{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Institute for Behavioral Genetics International Statistical Genetics 2023 Workshop \n",
    "\n",
    "# Rare Variant Analysis of Sequencing Data with Hail\n",
    "\n",
    "Learning objectives:\n",
    "\n",
    "1. Understand statistical models of rare variant effects on phenotype.\n",
    "2. Understand how to use Hail to perform a Burden test.\n",
    "3. Understand how to use Hail to perform a SKAT test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Burden Test: Theory\n",
    "\n",
    "The phrase \"genome-wide association study\" (GWAS) usually refers to independently testing every variant in a dataset against a phenotype. For a continuous phenotype, we symbolically state that as:\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "N &: \\mathbb{N} &\\textrm{The number of samples} \\\\\n",
    "M &: \\mathbb{N} &\\textrm{The number of variants} \\\\\n",
    "G &: \\{0, 1, 2\\}^{N \\times M} &\\textrm{The genotypes represented as the number of alternate alleles} \\\\\n",
    "y &: \\mathbb{R}^{N} &\\textrm{The value of the phenotype for each sample}\\\\\n",
    "\\beta &: \\mathbb{R}^{M} &\\textrm{The unknown effect of each variant on the phenotype}\\\\\n",
    "\\\\\n",
    "\\varepsilon_i &\\sim \\mathcal{N}(0, \\sigma^2) &\\textrm{Normally distributed measurement error of unknown variance, }\\sigma^2\\\\\n",
    "y_i &= \\beta_j G_{ij} + \\varepsilon_i\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "This model lacks sufficient statistical power to detect rare variants _because of_ their rarity. There are two ways to address this problem: collect more samples or combine multiple variants into a single association test. In this notebook, we explore two tests that combine multiple variants: the burden test and the squence kernel association test (SKAT).\n",
    "\n",
    "The burden test considers the sum of effects of a set of variants on a phenotype. When the set of variants is a gene, this test is called a gene burden set. Analagously to testing every variant in GWAS, we typically test many variant-sets. We symbolically state this model as:\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "S_k && \\textrm{The } k\\textrm{-th set of variants} \\\\\n",
    "\\\\\n",
    "\\varepsilon_i &\\sim \\mathcal{N}(0, \\sigma^2) \\\\\n",
    "y_i &= \\beta_k \\left( \\sum_{j \\in S_k} G_{ij} \\right) + \\varepsilon_i\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "This model is well-powered for rare variants whose effects have the same direction. For example, if all the variants in a gene increase the chance of disease, a burden test is well-powered. If the direction of effect of variants in the set is random and the effects size are all of similar magnitude, the sum of effects will trend towards zero. We can simulate and visualize this effect:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 546
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "effects = np.random.normal(0, 1, size=100)\n",
    "\n",
    "sum_of_effects = np.sum(effects)\n",
    "magnitude_of_effects = np.sqrt(effects.T @ effects)\n",
    "sum_of_abs_of_effects = np.sum(np.abs(effects))\n",
    "\n",
    "plt.bar(list(range(100)), effects,\n",
    "        label=(f'sum(y) = {sum_of_effects}\\n'\n",
    "               f'sqrt(y.T @ y) = {magnitude_of_effects}\\n'\n",
    "               f'sum(abs(y)) = {sum_of_abs_of_effects}'))\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Burden Test: Practice\n",
    "### Setup\n",
    "\n",
    "Import Hail and configure the plotting system for Notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23/03/10 07:58:28 WARN Utils: Your hostname, ip-10-0-201-238 resolves to a loopback address: 127.0.1.1; using 10.0.201.238 instead (on interface ens5)\n",
      "23/03/10 07:58:28 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address\n",
      "23/03/10 07:58:29 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "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": [
      "23/03/10 07:58:29 WARN Hail: This Hail JAR was compiled for Spark 3.3.0, running with Spark 3.3.2.\n",
      "  Compatibility is not guaranteed.\n",
      "23/03/10 07:58:30 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Running on Apache Spark version 3.3.2\n",
      "SparkUI available at http://10.0.201.238:4041\n",
      "Welcome to\n",
      "     __  __     <>__\n",
      "    / /_/ /__  __/ /\n",
      "   / __  / _ `/ / /\n",
      "  /_/ /_/\\_,_/_/_/   version 0.2.109-b123465fc0bf\n",
      "LOGGING: writing to /home/timp/2023_IBG_HAIL/2021_IBG_Hail/hail-20230310-0758-0.2.109-b123465fc0bf.log\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['PYSPARK_SUBMIT_ARGS'] = '--driver-memory 6G pyspark-shell'\n",
    "\n",
    "import hail as hl\n",
    "hl.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var 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",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var 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",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\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",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"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, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var 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",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var 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",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\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",
       "      var 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",
       "      var 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",
       "    var events = require('base/js/events');\n",
       "    var 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",
       "\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",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\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",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\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",
       "\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() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\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 (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\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",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\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",
       "  \n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var 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",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\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": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var 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\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  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\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     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\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\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() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\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 (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\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  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\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  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var 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      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\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": [
    "hl.plot.output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Quality Control & Sample Annotation\n",
    "\n",
    "The last notebook covered these steps in detail. We'll do them quickly here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mt = hl.read_matrix_table('resources/hgdp-tgp-rare-variants.mt')\n",
    "\n",
    "# remove non-PASS variants\n",
    "mt = mt.filter_rows(hl.len(mt.filters) == 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove Common Variants\n",
    "\n",
    "Next, we will keep variants with an allele frequency of under 1%. Including common variants will only reduce the power of a burden test.\n",
    "\n",
    "We could rerun `hl.variant_qc` here, or use an aggregator designed to compute allele frequencies and counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mt = mt.filter_rows(\n",
