doc/ipynb/fastlmm2021.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FaST-LMM: New Features for 2021 & 2022\n",
    "=====\n",
    "\n",
    "*updated: 4/25/2024*\n",
    "\n",
    "This notebook demonstrates these new features of FaST-LMM's `single_snp` function:\n",
    "\n",
    "* effect size\n",
    "* multiple phenotypes\n",
    "* filtering output by p-value and random sampling\n",
    "* caching to divide the work into parts (useful for running jobs that run from several hours to several days.)\n",
    "* controlling multi-processor runs\n",
    "\n",
    "and these [new features of `epistasis`](#epistasis):\n",
    "\n",
    "* reports β, the linear coefficient of the epistatic interaction, and its variance\n",
    "* supports a user-supplied eigenvalue decomposition of the Realized Relationship Matrix (RRM) matrix\n",
    "\n",
    "The notebook is an addition to the [main FaST-LMM notebook](https://github.com/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To prepare this notebook to run analyses, please run the following script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set some ipython notebook properties\n",
    "%matplotlib inline\n",
    "\n",
    "# set degree of verbosity (adapt to INFO for more verbose output)\n",
    "import logging\n",
    "logging.basicConfig(level=logging.WARNING)\n",
    "\n",
    "# set figure sizes\n",
    "import pylab\n",
    "pylab.rcParams['figure.figsize'] = (10.0, 8.0)\n",
    "#pylab.plot([1,2,3],[4,5,6])\n",
    "\n",
    "# set display width for pandas data frames\n",
    "import pandas as pd\n",
    "pd.set_option('display.width', 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effect Size\n",
    "\n",
    "The `single_snp` function now returns the effect size of the SNP. EffectSize is `SNPWeight^2 * \"raw\" test SNP variance / phenotype variance`, where\n",
    "* SNPWeight is the beta for the “standardized test SNP” (where \"standardized\" means made to have mean 0, stdev 1)\n",
    "* \"raw\" means the original test SNP values of “0,1,2” (the count of allele 1 or 2 [it doesn’t matter])\n",
    "* The phenotype’s original values are used (so, they are not, for example, changed by regressing out the covariates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Phenotype\n",
    "\n",
    "Previous version of `single_snp` looked for associations between SNPs and one phenotype. The new version supports multiple phenotypes, for example multiple conditions or diseases. The number of phenotypes can be a few, dozens, or thousands. Testing multiple phenotypes in one run is much faster than testing them one at a time.\n",
    "\n",
    "In this example, we'll expand the example from the [main FaST-LMM notebook](https://github.com/fastlmm/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb). Along with the original phenotype, for demonstration purposes, we'll also test its square and cube. We'll create this new phenotype data in memory, but you'll typically, instead, just read multiple phenotypes from a single file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pysnptools.snpreader import Pheno, SnpData\n",
    "from fastlmm.util import example_file # Download and return local file name\n",
    "\n",
    "# For example, create multiple phenotype in memory\n",
    "##############################\n",
    "pheno1 = Pheno(example_file(\"tests/datasets/synth/pheno_10_causals.txt\")).read()\n",
    "pheno3 = SnpData(iid=pheno1.iid,\n",
    "                 sid=[\"original\",\"sqrt\",\"square\"],\n",
    "                 val= np.c_[pheno1.val,pheno1.val**2,pheno1.val**3]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We run just as in the main FaST-LMM notebook but with pheno3, which contains multiple phenotypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda=1.0126\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid_index</th>\n",
       "      <th>SNP</th>\n",
       "      <th>Chr</th>\n",
       "      <th>GenDist</th>\n",
       "      <th>ChrPos</th>\n",
       "      <th>PValue</th>\n",
       "      <th>SnpWeight</th>\n",
       "      <th>SnpWeightSE</th>\n",
       "      <th>EffectSize</th>\n",
       "      <th>Mixing</th>\n",
       "      <th>Nullh2</th>\n",
       "      <th>Pheno</th>\n",
       "      <th>PhenoCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>snp495_m0_.01m1_.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>4.970585e-35</td>\n",
       "      <td>3.069400</td>\n",
       "      <td>0.229633</td>\n",
       "      <td>0.011049</td>\n",
       "      <td>0</td>\n",
       "      <td>0.300930</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>snp495_m0_.01m1_.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>7.180109e-32</td>\n",
       "      <td>-0.872371</td>\n",
       "      <td>0.069123</td>\n",
       "      <td>0.010028</td>\n",
       "      <td>0</td>\n",
       "      <td>0.451557</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52</td>\n",
       "      <td>snp495_m0_.01m1_.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>2.990684e-23</td>\n",
       "      <td>0.418653</td>\n",
       "      <td>0.040052</td>\n",
       "      <td>0.007373</td>\n",
       "      <td>0</td>\n",
       "      <td>0.451117</td>\n",
       "      <td>original</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>392</td>\n",
       "      <td>snp1422_m0_.49m1_.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>8.251922e-23</td>\n",
       "      <td>-0.416495</td>\n",
       "      <td>0.040300</td>\n",
       "      <td>0.082526</td>\n",
       "      <td>0</td>\n",
       "      <td>0.279710</td>\n",
       "      <td>original</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>650</td>\n",
       "      <td>snp1200_m0_.37m1_.36</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>3.048007e-14</td>\n",
       "      <td>0.328870</td>\n",
       "      <td>0.042021</td>\n",
       "      <td>0.054986</td>\n",
       "      <td>0</td>\n",
       "      <td>0.279710</td>\n",
       "      <td>original</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>392</td>\n",
       "      <td>snp1422_m0_.49m1_.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>9.732331e-11</td>\n",
       "      <td>-1.691786</td>\n",
       "      <td>0.255819</td>\n",
       "      <td>0.037965</td>\n",
       "      <td>0</td>\n",
       "      <td>0.263895</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>snp433_m0_.14m1_.11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>9.202499e-10</td>\n",
       "      <td>-0.268289</td>\n",
       "      <td>0.042973</td>\n",
       "      <td>0.016289</td>\n",
       "      <td>0</td>\n",
       "      <td>0.279710</td>\n",
       "      <td>original</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>725</td>\n",
       "      <td>snp1386_m0_m1_.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>1.383880e-08</td>\n",
       "      <td>1.480571</td>\n",
       "      <td>0.256529</td>\n",
       "      <td>0.000605</td>\n",
       "      <td>0</td>\n",
       "      <td>0.417260</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>725</td>\n",
       "      <td>snp1386_m0_m1_.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>1.308581e-05</td>\n",
       "      <td>-0.340112</td>\n",
       "      <td>0.077247</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0</td>\n",
       "      <td>0.495164</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>415</td>\n",
       "      <td>snp1166_m0_.08m1_.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>5.007490e-05</td>\n",
       "      <td>-0.315055</td>\n",
       "      <td>0.077009</td>\n",
       "      <td>0.005493</td>\n",
       "      <td>0</td>\n",
       "      <td>0.509244</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sid_index                   SNP  Chr  GenDist  ChrPos        PValue  SnpWeight  SnpWeightSE  EffectSize  Mixing    Nullh2     Pheno  PhenoCount\n",
       "0         52   snp495_m0_.01m1_.04  5.0   4052.0  4052.0  4.970585e-35   3.069400     0.229633    0.011049       0  0.300930    square           3\n",
       "1         52   snp495_m0_.01m1_.04  5.0   4052.0  4052.0  7.180109e-32  -0.872371     0.069123    0.010028       0  0.451557      sqrt           3\n",
       "2         52   snp495_m0_.01m1_.04  5.0   4052.0  4052.0  2.990684e-23   0.418653     0.040052    0.007373       0  0.451117  original           3\n",
