notebooks/fig2-literature-germline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f0e0ef72-7beb-4ab7-aa8b-ecb7bfc8da56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a0cd0f85-7988-40bc-b38f-7839b7ec5fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"/cellarold/users/mpagadal/Programs/anaconda3/lib/python3.7/site-packages\")\n",
    "import re\n",
    "import myvariant\n",
    "mv = myvariant.MyVariantInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3332c61d-d5e0-4648-9e80-38b882f3e680",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_clumps(directory,snp_type):\n",
    "    '''\n",
    "    Input: directory with all plink .clumped files to be aggregated\n",
    "    Output: dataframe with INDEX SNP, p-value, association paramter\n",
    "    '''\n",
    "    #get clumped files\n",
    "    files=os.listdir(directory)\n",
    "    file_lst=[x for x in files if \".clumped\" in x]\n",
    "    \n",
    "    if snp_type == \"index\":\n",
    "    \n",
    "        #iterate through all clumped files and extract index snps\n",
    "        snps=[]\n",
    "        assoc=[]\n",
    "        p=[]\n",
    "\n",
    "        for x in file_lst:\n",
    "            file=pd.read_csv(directory+\"/\"+x, delim_whitespace=True)\n",
    "            for i,row in file.iterrows():\n",
    "                snps.append(row[\"SNP\"])\n",
    "                p.append(row[\"P\"])\n",
    "                assoc.append(x.split(\".clump.clumped\")[0])\n",
    "    \n",
    "        df=pd.DataFrame({\"snps\":snps,\"file\":assoc,\"p-value\":p})\n",
    "        return(df)\n",
    "    \n",
    "    if snp_type == \"all\":\n",
    "        snps=[]\n",
    "        indexsnp=[]\n",
    "        assoc=[]\n",
    "        p=[]\n",
    "\n",
    "        for x in file_lst:\n",
    "            file=pd.read_csv(directory+\"/\"+x, delim_whitespace=True)\n",
    "            for i,row in file.iterrows():\n",
    "                for y in row[\"SP2\"].split(\"(1),\"):\n",
    "                    snps.append(y)\n",
    "                    indexsnp.append(row[\"SNP\"])\n",
    "                    p.append(row[\"P\"])\n",
    "                    assoc.append(x.split(\".clump.clumped\")[0])\n",
    "                \n",
    "        df=pd.DataFrame({\"indexsnps\":indexsnp,\"snps\":snps,\"file\":assoc,\"p-value\":p})\n",
    "        df=df[~(df[\"snps\"]==\"\")]\n",
    "        df=df[~(df[\"snps\"]==\"NONE\")]\n",
    "        df[\"snps\"]=df[\"snps\"].str.split(\"(\").str[0]\n",
    "        return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92efa219-7738-4109-b859-4c67e1d1f169",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_rsid(x):\n",
    "    '''\n",
    "    Input: SNP format (chr:bp:minor:major)\n",
    "    Output: tuple (variant,rsid)\n",
    "    '''\n",
    "    try:\n",
    "        if \"rs\" in x:\n",
    "            try:\n",
    "                var=(re.findall('\\d+',mv.getvariants(x)[-1][\"_id\"]))[0]+\":\"+(re.findall('\\d+',mv.getvariants(x)[-1][\"_id\"]))[1]+\":\"+re.split(\"[^a-zA-Z]*\",mv.getvariants(x)[-1][\"_id\"])[-4]+\":\"+re.split(\"[^a-zA-Z]*\",mv.getvariants(x)[-1][\"_id\"])[-2]\n",
    "                rs=x\n",
    "            except:\n",
    "                var=\"\"\n",
    "                rs=x\n",
    "        else:\n",
    "            try:\n",
    "                var=\"chr\"+x.split(\":\")[0]+\":g.\"+x.split(\":\")[1]+x.split(\":\")[2]+\">\"+x.split(\":\")[3]\n",
    "                rs=mv.getvariants(var,fields='dbsnp.rsid')[0][\"dbsnp\"][\"rsid\"]\n",
    "            except:\n",
    "                try:\n",
    "                    var=\"chr\"+x.split(\":\")[0]+\":g.\"+x.split(\":\")[1]+x.split(\":\")[3]+\">\"+x.split(\":\")[2]\n",