    "    hl.agg.call_stats(mt.GT, mt.alleles).AF[1] < 0.01\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also remove variants without any non-reference calls:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mt = mt.filter_rows(\n",
    "    hl.agg.all(mt.GT.is_hom_ref()),\n",
    "    keep=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Group by gene\n",
    "\n",
    "We imported gene names and intervals from GENCODE and created a Hail table keyed by interval. We'll use this table to annotate our genetic data with gene information. After annotation, we can group our variants and perform a linear regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "genes = hl.read_table('resources/genes.ht')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><thead><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">interval</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">gene_name</div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">interval&lt;locus&lt;GRCh38&gt;&gt;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">str</td></tr>\n",
       "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[chr1:11869-chr1:14409]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;DDX11L1&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[chr1:14404-chr1:29570]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;WASH7P&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[chr1:17369-chr1:17436]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;MIR6859-1&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[chr1:29554-chr1:31109]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;MIR1302-2HG&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[chr1:30366-chr1:30503]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;MIR1302-2&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[chr1:34554-chr1:36081]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;FAM138A&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[chr1:52473-chr1:53312]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;OR4G4P&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[chr1:57598-chr1:64116]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;OR4G11P&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[chr1:65419-chr1:71585]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;OR4F5&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[chr1:89295-chr1:133723]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;AL627309.1&quot;</td></tr>\n",
       "</tbody></table><p style=\"background: #fdd; padding: 0.4em;\">showing top 10 rows</p>\n"
      ],
      "text/plain": [
       "+--------------------------+---------------+\n",
       "| interval                 | gene_name     |\n",
       "+--------------------------+---------------+\n",
       "| interval<locus<GRCh38>>  | str           |\n",
       "+--------------------------+---------------+\n",
       "| [chr1:11869-chr1:14409]  | \"DDX11L1\"     |\n",
       "| [chr1:14404-chr1:29570]  | \"WASH7P\"      |\n",
       "| [chr1:17369-chr1:17436]  | \"MIR6859-1\"   |\n",
       "| [chr1:29554-chr1:31109]  | \"MIR1302-2HG\" |\n",
       "| [chr1:30366-chr1:30503]  | \"MIR1302-2\"   |\n",
       "| [chr1:34554-chr1:36081]  | \"FAM138A\"     |\n",
       "| [chr1:52473-chr1:53312]  | \"OR4G4P\"      |\n",
       "| [chr1:57598-chr1:64116]  | \"OR4G11P\"     |\n",
       "| [chr1:65419-chr1:71585]  | \"OR4F5\"       |\n",
       "| [chr1:89295-chr1:133723] | \"AL627309.1\"  |\n",
       "+--------------------------+---------------+\n",
       "showing top 10 rows"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genes.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall how we annotated sample phenotypes earlier in the common variant tutorial -- this looks very similar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mt = mt.annotate_rows(gene_name = genes[mt.locus].gene_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "phenos = hl.read_table('resources/rare-variant-phenotypes.ht')\n",
    "mt = mt.annotate_cols(\n",
    "    pheno1 = phenos[mt.s].pheno1,\n",
    "    pheno2 = phenos[mt.s].pheno2\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's `show` the resulting annotations on the matrix table to make sure everything worked:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 4:============================================>              (3 + 1) / 4]\r"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table><thead><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">locus</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">alleles</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">gene_name</div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">locus&lt;GRCh38&gt;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">array&lt;str&gt;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">str</td></tr>\n",
       "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr1:949490</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;NOC2L&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr1:950316</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;NOC2L&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr1:950751</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;C&quot;,&quot;T&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;NOC2L&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr1:951750</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;NOC2L&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr1:952412</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;C&quot;,&quot;T&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;NOC2L&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr1:953543</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;NOC2L&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr1:953730</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;NOC2L&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr1:954116</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;C&quot;,&quot;G&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;NOC2L&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr1:954815</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;C&quot;,&quot;T&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;NOC2L&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr1:955369</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;A&quot;,&quot;G&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;NOC2L&quot;</td></tr>\n",
       "</tbody></table><p style=\"background: #fdd; padding: 0.4em;\">showing top 10 rows</p>\n"
      ],
      "text/plain": [
       "+---------------+------------+-----------+\n",
       "| locus         | alleles    | gene_name |\n",
       "+---------------+------------+-----------+\n",
       "| locus<GRCh38> | array<str> | str       |\n",
       "+---------------+------------+-----------+\n",
       "| chr1:949490   | [\"G\",\"A\"]  | \"NOC2L\"   |\n",
       "| chr1:950316   | [\"G\",\"A\"]  | \"NOC2L\"   |\n",
       "| chr1:950751   | [\"C\",\"T\"]  | \"NOC2L\"   |\n",
       "| chr1:951750   | [\"G\",\"A\"]  | \"NOC2L\"   |\n",
       "| chr1:952412   | [\"C\",\"T\"]  | \"NOC2L\"   |\n",
       "| chr1:953543   | [\"G\",\"A\"]  | \"NOC2L\"   |\n",
       "| chr1:953730   | [\"G\",\"A\"]  | \"NOC2L\"   |\n",
       "| chr1:954116   | [\"C\",\"G\"]  | \"NOC2L\"   |\n",
       "| chr1:954815   | [\"C\",\"T\"]  | \"NOC2L\"   |\n",
       "| chr1:955369   | [\"A\",\"G\"]  | \"NOC2L\"   |\n",
       "+---------------+------------+-----------+\n",
       "showing top 10 rows"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.gene_name.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Aggregate by gene\n",
    "\n",
    "Hail's modularity makes it easy to perform non-kernel-based burden tests.\n",
    "\n",
    "We'll compose two general tools:\n",
    " - [group_rows_by](https://hail.is/docs/0.2/hail.MatrixTable.html#hail.MatrixTable.group_rows_by) / [aggregate](https://hail.is/docs/0.2/hail.GroupedMatrixTable.html#hail.GroupedMatrixTable.aggregate)\n",
    " - [hl.linear_regression_rows](https://hail.is/docs/0.2/methods/stats.html#hail.methods.linear_regression_rows).\n",
    " \n",
    "This means that you can flexibly specify the way genotypes are summarized per gene. Using other tools, you may have a few ways to aggregate, but if you want to do something different you are out of luck!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "burden_mt = mt.group_rows_by(mt.gene_name).aggregate(\n",