       "3        392   snp1422_m0_.49m1_.5  3.0   2392.0  2392.0  8.251922e-23  -0.416495     0.040300    0.082526       0  0.279710  original           3\n",
       "4        650  snp1200_m0_.37m1_.36  3.0   2650.0  2650.0  3.048007e-14   0.328870     0.042021    0.054986       0  0.279710  original           3\n",
       "5        392   snp1422_m0_.49m1_.5  3.0   2392.0  2392.0  9.732331e-11  -1.691786     0.255819    0.037965       0  0.263895    square           3\n",
       "6          3   snp433_m0_.14m1_.11  3.0   2003.0  2003.0  9.202499e-10  -0.268289     0.042973    0.016289       0  0.279710  original           3\n",
       "7        725     snp1386_m0_m1_.01  1.0    725.0   725.0  1.383880e-08   1.480571     0.256529    0.000605       0  0.417260    square           3\n",
       "8        725     snp1386_m0_m1_.01  1.0    725.0   725.0  1.308581e-05  -0.340112     0.077247    0.000359       0  0.495164      sqrt           3\n",
       "9        415   snp1166_m0_.08m1_.1  2.0   1415.0  1415.0  5.007490e-05  -0.315055     0.077009    0.005493       0  0.509244      sqrt           3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import the algorithm\n",
    "import numpy as np\n",
    "from fastlmm.association import single_snp\n",
    "from fastlmm.util import example_file # Download and return local file name\n",
    "\n",
    "# set up data\n",
    "##############################\n",
    "from fastlmm.util import example_file # Download and return local file name\n",
    "bed_fn = example_file('tests/datasets/synth/all.*','*.bed')\n",
    "cov_fn = example_file(\"tests/datasets/synth/cov.txt\")\n",
    "\n",
    "# run gwas with pheno3\n",
    "###################################################################\n",
    "results_df = single_snp(bed_fn, pheno3, covar=cov_fn, count_A1=False)\n",
    "\n",
    "# manhattan plot\n",
    "import pylab\n",
    "import fastlmm.util.util as flutil\n",
    "pylab.rcParams['figure.figsize'] = (10.0, 8.0)#For some reason, need again (appears above too) to get big figures\n",
    "flutil.manhattan_plot(results_df[[\"Chr\", \"ChrPos\", \"PValue\"]].values,pvalue_line=1e-5/pheno3.sid_count,xaxis_unit_bp=False)\n",
    "pylab.show()\n",
    "\n",
    "# qq plot\n",
    "from fastlmm.util.stats import plotp\n",
    "plotp.qqplot(results_df[\"PValue\"].values, xlim=[0,5], ylim=[0,5])\n",
    "\n",
    "# print head of results data frame\n",
    "import pandas as pd\n",
    "pd.set_option('display.width', 1000)\n",
    "results_df.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running with multiple phenotypes differs from running with a single phenotype in these ways:\n",
    "\n",
    "### More output lines (and tougher p-value threshold for significance)\n",
    "\n",
    "Instead of one output line per test SNP, we receive one output line per \\[test SNP, phenotype\\] pair. In the example, we have 5000 test SNPs and three phenotypes, so the number of output lines will be 15,000.\n",
    "\n",
    "As before, each output line will contain one p-value. Because we are testing more hypotheses, we need adjust the p-value threshold for significance. For example, if that threshold was 1e-5 for one phenotype, then the [Bonferroni correction](https://en.wikipedia.org/wiki/Bonferroni_correction) says that for three phenotypes should be 1e-5 divided by 3.\n",
    "\n",
    "### New columns\n",
    "\n",
    "The output contains two new columns: `Pheno` and `PhenoCount`. In this case, `Pheno` will be 'Original', 'Square', or 'Cube'. `PhenoCount` will be 3.\n",
    "\n",
    "### Missing data handling\n",
    "\n",
    "For a given individual, phonotype data must be entirely present or entirely missing. A user with some phenotype data present and some missing is not supported. In detail:\n",
    "\n",
    "* For a given individual, all phenotype data is present -- individual is included in computation.\n",
    "* For a given individual, all phenotype is missing -- individual is removed from computation.\n",
    "* For a given individual, some phenotype is present and some is missing -- an error is raised.\n",
    "\n",
    "In the last case, if an error is raised, we would either need to fix the data before giving it `single_snp` (perhaps by filling in missing data or by removing the individual) or create a loop around `single_snp` and running one phenotype at a time. Note that the 'create a loop' method is slower and produces results based on different subsets of individuals.\n",
    "\n",
    "### No support for 2nd kernel\n",
    "\n",
    "The `single_snp` function supports multiple similarity kernels when the number of phentotypes is 1. When multiple phenotypes are given, no second kernel is allowed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering Output by p-value and Random Sampling\n",
    "\n",
    "The `single_snp` function may compute more p-values that we wish to see in the output. Two new options let us filter out less interesting rows.\n",
    "\n",
    "Use `pvalue_threshold` to include all p-values smaller than the given threshold. With this we can create a censored Manhatten plot that shows the interesting values and some less interesting values, but that excludes very uninteresting values.\n",
    "\n",
    "Use `random_threshold` to include a random sample of output lines. Sampling works by first assigning each potential output line a random number drawn from a uniform distribution between 0.0 and 1.0. The program then checks if that random number is below our random threshold. With a random sample of output lines, we can create a Q-Q plot that lets us know whether the model is controlling for type-I error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2051\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda=0.9231\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid_index</th>\n",
       "      <th>SNP</th>\n",
       "      <th>Chr</th>\n",
       "      <th>GenDist</th>\n",
       "      <th>ChrPos</th>\n",
       "      <th>PValue</th>\n",
       "      <th>SnpWeight</th>\n",
       "      <th>SnpWeightSE</th>\n",
       "      <th>EffectSize</th>\n",
       "      <th>Mixing</th>\n",
       "      <th>Nullh2</th>\n",
       "      <th>Pheno</th>\n",
       "      <th>PhenoCount</th>\n",
       "      <th>PValueThreshold</th>\n",
       "      <th>RandomValue</th>\n",
       "      <th>RandomThreshold</th>\n",
       "      <th>RandomSeed</th>\n",
       "      <th>PValueCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>snp495_m0_.01m1_.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>4.970585e-35</td>\n",
       "      <td>3.069400</td>\n",
       "      <td>0.229633</td>\n",
       "      <td>0.011049</td>\n",
       "      <td>0</td>\n",
       "      <td>0.300930</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.429538</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>snp495_m0_.01m1_.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>7.180109e-32</td>\n",
       "      <td>-0.872371</td>\n",
       "      <td>0.069123</td>\n",
       "      <td>0.010028</td>\n",
       "      <td>0</td>\n",
       "      <td>0.451557</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.771803</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52</td>\n",
       "      <td>snp495_m0_.01m1_.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>2.990684e-23</td>\n",
       "      <td>0.418653</td>\n",
       "      <td>0.040052</td>\n",
       "      <td>0.007373</td>\n",
       "      <td>0</td>\n",
       "      <td>0.451117</td>\n",
       "      <td>original</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.155787</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>392</td>\n",
       "      <td>snp1422_m0_.49m1_.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>8.251922e-23</td>\n",
       "      <td>-0.416495</td>\n",
       "      <td>0.040300</td>\n",
       "      <td>0.082526</td>\n",
       "      <td>0</td>\n",
       "      <td>0.279710</td>\n",
       "      <td>original</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.707699</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>650</td>\n",
       "      <td>snp1200_m0_.37m1_.36</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>3.048007e-14</td>\n",
       "      <td>0.328870</td>\n",
       "      <td>0.042021</td>\n",