    "                    rs=mv.getvariants(var,fields='dbsnp.rsid')[0][\"dbsnp\"][\"rsid\"]\n",
    "                except:\n",
    "                    var=\"chr\"+x.split(\":\")[0]+\":g.\"+x.split(\":\")[1]+x.split(\":\")[3]+\">\"+x.split(\":\")[2]\n",
    "                    rs=\"\"\n",
    "    except:\n",
    "        var=\"\"\n",
    "        rs=\"\"\n",
    "        \n",
    "    return (var,rs)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f346b52-d7a0-4fe2-aa31-7a5106993a32",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Merge(dict1, dict2):\n",
    "    res = {**dict1, **dict2}\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4ebd01a-a440-4d00-80dc-83f4ee1ed2cb",
   "metadata": {},
   "source": [
    "### Collect variants from previous germline studies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "21300397-7f73-4a9a-b686-da170f4a494e",
   "metadata": {},
   "outputs": [],
   "source": [
    "lit_germline=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a6cdb123-9cbb-4598-b7ef-3a7d2eb0f5a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#FGFR4\n",
    "kogan=[\"rs351855\"]\n",
    "kogan_snp=[\"5:176520243:G:A\"]\n",
    "kogan_df=pd.DataFrame({\"snps\":kogan,\"key\":\"kogan\",\"association\":\"FGFR4\",\"variant\":kogan_snp})\n",
    "lit_germline=lit_germline.append(kogan_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4f2d67b-8eec-4995-97f8-7087c69e07a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#CTLA4\n",
    "queirolo=[\"rs4553808\",\"rs11571316\",\"rs11571317\",\"rs3087243\",\"rs5742909\",\"rs231775\"]\n",
    "queirolo_snp=[\"2:204731005:A:G\",\"2:204731089:G:A\",\"2:204732008:C:T\",\"2:204738919:G:A\",\"2:204732347:C:T\",\"2:204732714:A:T\"]\n",
    "queirolo_df=pd.DataFrame({\"snps\":queirolo,\"key\":\"queirolo\",\"association\":\"CTLA4\",\"variant\":queirolo_snp})\n",
    "lit_germline=lit_germline.append(queirolo_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "420b25e3-d80e-4c90-a1ac-875443781abd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#IRF5\n",
    "uccellini=[\"rs10954213\"]\n",
    "uccellini_snp=[\"7:128589427:G:A\"]\n",
    "uccellini_df=pd.DataFrame({\"snps\":uccellini,\"key\":\"uccellini\",\"association\":\"IRF5\",\"variant\":uccellini_snp})\n",
    "lit_germline=lit_germline.append(uccellini_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3e42b294-2e6a-40be-8f60-6edf74621b59",
   "metadata": {},
   "outputs": [],
   "source": [
    "#CXCR3, CCR5\n",
    "bedognetti=[\"rs2280964\", \"rs333\"]\n",
    "bedognetti_snp=[\"X:70838054:G:C\",np.nan]\n",
    "bedognetti_df=pd.DataFrame({\"snps\":bedognetti,\"key\":\"bedognetti\",\"association\":\"CXCR3/CCR5\",\"variant\":bedognetti_snp})\n",
    "lit_germline=lit_germline.append(bedognetti_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bd532247-91d6-4244-9154-14d78c3da4f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#lim et al PNAS\n",
    "lim=pd.read_csv(\"../data/yang/pnas.variant.csv\")\n",
    "lim_df=pd.DataFrame({\"snps\":lim[\"SNP\"].tolist(),\"key\":\"lim\",\"association\":lim[\"Geneset\"].tolist(),\"variant\":lim[\"variant\"].tolist()})\n",
    "lit_germline=lit_germline.append(lim_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5e22d151-a8d0-4b31-9a63-fd416b229f0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Shahamtdar\n",
    "shahamtdar=[\"rs4819959\",\"rs3366\"]\n",
    "shahamtdar_snp=[\"22:17586631:G:A\",\"21:44834825:G:T\"]\n",
    "shahamtdar_assoc=[\"Th17\",\"Tfh\"]\n",
    "shahamtdar_df=pd.DataFrame({\"snps\":shahamtdar,\"key\":\"shahamtdar\",\"association\":shahamtdar_assoc,\"variant\":shahamtdar_snp})\n",