    "    n_variants = hl.agg.count_where(mt.GT.n_alt_alleles() > 0)\n",
    ")\n",
    "\n",
    "# filter to genes with at least one rare variant!\n",
    "burden_mt = burden_mt.filter_rows(hl.agg.sum(burden_mt.n_variants) > 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore this new matrix table!\n",
    "\n",
    "We always start exploring a new matrix table with `describe`. The describe command does not perform any time-consuming or expensive operations. It just introspects on the fields and their \"types\" (meaning the kind of data, e.g. `float`, `int`, and `str`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "078a5ebafd564df88270df88c95a6ba1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Button(description='globals', layout=Layout(height='30px', width='65px'), style=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a8031d2a520452294acc03e95f40159",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tab(children=(VBox(children=(HTML(value='<p><big>Global fields, with one value in the dataset.</big></p>\\n<p>C…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "burden_mt.describe(widget=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also `show` a Matrix Table (or Table). This operation has to actually load and process the data so it might be take some time! We can limit the amount of data processed by specifying `n_cols` and `n_rows`. In the following cell, we look at the top-left, 5x5, corner of the Matrix Table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 11:===========================================>              (3 + 1) / 4]\r"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table><thead><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">&#x27;CHMI_CHMI3_WGS2&#x27;</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">&#x27;LP6005441-DNA_F08&#x27;</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">&#x27;HGDP00843&#x27;</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">&#x27;HGDP00392&#x27;</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">&#x27;LP6005441-DNA_H03&#x27;</div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">gene_name</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">n_variants</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">n_variants</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">n_variants</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">n_variants</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">n_variants</div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">str</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">int64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">int64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">int64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">int64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">int64</td></tr>\n",
       "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;AC003044.1&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;AC005014.1&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;AC005599.1&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;AC006296.3&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;AC007161.3&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "</tbody></table><p style=\"background: #fdd; padding: 0.4em;\">showing top 5 rows</p>\n",
       "<p style=\"background: #fdd; padding: 0.4em;\">showing the first 5 of 4151 columns</p>\n"
      ],
      "text/plain": [
       "+--------------+------------------------------+--------------------------------+\n",
       "| gene_name    | 'CHMI_CHMI3_WGS2'.n_variants | 'LP6005441-DNA_F08'.n_variants |\n",
       "+--------------+------------------------------+--------------------------------+\n",
       "| str          |                        int64 |                          int64 |\n",
       "+--------------+------------------------------+--------------------------------+\n",
       "| \"AC003044.1\" |                            0 |                              0 |\n",
       "| \"AC005014.1\" |                            0 |                              0 |\n",
       "| \"AC005599.1\" |                            0 |                              0 |\n",
       "| \"AC006296.3\" |                            0 |                              0 |\n",
       "| \"AC007161.3\" |                            0 |                              0 |\n",
       "+--------------+------------------------------+--------------------------------+\n",
       "\n",
       "+------------------------+------------------------+\n",
       "| 'HGDP00843'.n_variants | 'HGDP00392'.n_variants |\n",
       "+------------------------+------------------------+\n",
       "|                  int64 |                  int64 |\n",
       "+------------------------+------------------------+\n",
       "|                      0 |                      1 |\n",
       "|                      0 |                      0 |\n",
       "|                      0 |                      0 |\n",
       "|                      0 |                      0 |\n",
       "|                      0 |                      0 |\n",
       "+------------------------+------------------------+\n",
       "\n",
       "+--------------------------------+\n",
       "| 'LP6005441-DNA_H03'.n_variants |\n",
       "+--------------------------------+\n",
       "|                          int64 |\n",
       "+--------------------------------+\n",
       "|                              0 |\n",
       "|                              0 |\n",
       "|                              0 |\n",
       "|                              0 |\n",
       "|                              0 |\n",
       "+--------------------------------+\n",
       "showing top 5 rows\n",
       "showing the first 5 of 4151 columns"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "burden_mt.show(n_cols=5, n_rows=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even this small dataset is too large for us to inspect the value of `n_variants` for every sample at every variant. Instead, we need to use methods to aggregate or summarize data. Hail has some automagical summarization methods such as:\n",
    "\n",
    "- [`hl.summarize_variants(mt)`](https://hail.is/docs/0.2/methods/genetics.html#hail.methods.summarize_variants)\n",
    "- [`mt.field.summarize()`](https://hail.is/docs/0.2/hail.expr.Expression.html#hail.expr.Expression.summarize)\n",
    "\n",
    "If your scientific questions are not answered by those methods, you probably need to use an [aggregator](https://hail.is/docs/0.2/aggregators.html). Aggregators collapse many values into one value. For example, `hl.agg.mean(mt.field)` computes the mean of all the values of `mt.field`. We can calculate the mean depth for each variant:\n",
    "\n",
    "```\n",
    "mt = mt.annotate_rows(mean_DP_per_variant = hl.agg.mean(mt.DP))\n",
    "```\n",
    "\n",
    "as well as the mean depth for each sample:\n",
    "\n",
    "```\n",
    "mt = mt.annotate_cols(mean_DP_per_sample = hl.agg.mean(mt.DP))\n",
    "```\n",
    "\n",
    "and the mean depth over all genotypes:\n",
    "\n",
    "```\n",
    "mt = mt.annotate_globals(mean_DP_overall = hl.agg.mean(mt.DP))\n",
    "```\n",
    "\n",
    "In the next exercise, you will need to use either a summarize function or an aggregator.\n",
    "\n",
    "#### Exercise\n",
    "\n",
    "Is this a dense (mostly non-zero) or sparse (mostly zero) matrix? Is this expected? How many variants are in our dataset, and how many genes are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to using `annotate_global` to compute dataset-wide aggregations, we can combine row-wise and column-wise aggregations with `hail.ggplot` to produce visualizations. Instead of relying on our brains to make sense of things like mean and variance, we can let our brain consume the entire distribution of the data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 20:===========================================>              (3 + 1) / 4]\r"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe\n",
       "    scrolling=\"no\"\n",
       "    width=\"100%\"\n",
       "    height=\"545px\"\n",
       "    src=\"iframe_figures/figure_16.html\"\n",
       "    frameborder=\"0\"\n",
       "    allowfullscreen\n",
       "></iframe>\n"
      ],
      "text/plain": [
       "<hail.ggplot.ggplot.GGPlot at 0x7f55cf094cd0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from hail.ggplot import *\n",