       "      <td>0.054986</td>\n",
       "      <td>0</td>\n",
       "      <td>0.279710</td>\n",
       "      <td>original</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.540116</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>392</td>\n",
       "      <td>snp1422_m0_.49m1_.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>9.732331e-11</td>\n",
       "      <td>-1.691786</td>\n",
       "      <td>0.255819</td>\n",
       "      <td>0.037965</td>\n",
       "      <td>0</td>\n",
       "      <td>0.263895</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.697988</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>snp433_m0_.14m1_.11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>9.202499e-10</td>\n",
       "      <td>-0.268289</td>\n",
       "      <td>0.042973</td>\n",
       "      <td>0.016289</td>\n",
       "      <td>0</td>\n",
       "      <td>0.279710</td>\n",
       "      <td>original</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.086047</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>725</td>\n",
       "      <td>snp1386_m0_m1_.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>1.383880e-08</td>\n",
       "      <td>1.480571</td>\n",
       "      <td>0.256529</td>\n",
       "      <td>0.000605</td>\n",
       "      <td>0</td>\n",
       "      <td>0.417260</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.055916</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>725</td>\n",
       "      <td>snp1386_m0_m1_.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>1.308581e-05</td>\n",
       "      <td>-0.340112</td>\n",
       "      <td>0.077247</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0</td>\n",
       "      <td>0.495164</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.102950</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>415</td>\n",
       "      <td>snp1166_m0_.08m1_.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>5.007490e-05</td>\n",
       "      <td>-0.315055</td>\n",
       "      <td>0.077009</td>\n",
       "      <td>0.005493</td>\n",
       "      <td>0</td>\n",
       "      <td>0.509244</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.433384</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sid_index                   SNP  Chr  GenDist  ChrPos        PValue  SnpWeight  SnpWeightSE  EffectSize  Mixing    Nullh2     Pheno  PhenoCount  PValueThreshold  RandomValue  RandomThreshold  RandomSeed  PValueCount\n",
       "0         52   snp495_m0_.01m1_.04  5.0   4052.0  4052.0  4.970585e-35   3.069400     0.229633    0.011049       0  0.300930    square           3             0.05     0.429538              0.1           0         3000\n",
       "1         52   snp495_m0_.01m1_.04  5.0   4052.0  4052.0  7.180109e-32  -0.872371     0.069123    0.010028       0  0.451557      sqrt           3             0.05     0.771803              0.1           0         3000\n",
       "2         52   snp495_m0_.01m1_.04  5.0   4052.0  4052.0  2.990684e-23   0.418653     0.040052    0.007373       0  0.451117  original           3             0.05     0.155787              0.1           0         3000\n",
       "3        392   snp1422_m0_.49m1_.5  3.0   2392.0  2392.0  8.251922e-23  -0.416495     0.040300    0.082526       0  0.279710  original           3             0.05     0.707699              0.1           0         3000\n",
       "4        650  snp1200_m0_.37m1_.36  3.0   2650.0  2650.0  3.048007e-14   0.328870     0.042021    0.054986       0  0.279710  original           3             0.05     0.540116              0.1           0         3000\n",
       "5        392   snp1422_m0_.49m1_.5  3.0   2392.0  2392.0  9.732331e-11  -1.691786     0.255819    0.037965       0  0.263895    square           3             0.05     0.697988              0.1           0         3000\n",
       "6          3   snp433_m0_.14m1_.11  3.0   2003.0  2003.0  9.202499e-10  -0.268289     0.042973    0.016289       0  0.279710  original           3             0.05     0.086047              0.1           0         3000\n",
       "7        725     snp1386_m0_m1_.01  1.0    725.0   725.0  1.383880e-08   1.480571     0.256529    0.000605       0  0.417260    square           3             0.05     0.055916              0.1           0         3000\n",
       "8        725     snp1386_m0_m1_.01  1.0    725.0   725.0  1.308581e-05  -0.340112     0.077247    0.000359       0  0.495164      sqrt           3             0.05     0.102950              0.1           0         3000\n",
       "9        415   snp1166_m0_.08m1_.1  2.0   1415.0  1415.0  5.007490e-05  -0.315055     0.077009    0.005493       0  0.509244      sqrt           3             0.05     0.433384              0.1           0         3000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import the algorithm\n",
    "import numpy as np\n",
    "from pysnptools.snpreader import Pheno\n",
    "from fastlmm.association import single_snp\n",
    "from fastlmm.util import example_file # Download and return local file name\n",
    "\n",
    "# set up data\n",
    "##############################\n",
    "from fastlmm.util import example_file # Download and return local file name\n",
    "bed_fn = example_file('tests/datasets/synth/all.*','*.bed')\n",
    "cov_fn = example_file(\"tests/datasets/synth/cov.txt\")\n",
    "pheno1 = Pheno(example_file(\"tests/datasets/synth/pheno_10_causals.txt\")).read()\n",
    "pheno3 = SnpData(iid=pheno1.iid,\n",
    "                 sid=[\"original\",\"sqrt\",\"square\"],\n",
    "                 val= np.c_[pheno1.val,pheno1.val**2,pheno1.val**3]\n",
    ")\n",
    "\n",
    "# run gwas\n",
    "###################################################################\n",
    "results_df = single_snp(bed_fn, pheno3, covar=cov_fn, count_A1=False, pvalue_threshold=5e-2, random_threshold=.1)\n",
    "print(len(results_df))\n",
    "\n",
    "# manhattan plot\n",
    "import pylab\n",
    "import fastlmm.util.util as flutil\n",
    "pylab.rcParams['figure.figsize'] = (10.0, 8.0)#For some reason, need again (appears above too) to get big figures\n",
    "top_pvalues_df = results_df[results_df['PValue']<=results_df['PValueThreshold']]\n",
    "flutil.manhattan_plot(top_pvalues_df[[\"Chr\", \"ChrPos\", \"PValue\"]].values,pvalue_line=1e-5/pheno3.sid_count,xaxis_unit_bp=False)\n",
    "pylab.show()\n",
    "\n",
    "# qq plot\n",
    "from fastlmm.util.stats import plotp\n",
    "random_df = results_df[results_df['RandomValue']<=results_df['RandomThreshold']]\n",
    "plotp.qqplot(random_df[\"PValue\"].values, xlim=[0,5], ylim=[0,5])\n",
    "\n",
    "# print head of results data frame\n",
    "import pandas as pd\n",
    "pd.set_option('display.width', 1000)\n",
    "results_df.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, using a `PValueThreshold` and `RandomThreshold` lets us reduce the number of output lines from 15,000 to 2,051. Filtering becomes even more important when the number of \\[test SNP, phenotypes\\] pairs is in the millions or billions. Filtering allows us to reduce output line from billions to, say, tens of thousands.\n",
    "\n",
    "Setting either `PValueThreshold` or  `RandomThreshold` produces this new column in the output:\n",
    "\n",
    "* `PValueCount` -- the number of p-values computed. This is the number of output lines that will be produced with no filtering. It is also the number of \\[test SNPs,  phenotype pairs\\]. In the example, it is 15,000.\n",
    "\n",
    "Setting a `PValueThreshold` produces this column:\n",
    "\n",
    "* `PValueThreshold` -- the `PValueThreshold` used\n",
    "\n",
    "Setting a `RandomThreshold` produces these columns:\n",
    "\n",
    "* `RandomValue` -- the random value assigned to this output row\n",
    "* `RandomThreshold` -- the `RandomThreshold` used\n",
    "* `RandomSeed` -- the random seed used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Caching to Divide the Work into Parts\n",
    "\n",
    "Internally, the `single_snp` function works in two parts:\n",
    "\n",
    "* Part 1 -- Merge similarity kernel information with covariate and phenotype information, process the kernel(s) via a matrix operation (SVD), and find best mix between kernel(s) and the identity matrix. (By default, do this with respect to each chromosome.)\n",
    "\n",
    "* Part 2 -- Test SNPs\n",
