14648,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.11.11"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/requirements.txt b/requirements.txt
index 6604853..c2a1eb5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,5 +6,4 @@ jupyter_nbextensions_configurator
 jupyterlab
 nodejs
 npm
-jupyter_bokeh
 jupyter-server<2.0.0

From b7e6cfadf008591bfaa45b6c96c662bb5ed2f75a Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 21 Jan 2025 19:27:09 -0700
Subject: [PATCH 107/121] Change to use the Cloud Storage Connector

---
 README.md | 49 ++++++-------------------------------------------
 1 file changed, 6 insertions(+), 43 deletions(-)

diff --git a/README.md b/README.md
index 5aa9762..9bd6be8 100644
--- a/README.md
+++ b/README.md
@@ -109,43 +109,12 @@ print("Hail and gnomad_toolbox setup is complete!")
 If you already have experience with gcloud and have no problem running these notebooks,
 you can skip this section.
 
-### Prerequisites
-You must have a Google Cloud account and a project set up. If you don't have a Google Cloud account, you can sign up
-for a free trial [here](https://cloud.google.com/free).
-
-### Install Google Cloud SDK (gcloud)
-
-The Google Cloud SDK is required to interact with Google Cloud services and access gnomAD public data locally.
-1. Follow the official Google Cloud SDK installation [guide](https://cloud.google.com/sdk/docs/install) for your operating system.
-2. After installation, initialize gcloud to log in and set up your default project:
-   ```commandline
-   gcloud init
-   ```
-3. You can check your gcloud config by:
-   ```commandline
-   gcloud config list
-   ```
-   or set the default project:
-   ```commandline
-   gcloud config set project {YOUR_PROJECT_ID}
-   ```
-
-### Configure a Service Account
-You will need to create a service account in gcloud console IAM & Admin or using
-cloud CLI. Then you can create a key for service account and set the key.
-
-1. Create a service account:
-   ```commandline
-   gcloud iam service-accounts create hail-local-sa --display-name "Hail Local Service Account"
-   ```
-2. Create service account key:
-   ```commandline
-   gcloud iam service-accounts keys create hail-local-sa-key.json --iam-account hail-local-sa@{YOUR_PROJECT_ID}.iam.gserviceaccount.com
-   ```
-3. Set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path of the service account key:
-   ```commandline
-   export GOOGLE_APPLICATION_CREDENTIALS=/full/path/to/hail-local-sa-key.json
-   ```
+### Install the Cloud Storage Connector
+Hail uses the Google Cloud Storage Connector to read and write data from Google Cloud Storage. The easiest way to
+install the connector is to use the `install-gcs-connector` script provided by the Broad Institute:
+```commandline
+curl -sSL https://broad.io/install-gcs-connector | python3 - --auth-type UNAUTHENTICATED
+```
 
 ### Using the Example Notebooks
 The gnomAD tool-box package includes example notebooks to help you get started with
@@ -196,9 +165,3 @@ clicking on the >> button in the toolbar (shown in the image below) or by select
 ### Hail:
    * [Hail Documentation](https://hail.is/docs/0.2/index.html)
    * [Hail Discussion Forum](https://discuss.hail.is/)
-
-### Google Cloud:
-   * [SDK Documentation](https://cloud.google.com/sdk/docs)
-   * [Free Trial](https://cloud.google.com/free)
-   * [Service Account Creation](https://cloud.google.com/iam/docs/service-accounts-create#creating)
-   * [Service Account Key Creation](https://cloud.google.com/iam/docs/keys-create-delete#creating)

From f67bd315e3cbe5035680372be438c8836aa45297 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 21 Jan 2025 20:37:16 -0700
Subject: [PATCH 108/121] Add image for README.md

---
 README.md                  |   2 ++
 images/jupyter_run_all.png | Bin 0 -> 484090 bytes
 2 files changed, 2 insertions(+)
 create mode 100644 images/jupyter_run_all.png