    "import plotly\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = 'iframe'\n",
    "\n",
    "xx = burden_mt\n",
    "xx = xx.annotate_rows(\n",
    "    n_zero = hl.agg.count_where(xx.n_variants != 0)\n",
    ")\n",
    "\n",
    "ggplot(xx) + geom_col(aes(x=xx.gene_name, y=xx.n_zero))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Run linear regression per gene\n",
    "\n",
    "Hail is designed as a set of resuable modules and functions. In this section, we will re-use several functions from the first notebook but apply them to our =burden_mt= which is keyed by gene instead of locus and contains combined variants rather than genotype calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 99:===========================================>              (3 + 1) / 4]\r"
     ]
    }
   ],
   "source": [
    "_, pca_scores, _ = hl.hwe_normalized_pca(mt.GT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 107:============================>                            (2 + 2) / 4]\r"
     ]
    }
   ],
   "source": [
    "burden_mt = burden_mt.annotate_cols(pca = pca_scores[burden_mt.s])\n",
    "\n",
    "burden_results = hl.linear_regression_rows(\n",
    "    y=burden_mt.pheno1, \n",
    "    x=burden_mt.n_variants,\n",
    "    covariates=[1.0, \n",
    "                burden_mt.pca.scores[0], \n",
    "                burden_mt.pca.scores[1], \n",
    "                burden_mt.pca.scores[2]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use Hail's new plotting system, `hl.ggplot`, to show a bar graph of the burden results. Notice that the genes are sorted alphabetically, not by genomic location!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe\n",
       "    scrolling=\"no\"\n",
       "    width=\"100%\"\n",
       "    height=\"545px\"\n",
       "    src=\"iframe_figures/figure_19.html\"\n",
       "    frameborder=\"0\"\n",
       "    allowfullscreen\n",
       "></iframe>\n"
      ],
      "text/plain": [
       "<hail.ggplot.ggplot.GGPlot at 0x7f55cdafd910>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from hail.ggplot import *\n",
    "import plotly\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = 'iframe'\n",
    "\n",
    "ht = burden_results\n",
    "ggplot(ht) + geom_col(aes(x=ht.gene_name, y=-hl.log(ht.p_value, base=10)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also look at the first ten results in ascending order of p-value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><thead><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">gene_name</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">n</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">sum_x</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">y_transpose_x</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">beta</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">standard_error</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">t_stat</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">p_value</div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">str</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">int32</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td></tr>\n",
       "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;MREG&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.36e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.26e+03</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.38e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.29e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.17e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.11e-05</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;TFB2M&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.44e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">7.31e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.07e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.68e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.03e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.48e-03</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;AC131235.2&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">7.11e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.58e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.42e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.53e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.15e-02</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;EGLN1&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.56e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">6.24e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.18e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.66e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.51e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.20e-02</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;TLCD4&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">6.00e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.35e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.49e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.57e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.14e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.27e-02</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;LINC02591&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.21e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.22e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.00e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.11e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.50e-02</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;NOC2L&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.60e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-2.35e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-6.86e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.31e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-2.07e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.84e-02</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;LINC01397&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.43e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.63e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.36e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.72e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.95e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.13e-02</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;EBNA1BP2&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.57e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-5.99e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-2.22e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.24e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-1.79e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">7.31e-02</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;EFCAB10&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.07e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.03e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.47e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.41e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.75e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">8.07e-02</td></tr>\n",
       "</tbody></table><p style=\"background: #fdd; padding: 0.4em;\">showing top 10 rows</p>\n"
      ],
      "text/plain": [
       "+--------------+-------+----------+---------------+-----------+----------------+\n",
       "| gene_name    |     n |    sum_x | y_transpose_x |      beta | standard_error |\n",
       "+--------------+-------+----------+---------------+-----------+----------------+\n",
       "| str          | int32 |  float64 |       float64 |   float64 |        float64 |\n",
       "+--------------+-------+----------+---------------+-----------+----------------+\n",
       "| \"MREG\"       |  4151 | 2.36e+02 |      1.26e+03 |  5.38e+00 |       1.29e+00 |\n",
       "| \"TFB2M\"      |  4151 | 1.44e+02 |      7.31e+02 |  5.07e+00 |       1.68e+00 |\n",
       "| \"AC131235.2\" |  4151 | 2.00e+00 |      7.11e+01 |  3.58e+01 |       1.42e+01 |\n",
       "| \"EGLN1\"      |  4151 | 1.56e+02 |      6.24e+02 |  4.18e+00 |       1.66e+00 |\n",
       "| \"TLCD4\"      |  4151 | 6.00e+01 |      3.35e+02 |  5.49e+00 |       2.57e+00 |\n",
       "| \"LINC02591\"  |  4151 | 1.00e+00 |      4.21e+01 |  4.22e+01 |       2.00e+01 |\n",
       "| \"NOC2L\"      |  4151 | 3.60e+01 |     -2.35e+02 | -6.86e+00 |       3.31e+00 |\n",
       "| \"LINC01397\"  |  4151 | 1.43e+02 |      4.63e+02 |  3.36e+00 |       1.72e+00 |\n",