    "\n",
    "Caching lets us save the results from Part 1 before doing Part 2. In addition, if we have cached results for Part 1, we can easily do Part 2 pieces. This is especially useful if the parts take days to run. It also makes it easy to run Part 2 on multiple computers.\n",
    "\n",
    "Let's rerun our example with this caching. First, we do a run that will save Part 1's results to the cache files. This run still requires some test SNPs, so we give it just one test SNP per chromosome and ignore the results.\n",
    "\n",
    "Listing the files in the cache folder we see a file(s) created for each chromosome. Only runs with multiple phenotypes will have '*.extra.npz' files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fastlmm2021sample.1.0.npz',\n",
       " 'fastlmm2021sample.1.0.npz.extra.npz',\n",
       " 'fastlmm2021sample.2.0.npz',\n",
       " 'fastlmm2021sample.2.0.npz.extra.npz',\n",
       " 'fastlmm2021sample.3.0.npz',\n",
       " 'fastlmm2021sample.3.0.npz.extra.npz',\n",
       " 'fastlmm2021sample.4.0.npz',\n",
       " 'fastlmm2021sample.4.0.npz.extra.npz',\n",
       " 'fastlmm2021sample.5.0.npz',\n",
       " 'fastlmm2021sample.5.0.npz.extra.npz',\n",
       " 'us.npz']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the algorithm\n",
    "import os\n",
    "import numpy as np\n",
    "from pysnptools.snpreader import Bed, Pheno\n",
    "from fastlmm.association import single_snp\n",
    "from fastlmm.util import example_file # Download and return local file name\n",
    "\n",
    "# set up data\n",
    "##############################\n",
    "from fastlmm.util import example_file # Download and return local file name\n",
    "all_snps = Bed(example_file('tests/datasets/synth/all.*','*.bed'),count_A1=False)\n",
    "# Find one test_snps per chromsome\n",
    "index_to_1_snp_per_chrom = np.unique(all_snps.pos[:,0],return_index=True)[1]\n",
    "small_test_snps = all_snps[:,index_to_1_snp_per_chrom]\n",
    "\n",
    "cov_fn = example_file(\"tests/datasets/synth/cov.txt\")\n",
    "pheno1 = Pheno(example_file(\"tests/datasets/synth/pheno_10_causals.txt\")).read()\n",
    "pheno3 = SnpData(iid=pheno1.iid,\n",
    "                 sid=[\"original\",\"sqrt\",\"square\"],\n",
    "                 val= np.c_[pheno1.val,pheno1.val**2,pheno1.val**3]\n",
    ")\n",
    "\n",
    "# run gwas\n",
    "###################################################################\n",
    "ignore_df = single_snp(test_snps=small_test_snps, K0=all_snps, pheno=pheno3, covar=cov_fn, count_A1=False,\n",
    "             cache_file=\"cache/fastlmm2021sample\")\n",
    "\n",
    "os.listdir(\"cache\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's test SNPs in pieces. We can divide the test SNPs any way we wish. In this example, we'll divide by chromosome."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda=0.9231\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid_index</th>\n",
       "      <th>SNP</th>\n",
       "      <th>Chr</th>\n",
       "      <th>GenDist</th>\n",
       "      <th>ChrPos</th>\n",
       "      <th>PValue</th>\n",
       "      <th>SnpWeight</th>\n",
       "      <th>SnpWeightSE</th>\n",
       "      <th>EffectSize</th>\n",
       "      <th>Mixing</th>\n",
       "      <th>Nullh2</th>\n",
       "      <th>Pheno</th>\n",
       "      <th>PhenoCount</th>\n",
       "      <th>PValueThreshold</th>\n",
       "      <th>RandomValue</th>\n",
       "      <th>RandomThreshold</th>\n",
       "      <th>RandomSeed</th>\n",
       "      <th>PValueCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>725</td>\n",
       "      <td>snp1386_m0_m1_.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>1.383880e-08</td>\n",
       "      <td>1.480571</td>\n",
       "      <td>0.256529</td>\n",
       "      <td>0.000605</td>\n",
       "      <td>0</td>\n",
       "      <td>0.417260</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.055916</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>725</td>\n",
       "      <td>snp1386_m0_m1_.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>1.308581e-05</td>\n",
       "      <td>-0.340112</td>\n",
       "      <td>0.077247</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0</td>\n",
       "      <td>0.495164</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.102950</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206</td>\n",
       "      <td>snp4879_m0_.01m1_.08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>1.231412e-04</td>\n",
       "      <td>1.027894</td>\n",
       "      <td>0.265577</td>\n",
       "      <td>0.002718</td>\n",
       "      <td>0</td>\n",
       "      <td>0.417260</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.941165</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>853</td>\n",
       "      <td>snp3520_m0_.03m1_.05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>853.0</td>\n",
       "      <td>853.0</td>\n",
       "      <td>1.493474e-04</td>\n",
       "      <td>1.003177</td>\n",
       "      <td>0.262495</td>\n",
       "      <td>0.002430</td>\n",
       "      <td>0</td>\n",
       "      <td>0.417260</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.485523</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>465</td>\n",
       "      <td>snp2263_m0_.33m1_.55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>2.682321e-04</td>\n",
       "      <td>0.295622</td>\n",
       "      <td>0.080539</td>\n",
       "      <td>0.013884</td>\n",
       "      <td>0</td>\n",
       "      <td>0.495164</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.900238</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>206</td>\n",
       "      <td>snp4879_m0_.01m1_.08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>4.993080e-04</td>\n",
       "      <td>-0.277294</td>\n",
       "      <td>0.079131</td>\n",
       "      <td>0.002222</td>\n",
       "      <td>0</td>\n",
       "      <td>0.495164</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.357896</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>571</td>\n",
       "      <td>snp3264_m0_.69m1_.51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>5.098300e-04</td>\n",
       "      <td>0.277261</td>\n",
       "      <td>0.079251</td>\n",
       "      <td>0.011858</td>\n",
       "      <td>0</td>\n",
       "      <td>0.495164</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.110969</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>389</td>\n",
       "      <td>snp2136_m0_.35m1_.29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>1.104407e-03</td>\n",
       "      <td>-0.257725</td>\n",
       "      <td>0.078534</td>\n",
       "      <td>0.008628</td>\n",
       "      <td>0</td>\n",
       "      <td>0.495164</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.767119</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>535</td>\n",
       "      <td>snp513_m0_.34m1_.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>1.235205e-03</td>\n",
       "      <td>-0.906270</td>\n",
       "      <td>0.278912</td>\n",
       "      <td>0.012012</td>\n",
       "      <td>0</td>\n",
       "      <td>0.417260</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.691705</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>460</td>\n",
       "      <td>snp2888_m0_.3m1_.22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1.526408e-03</td>\n",
       "      <td>0.832640</td>\n",
       "      <td>0.261235</td>\n",
       "      <td>0.006842</td>\n",
       "      <td>0</td>\n",
       "      <td>0.417260</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.067145</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sid_index                   SNP  Chr  GenDist  ChrPos        PValue  SnpWeight  SnpWeightSE  EffectSize  Mixing    Nullh2   Pheno  PhenoCount  PValueThreshold  RandomValue  RandomThreshold  RandomSeed  PValueCount\n",
       "0        725     snp1386_m0_m1_.01  1.0    725.0   725.0  1.383880e-08   1.480571     0.256529    0.000605       0  0.417260  square           3             0.05     0.055916              0.1           0         3000\n",
       "1        725     snp1386_m0_m1_.01  1.0    725.0   725.0  1.308581e-05  -0.340112     0.077247    0.000359       0  0.495164    sqrt           3             0.05     0.102950              0.1           0         3000\n",
