smoking-ipf-mr-prep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_table('allen_et_al_2019_ipf_meta_gwas_summary_statistics_public_download.txt', sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('smoking_exposure_snps.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df[df.rsid.isin(df1['SNP'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df1[df1['SNP'].isin(df2['rsid'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.sort_values(by='rsid')\n",
    "df3 = df3.sort_values(by='SNP')\n",
    "df3['rsid'] = df3['SNP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert df2['rsid'].values.all() == df3['SNP'].values.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = pd.merge(df2, df3, on='rsid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4.to_csv('2samplemr.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# how R TwoSampleMR library would like data formatted\n",
    "\n",
    "# outcome_dat: - SNP - beta.outcome - se.outcome - effect_allele.outcome - other_allele.outcome - eaf.outcome - outcome\n",
    "\n",
    "# exposure_dat: - SNP - beta.exposure - se.exposure - effect_allele.exposure - other_allele.exposure - eaf.exposure\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome_dat = df4[['rsid', 'chromosome', 'position', 'non_effect_allele', 'effect_allele',\n",
    "       'effect_allele_frequency', 'studies_included',\n",
    "       'imputation_quality_by_study', 'effect_direction_by_study', 'beta',\n",
    "       'standard_error', 'p']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exposure_dat =  df4[['SNP', 'CHR', 'BP', 'EA', 'NonEA', 'EAF', 'Beta', 'SE', 'p-value']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome_dat = outcome_dat[['rsid', 'beta', 'standard_error', 'effect_allele', 'non_effect_allele', 'effect_allele_frequency']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome_dat.columns = ['SNP', 'beta.outcome', 'se.outcome', 'effect_allele.outcome', 'other_allele.outcome', 'eaf.outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome_dat['outcome'] = 'IPF'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome_dat['id.outcome'] = 'IPF'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exposure_dat = exposure_dat[['SNP', 'Beta', 'SE', 'EA', 'NonEA', 'EAF']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exposure_dat.columns = ['SNP', 'beta.exposure', 'se.exposure', 'effect_allele.exposure', 'other_allele.exposure', 'eaf.exposure']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exposure_dat['id.exposure'] = 'smoking'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exposure_dat['exposure'] = 'smoking'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome_dat.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome_dat.to_csv('outcome_dat.csv', index=False)\n",
    "exposure_dat.to_csv('exposure_dat.csv', index=False)"
   ]
  }
 ],
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