       "| \"EBNA1BP2\"   |  4151 | 2.57e+02 |     -5.99e+02 | -2.22e+00 |       1.24e+00 |\n",
       "| \"EFCAB10\"    |  4151 | 2.07e+02 |      5.03e+02 |  2.47e+00 |       1.41e+00 |\n",
       "+--------------+-------+----------+---------------+-----------+----------------+\n",
       "\n",
       "+-----------+----------+\n",
       "|    t_stat |  p_value |\n",
       "+-----------+----------+\n",
       "|   float64 |  float64 |\n",
       "+-----------+----------+\n",
       "|  4.17e+00 | 3.11e-05 |\n",
       "|  3.03e+00 | 2.48e-03 |\n",
       "|  2.53e+00 | 1.15e-02 |\n",
       "|  2.51e+00 | 1.20e-02 |\n",
       "|  2.14e+00 | 3.27e-02 |\n",
       "|  2.11e+00 | 3.50e-02 |\n",
       "| -2.07e+00 | 3.84e-02 |\n",
       "|  1.95e+00 | 5.13e-02 |\n",
       "| -1.79e+00 | 7.31e-02 |\n",
       "|  1.75e+00 | 8.07e-02 |\n",
       "+-----------+----------+\n",
       "showing top 10 rows"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "burden_results.order_by(burden_results.p_value).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, a Q-Q plot is meaningful on genes. Let's plot one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"f0b2e389-6c34-4fbd-b14f-7a3da8b55d69\" data-root-id=\"1002\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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       "  var render_items = [{\"docid\":\"9bc32c76-f02b-420c-970d-6230bb979b1f\",\"roots\":{\"1002\":\"f0b2e389-6c34-4fbd-b14f-7a3da8b55d69\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = hl.plot.qq(burden_results.p_value)\n",
    "hl.plot.show(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With fewer tests performed (one per gene, instead of one per variant), the X and Y range of the Q-Q plot is much smaller than in the common variant association practical.\n",
    "\n",
    "Let's compare the burden test to a standard GWAS. Recall that a standard GWAS performs a large number of tests and therefore must overcome a substantial multiple testing burden. We also look at the genomic locations for some of our top burden genes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><thead><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">interval</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">gene_name</div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">interval&lt;locus&lt;GRCh38&gt;&gt;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">str</td></tr>\n",
       "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[chr1:246540561-chr1:246566261]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;TFB2M&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[chr2:215942584-chr2:216034096]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;MREG&quot;</td></tr>\n",
       "</tbody></table>"
      ],
      "text/plain": [
       "+---------------------------------+-----------+\n",
       "| interval                        | gene_name |\n",
       "+---------------------------------+-----------+\n",
       "| interval<locus<GRCh38>>         | str       |\n",
       "+---------------------------------+-----------+\n",
       "| [chr1:246540561-chr1:246566261] | \"TFB2M\"   |\n",
       "| [chr2:215942584-chr2:216034096] | \"MREG\"    |\n",
       "+---------------------------------+-----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
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     "text": [
      "[Stage 129:============================>                            (2 + 2) / 4]\r"
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\",\"dtype\":\"float64\",\"shape\":[2929]},\"_pval\":{\"__ndarray__\":\"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       "  var render_items = [{\"docid\":\"60d9eab5-c3e5-4b27-86fe-4ccd025ed019\",\"roots\":{\"1105\":\"7247a86a-3641-4f41-81d7-60af7cca9229\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1105"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table><thead><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">locus</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">alleles</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">n</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">sum_x</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">y_transpose_x</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">beta</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">standard_error</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">t_stat</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">p_value</div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">locus&lt;GRCh38&gt;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">array&lt;str&gt;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">int32</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td></tr>\n",
       "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr2:216026821</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;A&quot;,&quot;G&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">7.00e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">7.38e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.08e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.41e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.48e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">7.59e-06</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr1:95163286</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;C&quot;,&quot;T&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">9.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.29e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.56e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">6.67e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.83e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.29e-04</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr1:61872068</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;A&quot;,&quot;G&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.01e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.07e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.41e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.59e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.40e-04</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr7:117738954</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">7.11e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">7.11e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.00e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.56e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.81e-04</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr6:143088243</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.01e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">7.11e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">7.11e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.00e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.56e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.82e-04</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr6:143251809</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;C&quot;,&quot;T&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">7.05e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">7.06e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.00e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.53e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.17e-04</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr5:156232994</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-6.96e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-6.94e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.00e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-3.47e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.29e-04</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr5:156431319</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;A&quot;,&quot;G&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.00e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.37e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.07e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.10e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.44e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.87e-04</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr6:8904959</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;C&quot;,&quot;T&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">6.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-1.62e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-2.68e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">8.17e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-3.28e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.06e-03</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr8:118665470</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;GTTTT&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.75e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-9.03e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-4.49e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.42e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-3.18e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.50e-03</td></tr>\n",