       "2        206  snp4879_m0_.01m1_.08  1.0    206.0   206.0  1.231412e-04   1.027894     0.265577    0.002718       0  0.417260  square           3             0.05     0.941165              0.1           0         3000\n",
       "3        853  snp3520_m0_.03m1_.05  1.0    853.0   853.0  1.493474e-04   1.003177     0.262495    0.002430       0  0.417260  square           3             0.05     0.485523              0.1           0         3000\n",
       "4        465  snp2263_m0_.33m1_.55  1.0    465.0   465.0  2.682321e-04   0.295622     0.080539    0.013884       0  0.495164    sqrt           3             0.05     0.900238              0.1           0         3000\n",
       "5        206  snp4879_m0_.01m1_.08  1.0    206.0   206.0  4.993080e-04  -0.277294     0.079131    0.002222       0  0.495164    sqrt           3             0.05     0.357896              0.1           0         3000\n",
       "6        571  snp3264_m0_.69m1_.51  1.0    571.0   571.0  5.098300e-04   0.277261     0.079251    0.011858       0  0.495164    sqrt           3             0.05     0.110969              0.1           0         3000\n",
       "7        389  snp2136_m0_.35m1_.29  1.0    389.0   389.0  1.104407e-03  -0.257725     0.078534    0.008628       0  0.495164    sqrt           3             0.05     0.767119              0.1           0         3000\n",
       "8        535    snp513_m0_.34m1_.6  1.0    535.0   535.0  1.235205e-03  -0.906270     0.278912    0.012012       0  0.417260  square           3             0.05     0.691705              0.1           0         3000\n",
       "9        460   snp2888_m0_.3m1_.22  1.0    460.0   460.0  1.526408e-03   0.832640     0.261235    0.006842       0  0.417260  square           3             0.05     0.067145              0.1           0         3000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_list = []\n",
    "for chrom in np.unique(all_snps.pos[:,0]): # For each chrom\n",
    "    test_snps = all_snps[:,all_snps.pos[:,0]==chrom] # Find the test SNPs in the chrom\n",
    "    chrom_df = single_snp(test_snps=test_snps,K0=all_snps, pheno=pheno3, covar=cov_fn, count_A1=False, \n",
    "                          cache_file=\"cache/fastlmm2021sample\",pvalue_threshold=5e-2, random_threshold=.1)\n",
    "    results_list.append(chrom_df)\n",
    "results_df = pd.concat(results_list)\n",
    "results_df\n",
    "\n",
    "# manhattan plot\n",
    "import pylab\n",
    "import fastlmm.util.util as flutil\n",
    "pylab.rcParams['figure.figsize'] = (10.0, 8.0)#For some reason, need again (appears above too) to get big figures\n",
    "top_pvalues_df = results_df[results_df['PValue']<=results_df['PValueThreshold']]\n",
    "flutil.manhattan_plot(top_pvalues_df[[\"Chr\", \"ChrPos\", \"PValue\"]].values,pvalue_line=1e-5/pheno3.sid_count,xaxis_unit_bp=False)\n",
    "pylab.show()\n",
    "\n",
    "# qq plot\n",
    "from fastlmm.util.stats import plotp\n",
    "random_df = results_df[results_df['RandomValue']<=results_df['RandomThreshold']]\n",
    "plotp.qqplot(random_df[\"PValue\"].values, xlim=[0,5], ylim=[0,5])\n",
    "\n",
    "# print head of results data frame\n",
    "import pandas as pd\n",
    "pd.set_option('display.width', 1000)\n",
    "results_df.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Controlling Multi-Processor Runs\n",
    "\n",
    "The `single_snps` function can be run on multiple processors. By default, it divides the work across processors by chromosome. In our example, this makes sense for Part 1 but not Part 2 (because we are testing one chromosome at a time). Setting `map_reduce_outer=False` will instead internally divide the work by testing SNPs in chucks.\n",
    "\n",
    "Here is our example again, now using `runner` and `map_reduce_outer`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To clear the cache, set this to True and run\n",
    "if False:\n",
    "    for cache_file in os.listdir(\"cache\"):\n",
    "        os.unlink(\"cache/\" + cache_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fastlmm2021sample.1.0.npz',\n",
       " 'fastlmm2021sample.1.0.npz.extra.npz',\n",
       " 'fastlmm2021sample.2.0.npz',\n",
       " 'fastlmm2021sample.2.0.npz.extra.npz',\n",
       " 'fastlmm2021sample.3.0.npz',\n",
       " 'fastlmm2021sample.3.0.npz.extra.npz',\n",
       " 'fastlmm2021sample.4.0.npz',\n",
       " 'fastlmm2021sample.4.0.npz.extra.npz',\n",
       " 'fastlmm2021sample.5.0.npz',\n",
       " 'fastlmm2021sample.5.0.npz.extra.npz',\n",
       " 'us.npz']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the algorithm\n",
    "import numpy as np\n",
    "from pysnptools.snpreader import Bed, Pheno\n",
    "from pysnptools.util.mapreduce1.runner import LocalMultiProc\n",
    "from fastlmm.association import single_snp\n",
    "from fastlmm.util import example_file # Download and return local file name\n",
    "\n",
    "# set up data\n",
    "##############################\n",
    "from fastlmm.util import example_file # Download and return local file name\n",
    "all_snps = Bed(example_file('tests/datasets/synth/all.*','*.bed'),count_A1=False)\n",
    "# Find one test_snps per chromsome\n",
    "index_to_1_snp_per_chrom = np.unique(all_snps.pos[:,0],return_index=True)[1]\n",
    "small_test_snps = all_snps[:,index_to_1_snp_per_chrom]\n",
    "\n",
    "cov_fn = example_file(\"tests/datasets/synth/cov.txt\")\n",
    "pheno1 = Pheno(example_file(\"tests/datasets/synth/pheno_10_causals.txt\")).read()\n",
    "pheno3 = SnpData(iid=pheno1.iid,\n",
    "                 sid=[\"original\",\"sqrt\",\"square\"],\n",
    "                 val= np.c_[pheno1.val,pheno1.val**2,pheno1.val**3]\n",
    ")\n",
    "\n",
    "# set the runner to run on multiple processors\n",
    "##############################\n",
    "runner = LocalMultiProc(6) #Set to None to run locally\n",
    "\n",
    "# run gwas\n",
    "###################################################################\n",
    "ignore_df = single_snp(test_snps=small_test_snps, K0=all_snps, pheno=pheno3, covar=cov_fn, count_A1=False,\n",
    "             cache_file=\"cache/fastlmm2021sample\",\n",
    "             runner=runner)\n",
    "\n",
    "os.listdir(\"cache\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda=0.9231\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid_index</th>\n",
       "      <th>SNP</th>\n",
       "      <th>Chr</th>\n",
       "      <th>GenDist</th>\n",
       "      <th>ChrPos</th>\n",
       "      <th>PValue</th>\n",
       "      <th>SnpWeight</th>\n",
       "      <th>SnpWeightSE</th>\n",
       "      <th>EffectSize</th>\n",
       "      <th>Mixing</th>\n",
       "      <th>Nullh2</th>\n",
       "      <th>Pheno</th>\n",
       "      <th>PhenoCount</th>\n",
       "      <th>PValueThreshold</th>\n",
       "      <th>RandomValue</th>\n",
       "      <th>RandomThreshold</th>\n",
       "      <th>RandomSeed</th>\n",
       "      <th>PValueCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>725</td>\n",
       "      <td>snp1386_m0_m1_.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>1.383880e-08</td>\n",
       "      <td>1.480571</td>\n",
       "      <td>0.256529</td>\n",
       "      <td>0.000605</td>\n",
       "      <td>0</td>\n",
       "      <td>0.417260</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.055916</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>725</td>\n",
       "      <td>snp1386_m0_m1_.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>1.308581e-05</td>\n",
       "      <td>-0.340112</td>\n",
       "      <td>0.077247</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0</td>\n",
       "      <td>0.495164</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.102950</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>206</td>\n",
       "      <td>snp4879_m0_.01m1_.08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>1.231412e-04</td>\n",
       "      <td>1.027894</td>\n",
       "      <td>0.265577</td>\n",
       "      <td>0.002718</td>\n",
       "      <td>0</td>\n",
       "      <td>0.417260</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.941165</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>853</td>\n",
       "      <td>snp3520_m0_.03m1_.05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>853.0</td>\n",
       "      <td>853.0</td>\n",