diff --git a/README.md b/README.md
index 9bd6be8..8fdeb54 100644
--- a/README.md
+++ b/README.md
@@ -105,6 +105,8 @@ hl.init()
 print("Hail and gnomad_toolbox setup is complete!")
 ```
 
+---
+
 ## Accessing gnomAD Data Locally with example notebooks
 If you already have experience with gcloud and have no problem running these notebooks,
 you can skip this section.
diff --git a/images/jupyter_run_all.png b/images/jupyter_run_all.png
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zu^S~kANMlZtnf_WoOU%b<6L6gkKM5zCtpWe#ck4^Gb#A8@7<~TPd(1t2e!?7)RN_K
z_ZSmnmFar+C7I!Vs__O(=6QKt&)pF%webPR+tiP?7p~7dR=VD8U)#>uJ$1pHAFKV2
zUHI`X)bipT@B3M+(sMW#%5QTIXs=rOve!&B)XF;Opt{cQZmG|6CWOU)ddJOn^0WKzoS84T_Q9%2C%!T(WUs#^d$9A?M6V6kpa0&b;~!cf?~@#8
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z>z;L;Oiq?u@3P}oi+oKuCwH`~;LuJhU3+jz^V18s_EuYH2XGqm{f!$pCQP0jyf07b
zr4O{PeBiK>0(fnkbTUOtIa&Q5Y+Gj9Xu~5Yd1*(o;BA?sPgVR@^!52a+y9}a<&I_N
z?TJgj{V7Wqo_zUbmeVG$W8V@2uix`&(#yzhn(}G2PH}VW#zn0suX3)u_tW?3!}C_a
z>L|9)ds)e}GZQV|0aspmexCN6@h5O`%Jv`oCarVbBr$?uVMZ5!o#
z-Uq$$EqY*5R=)3je8&w7Sxex6@`HE>kpG2ud{mpRQt+R9-%KMHP;P4w2!Q6M+d>r8
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zhXzhC?_dt}76RHh9d6@yU>yWnxG1elbsOCu{Ij2J7O!&qM+FN55O})!xvX
Date: Tue, 21 Jan 2025 20:40:25 -0700
Subject: [PATCH 109/121] Use correct name for the jupyter notebook -- run all
 cells

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 8fdeb54..c9618d5 100644
--- a/README.md
+++ b/README.md
@@ -147,7 +147,7 @@ loading and filtering gnomAD data.
 
 3. Open the `explore_release_data.ipynb` notebook to learn how to load gnomAD release data. You can run all cells by
 clicking on the >> button in the toolbar (shown in the image below) or by selecting "Run All" from the "Cell" menu.
-   ![jupyter notebook -- run all cells](images/run_all_cells.png)
+   ![jupyter notebook -- run all cells](images/jupyter_run_all.png)
 
 4. Explore the other notebooks to learn about additional functionalities and analyses you can perform with gnomAD data.
 

From 8e1f73b22326f2a39477fb1a054d7391e3c5176e Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 21 Jan 2025 22:03:57 -0700
Subject: [PATCH 110/121] Formatting and clean-up of the README.md

---
 README.md | 184 ++++++++++++++++++++++++++++--------------------------
 1 file changed, 96 insertions(+), 88 deletions(-)

diff --git a/README.md b/README.md
index c9618d5..57492ad 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,14 @@
 # gnomad-toolbox
-This repository provides a set of Python functions to simplify working with gnomAD Hail Tables. It includes tools for
-data access, filtering, and analysis.
 
-**Disclaimer: This package is in its early stages of development, and we are actively working on improving it. There may
-be bugs, and the API may change. We welcome feedback and contributions.**
+![License](https://img.shields.io/github/license/broadinstitute/gnomad-toolbox)
 
-## Repository structure
+This repository provides a set of Python functions to simplify working with gnomAD Hail Tables. It includes tools for data access, filtering, and analysis.
+
+> **Disclaimer:** This package is in its early stages of development, and we are actively working on improving it. There may be bugs, and the API may change. Feedback and contributions are welcome.
+
+---
+
+## Repository Structure
 ```
 gnomad_toolbox/
 │
@@ -16,69 +19,61 @@ gnomad_toolbox/
 │   ├── coverage.py      # Functions to filter variants or regions based on coverage thresholds.
 │   ├── frequency.py     # Functions to filter variants by allele frequency thresholds.
 │   ├── pext.py          # Functions to filter variants using predicted expression (pext) scores.
-|   ├── variant.py       # Functions to filter to a specific variant or set of variants.
+│   ├── variant.py       # Functions to filter to a specific variant or set of variants.
 │   ├── vep.py           # Functions to filter variants based on VEP (Variant Effect Predictor) annotations.
 │
 ├── analysis/
 │   ├── general.py       # General analysis functions, such as summarizing variant statistics.
 │
 ├── notebooks/
-│   ├── explore_release_data.ipynb    # Jupyter notebook introducing the loading of gnomAD release data.
-|   |── intro_to_filtering_variant_data.ipynb    # Jupyter notebook introducing the filtering of gnomAD variant data.
-|   |── dive_into_secondary_analyses.ipynb    # Jupyter notebook introducing secondary analyses using gnomAD data.
+│   ├── explore_release_data.ipynb      # Guide to loading gnomAD release data.
+│   ├── intro_to_filtering_variant_data.ipynb  # Introduction to filtering gnomAD variants.
+│   ├── dive_into_secondary_analyses.ipynb  # Analyses with gnomAD data.
 ```
 
 ---
 
-## Setting Up Your Environment for Hail and gnomAD Toolbox
+## Set Up Your Environment for Hail and gnomAD Toolbox
 
-This guide provides step-by-step instructions to set up a working environment for
-using Hail and the gnomAD Toolbox.
+This guide provides step-by-step instructions to set up a working environment for using Hail and the gnomAD Toolbox.
 
-**Disclaimer: We provide this guide to help you set up your environment, but we cannot
-guarantee that it will work on all systems. If you encounter any issues, you can
-reach out to us on the [gnomAD Forum](https://discuss.gnomad.broadinstitute.org), and
-if it is something that we have come across before, we will try to help you out.**
+> **Note:** We provide this guide to help you set up your environment, but we cannot guarantee that it will work on all systems. If you encounter any issues, you can reach out to us on the [gnomAD Forum](https://discuss.gnomad.broadinstitute.org), and if it is something that we have come across before, we will try to help you out.
 
 ### Prerequisites
 
-Before starting, ensure you have the following:
-* Administrator access to your system to install software.
-* Internet connection for downloading dependencies.
-* **Note: Hail 0.2.127+ requires Java 8 or Java 11 and jupyter labs requires Java
-11+, and if you have an Apple M1 or M2, you must have arm64 Java installed, you
-can first check your Java version by running:**
-  ```commandline
-  java -version
-  ```
-  and check if you have the arm64 Java by running:
-  ```commandline
-  file $JAVA_HOME/bin/java
-  ```
-  If you don't have the arm64 Java, you can find it [here](https://www.azul.com/downloads/?os=macos&architecture=arm-64-bit&package=jre#zulu)
+Ensure you have the following:
+- Administrator access to your system to install software.
+- Internet connection for downloading dependencies.
+
+> **Note:** Hail 0.2.127+ requires Java 8 or Java 11. If you use an Apple M1/M2 chip, you must have arm64 Java installed. Verify your setup:
+>   ```commandline
+>   java -version
+>   file $JAVA_HOME/bin/java
+>   ```
+If you don’t have arm64 Java, download it [here](https://www.azul.com/downloads/?os=macos&architecture=arm-64-bit&package=jre#zulu).
+
+---
 
 ### Install Miniconda
-Miniconda is a lightweight distribution of Conda that includes just Conda and its dependencies.
-1. Download Miniconda for your system from the [official website](https://docs.anaconda.com/miniconda/install/).
-2. Follow the installation instructions for your operating system:
-   * Linux/macOS: Run the installer script in your terminal:
-      ```commandline
-      bash Miniconda3-latest-Linux-x86_64.sh
-      ```
-   * Windows: Run the installer executable and follow the installation wizard.
-   **Note: Our team has not tested the gnomad-toolbox on Windows, so we cannot guarantee that it will
-   work as expected or support any issues that may arise.**
-3. Confirm the installation by running:
+
+Miniconda is a lightweight distribution of Conda.
+
+1. Download Miniconda from the [official website](https://docs.anaconda.com/miniconda/install/).
+2. Follow the installation instructions described on the download page for your operating system.
+3. Verify installation:
    ```commandline
    conda --version
    ```
 
+---
+
 ### Create a Conda Environment
-1. Create a new Conda environment with a specific version of Python:
+
+1. Create a new environment with a specified Python version:
    ```commandline
    conda create -n gnomad-toolbox python=3.11
    ```
-2. Activate the Conda environment:
+2. Activate the environment:
    ```commandline
    conda activate gnomad-toolbox
    ```
@@ -91,12 +86,13 @@ Miniconda is a lightweight distribution of Conda that includes just Conda and it
       ```commandline
       pip install git+https://github.com/broadinstitute/gnomad-toolbox@main
       ```
-   Note: If you encounter an error like: `Error: pg_config executable not found.`, you may need to install the `postgresql` package:
-   ```commandline
-   conda install postgresql
-   ```
+   > **Note:** If you encounter an error like: `Error: pg_config executable not found`, you may need to install the `postgresql` package:
+   >   ```commandline
+   >   conda install postgresql
+   >   ```
 
 ### Verify the Setup
+
 Start a Python shell and test if Hail and gnomad_toolbox are working:
 ```python
 import hail as hl
@@ -107,63 +103,75 @@ print("Hail and gnomad_toolbox setup is complete!")
 