       "</tbody></table><p style=\"background: #fdd; padding: 0.4em;\">showing top 10 rows</p>\n"
      ],
      "text/plain": [
       "+----------------+---------------+-------+----------+---------------+\n",
       "| locus          | alleles       |     n |    sum_x | y_transpose_x |\n",
       "+----------------+---------------+-------+----------+---------------+\n",
       "| locus<GRCh38>  | array<str>    | int32 |  float64 |       float64 |\n",
       "+----------------+---------------+-------+----------+---------------+\n",
       "| chr2:216026821 | [\"A\",\"G\"]     |  4151 | 7.00e+01 |      7.38e+02 |\n",
       "| chr1:95163286  | [\"C\",\"T\"]     |  4151 | 9.00e+00 |      2.29e+02 |\n",
       "| chr1:61872068  | [\"A\",\"G\"]     |  4151 | 2.00e+00 |      1.01e+02 |\n",
       "| chr7:117738954 | [\"G\",\"A\"]     |  4151 | 1.00e+00 |      7.11e+01 |\n",
       "| chr6:143088243 | [\"G\",\"A\"]     |  4151 | 1.01e+00 |      7.11e+01 |\n",
       "| chr6:143251809 | [\"C\",\"T\"]     |  4151 | 1.00e+00 |      7.05e+01 |\n",
       "| chr5:156232994 | [\"G\",\"A\"]     |  4151 | 1.00e+00 |     -6.96e+01 |\n",
       "| chr5:156431319 | [\"A\",\"G\"]     |  4151 | 4.00e+01 |      4.37e+02 |\n",
       "| chr6:8904959   | [\"C\",\"T\"]     |  4151 | 6.00e+00 |     -1.62e+02 |\n",
       "| chr8:118665470 | [\"G\",\"GTTTT\"] |  4151 | 2.75e+00 |     -9.03e+01 |\n",
       "+----------------+---------------+-------+----------+---------------+\n",
       "\n",
       "+-----------+----------------+-----------+----------+\n",
       "|      beta | standard_error |    t_stat |  p_value |\n",
       "+-----------+----------------+-----------+----------+\n",
       "|   float64 |        float64 |   float64 |  float64 |\n",
       "+-----------+----------------+-----------+----------+\n",
       "|  1.08e+01 |       2.41e+00 |  4.48e+00 | 7.59e-06 |\n",
       "|  2.56e+01 |       6.67e+00 |  3.83e+00 | 1.29e-04 |\n",
       "|  5.07e+01 |       1.41e+01 |  3.59e+00 | 3.40e-04 |\n",
       "|  7.11e+01 |       2.00e+01 |  3.56e+00 | 3.81e-04 |\n",
       "|  7.11e+01 |       2.00e+01 |  3.56e+00 | 3.82e-04 |\n",
       "|  7.06e+01 |       2.00e+01 |  3.53e+00 | 4.17e-04 |\n",
       "| -6.94e+01 |       2.00e+01 | -3.47e+00 | 5.29e-04 |\n",
       "|  1.07e+01 |       3.10e+00 |  3.44e+00 | 5.87e-04 |\n",
       "| -2.68e+01 |       8.17e+00 | -3.28e+00 | 1.06e-03 |\n",
       "| -4.49e+01 |       1.42e+01 | -3.18e+00 | 1.50e-03 |\n",
       "+-----------+----------------+-----------+----------+\n",
       "showing top 10 rows"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt = mt.annotate_cols(pca = pca_scores[mt.s])\n",
    "\n",
    "\n",
    "linreg_results = hl.linear_regression_rows(\n",
    "    y=mt.pheno1, \n",
    "    x=mt.GT.n_alt_alleles(),\n",
    "    covariates=[1.0, \n",
    "                mt.pca.scores[0], \n",
    "                mt.pca.scores[1], \n",
    "                mt.pca.scores[2]])\n",
    "ht = linreg_results\n",
    "hl.plot.show(hl.plot.manhattan(ht.p_value))\n",
    "linreg_results.order_by(linreg_results.p_value).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Weighted Burden Test: Theory\n",
    "\n",
    "If we can confidently predict the directions of effects (while the effect sizes themselves are still unknown), we can encode that knowledge as a \"weight\". A burden test with weights is known as a weighted burden test. We symbolically represent it as:\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "S_k && \\textrm{The } k\\textrm{-th set of variants} \\\\\n",
    "w &: \\mathbb{R}^M &\\textrm{The weights for each variant}\\\\\n",
    "\\\\\n",
    "\\varepsilon_i &\\sim \\mathcal{N}(0, \\sigma^2) \\\\\n",
    "y_i &= \\beta_k \\left( \\sum_{j \\in S_k} w_j G_{ij} \\right) + \\varepsilon_i\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "## The Weighted Burden Test: Practice\n",
    "\n",
    "An effective choice of weights can increase the power of a burden test. For example, we may weight variants which are predicted to be damaging higher than synonymous variants. The HGDP+1kG subset dataset we have here, `mt`, contains a few different annotations. Your tasks in this section are:\n",
    "\n",
    "#### Exercise\n",
    "\n",
    "1. Explore these annotations using `show`, aggregations, and `summarize`.\n",
    "2. Use a numeric annotation as a weight or compute a new numeric annotaiton from a non-numeric annotation (you might need [`hl.case`](https://hail.is/docs/0.2/functions/core.html#hail.expr.functions.case)).\n",
    "3. Perform a new burden test using `mt.group_rows_by(...).aggregate(...)`, aggregators, `hl.linear_regression_rows`, and your new weight annotation. Do not use `burden_mt` again!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Sequence Kernel Association Test (SKAT): Theory\n",
    "\n",
    "If the directions of effects are unpredictably random, then neither a burden test nor a weighted burden test is well-powered. Instead we can test for _excess variance_ of the effect sizes of a set of variants. The sequence kernel association test (SKAT) is one such test. It does not report an effect size because it does not test the strength of the association. Instead, SKAT reports a $p$-value of rejecting its null hypothesis: that the effect of the genotypes on the phenotypes is zero. The SKAT test involves two models, a null model and a full model. Both models include a set of covariates per sample. The full model is:\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "K && \\textrm{The number of covariates} \\\\\n",
    "X &: \\mathbb{R}^{N \\times K} &\\textrm{The covariates for each sample} \\\\\n",
    "\\\\\n",
    "\\varepsilon_i &\\sim \\mathcal{N}(0, \\sigma^2) \\\\\n",
    "y_i &= X \\vec{\\alpha} + G \\vec{\\beta} + \\varepsilon_i\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "The null model considers only the covariates:\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "y_i &= X \\vec{\\alpha}_{\\textrm{null}} + \\varepsilon_i\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "The null hypothesis supposes that $\\beta = 0$. The test of the null hypothesis essentially investigates the likelihood that the residual variance (i.e. $y - X \\widehat{\\vec{\\alpha}_{\\textrm{null}}}$) is truly independently, identically, and normally distributed noise. The details of how to test that are somewhat complex and involve a distribution without a closed form. We refer the interested reader to the [SKAT paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3135811/).\n",
    "\n",
    "## The Sequence Kernel Association Test (SKAT): Practice\n",
    "\n",
    "The sequence kernel association test is one of Hail's built-in methods. The SKAT also permits a non-negative weight paramter for each variant. The SKAT paper suggests using weights taken from the CDF of a 1,25-Beta distribution evaluated at the allele frequency of the variant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 142:==========================================>              (3 + 1) / 4]\r"
     ]
    }
   ],
   "source": [
    "skat_mt = mt\n",
    "skat_mt = skat_mt.annotate_cols(\n",
    "    pca = pca_scores[skat_mt.s]\n",
    ")\n",
    "skat_mt = hl.variant_qc(skat_mt)\n",
    "skat_mt = skat_mt.annotate_rows(\n",
    "    weight = hl.dbeta(skat_mt.variant_qc.AF[1], 1, 25)\n",
    ")\n",
    "\n",
    "skat_results = hl.skat(\n",
    "    skat_mt.gene_name,\n",
    "    skat_mt.weight,\n",
    "    y = skat_mt.pheno2, \n",
    "    x = skat_mt.GT.n_alt_alleles(),\n",
    "    covariates = [1.0, \n",
    "                  skat_mt.pca.scores[0], \n",
    "                  skat_mt.pca.scores[1], \n",
    "                  skat_mt.pca.scores[2]]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "Global fields:\n",