    "lit_germline=lit_germline.append(shahamtdar_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f839e05c-38b8-4871-aa36-3c2146fff884",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Zhang\n",
    "zhang=[\"rs3903072\"]\n",
    "zhang_snp=[\"11:65583066:G:T\"]\n",
    "zhang_df=pd.DataFrame({\"snps\":zhang,\"key\":\"zhang\",\"association\":\"CTSW\",\"variant\":zhang_snp})\n",
    "lit_germline=lit_germline.append(zhang_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "604a34ef-6586-4eec-a7a8-542e79a04d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#APOE\n",
    "ostendorf=[\"rs429358\",\"rs7412\"]\n",
    "ostendorf_snp=[\"19:45411941:T:C\",\"19:45412079:C:T\"]\n",
    "ostendorf_df=pd.DataFrame({\"snps\":ostendorf,\"key\":\"ostendorf\",\"association\":\"APOE\",\"variant\":ostendorf_snp})\n",
    "lit_germline=lit_germline.append(ostendorf_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5207c0da-fa4e-43e9-b672-1b5d6eaf878b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#PD-1\n",
    "salmaninejad=[\"rs36084323\",\"rs11568821\",\"rs2227981\",\"rs2227982\",\"rs7421861\"]\n",
    "salmaninejad_snp=[\"2:242801596:C:T\",\"2:242793912:C:T\",\"2:242793273:A:G\",\"2:242793433:G:A\",\"2:242795350:A:G\"]\n",
    "salmaninejad_df=pd.DataFrame({\"snps\":salmaninejad,\"key\":\"salmaninejad\",\"association\":\"PD-1\",\"variant\":salmaninejad_snp})\n",
    "lit_germline=lit_germline.append(salmaninejad_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8be21b7c-95c2-4e95-8c18-9f0d9bc3e279",
   "metadata": {},
   "outputs": [],
   "source": [
    "#PD-1\n",
    "sasaki=[\"rs36084323\"]\n",
    "sasaki_snp=[\"2:242801596:C:T\"]\n",
    "sasaki_df=pd.DataFrame({\"snps\":sasaki,\"key\":\"sasaki\",\"association\":\"PD-1\",\"variant\":sasaki_snp})\n",
    "lit_germline=lit_germline.append(sasaki_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d0689767-dabf-40c8-b0ea-51a83df89203",
   "metadata": {},
   "outputs": [],
   "source": [
    "#PD-1\n",
    "tang=[\"rs10204525\",\"rs7421861\",\"rs2227982\"]\n",
    "tang_snp=[\"2:242792321:C:T\",\"2:242795350:A:G\",\"2:242793433:G:A\"]\n",
    "tang_df=pd.DataFrame({\"snps\":tang,\"key\":\"tang\",\"association\":\"PD-1\",\"variant\":tang_snp})\n",
    "lit_germline=lit_germline.append(tang_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a334db0a-12b9-4994-9e65-48baadd3204e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#PD-L1\n",
    "kula=[\"rs4143815\",\"rs2297136\",\"rs822336\",\"rs822337\",\"rs2282055\",\"rs2890658\",\"rs10815225\",\"rs2890657\",\"rs822338\",\"rs17718883\"]\n",
    "kula_snp=[\"9:5468257:G:C\",\"9:5467955:G:A\",\"9:5448690:G:C\",\"9:5449154:T:A\",\"9:5455732:T:A\",\"9:5465130:C:A\",\"9:5450497:G:A\",\"9:5452560:G:C\",\"9:5451557:C:A\",\"9:5462876:C:G\"]\n",
    "kula_df=pd.DataFrame({\"snps\":kula,\"key\":\"kula\",\"association\":\"PD-1\",\"variant\":kula_snp})\n",
    "lit_germline=lit_germline.append(kula_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7dcfbdf0-bd20-4a6d-991d-ce98bbf556a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#PD-L1\n",
    "yoshida=[\"rs822339\",\"rs1411262\"]\n",
    "yoshida_snp=[\"9:5453172:A:G\",\"9:5459419:C:T\"]\n",
    "yoshida_df=pd.DataFrame({\"snps\":yoshida,\"key\":\"yoshida\",\"association\":\"PD-L1\",\"variant\":yoshida_snp})\n",
    "lit_germline=lit_germline.append(yoshida_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "97e513e7-0aa9-42f7-b7a4-7aa6863f1ba0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "584\n"
     ]
    }
   ],
   "source": [
    "#Sayaman Immunity\n",
    "sayaman=pd.read_excel(\"../data/sayaman/1-s2.0-S1074761321000340-mmc5.xlsx\",sheet_name=1)\n",
    "sayaman[\"snp_noallele\"]=sayaman[\"PLINK.SNP\"].str.rsplit(\":\",2).str[0]\n",