 ---
 
-## Accessing gnomAD Data Locally with example notebooks
-If you already have experience with gcloud and have no problem running these notebooks,
-you can skip this section.
+## Access gnomAD Data Locally with example notebooks
+
+If you already have experience with gcloud and have no problem running these notebooks, you can skip this section.
 
 ### Install the Cloud Storage Connector
-Hail uses the Google Cloud Storage Connector to read and write data from Google Cloud Storage. The easiest way to
-install the connector is to use the `install-gcs-connector` script provided by the Broad Institute:
+
+Hail uses the Google Cloud Storage Connector to read and write data from Google Cloud Storage. The easiest way to install the connector is to use the `install-gcs-connector` script provided by the Broad Institute:
 ```commandline
 curl -sSL https://broad.io/install-gcs-connector | python3 - --auth-type UNAUTHENTICATED
 ```
 
-### Using the Example Notebooks
-The gnomAD tool-box package includes example notebooks to help you get started with
-loading and filtering gnomAD data.
+### Use the Example Notebooks
 
-1. Run the `copy-gnomad-toolbox-notebooks` command to copy the example notebooks to a new directory:
-   ```commandline
-   copy-gnomad-toolbox-notebooks /path/to/copy/notebooks
-   ```
-   Note: If the specified directory already exists, you will need to provide a different path, or if you want to
-   overwrite the existing directory, you will need to add the `--overwrite` flag:
+The gnomAD tool-box package includes example notebooks to help you get started with loading and filtering gnomAD data.
+
+1. Copy example notebooks to a new directory:
    ```commandline
-   copy-gnomad-toolbox-notebooks /path/to/copy/notebooks --overwrite
+   copy-gnomad-toolbox-notebooks /path/to/copy/notebooks/to
    ```
-
-2. Use the `gnomad-toolbox-jupyter` command to start a Jupyter server:
-   * To start jupyter Notebook:
-      ```commandline
-      gnomad-toolbox-jupyter notebook
-      ```
-   * To start jupyter Lab:
-      ```commandline
-      gnomad-toolbox-jupyter lab
-      ```
-
-   These commands will start a Jupyter notebook/lab server and open a new tab in your default web browser. The notebook
-   directory containing the example notebooks will be displayed.
-
-3. Open the `explore_release_data.ipynb` notebook to learn how to load gnomAD release data. You can run all cells by
-clicking on the >> button in the toolbar (shown in the image below) or by selecting "Run All" from the "Cell" menu.
-   ![jupyter notebook -- run all cells](images/jupyter_run_all.png)
-
-4. Explore the other notebooks to learn about additional functionalities and analyses you can perform with gnomAD data.
+   > **Note:** If the specified directory already exists, you will need to provide a different path, or if you want to overwrite the existing directory, you will need to add the `--overwrite` flag:
+   >   ```commandline
+   >   copy-gnomad-toolbox-notebooks /path/to/copy/notebooks/to --overwrite
+   >   ```
+
+2. Start Jupyter with gnomad-toolbox specific configurations:
+   - jupyter Notebook:
+     ```commandline
+     gnomad-toolbox-jupyter notebook
+     ```
+   - jupyter Lab:
+     ```commandline
+     gnomad-toolbox-jupyter lab
+     ```
+
+   > **Note:** These commands will start a Jupyter notebook/lab server and open a new tab in your default web browser. The notebook directory containing the example notebooks will be displayed.
+
+3. Open the `explore_release_data.ipynb` notebook to learn how to load gnomAD release data:
+   - Run all cells by clicking on the >> button in the toolbar (shown in the image below) or by selecting "Run All" from the "Cell" menu.
+      ![jupyter notebook -- run all cells](images/jupyter_run_all.png)
+
+4. Explore the other notebooks:
+   - `intro_to_filtering_variant_data.ipynb`: Introduction to filtering variants.
+   - `dive_into_secondary_analyses.ipynb`: Examples of some simple analyses using gnomAD data.
 
 5. Try adding your own queries to the notebooks to explore the data further.
-**WARNING: you should avoid running queries on the full dataset as it may take a long time.**
+> **WARNING:** Avoid running queries on the full dataset as it may take a long time.
 
 ---
 
 ## Resources
 
 ### gnomAD:
-   * [gnomAD Toolbox Documentation](https://broadinstitute.github.io/gnomad-toolbox/)
-   * [gnomAD Browser](https://gnomad.broadinstitute.org/)
-   * [gnomAD Download Page](https://gnomad.broadinstitute.org/downloads)
-   * [gnomAD Forum](https://discuss.gnomad.broadinstitute.org)
+- [gnomAD Toolbox Documentation](https://broadinstitute.github.io/gnomad-toolbox/)
+- [gnomAD Browser](https://gnomad.broadinstitute.org/)
+- [gnomAD Download Page](https://gnomad.broadinstitute.org/downloads)
+- [gnomAD Forum](https://discuss.gnomad.broadinstitute.org)
 
 ### Hail:
-   * [Hail Documentation](https://hail.is/docs/0.2/index.html)
-   * [Hail Discussion Forum](https://discuss.hail.is/)
+- [Hail Documentation](https://hail.is/docs/0.2/index.html)
+- [Hail Discussion Forum](https://discuss.hail.is/)
+
+---
+
+## Contributing
+
+We welcome contributions! Please submit issues and pull requests on our [GitHub repository](https://github.com/broadinstitute/gnomad-toolbox).
+
+---
+
+## License
+
+This project is licensed under the BSD 3-Clause License - see the [LICENSE](LICENSE) file for details.

From 47d54faa032c9f9732329ec2f4960c1fc4d65244 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Tue, 21 Jan 2025 23:10:45 -0700
Subject: [PATCH 111/121] A bit more README.md clean-up

---
 README.md | 147 +++++++++++++++++++++++++++++-------------------------
 1 file changed, 78 insertions(+), 69 deletions(-)

diff --git a/README.md b/README.md
index 57492ad..b54b674 100644
--- a/README.md
+++ b/README.md
@@ -1,58 +1,59 @@
-# gnomad-toolbox
+# gnomad-toolbox: Simplifying Access and Analysis of gnomAD Data
 
 ![License](https://img.shields.io/github/license/broadinstitute/gnomad-toolbox)
 
-This repository provides a set of Python functions to simplify working with gnomAD Hail Tables. It includes tools for data access, filtering, and analysis.
+The gnomAD Toolbox is a Python package designed to streamline the use of gnomAD Hail Tables. The Genome Aggregation Database (gnomAD) is a widely used resource for understanding genetic variation, offering large-scale data on millions of variants across diverse populations. This toolbox simplifies tasks like loading, filtering, and analyzing gnomAD data, making it more accessible to researchers.
 
-> **Disclaimer:** This package is in its early stages of development, and we are actively working on improving it. There may be bugs, and the API may change. Feedback and contributions are welcome.
+> **Disclaimer:** This package is in its early stages of development, and we are actively working on improving it. There may be bugs, and the API is subject to change. Feedback and contributions are highly encouraged.
 