       "      <td>1.493474e-04</td>\n",
       "      <td>1.003177</td>\n",
       "      <td>0.262495</td>\n",
       "      <td>0.002430</td>\n",
       "      <td>0</td>\n",
       "      <td>0.417260</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.485523</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>465</td>\n",
       "      <td>snp2263_m0_.33m1_.55</td>\n",
       "      <td>1.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>2.682321e-04</td>\n",
       "      <td>0.295622</td>\n",
       "      <td>0.080539</td>\n",
       "      <td>0.013884</td>\n",
       "      <td>0</td>\n",
       "      <td>0.495164</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.900238</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>206</td>\n",
       "      <td>snp4879_m0_.01m1_.08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>4.993080e-04</td>\n",
       "      <td>-0.277294</td>\n",
       "      <td>0.079131</td>\n",
       "      <td>0.002222</td>\n",
       "      <td>0</td>\n",
       "      <td>0.495164</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.357896</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>571</td>\n",
       "      <td>snp3264_m0_.69m1_.51</td>\n",
       "      <td>1.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>5.098300e-04</td>\n",
       "      <td>0.277261</td>\n",
       "      <td>0.079251</td>\n",
       "      <td>0.011858</td>\n",
       "      <td>0</td>\n",
       "      <td>0.495164</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.110969</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>389</td>\n",
       "      <td>snp2136_m0_.35m1_.29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>1.104407e-03</td>\n",
       "      <td>-0.257725</td>\n",
       "      <td>0.078534</td>\n",
       "      <td>0.008628</td>\n",
       "      <td>0</td>\n",
       "      <td>0.495164</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.767119</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>535</td>\n",
       "      <td>snp513_m0_.34m1_.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>1.235205e-03</td>\n",
       "      <td>-0.906270</td>\n",
       "      <td>0.278912</td>\n",
       "      <td>0.012012</td>\n",
       "      <td>0</td>\n",
       "      <td>0.417260</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.691705</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>460</td>\n",
       "      <td>snp2888_m0_.3m1_.22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1.526408e-03</td>\n",
       "      <td>0.832640</td>\n",
       "      <td>0.261235</td>\n",
       "      <td>0.006842</td>\n",
       "      <td>0</td>\n",
       "      <td>0.417260</td>\n",
       "      <td>square</td>\n",
       "      <td>3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.067145</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sid_index                   SNP  Chr  GenDist  ChrPos        PValue  SnpWeight  SnpWeightSE  EffectSize  Mixing    Nullh2   Pheno  PhenoCount  PValueThreshold  RandomValue  RandomThreshold  RandomSeed  PValueCount\n",
       "0        725     snp1386_m0_m1_.01  1.0    725.0   725.0  1.383880e-08   1.480571     0.256529    0.000605       0  0.417260  square           3             0.05     0.055916              0.1           0         3000\n",
       "1        725     snp1386_m0_m1_.01  1.0    725.0   725.0  1.308581e-05  -0.340112     0.077247    0.000359       0  0.495164    sqrt           3             0.05     0.102950              0.1           0         3000\n",
       "2        206  snp4879_m0_.01m1_.08  1.0    206.0   206.0  1.231412e-04   1.027894     0.265577    0.002718       0  0.417260  square           3             0.05     0.941165              0.1           0         3000\n",
       "3        853  snp3520_m0_.03m1_.05  1.0    853.0   853.0  1.493474e-04   1.003177     0.262495    0.002430       0  0.417260  square           3             0.05     0.485523              0.1           0         3000\n",
       "4        465  snp2263_m0_.33m1_.55  1.0    465.0   465.0  2.682321e-04   0.295622     0.080539    0.013884       0  0.495164    sqrt           3             0.05     0.900238              0.1           0         3000\n",
       "5        206  snp4879_m0_.01m1_.08  1.0    206.0   206.0  4.993080e-04  -0.277294     0.079131    0.002222       0  0.495164    sqrt           3             0.05     0.357896              0.1           0         3000\n",
       "6        571  snp3264_m0_.69m1_.51  1.0    571.0   571.0  5.098300e-04   0.277261     0.079251    0.011858       0  0.495164    sqrt           3             0.05     0.110969              0.1           0         3000\n",
       "7        389  snp2136_m0_.35m1_.29  1.0    389.0   389.0  1.104407e-03  -0.257725     0.078534    0.008628       0  0.495164    sqrt           3             0.05     0.767119              0.1           0         3000\n",
       "8        535    snp513_m0_.34m1_.6  1.0    535.0   535.0  1.235205e-03  -0.906270     0.278912    0.012012       0  0.417260  square           3             0.05     0.691705              0.1           0         3000\n",
       "9        460   snp2888_m0_.3m1_.22  1.0    460.0   460.0  1.526408e-03   0.832640     0.261235    0.006842       0  0.417260  square           3             0.05     0.067145              0.1           0         3000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_list = []\n",
    "for chrom in np.unique(all_snps.pos[:,0]): # For each chrom\n",
    "    test_snps = all_snps[:,all_snps.pos[:,0]==chrom] # Find the test SNPs in the chrom\n",
    "    chrom_df = single_snp(test_snps=test_snps,K0=all_snps, pheno=pheno3, covar=cov_fn, count_A1=False, \n",
    "                          cache_file=\"cache/fastlmm2021sample\",pvalue_threshold=5e-2, random_threshold=.1,\n",
    "                          runner=runner, map_reduce_outer=False)\n",
    "    results_list.append(chrom_df)\n",
    "results_df = pd.concat(results_list)\n",
    "results_df\n",
    "\n",
    "# manhattan plot\n",
    "import pylab\n",
    "import fastlmm.util.util as flutil\n",
    "pylab.rcParams['figure.figsize'] = (10.0, 8.0)#For some reason, need again (appears above too) to get big figures\n",
    "top_pvalues_df = results_df[results_df['PValue']<=results_df['PValueThreshold']]\n",
    "flutil.manhattan_plot(top_pvalues_df[[\"Chr\", \"ChrPos\", \"PValue\"]].values,pvalue_line=1e-5/pheno3.sid_count,xaxis_unit_bp=False)\n",
    "pylab.show()\n",
    "\n",
    "# qq plot\n",
    "from fastlmm.util.stats import plotp\n",
    "random_df = results_df[results_df['RandomValue']<=results_df['RandomThreshold']]\n",
    "plotp.qqplot(random_df[\"PValue\"].values, xlim=[0,5], ylim=[0,5])\n",
    "\n",
    "# print head of results data frame\n",
    "import pandas as pd\n",
    "pd.set_option('display.width', 1000)\n",
    "results_df.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single_Snp Summary\n",
    "\n",
    "We've seen that the `single_snp` function now supports multiple phenotypes. This support typically creates more output. Additional new features make longer runs and runs with more output more manageable.\n",
    "\n",
    "FaST-LMM also offers the `single_snp_scale` function [(notebook)](https://github.com/fastlmm/FaST-LMM/blob/master/doc/ipynb/SingleSnpScale.ipynb). It also supports multiple phenotypes, can run on a cluster of dozens or hundreds of CPUs, and is more memory efficient. However, its memory efficiency makes it a bit slower.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"epistasis\"></a>\n",
    "## Epistasis\n",
    "\n",
    "Recall that FaST-LMM allows you to test for epistatic interactions between pairs of SNPs. The code optimizes (δ and thus h2) using REML, and computes P-values for an epistatic interaction via a likelihood-ratio test based on ML (see [(Lippert, et al., 2011)](https://static-content.springer.com/esm/art%3A10.1038%2Fnmeth.1681/MediaObjects/41592_2011_BFnmeth1681_MOESM290_ESM.pdf).\n",
    "\n",
    "New for 2021, the output includes β, the linear coefficient of the epistatic interaction, and its variance. Specifically, the epistatic interaction is the product of standardized SNPs (SNPs values scaled to have mean 0 and variance 1), and is a fixed effect.\n",
    "\n",