    "sayaman_sig=sayaman[sayaman[\"Annot.Figure.GenomewideSignificant.SNP\"]==\"GW Significant\"]\n",
    "sayaman_snps=sayaman_sig[\"PLINK.SNP\"].unique().tolist()\n",
    "print(len(sayaman_snps))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c90fa0-c5ac-4b80-96c1-5a48878a4060",
   "metadata": {},
   "source": [
    "#### conduct clumping for sayaman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9172792e-5ce9-489e-a813-4e5f89f85903",
   "metadata": {},
   "outputs": [],
   "source": [
    "bim=pd.read_csv(\"/cellar/controlled/dbgap-genetic/phs000178_TCGA/imputation/michigan-imputation/HRC/european.final.noimmunecancers.clean.bim\",header=None,delim_whitespace=True)\n",
    "bim[\"snp_noallele\"]=bim[1].str.rsplit(\":\",2).str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6d24bebf-7d64-4306-b337-0a6799c472fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigs160.Bindea_T.helper.cells\n",
      "(0, 9)\n",
      "Sigs160.Wolf_IFN.21978456\n",
      "(6, 9)\n",
      "Sigs160.Wolf_Interferon.19272155\n",
      "(3, 9)\n",
      "Sigs160.Attractors_IFIT3\n",
      "(2, 9)\n",
      "Sigs160.Bindea_CD8.T.cells\n",
      "(0, 9)\n",
      "Sigs160.Wolf_Module3.IFN.score\n",
      "(5, 9)\n",
      "Sigs160.Wolf_Interferon.Cluster.21214954\n",
      "(1, 9)\n",
      "Sigs160.Senbabaoglu_APM1\n",
      "(0, 9)\n",
      "Sigs160.Bindea_Tfh.cells\n",
      "(2, 9)\n",
      "Sigs160.Wolf_CD8.CD68.ratio\n",
      "(6, 9)\n",
      "Sigs160.Wolf_MHC2.21978456\n",
      "(522, 9)\n",
      "Sigs160.Bindea_aDC\n",
      "(1, 9)\n",
      "Sigs160.Bindea_NK.cells\n",
      "(0, 9)\n",
      "Sigs160.Bindea_Cytotoxic.cells\n",
      "(2, 9)\n",
      "Sigs160.Wolf_LYMPHS.PCA.16704732\n",
      "(0, 9)\n",
      "Core56.Cell.Proportion_Eosinophils_Binary.MedianLowHigh\n",
      "(7, 9)\n",
      "Sigs160.Bindea_Eosinophils\n",
      "(0, 9)\n",
      "Sigs160.Bindea_Tcm.cells\n",
      "(0, 9)\n",
      "Sigs160.Bindea_Th17.cells\n",
      "(19, 9)\n"
     ]
    }
   ],
   "source": [
    "for x in sayaman_sig[\"Sayaman.InternalLabel\"].unique():\n",
    "    print(x)\n",
    "    assoc=sayaman_sig[sayaman_sig[\"Sayaman.InternalLabel\"]==x]\n",
    "    snps=assoc[\"PLINK.SNP\"].tolist()+[x.rsplit(\":\",2)[0]+\":\"+x.split(\":\")[3]+\":\"+x.split(\":\")[2] for x in assoc[\"PLINK.SNP\"].tolist()]\n",
    "    assoc_bim=bim[bim[1].isin(snps)]\n",
    "    assoc=pd.merge(assoc_bim, assoc[[\"snp_noallele\",\"PLINK.P\",\"Sayaman.InternalLabel\"]],on=\"snp_noallele\",how=\"left\")\n",
    "    print(assoc.shape)\n",
    "    if len(assoc)>0:\n",
    "        assoc=assoc[[1,\"PLINK.P\"]]\n",
    "        assoc.columns=[\"SNP\",\"P\"]\n",
    "        assoc.to_csv(\"../data/sayaman/supp-associations/\"+x+\".assoc\",index=None,sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f292efe-1a00-43b8-90b8-efd7e7122370",
   "metadata": {},
   "source": [
    "run script: **sayaman-clumping.sh**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0e0e50cf-7b87-4115-b59d-6fa2bc784a01",
   "metadata": {},
   "outputs": [],
   "source": [
    "sayaman_clump=get_clumps(\"../data/sayaman/supp-associations/\",\"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ef211218-95e9-4498-a081-20769734f5b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sayaman[\"variant\"]=sayaman[\"PLINK.SNP\"]\n",
    "sayaman[\"variant2\"]=sayaman[\"PLINK.SNP\"].str.rsplit(\":\",2).str[0]+\":\"+sayaman[\"PLINK.SNP\"].str.split(\":\").str[3]+\":\"+sayaman[\"PLINK.SNP\"].str.split(\":\").str[2]\n",
    "\n",
    "mp_beta=Merge(dict(zip(sayaman[\"variant\"],sayaman[\"PLINK.BETA.OR\"])),dict(zip(sayaman[\"variant2\"],sayaman[\"PLINK.BETA.OR\"])))\n",