 ---
 
 ## Repository Structure
+
+The package is organized as follows:
+
 ```
 gnomad_toolbox/
 │
 ├── load_data.py         # Functions to load gnomAD release Hail Tables.
 │
-├── filtering/
-│   ├── constraint.py    # Functions to filter constraint metrics (e.g., observed/expected ratios).
-│   ├── coverage.py      # Functions to filter variants or regions based on coverage thresholds.
-│   ├── frequency.py     # Functions to filter variants by allele frequency thresholds.
-│   ├── pext.py          # Functions to filter variants using predicted expression (pext) scores.
-│   ├── variant.py       # Functions to filter to a specific variant or set of variants.
-│   ├── vep.py           # Functions to filter variants based on VEP (Variant Effect Predictor) annotations.
+├── filtering/           # Modules for filtering gnomAD data.
+│   ├── constraint.py    # Filter by constraint metrics (e.g., observed/expected ratios).
+│   ├── coverage.py      # Filter by coverage thresholds.
+│   ├── frequency.py     # Filter by allele frequency thresholds.
+│   ├── pext.py          # Filter by predicted expression (pext) scores.
+│   ├── variant.py       # Filter specific variants or sets of variants.
+│   ├── vep.py           # Filter by VEP (Variant Effect Predictor) annotations.
 │
-├── analysis/
-│   ├── general.py       # General analysis functions, such as summarizing variant statistics.
+├── analysis/            # Analysis functions.
+│   ├── general.py       # General-purpose analyses, such as summarizing variant statistics.
 │
-├── notebooks/
-│   ├── explore_release_data.ipynb      # Guide to loading gnomAD release data.
-│   ├── intro_to_filtering_variant_data.ipynb  # Introduction to filtering gnomAD variants.
-│   ├── dive_into_secondary_analyses.ipynb  # Analyses with gnomAD data.
+├── notebooks/           # Example Jupyter notebooks.
+│   ├── explore_release_data.ipynb       # Guide to loading gnomAD release data.
+│   ├── intro_to_filtering_variant_data.ipynb # Introduction to filtering gnomAD variants.
+│   ├── dive_into_secondary_analyses.ipynb   # Secondary analyses using gnomAD data.
 ```
 
 ---
 
 ## Set Up Your Environment for Hail and gnomAD Toolbox
 
-This guide provides step-by-step instructions to set up a working environment for using Hail and the gnomAD Toolbox.
+This section provides step-by-step instructions to set up a working environment for using Hail and the gnomAD Toolbox.
 
 > **Note:** We provide this guide to help you set up your environment, but we cannot guarantee that it will work on all systems. If you encounter any issues, you can reach out to us on the [gnomAD Forum](https://discuss.gnomad.broadinstitute.org), and if it is something that we have come across before, we will try to help you out.
 
 ### Prerequisites
 
-Ensure you have the following:
-- Administrator access to your system to install software.
-- Internet connection for downloading dependencies.
-
-> **Note:** Hail 0.2.127+ requires Java 8 or Java 11. If you use an Apple M1/M2 chip, you must have arm64 Java installed. Verify your setup:
->   ```commandline
->   java -version
->   file $JAVA_HOME/bin/java
->   ```
-If you don’t have arm64 Java, download it [here](https://www.azul.com/downloads/?os=macos&architecture=arm-64-bit&package=jre#zulu).
-
----
+Before installing the toolbox, ensure the following:
+- Administrator access to install software.
+- A working Python >3.9 environment.
+- Java **8** or Java **11** for Hail.
+   > **Note:** If you are using an Apple M1/M2 chip, you must have arm64 Java installed. Verify your Java installation:
+   > ```bash
+   > java -version
+   > file $JAVA_HOME/bin/java
+   > ```
+   > If you need arm64 Java, download it [here](https://www.azul.com/downloads/?os=macos&architecture=arm-64-bit&package=jre#zulu).
 
 ### Install Miniconda
 
@@ -65,35 +66,33 @@ Miniconda is a lightweight distribution of Conda.
    conda --version
    ```
 
----
+### Set Up a Conda Environment
 
-### Create a Conda Environment
+Create and activate a new environment with a specified Python version for the gnomAD Toolbox:
+```commandline
+conda create -n gnomad-toolbox python=3.11
+conda activate gnomad-toolbox
+```
+
+### Install gnomAD Toolbox
+- To install from PyPI:
+  ```commandline
+  pip install gnomad-toolbox
+  ```
+- To install the latest development version from GitHub:
+  ```commandline
+  pip install git+https://github.com/broadinstitute/gnomad-toolbox@main
+  ```
+
+> **Troubleshooting:** If you encounter an error such as `Error: pg_config executable not found`, install the `postgresql` package:
+> ```commandline
+> conda install postgresql
+> ```
 
-1. Create a new environment with a specified Python version:
-   ```commandline
-   conda create -n gnomad-toolbox python=3.11
-   ```
-2. Activate the environment:
-   ```commandline
-   conda activate gnomad-toolbox
-   ```
-3. Install the gnomad-toolbox package and its dependencies:
-   * To install the latest version from PyPI:
-      ```commandline
-      pip install gnomad-toolbox
-      ```
-   * To install the most up-to-date version from GitHub:
-      ```commandline
-      pip install git+https://github.com/broadinstitute/gnomad-toolbox@main
-      ```
-   > **Note:** If you encounter an error like: `Error: pg_config executable not found`, you may need to install the `postgresql` package:
-   >   ```commandline
-   >   conda install postgresql
-   >   ```
 
-### Verify the Setup
+### Verify the Installation
 
-Start a Python shell and test if Hail and gnomad_toolbox are working:
+Start a Python shell and ensure that Hail and the gnomAD Toolbox are set up correctly:
 ```python
 import hail as hl
 import gnomad_toolbox
@@ -105,7 +104,21 @@ print("Hail and gnomad_toolbox setup is complete!")
 
 ## Access gnomAD Data Locally with example notebooks
 
-If you already have experience with gcloud and have no problem running these notebooks, you can skip this section.
+> **Note:** If you already have experience with gcloud and have no problem running these notebooks, you can skip this section.
+
+The gnomAD Toolbox includes Jupyter notebooks to help you get started with gnomAD data:
+
+- **Explore Release Data:**
+   - Learn how to load and inspect gnomAD release data.
+   - Notebook: `explore_release_data.ipynb`
+
+- **Filter Variants:**
+   - Understand how to filter variants using different criteria.
+   - Notebook: `intro_to_filtering_variant_data.ipynb`
+
+- **Perform Secondary Analyses:**
+   - Explore more advanced analyses using gnomAD data.
+   - Notebook: `dive_into_secondary_analyses.ipynb`
 
 ### Install the Cloud Storage Connector
 
@@ -114,25 +127,23 @@ Hail uses the Google Cloud Storage Connector to read and write data from Google
 curl -sSL https://broad.io/install-gcs-connector | python3 - --auth-type UNAUTHENTICATED
 ```
 