    "Thanks to Ava Khamseh of the University of University of Edinburgh for [her derivation of the formula for the variance](https://github.com/fastlmm/FaST-LMM/raw/epibeta/doc/notes/FaST_LMM_variance.pdf) and for requesting this feature.\n",
    "\n",
    "Here is the example from the main FaST-LMM notebook, showing the new output columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNP0</th>\n",
       "      <th>Chr0</th>\n",
       "      <th>GenDist0</th>\n",
       "      <th>ChrPos0</th>\n",
       "      <th>SNP1</th>\n",
       "      <th>Chr1</th>\n",
       "      <th>GenDist1</th>\n",
       "      <th>ChrPos1</th>\n",
       "      <th>PValue</th>\n",
       "      <th>NullLogLike</th>\n",
       "      <th>AltLogLike</th>\n",
       "      <th>H2</th>\n",
       "      <th>Beta</th>\n",
       "      <th>Variance_Beta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>snp2376_m0_.38m1_.49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>snp1754_m0_.19m1_.42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.000451</td>\n",
       "      <td>-700.878718</td>\n",
       "      <td>-694.724583</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.151714</td>\n",
       "      <td>7.372408e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>snp625_m0_.03m1_.07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>snp3000_m0_.28m1_.34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.001306</td>\n",
       "      <td>-700.387891</td>\n",
       "      <td>-695.220652</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.117448</td>\n",
       "      <td>5.272537e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>snp250_m0_.23m1_.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>snp2377_m0_.06m1_.02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.002279</td>\n",
       "      <td>-698.768488</td>\n",
       "      <td>-694.113511</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.145412</td>\n",
       "      <td>8.980881e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>snp625_m0_.03m1_.07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>snp376_m0_.17m1_.17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.002287</td>\n",
       "      <td>-700.206861</td>\n",
       "      <td>-695.555229</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.132940</td>\n",
       "      <td>7.511802e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>snp125_m0_.64m1_.41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>snp1754_m0_.19m1_.42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.002404</td>\n",
       "      <td>-700.873861</td>\n",
       "      <td>-696.267594</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.131984</td>\n",
       "      <td>7.477779e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>snp2878_m0_.82m1_.58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>snp1878_m0_.58m1_.36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.002568</td>\n",
       "      <td>-698.579519</td>\n",
       "      <td>-694.033792</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.131828</td>\n",
       "      <td>7.560385e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>snp1625_m0_.4m1_.47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>snp1128_m0_.14m1_.13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.003067</td>\n",
       "      <td>-700.445456</td>\n",
       "      <td>-696.061998</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.129267</td>\n",
       "      <td>7.540972e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>snp1753_m0_.17m1_.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>snp3_m0_.55m1_.46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.004327</td>\n",
       "      <td>-700.735073</td>\n",
       "      <td>-696.664365</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.126579</td>\n",
       "      <td>7.791090e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>snp250_m0_.23m1_.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>snp3_m0_.55m1_.46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.004486</td>\n",
       "      <td>-700.927439</td>\n",
       "      <td>-696.889564</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.120030</td>\n",
       "      <td>7.063126e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>snp1753_m0_.17m1_.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>snp251_m0_.1m1_.49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.006503</td>\n",
       "      <td>-700.728933</td>\n",
       "      <td>-697.026244</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.115623</td>\n",
       "      <td>7.152112e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SNP0  Chr0  GenDist0  ChrPos0                  SNP1  Chr1  GenDist1  ChrPos1    PValue  NullLogLike  AltLogLike        H2      Beta  Variance_Beta\n",
       "0  snp2376_m0_.38m1_.49   1.0      20.0     20.0  snp1754_m0_.19m1_.42   1.0      38.0     38.0  0.000451  -700.878718 -694.724583  0.000399  0.151714   7.372408e-07\n",
       "1   snp625_m0_.03m1_.07   1.0       NaN      NaN  snp3000_m0_.28m1_.34   1.0       5.0      5.0  0.001306  -700.387891 -695.220652  0.000399 -0.117448   5.272537e-07\n",
       "2   snp250_m0_.23m1_.19   1.0      15.0     15.0  snp2377_m0_.06m1_.02   1.0      30.0     30.0  0.002279  -698.768488 -694.113511  0.000399 -0.145412   8.980881e-07\n",
       "3   snp625_m0_.03m1_.07   1.0       NaN      NaN   snp376_m0_.17m1_.17   1.0       9.0      9.0  0.002287  -700.206861 -695.555229  0.000399 -0.132940   7.511802e-07\n",
       "4   snp125_m0_.64m1_.41   1.0      21.0     21.0  snp1754_m0_.19m1_.42   1.0      38.0     38.0  0.002404  -700.873861 -696.267594  0.000399 -0.131984   7.477779e-07\n",
       "5  snp2878_m0_.82m1_.58   1.0      42.0     42.0  snp1878_m0_.58m1_.36   1.0      48.0     48.0  0.002568  -698.579519 -694.033792  0.000399 -0.131828   7.560385e-07\n",
       "6   snp1625_m0_.4m1_.47   1.0      23.0     23.0  snp1128_m0_.14m1_.13   1.0      35.0     35.0  0.003067  -700.445456 -696.061998  0.000399  0.129267   7.540972e-07\n",
       "7  snp1753_m0_.17m1_.19   1.0      28.0     28.0     snp3_m0_.55m1_.46   1.0      32.0     32.0  0.004327  -700.735073 -696.664365  0.000399  0.126579   7.791090e-07\n",
       "8   snp250_m0_.23m1_.19   1.0      15.0     15.0     snp3_m0_.55m1_.46   1.0      32.0     32.0  0.004486  -700.927439 -696.889564  0.000399 -0.120030   7.063126e-07\n",
       "9  snp1753_m0_.17m1_.19   1.0      28.0     28.0    snp251_m0_.1m1_.49   1.0      33.0     33.0  0.006503  -700.728933 -697.026244  0.000399 -0.115623   7.152112e-07"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from fastlmm.association import epistasis\n",
    "from pysnptools.snpreader import Bed\n",
    "from fastlmm.util import example_file # Download and return local file name\n",
    "\n",
    "# define file names\n",
    "bed_fn = example_file('tests/datasets/synth/all.*','*.bed')\n",
    "bed_reader = Bed(bed_fn, count_A1=True)\n",
    "pheno_fn = example_file(\"tests/datasets/synth/pheno_10_causals.txt\")\n",
    "cov_fn = example_file(\"tests/datasets/synth/cov.txt\")\n",
    "\n",
    "# partition data into the first 50 SNPs on chr1 and all but chr1\n",
    "G0 = bed_reader[:,bed_reader.pos[:,0] != 1]\n",
    "test_snps = bed_reader[:,bed_reader.pos[:,0] == 1][:,0:50]\n",
    "\n",
    "# run epistasis analysis\n",
    "results_df = epistasis(test_snps, pheno_fn, G0=G0, covar=cov_fn)\n",
    "\n",
    "# print head of results data frame\n",
    "pd.set_option('display.width', 1000)\n",
    "results_df.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User-supplied eigenvalue decomposition for epistasis\n",
    "\n",
    "Now, let's look at the same example where the eigenvalue decomposition of realized relationship matrix (RRM) is known.\n",
    "\n",
    "We can provide the eigenvalues and eigenvector by writing a npz file and passing it to 'epistasis'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To start the example, we need to create our new inputs\n",
    "* S and U from the eigenvalue decomposition of the standardized RRM. Standardized means that the SNPs used to compute the matrix have mean 0 and standard deviation 1.\n",
    "* A list of the individuals in the order they appear in the decomposition\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['cid499P1' 'cid499P1']\n",
      " ['cid498P1' 'cid498P1']\n",
      " ['cid497P1' 'cid497P1']]...\n"