    "mp_a1=Merge(dict(zip(sayaman[\"variant\"],sayaman[\"PLINK.A1\"])),dict(zip(sayaman[\"variant2\"],sayaman[\"PLINK.A1\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "54580bc9-1ba1-4fb6-b452-e4e076180ae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sayaman_clump[\"beta\"]=sayaman_clump[\"snps\"].map(mp_beta)\n",
    "sayaman_clump[\"A1\"]=sayaman_clump[\"snps\"].map(mp_a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aaeb1f34-a79e-4082-9cb4-85f998392abd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(55, 5)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sayaman_clump.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "43ae44f5-a472-4300-be5f-ffde8a3c9455",
   "metadata": {},
   "outputs": [],
   "source": [
    "sayaman_clump=sayaman_clump.rename(columns={\"snps\":\"variant\",\"file\":\"association\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5b1840d5-f38a-4676-a812-777302976d1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sayaman_clump[\"key\"]=\"sayaman\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e9f6c631-d916-401e-8dd9-b7acdee6d87b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/cellar/users/mpagadal/Programs/miniconda3/envs/baseold/lib/python3.7/site-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n"
     ]
    }
   ],
   "source": [
    "lit_germline=lit_germline.append(sayaman_clump)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2e676c-16a3-4046-b16a-1d64fb6d5770",
   "metadata": {},
   "source": [
    "### make literature review snps dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b213e329-ee5a-403c-ba37-258bbe1a0998",
   "metadata": {},
   "source": [
    "##### map the rsid to coord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "58e082f5-b36d-48e0-a09b-c30805fda8de",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "lit_germline['mv_variant'] = lit_germline['snps'].apply(lambda x: map_rsid(x)[0])\n",
    "lit_germline['mv_rsid'] = lit_germline['variant'].apply(lambda x: map_rsid(x)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d37a68cf-76ce-4d66-b365-11cce0e9b6bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "lit_germline[\"snps\"]=lit_germline[\"snps\"].fillna(lit_germline[\"mv_rsid\"])\n",
    "lit_germline[\"variant\"]=lit_germline[\"variant\"].fillna(lit_germline[\"mv_variant\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "223f84b6-820c-48d2-bcf8-6ecb18bae3b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "lit_germline=lit_germline[[\"variant\",\"snps\",\"association\",\"key\",\"A1\",\"beta\",\"p-value\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a4e23fa0-df44-48b8-ae02-a94d7850c682",
   "metadata": {},
   "outputs": [],
   "source": [
    "lit_germline.columns=[\"variant\",\"rsid\",\"file\",\"lit_source\",\"A1\",\"beta\",\"p-value\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "316ed522-2914-4a7b-91f8-3bd5d23877c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "lit_germline.to_csv(\"../data/supplemental/Supplemental_Table_5.csv\",index=None,sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "038b1fd2-1ec3-47c9-b88d-a6a63a5f2b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lim             103\n",
       "sayaman          55\n",
       "kula             10\n",
       "queirolo          6\n",
       "salmaninejad      5\n",
       "tang              3\n",
       "shahamtdar        2\n",
       "bedognetti        2\n",
       "yoshida           2\n",
       "ostendorf         2\n",
       "zhang             1\n",
       "sasaki            1\n",
       "uccellini         1\n",
       "kogan             1\n",
       "Name: lit_source, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lit_germline[\"lit_source\"].value_counts()"
   ]
  }
 ],
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