-### Use the Example Notebooks
-
-The gnomAD tool-box package includes example notebooks to help you get started with loading and filtering gnomAD data.
+### Copy and Open the Notebooks
 
-1. Copy example notebooks to a new directory:
+1. Copy the notebooks to a directory of your choice:
    ```commandline
-   copy-gnomad-toolbox-notebooks /path/to/copy/notebooks/to
+   copy-gnomad-toolbox-notebooks /path/to/your/notebooks
    ```
    > **Note:** If the specified directory already exists, you will need to provide a different path, or if you want to overwrite the existing directory, you will need to add the `--overwrite` flag:
    >   ```commandline
-   >   copy-gnomad-toolbox-notebooks /path/to/copy/notebooks/to --overwrite
+   >   copy-gnomad-toolbox-notebooks /path/to/your/notebooks --overwrite
    >   ```
 
 2. Start Jupyter with gnomad-toolbox specific configurations:
-   - jupyter Notebook:
+   - For Jupyter Notebook:
      ```commandline
      gnomad-toolbox-jupyter notebook
      ```
-   - jupyter Lab:
+   - For Jupyter Lab:
      ```commandline
      gnomad-toolbox-jupyter lab
      ```
@@ -143,12 +154,10 @@ The gnomAD tool-box package includes example notebooks to help you get started w
    - Run all cells by clicking on the >> button in the toolbar (shown in the image below) or by selecting "Run All" from the "Cell" menu.
       ![jupyter notebook -- run all cells](images/jupyter_run_all.png)
 
-4. Explore the other notebooks:
-   - `intro_to_filtering_variant_data.ipynb`: Introduction to filtering variants.
-   - `dive_into_secondary_analyses.ipynb`: Examples of some simple analyses using gnomAD data.
+4. Explore the other notebooks described above.
 
 5. Try adding your own queries to the notebooks to explore the data further.
-> **WARNING:** Avoid running queries on the full dataset as it may take a long time.
+   > **WARNING:** Avoid running queries on the full dataset as it may take a long time.
 
 ---
 
@@ -168,10 +177,10 @@ The gnomAD tool-box package includes example notebooks to help you get started w
 
 ## Contributing
 
-We welcome contributions! Please submit issues and pull requests on our [GitHub repository](https://github.com/broadinstitute/gnomad-toolbox).
+We welcome contributions to the gnomAD Toolbox! See the [CONTRIBUTING.md](CONTRIBUTING.md) file for more information.
 
 ---
 
 ## License
 
-This project is licensed under the BSD 3-Clause License - see the [LICENSE](LICENSE) file for details.
+This project is licensed under the BSD 3-Clause License. See the [LICENSE](LICENSE) file for details.

From efe069b9309c5537b6607f4293fec80f0a275dd7 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 22 Jan 2025 08:14:53 -0700
Subject: [PATCH 112/121] Update the Java prereq section

---
 README.md | 27 ++++++++++++++-------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/README.md b/README.md
index b54b674..1e0b768 100644
--- a/README.md
+++ b/README.md
@@ -40,20 +40,20 @@ gnomad_toolbox/
 
 This section provides step-by-step instructions to set up a working environment for using Hail and the gnomAD Toolbox.
 
-> **Note:** We provide this guide to help you set up your environment, but we cannot guarantee that it will work on all systems. If you encounter any issues, you can reach out to us on the [gnomAD Forum](https://discuss.gnomad.broadinstitute.org), and if it is something that we have come across before, we will try to help you out.
+> We provide this guide to help you set up your environment, but we cannot guarantee that it will work on all systems. If you encounter any issues, you can reach out to us on the [gnomAD Forum](https://discuss.gnomad.broadinstitute.org), and if it is something that we have come across before, we will try to help you out.
 
 ### Prerequisites
 
 Before installing the toolbox, ensure the following:
 - Administrator access to install software.
 - A working Python >3.9 environment.
-- Java **8** or Java **11** for Hail.
-   > **Note:** If you are using an Apple M1/M2 chip, you must have arm64 Java installed. Verify your Java installation:
-   > ```bash
-   > java -version
-   > file $JAVA_HOME/bin/java
-   > ```
-   > If you need arm64 Java, download it [here](https://www.azul.com/downloads/?os=macos&architecture=arm-64-bit&package=jre#zulu).
+- Java **11**.
+  > For macOS: [Hail recommends](https://hail.is/docs/0.2/install/macosx.html) using a packaged installation from [Azul](https://www.azul.com/downloads/?version=java-11-lts&os=macos&package=jdk&show-old-builds=true) or using [Homebrew](https://brew.sh/):
+  > ```commandline
+  > brew tap homebrew/cask-versions
+  > brew install --cask temurin8
+  > ```
+  > Make sure to choose a Java installation with a compatible architecture (Can be found in “Apple Menu > About This Mac”), Apple M1/M2 must use an “arm64” Java, otherwise use an “x86_64” Java.
 
 ### Install Miniconda
 
@@ -102,9 +102,7 @@ print("Hail and gnomad_toolbox setup is complete!")
 
 ---
 
-## Access gnomAD Data Locally with example notebooks
-
-> **Note:** If you already have experience with gcloud and have no problem running these notebooks, you can skip this section.
+## Available Example Notebooks
 
 The gnomAD Toolbox includes Jupyter notebooks to help you get started with gnomAD data:
 
@@ -120,6 +118,9 @@ The gnomAD Toolbox includes Jupyter notebooks to help you get started with gnomA
    - Explore more advanced analyses using gnomAD data.
    - Notebook: `dive_into_secondary_analyses.ipynb`
 
+## Run the Example Notebooks Locally
+> If you already have experience with Google Cloud and using Jupyter notebooks, you can skip this section and use the notebooks in your preferred environment.
+
 ### Install the Cloud Storage Connector
 
 Hail uses the Google Cloud Storage Connector to read and write data from Google Cloud Storage. The easiest way to install the connector is to use the `install-gcs-connector` script provided by the Broad Institute:
@@ -133,7 +134,7 @@ curl -sSL https://broad.io/install-gcs-connector | python3 - --auth-type UNAUTHE
    ```commandline
    copy-gnomad-toolbox-notebooks /path/to/your/notebooks
    ```
-   > **Note:** If the specified directory already exists, you will need to provide a different path, or if you want to overwrite the existing directory, you will need to add the `--overwrite` flag:
+   > If the specified directory already exists, you will need to provide a different path, or if you want to overwrite the existing directory, you will need to add the `--overwrite` flag:
    >   ```commandline
    >   copy-gnomad-toolbox-notebooks /path/to/your/notebooks --overwrite
    >   ```
@@ -148,7 +149,7 @@ curl -sSL https://broad.io/install-gcs-connector | python3 - --auth-type UNAUTHE
      gnomad-toolbox-jupyter lab
      ```
 
-   > **Note:** These commands will start a Jupyter notebook/lab server and open a new tab in your default web browser. The notebook directory containing the example notebooks will be displayed.
+   > These commands will start a Jupyter notebook/lab server and open a new tab in your default web browser. The notebook directory containing the example notebooks will be displayed.
 