     ]
    }
   ],
   "source": [
    "# Precompute the decomposition of the kinship kernel.\n",
    "G = G0[::-1,:] # We reverse the order of the individuals, just to show that any order is allowed\n",
    "Gval = G.read().standardize().val\n",
    "K = Gval.dot(Gval.T)\n",
    "# So the Eigen values and vector are:\n",
    "S,U = np.linalg.eigh(K)\n",
    "# and the order of the individuals in the eigenvector is\n",
    "eigen_iid = G.iid\n",
    "print(f\"{eigen_iid[:3]}...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we have the eigenvalue decomposition, we save it to a file. We also create a \"fake_G\" file. The \"fake_G\" file is then used that to create a test SNP reader that orders individuals to match the order in the eigenvalue decomposition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from pysnptools.snpreader import SnpData\n",
    "from pysnptools.util import intersect_apply\n",
    "\n",
    "\n",
    "# We write the vector and the values to a npz file.\n",
    "cache_file = \"cache/us.npz\"\n",
    "Path(cache_file).parent.mkdir(exist_ok=True)\n",
    "np.savez(cache_file,U,S) # note that U goes before S\n",
    "\n",
    "# We create a fake G, the SNPs that created the kinship matrix\n",
    "fake_G = SnpData(iid=eigen_iid,sid=[\"fake_sid\"],val=np.full((test_snps.iid_count,1),np.nan))\n",
    "\n",
    "# Reorder test_snps to match the order in the eigen_iid\n",
    "fake_G_after, test_snps_reordered = intersect_apply([fake_G, test_snps])\n",
    "assert np.all(fake_G_after.iid == fake_G.iid) # prove that eigen_iid are unchanged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we run epistasis using\n",
    "* the test SNPs reordered to match eigen_iid\n",
    "* the fake G file\n",
    "* the cached file containing the decomposition\n",
    "\n",
    "We get the same results as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNP0</th>\n",
       "      <th>Chr0</th>\n",
       "      <th>GenDist0</th>\n",
       "      <th>ChrPos0</th>\n",
       "      <th>SNP1</th>\n",
       "      <th>Chr1</th>\n",
       "      <th>GenDist1</th>\n",
       "      <th>ChrPos1</th>\n",
       "      <th>PValue</th>\n",
       "      <th>NullLogLike</th>\n",
       "      <th>AltLogLike</th>\n",
       "      <th>H2</th>\n",
       "      <th>Beta</th>\n",
       "      <th>Variance_Beta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>snp2376_m0_.38m1_.49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>snp1754_m0_.19m1_.42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.000451</td>\n",
       "      <td>-700.878718</td>\n",
       "      <td>-694.724583</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.151714</td>\n",
       "      <td>7.372408e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>snp625_m0_.03m1_.07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>snp3000_m0_.28m1_.34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.001306</td>\n",
       "      <td>-700.387891</td>\n",
       "      <td>-695.220652</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.117448</td>\n",
       "      <td>5.272537e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>snp250_m0_.23m1_.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>snp2377_m0_.06m1_.02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.002279</td>\n",
       "      <td>-698.768488</td>\n",
       "      <td>-694.113511</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.145412</td>\n",
       "      <td>8.980881e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>snp625_m0_.03m1_.07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>snp376_m0_.17m1_.17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.002287</td>\n",
       "      <td>-700.206861</td>\n",
       "      <td>-695.555229</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.132940</td>\n",
       "      <td>7.511802e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>snp125_m0_.64m1_.41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>snp1754_m0_.19m1_.42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.002404</td>\n",
       "      <td>-700.873861</td>\n",
       "      <td>-696.267594</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.131984</td>\n",
       "      <td>7.477779e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>snp2878_m0_.82m1_.58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>snp1878_m0_.58m1_.36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.002568</td>\n",
       "      <td>-698.579519</td>\n",
       "      <td>-694.033792</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.131828</td>\n",
       "      <td>7.560385e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>snp1625_m0_.4m1_.47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>snp1128_m0_.14m1_.13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.003067</td>\n",
       "      <td>-700.445456</td>\n",
       "      <td>-696.061998</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.129267</td>\n",
       "      <td>7.540972e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>snp1753_m0_.17m1_.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>snp3_m0_.55m1_.46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.004327</td>\n",
       "      <td>-700.735073</td>\n",
       "      <td>-696.664365</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.126579</td>\n",
       "      <td>7.791090e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>snp250_m0_.23m1_.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>snp3_m0_.55m1_.46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.004486</td>\n",
       "      <td>-700.927439</td>\n",
       "      <td>-696.889564</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.120030</td>\n",
       "      <td>7.063126e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>snp1753_m0_.17m1_.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>snp251_m0_.1m1_.49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.006503</td>\n",
       "      <td>-700.728933</td>\n",
       "      <td>-697.026244</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.115623</td>\n",
       "      <td>7.152112e-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   SNP0  Chr0  GenDist0  ChrPos0                  SNP1  Chr1  GenDist1  ChrPos1    PValue  NullLogLike  AltLogLike        H2      Beta  Variance_Beta\n",
       "0  snp2376_m0_.38m1_.49   1.0      20.0     20.0  snp1754_m0_.19m1_.42   1.0      38.0     38.0  0.000451  -700.878718 -694.724583  0.000399  0.151714   7.372408e-07\n",
       "1   snp625_m0_.03m1_.07   1.0       NaN      NaN  snp3000_m0_.28m1_.34   1.0       5.0      5.0  0.001306  -700.387891 -695.220652  0.000399 -0.117448   5.272537e-07\n",
       "2   snp250_m0_.23m1_.19   1.0      15.0     15.0  snp2377_m0_.06m1_.02   1.0      30.0     30.0  0.002279  -698.768488 -694.113511  0.000399 -0.145412   8.980881e-07\n",
       "3   snp625_m0_.03m1_.07   1.0       NaN      NaN   snp376_m0_.17m1_.17   1.0       9.0      9.0  0.002287  -700.206861 -695.555229  0.000399 -0.132940   7.511802e-07\n",
       "4   snp125_m0_.64m1_.41   1.0      21.0     21.0  snp1754_m0_.19m1_.42   1.0      38.0     38.0  0.002404  -700.873861 -696.267594  0.000399 -0.131984   7.477779e-07\n",
       "5  snp2878_m0_.82m1_.58   1.0      42.0     42.0  snp1878_m0_.58m1_.36   1.0      48.0     48.0  0.002568  -698.579519 -694.033792  0.000399 -0.131828   7.560385e-07\n",
       "6   snp1625_m0_.4m1_.47   1.0      23.0     23.0  snp1128_m0_.14m1_.13   1.0      35.0     35.0  0.003067  -700.445456 -696.061998  0.000399  0.129267   7.540972e-07\n",
       "7  snp1753_m0_.17m1_.19   1.0      28.0     28.0     snp3_m0_.55m1_.46   1.0      32.0     32.0  0.004327  -700.735073 -696.664365  0.000399  0.126579   7.791090e-07\n",
       "8   snp250_m0_.23m1_.19   1.0      15.0     15.0     snp3_m0_.55m1_.46   1.0      32.0     32.0  0.004486  -700.927439 -696.889564  0.000399 -0.120030   7.063126e-07\n",
       "9  snp1753_m0_.17m1_.19   1.0      28.0     28.0    snp251_m0_.1m1_.49   1.0      33.0     33.0  0.006503  -700.728933 -697.026244  0.000399 -0.115623   7.152112e-07"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = epistasis(test_snps_reordered, pheno_fn, G0=fake_G, covar=cov_fn, cache_file=cache_file)\n",
    "df.head(n=10)"
   ]
  }
 ],
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