      "    None\n",
      "----------------------------------------\n",
      "Row fields:\n",
      "    'locus': locus<GRCh38> \n",
      "    'alleles': array<str> \n",
      "    'n': int32 \n",
      "    'sum_x': float64 \n",
      "    'y_transpose_x': float64 \n",
      "    'beta': float64 \n",
      "    'standard_error': float64 \n",
      "    't_stat': float64 \n",
      "    'p_value': float64 \n",
      "----------------------------------------\n",
      "Key: ['locus', 'alleles']\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "ht.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe\n",
       "    scrolling=\"no\"\n",
       "    width=\"100%\"\n",
       "    height=\"545px\"\n",
       "    src=\"iframe_figures/figure_26.html\"\n",
       "    frameborder=\"0\"\n",
       "    allowfullscreen\n",
       "></iframe>\n"
      ],
      "text/plain": [
       "<hail.ggplot.ggplot.GGPlot at 0x7f55cdb1f730>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ht = skat_results\n",
    "ht = ht.annotate(\n",
    "    p_value = hl.if_else(ht.fault == 0, ht.p_value, 1)\n",
    ")\n",
    "ggplot(ht) + geom_col(aes(x=ht.id, y=-hl.log(ht.p_value, base=10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><thead><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">id</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">size</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">q_stat</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">p_value</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">fault</div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">str</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">int32</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">int32</td></tr>\n",
       "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;KLHL5&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">25</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">7.96e+03</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">9.41e-05</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;SFT2D2&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">11</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">6.31e+03</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.60e-03</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;TLCD4&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">9</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.52e+03</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.21e-02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;TLCD4-RWDD3&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">21</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.64e+03</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.41e-02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;WDR35&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">31</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">6.82e+03</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.47e-02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;DUX4&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">22</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.12e+03</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.80e-02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;FBLIM1&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">16</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">8.36e+02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.14e-02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;AC055764.2&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.65e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.35e-02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;AC067960.1&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.03e+03</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.65e-02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;AC016831.6&quot;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">134</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.44e+04</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">5.76e-02</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">0</td></tr>\n",
       "</tbody></table><p style=\"background: #fdd; padding: 0.4em;\">showing top 10 rows</p>\n"
      ],
      "text/plain": [
       "+---------------+-------+----------+----------+-------+\n",
       "| id            |  size |   q_stat |  p_value | fault |\n",
       "+---------------+-------+----------+----------+-------+\n",
       "| str           | int32 |  float64 |  float64 | int32 |\n",
       "+---------------+-------+----------+----------+-------+\n",
       "| \"KLHL5\"       |    25 | 7.96e+03 | 9.41e-05 |     0 |\n",
       "| \"SFT2D2\"      |    11 | 6.31e+03 | 5.60e-03 |     0 |\n",
       "| \"TLCD4\"       |     9 | 2.52e+03 | 1.21e-02 |     0 |\n",
       "| \"TLCD4-RWDD3\" |    21 | 4.64e+03 | 1.41e-02 |     0 |\n",
       "| \"WDR35\"       |    31 | 6.82e+03 | 1.47e-02 |     0 |\n",
       "| \"DUX4\"        |    22 | 5.12e+03 | 2.80e-02 |     0 |\n",
       "| \"FBLIM1\"      |    16 | 8.36e+02 | 5.14e-02 |     0 |\n",
       "| \"AC055764.2\"  |     1 | 4.65e+01 | 5.35e-02 |     0 |\n",
       "| \"AC067960.1\"  |     5 | 2.03e+03 | 5.65e-02 |     0 |\n",
       "| \"AC016831.6\"  |   134 | 1.44e+04 | 5.76e-02 |     0 |\n",
       "+---------------+-------+----------+----------+-------+\n",
       "showing top 10 rows"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "skat_results.order_by(skat_results.p_value).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, let's compare to a standard GWAS on this phenotype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><thead><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">interval</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">gene_name</div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">interval&lt;locus&lt;GRCh38&gt;&gt;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">str</td></tr>\n",
       "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[chr1:168225938-chr1:168253021]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;SFT2D2&quot;</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[chr4:39045039-chr4:39126857]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">&quot;KLHL5&quot;</td></tr>\n",
       "</tbody></table>"
      ],
      "text/plain": [
       "+---------------------------------+-----------+\n",
       "| interval                        | gene_name |\n",
       "+---------------------------------+-----------+\n",
       "| interval<locus<GRCh38>>         | str       |\n",
       "+---------------------------------+-----------+\n",
       "| [chr1:168225938-chr1:168253021] | \"SFT2D2\"  |\n",
       "| [chr4:39045039-chr4:39126857]   | \"KLHL5\"   |\n",
       "+---------------------------------+-----------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genes.filter(hl.set(['KLHL5', 'SFT2D2']).contains(genes.gene_name)).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 153:============================>                            (2 + 2) / 4]\r"
     ]
    },
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     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"f21387b6-41fd-4ada-8f60-55cc07852f0a\" data-root-id=\"1292\"></div>\n"
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       "  function embed_document(root) {\n",
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Application\",\"version\":\"1.4.0\"}};\n",
       "  var render_items = [{\"docid\":\"063562a4-805f-41de-bdc2-281537e40e44\",\"roots\":{\"1292\":\"f21387b6-41fd-4ada-8f60-55cc07852f0a\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1292"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table><thead><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;\"></div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">locus</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">alleles</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">n</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">sum_x</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">y_transpose_x</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">beta</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">standard_error</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">t_stat</div></td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \" colspan=\"1\"><div style=\"text-align: left;border-bottom: solid 2px #000; padding-bottom: 5px\">p_value</div></td></tr><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">locus&lt;GRCh38&gt;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">array&lt;str&gt;</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">int32</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; text-align: left;\">float64</td></tr>\n",