 3. Open the `explore_release_data.ipynb` notebook to learn how to load gnomAD release data:
    - Run all cells by clicking on the >> button in the toolbar (shown in the image below) or by selecting "Run All" from the "Cell" menu.

From f8fac364c997c8bfcc8a88bf5108d69bd9b11cba Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 22 Jan 2025 08:20:09 -0700
Subject: [PATCH 113/121] Wrap lines for easier reading

---
 README.md | 37 ++++++++++++++++++++++++++-----------
 1 file changed, 26 insertions(+), 11 deletions(-)

diff --git a/README.md b/README.md
index 1e0b768..d845e6a 100644
--- a/README.md
+++ b/README.md
@@ -2,9 +2,13 @@
 
 ![License](https://img.shields.io/github/license/broadinstitute/gnomad-toolbox)
 
-The gnomAD Toolbox is a Python package designed to streamline the use of gnomAD Hail Tables. The Genome Aggregation Database (gnomAD) is a widely used resource for understanding genetic variation, offering large-scale data on millions of variants across diverse populations. This toolbox simplifies tasks like loading, filtering, and analyzing gnomAD data, making it more accessible to researchers.
+The gnomAD Toolbox is a Python package designed to streamline the use of gnomAD Hail Tables. The Genome Aggregation
+Database (gnomAD) is a widely used resource for understanding genetic variation, offering large-scale data on millions
+of variants across diverse populations. This toolbox simplifies tasks like loading, filtering, and analyzing gnomAD
+data, making it more accessible to researchers.
 
-> **Disclaimer:** This package is in its early stages of development, and we are actively working on improving it. There may be bugs, and the API is subject to change. Feedback and contributions are highly encouraged.
+> **Disclaimer:** This package is in its early stages of development, and we are actively working on improving it.
+> There may be bugs, and the API is subject to change. Feedback and contributions are highly encouraged.
 
 ---
 
@@ -40,7 +44,9 @@ gnomad_toolbox/
 
 This section provides step-by-step instructions to set up a working environment for using Hail and the gnomAD Toolbox.
 
-> We provide this guide to help you set up your environment, but we cannot guarantee that it will work on all systems. If you encounter any issues, you can reach out to us on the [gnomAD Forum](https://discuss.gnomad.broadinstitute.org), and if it is something that we have come across before, we will try to help you out.
+> We provide this guide to help you set up your environment, but we cannot guarantee that it will work on all systems.
+> If you encounter any issues, you can reach out to us on the [gnomAD Forum](https://discuss.gnomad.broadinstitute.org),
+> and if it is something that we have come across before, we will try to help you out.
 
 ### Prerequisites
 
@@ -48,12 +54,15 @@ Before installing the toolbox, ensure the following:
 - Administrator access to install software.
 - A working Python >3.9 environment.
 - Java **11**.
-  > For macOS: [Hail recommends](https://hail.is/docs/0.2/install/macosx.html) using a packaged installation from [Azul](https://www.azul.com/downloads/?version=java-11-lts&os=macos&package=jdk&show-old-builds=true) or using [Homebrew](https://brew.sh/):
+  > For macOS: [Hail recommends](https://hail.is/docs/0.2/install/macosx.html) using a packaged installation from
+  > [Azul](https://www.azul.com/downloads/?version=java-11-lts&os=macos&package=jdk&show-old-builds=true) or using
+  > [Homebrew](https://brew.sh/):
   > ```commandline
   > brew tap homebrew/cask-versions
   > brew install --cask temurin8
   > ```
-  > Make sure to choose a Java installation with a compatible architecture (Can be found in “Apple Menu > About This Mac”), Apple M1/M2 must use an “arm64” Java, otherwise use an “x86_64” Java.
+  > Make sure to choose a Java installation with a compatible architecture (Can be found in “Apple Menu > About This
+  > Mac”), Apple M1/M2 must use an “arm64” Java, otherwise use an “x86_64” Java.
 
 ### Install Miniconda
 
@@ -84,7 +93,8 @@ conda activate gnomad-toolbox
   pip install git+https://github.com/broadinstitute/gnomad-toolbox@main
   ```
 
-> **Troubleshooting:** If you encounter an error such as `Error: pg_config executable not found`, install the `postgresql` package:
+> **Troubleshooting:** If you encounter an error such as `Error: pg_config executable not found`, install the
+> `postgresql` package:
 > ```commandline
 > conda install postgresql
 > ```
@@ -119,11 +129,13 @@ The gnomAD Toolbox includes Jupyter notebooks to help you get started with gnomA
    - Notebook: `dive_into_secondary_analyses.ipynb`
 
 ## Run the Example Notebooks Locally
-> If you already have experience with Google Cloud and using Jupyter notebooks, you can skip this section and use the notebooks in your preferred environment.
+> If you already have experience with Google Cloud and using Jupyter notebooks, you can skip this section and use the
+> notebooks in your preferred environment.
 
 ### Install the Cloud Storage Connector
 
-Hail uses the Google Cloud Storage Connector to read and write data from Google Cloud Storage. The easiest way to install the connector is to use the `install-gcs-connector` script provided by the Broad Institute:
+Hail uses the Google Cloud Storage Connector to read and write data from Google Cloud Storage. The easiest way to
+install the connector is to use the `install-gcs-connector` script provided by the Broad Institute:
 ```commandline
 curl -sSL https://broad.io/install-gcs-connector | python3 - --auth-type UNAUTHENTICATED
 ```
@@ -134,7 +146,8 @@ curl -sSL https://broad.io/install-gcs-connector | python3 - --auth-type UNAUTHE
    ```commandline
    copy-gnomad-toolbox-notebooks /path/to/your/notebooks
    ```
-   > If the specified directory already exists, you will need to provide a different path, or if you want to overwrite the existing directory, you will need to add the `--overwrite` flag:
+   > If the specified directory already exists, you will need to provide a different path, or if you want to overwrite
+   > the existing directory, you will need to add the `--overwrite` flag:
    >   ```commandline
    >   copy-gnomad-toolbox-notebooks /path/to/your/notebooks --overwrite
    >   ```
@@ -149,10 +162,12 @@ curl -sSL https://broad.io/install-gcs-connector | python3 - --auth-type UNAUTHE
      gnomad-toolbox-jupyter lab
      ```
 
-   > These commands will start a Jupyter notebook/lab server and open a new tab in your default web browser. The notebook directory containing the example notebooks will be displayed.
+   > These commands will start a Jupyter notebook/lab server and open a new tab in your default web browser. The
+   > notebook directory containing the example notebooks will be displayed.
 
 3. Open the `explore_release_data.ipynb` notebook to learn how to load gnomAD release data:
-   - Run all cells by clicking on the >> button in the toolbar (shown in the image below) or by selecting "Run All" from the "Cell" menu.
+   - Run all cells by clicking on the >> button in the toolbar (shown in the image below) or by selecting "Run All"
+   - from the "Cell" menu.
       ![jupyter notebook -- run all cells](images/jupyter_run_all.png)
 
 4. Explore the other notebooks described above.

From 3527120dfaed7de5dee1980e8a4c815475f41700 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 22 Jan 2025 09:13:24 -0700
Subject: [PATCH 114/121] Apply suggestions from code review

Co-authored-by: Katherine Chao 
---
 README.md | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index d845e6a..e12d3a2 100644
--- a/README.md
+++ b/README.md
@@ -2,10 +2,7 @@
 
 ![License](https://img.shields.io/github/license/broadinstitute/gnomad-toolbox)
 
-The gnomAD Toolbox is a Python package designed to streamline the use of gnomAD Hail Tables. The Genome Aggregation
-Database (gnomAD) is a widely used resource for understanding genetic variation, offering large-scale data on millions
-of variants across diverse populations. This toolbox simplifies tasks like loading, filtering, and analyzing gnomAD
-data, making it more accessible to researchers.
+The Genome Aggregation Database ([gnomAD](https://gnomad.broadinstitute.org/)) is a widely used resource for understanding genetic variation, offering large-scale data on millions of variants across diverse populations. This toolbox is a Python package designed to streamline use of gnomAD data, simplifying tasks like loading, filtering, and analysis, to make it more accessible to researchers.
 
 > **Disclaimer:** This package is in its early stages of development, and we are actively working on improving it.
 > There may be bugs, and the API is subject to change. Feedback and contributions are highly encouraged.
@@ -42,7 +39,7 @@ gnomad_toolbox/
 
 ## Set Up Your Environment for Hail and gnomAD Toolbox
 
-This section provides step-by-step instructions to set up a working environment for using Hail and the gnomAD Toolbox.
+This section provides step-by-step instructions to set up a working environment for using [Hail](https://hail.is/) and the gnomAD Toolbox.
 
 > We provide this guide to help you set up your environment, but we cannot guarantee that it will work on all systems.
 > If you encounter any issues, you can reach out to us on the [gnomAD Forum](https://discuss.gnomad.broadinstitute.org),
@@ -62,7 +59,7 @@ Before installing the toolbox, ensure the following:
   > brew install --cask temurin8
   > ```
   > Make sure to choose a Java installation with a compatible architecture (Can be found in “Apple Menu > About This
-  > Mac”), Apple M1/M2 must use an “arm64” Java, otherwise use an “x86_64” Java.
+  > Mac”); Apple M1/M2 must use an “arm64” Java, otherwise use an “x86_64” Java.
 
 ### Install Miniconda
 

From d6629092d900468c0516b28f2f29ff7da1fccf45 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 22 Jan 2025 09:43:15 -0700
Subject: [PATCH 115/121] Add more infor to java install

---
 README.md | 31 +++++++++++++++++++++----------
 1 file changed, 21 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index d845e6a..a14943e 100644
--- a/README.md
+++ b/README.md
@@ -52,17 +52,28 @@ This section provides step-by-step instructions to set up a working environment
 
 Before installing the toolbox, ensure the following:
 - Administrator access to install software.
-- A working Python >3.9 environment.
+- A working internet connection.
 - Java **11**.
-  > For macOS: [Hail recommends](https://hail.is/docs/0.2/install/macosx.html) using a packaged installation from
-  > [Azul](https://www.azul.com/downloads/?version=java-11-lts&os=macos&package=jdk&show-old-builds=true) or using
-  > [Homebrew](https://brew.sh/):
-  > ```commandline
-  > brew tap homebrew/cask-versions
-  > brew install --cask temurin8
-  > ```
-  > Make sure to choose a Java installation with a compatible architecture (Can be found in “Apple Menu > About This
-  > Mac”), Apple M1/M2 must use an “arm64” Java, otherwise use an “x86_64” Java.
+  - Check your Java version:
+    ```commandline
+    java -version
+    ```
+  - If you do not have Java 11 installed:
+    - For Linux, use `apt-get` or `yum` to install OpenJDK 11.
+    - For macOS, [Hail recommends](https://hail.is/docs/0.2/install/macosx.html) using [Homebrew](https://brew.sh/):
+      ```commandline
+      brew tap homebrew/cask-versions
+      brew install --cask temurin8
+      ```
+      or using a packaged installation from [Azul](https://www.azul.com/downloads/?version=java-11-lts&os=macos&package=jdk&show-old-builds=true)
+      > Make sure to choose a Java installation with a compatible architecture (Can be found in “Apple Menu > About
+      > This Mac”), Apple M1/M2 must use an “arm64” Java, otherwise use an “x86_64” Java.
+      >
+      > You may also need to set the `JAVA_HOME` environment variable to the path of the installed Java version. For example:
+      > ```commandline
+      > export JAVA_HOME=/Library/Java/JavaVirtualMachines/zulu-11.jdk/Contents/Home
+      > export PATH=$JAVA_HOME/bin:$PATH
+      > ```
 
 ### Install Miniconda
 

From fc766e3f706ad5bc7793cc52ef3fefb27bbb3a82 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 22 Jan 2025 09:53:18 -0700
Subject: [PATCH 116/121] Add Zulip to resources

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index c1b7d77..1695538 100644
--- a/README.md
+++ b/README.md
@@ -196,6 +196,7 @@ curl -sSL https://broad.io/install-gcs-connector | python3 - --auth-type UNAUTHE
 ### Hail:
 - [Hail Documentation](https://hail.is/docs/0.2/index.html)
 - [Hail Discussion Forum](https://discuss.hail.is/)
+- [Hail Zulip Chat](https://hail.zulipchat.com/)
 
 ---
 

From a749531769ed49e38b16a0d10879c9ca1f45f2ef Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 22 Jan 2025 10:28:26 -0700
Subject: [PATCH 117/121] Add more info about running notebooks locally

---
 README.md | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 1695538..53cf751 100644
--- a/README.md
+++ b/README.md
@@ -140,10 +140,24 @@ The gnomAD Toolbox includes Jupyter notebooks to help you get started with gnomA
 > If you already have experience with Google Cloud and using Jupyter notebooks, you can skip this section and use the
 > notebooks in your preferred environment.
 
+Hail can be [initialized](https://hail.is/docs/0.2/api.html#hail.init) with different backends depending on
+where you want to run your analysis. For analyses that require a lot of computational resources, a cloud-based
+environment will be most suitable.
+
+However, running the gnomaAD Toolbox example notebooks can be done locally using the
+`local` backend. At the beginning of each notebook, Hail is initialized with the `local` backend using:
+   ```python
+   hl.init(backend="local")
+   ```
+
+To run the example notebooks locally, there are a few additional steps needed to set up your environment:
+
 ### Install the Cloud Storage Connector
+The gnomAD Hail tables are stored in Google Cloud Storage, and in order to avoid downloading the entire dataset to your local machine,
+we recommend using the [Google Cloud Storage Connector](https://cloud.google.com/dataproc/docs/concepts/connectors/cloud-storage)
+to access the data.
 
-Hail uses the Google Cloud Storage Connector to read and write data from Google Cloud Storage. The easiest way to
-install the connector is to use the `install-gcs-connector` script provided by the Broad Institute:
+The easiest way to install the connector is to use the `install-gcs-connector` script provided by the Broad Institute:
 ```commandline
 curl -sSL https://broad.io/install-gcs-connector | python3 - --auth-type UNAUTHENTICATED
 ```

From a4866dcc2aea55e25434564074af3d4913ed5f12 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 22 Jan 2025 10:39:36 -0700
Subject: [PATCH 118/121] Small format addition

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 53cf751..e49fad4 100644
--- a/README.md
+++ b/README.md
@@ -136,6 +136,8 @@ The gnomAD Toolbox includes Jupyter notebooks to help you get started with gnomA
    - Explore more advanced analyses using gnomAD data.
    - Notebook: `dive_into_secondary_analyses.ipynb`
 
+---
+
 ## Run the Example Notebooks Locally
 > If you already have experience with Google Cloud and using Jupyter notebooks, you can skip this section and use the
 > notebooks in your preferred environment.

From 6207d60f6e9eb617dd09d3bfcaf7e50cbd53c204 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 22 Jan 2025 11:03:02 -0700
Subject: [PATCH 119/121] Apply suggestions from code review

Co-authored-by: Qin He <44242118+KoalaQin@users.noreply.github.com>
---
 README.md | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index e49fad4..6aa7cbd 100644
--- a/README.md
+++ b/README.md
@@ -30,8 +30,8 @@ gnomad_toolbox/
 │   ├── general.py       # General-purpose analyses, such as summarizing variant statistics.
 │
 ├── notebooks/           # Example Jupyter notebooks.
-│   ├── explore_release_data.ipynb       # Guide to loading gnomAD release data.
-│   ├── intro_to_filtering_variant_data.ipynb # Introduction to filtering gnomAD variants.
+│   ├── explore_release_data.ipynb                  # Guide to loading gnomAD release data.
+│   ├── intro_to_filtering_variant_data.ipynb    # Introduction to filtering gnomAD variants.
 │   ├── dive_into_secondary_analyses.ipynb   # Secondary analyses using gnomAD data.
 ```
 