       "</thead><tbody><tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr4:39099054</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;T&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.21e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.22e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.54e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.79e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.76e-06</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr7:130881591</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;C&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.21e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.22e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.54e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.79e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.76e-06</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr4:39123752</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;C&quot;,&quot;T&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.15e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.15e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.54e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.52e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">6.45e-06</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr4:4193655</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;T&quot;,&quot;C&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.15e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.15e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.54e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4.52e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">6.46e-06</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr4:10628077</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;C&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-1.13e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-1.13e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.54e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-4.45e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">8.78e-06</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr4:39077705</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;A&quot;,&quot;C&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.01e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-1.13e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-1.13e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.54e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-4.45e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">8.91e-06</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr5:156597958</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;C&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-9.28e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-1.09e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.63e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-4.16e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.29e-05</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr1:51781907</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;G&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-9.28e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-1.09e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.63e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-4.16e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.29e-05</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr1:95164390</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;ACCATCTGC&quot;,&quot;A&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-9.28e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-1.09e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.63e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-4.16e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.29e-05</td></tr>\n",
       "<tr><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">chr3:98888520</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">[&quot;C&quot;,&quot;T&quot;]</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">4151</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">1.00e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-9.28e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-1.09e+01</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">2.63e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">-4.16e+00</td><td style=\"white-space: nowrap; max-width: 500px; overflow: hidden; text-overflow: ellipsis; \">3.29e-05</td></tr>\n",
       "</tbody></table><p style=\"background: #fdd; padding: 0.4em;\">showing top 10 rows</p>\n"
      ],
      "text/plain": [
       "+----------------+-------------------+-------+----------+---------------+\n",
       "| locus          | alleles           |     n |    sum_x | y_transpose_x |\n",
       "+----------------+-------------------+-------+----------+---------------+\n",
       "| locus<GRCh38>  | array<str>        | int32 |  float64 |       float64 |\n",
       "+----------------+-------------------+-------+----------+---------------+\n",
       "| chr4:39099054  | [\"G\",\"T\"]         |  4151 | 1.00e+00 |      1.21e+01 |\n",
       "| chr7:130881591 | [\"C\",\"A\"]         |  4151 | 1.00e+00 |      1.21e+01 |\n",
       "| chr4:39123752  | [\"C\",\"T\"]         |  4151 | 1.00e+00 |      1.15e+01 |\n",
       "| chr4:4193655   | [\"T\",\"C\"]         |  4151 | 1.00e+00 |      1.15e+01 |\n",
       "| chr4:10628077  | [\"C\",\"A\"]         |  4151 | 1.00e+00 |     -1.13e+01 |\n",
       "| chr4:39077705  | [\"A\",\"C\"]         |  4151 | 1.01e+00 |     -1.13e+01 |\n",
       "| chr5:156597958 | [\"G\",\"C\"]         |  4151 | 1.00e+00 |     -9.28e+00 |\n",
       "| chr1:51781907  | [\"G\",\"A\"]         |  4151 | 1.00e+00 |     -9.28e+00 |\n",
       "| chr1:95164390  | [\"ACCATCTGC\",\"A\"] |  4151 | 1.00e+00 |     -9.28e+00 |\n",
       "| chr3:98888520  | [\"C\",\"T\"]         |  4151 | 1.00e+00 |     -9.28e+00 |\n",
       "+----------------+-------------------+-------+----------+---------------+\n",
       "\n",
       "+-----------+----------------+-----------+----------+\n",
       "|      beta | standard_error |    t_stat |  p_value |\n",
       "+-----------+----------------+-----------+----------+\n",
       "|   float64 |        float64 |   float64 |  float64 |\n",
       "+-----------+----------------+-----------+----------+\n",
       "|  1.22e+01 |       2.54e+00 |  4.79e+00 | 1.76e-06 |\n",
       "|  1.22e+01 |       2.54e+00 |  4.79e+00 | 1.76e-06 |\n",
       "|  1.15e+01 |       2.54e+00 |  4.52e+00 | 6.45e-06 |\n",
       "|  1.15e+01 |       2.54e+00 |  4.52e+00 | 6.46e-06 |\n",
       "| -1.13e+01 |       2.54e+00 | -4.45e+00 | 8.78e-06 |\n",
       "| -1.13e+01 |       2.54e+00 | -4.45e+00 | 8.91e-06 |\n",
       "| -1.09e+01 |       2.63e+00 | -4.16e+00 | 3.29e-05 |\n",
       "| -1.09e+01 |       2.63e+00 | -4.16e+00 | 3.29e-05 |\n",
       "| -1.09e+01 |       2.63e+00 | -4.16e+00 | 3.29e-05 |\n",
       "| -1.09e+01 |       2.63e+00 | -4.16e+00 | 3.29e-05 |\n",
       "+-----------+----------------+-----------+----------+\n",
       "showing top 10 rows"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt = mt.annotate_cols(pca = pca_scores[mt.s])\n",
    "\n",
    "linreg_results = hl.linear_regression_rows(\n",
    "    y=mt.pheno2, \n",
    "    x=mt.GT.n_alt_alleles(),\n",
    "    covariates=[1.0, \n",
    "                mt.pca.scores[0], \n",
    "                mt.pca.scores[1], \n",
    "                mt.pca.scores[2]])\n",
    "ht = linreg_results\n",
    "hl.plot.show(hl.plot.manhattan(ht.p_value))\n",
    "linreg_results.order_by(linreg_results.p_value).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Phenotypes\n",
    "\n",
    "For binary phenotypes, for example from a case-control study, [`hl.logistic_regression_rows`](https://hail.is/docs/0.2/methods/stats.html#hail.methods.logistic_regression_rows) and [`hl.skat(..., logistic=True)`](https://hail.is/docs/0.2/methods/genetics.html#hail.methods.skat) can be used instead of their linear analogues. No other changes to the code are necessary."
   ]
  }
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