@@ -62,9 +62,10 @@ Before installing the toolbox, ensure the following:
       brew tap homebrew/cask-versions
       brew install --cask temurin8
       ```
-      or using a packaged installation from [Azul](https://www.azul.com/downloads/?version=java-11-lts&os=macos&package=jdk&show-old-builds=true)
-      > Make sure to choose a Java installation with a compatible architecture (Can be found in “Apple Menu > About
-      > This Mac”); Apple M1/M2 must use an “arm64” Java, otherwise use an “x86_64” Java.
+      or using a packaged installation from [Azul](https://www.azul.com/downloads/?version=java-11-lts&os=macos&package=jdk&show-old-builds=true).
+      > Ensure you choose a Java installation that matches your system architecture (found in **Apple Menu > About This Mac**).  
+      > - For Apple M1/M2 chips, select an **arm64** Java package.  
+      > - For Intel-based Macs, choose an **x86_64** Java package.
       >
       > You may also need to set the `JAVA_HOME` environment variable to the path of the installed Java version. For example:
       > ```commandline

From 25f108b2e6465c30aaacba4044d10d848c166b25 Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 22 Jan 2025 11:16:25 -0700
Subject: [PATCH 120/121] Align comments in repo structure

---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 6aa7cbd..5e952b9 100644
--- a/README.md
+++ b/README.md
@@ -30,9 +30,9 @@ gnomad_toolbox/
 │   ├── general.py       # General-purpose analyses, such as summarizing variant statistics.
 │
 ├── notebooks/           # Example Jupyter notebooks.
-│   ├── explore_release_data.ipynb                  # Guide to loading gnomAD release data.
+│   ├── explore_release_data.ipynb               # Guide to loading gnomAD release data.
 │   ├── intro_to_filtering_variant_data.ipynb    # Introduction to filtering gnomAD variants.
-│   ├── dive_into_secondary_analyses.ipynb   # Secondary analyses using gnomAD data.
+│   ├── dive_into_secondary_analyses.ipynb       # Secondary analyses using gnomAD data.
 ```
 
 ---
@@ -63,8 +63,8 @@ Before installing the toolbox, ensure the following:
       brew install --cask temurin8
       ```
       or using a packaged installation from [Azul](https://www.azul.com/downloads/?version=java-11-lts&os=macos&package=jdk&show-old-builds=true).
-      > Ensure you choose a Java installation that matches your system architecture (found in **Apple Menu > About This Mac**).  
-      > - For Apple M1/M2 chips, select an **arm64** Java package.  
+      > Ensure you choose a Java installation that matches your system architecture (found in **Apple Menu > About This Mac**).
+      > - For Apple M1/M2 chips, select an **arm64** Java package.
       > - For Intel-based Macs, choose an **x86_64** Java package.
       >
       > You may also need to set the `JAVA_HOME` environment variable to the path of the installed Java version. For example:

From bc46c3b3d3bec0166c2867e9b7cdcbce4bb3d45d Mon Sep 17 00:00:00 2001
From: jkgoodrich <33063077+jkgoodrich@users.noreply.github.com>
Date: Wed, 22 Jan 2025 11:53:46 -0700
Subject: [PATCH 121/121] Remove toc from config, we have toc2

---
 gnomad_toolbox/configs/nbconfig/notebook.json | 1 -
 1 file changed, 1 deletion(-)

diff --git a/gnomad_toolbox/configs/nbconfig/notebook.json b/gnomad_toolbox/configs/nbconfig/notebook.json
index a53aa71..64ed80b 100644
--- a/gnomad_toolbox/configs/nbconfig/notebook.json
+++ b/gnomad_toolbox/configs/nbconfig/notebook.json
@@ -2,7 +2,6 @@
   "load_extensions": {
     "nbextensions_configurator/config_menu/main": true,
     "contrib_nbextensions_help_item/main": true,
-    "toc": true,
     "toc2/main": true
   }
 }

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