fine_mapping.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rpy2.situation:cffi mode is CFFI_MODE.ANY\n",
      "INFO:rpy2.situation:R home found: /usr/lib/R\n",
      "INFO:rpy2.situation:R library path: /usr/lib/R/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/default-java/lib/server\n",
      "INFO:rpy2.situation:LD_LIBRARY_PATH: /usr/lib/R/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/default-java/lib/server\n",
      "INFO:rpy2.rinterface_lib.embedded:Default options to initialize R: rpy2, --quiet, --no-save\n",
      "INFO:rpy2.rinterface_lib.embedded:R is already initialized. No need to initialize.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import susie_fine_mapping_utils as fm\n",
    "\n",
    "loci = fm.get_loci(\"/local/home/ssigfstead/GWAS/filtered_snps_threshold=0.5.csv\", chr_col=\"chr\", pos_col=\"pos\", rsid_col=\"snp\")\n",
    "loci_mapped = {}\n",
    "\n",
    "for locus in loci:\n",
    "    loci_mapped[locus[3]] = pd.read_csv(f\"/local/home/ssigfstead/GWAS/fine_mapping_results/locus_{locus[3]}_{locus[4]}_mapped.tsv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of loci mapped: 7\n",
      "Number of loci: 52\n"
     ]
    }
   ],
   "source": [
    "n_mapped = 0\n",
    "mapped_loci = []\n",
    "for rsid, locus in loci_mapped.items():\n",
    "    if rsid in locus[locus[\"cs\"]>0][\"rsID\"].values:\n",
    "        n_mapped += 1\n",
    "        mapped_loci.append(rsid)\n",
    "\n",
    "print(f\"Number of loci mapped: {n_mapped}\")\n",
    "print(f\"Number of loci: {len(loci_mapped)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024/08/05 10:54:51 GWASLab v3.4.47 https://cloufield.github.io/gwaslab/\n",
      "2024/08/05 10:54:51 (C) 2022-2024, Yunye He, Kamatani Lab, MIT License, gwaslab@gmail.com\n",
      "2024/08/05 10:54:51 Start to load format from formatbook....\n",
      "2024/08/05 10:54:51  -ssf format meta info:\n",
      "2024/08/05 10:54:51   - format_name  : ssf\n",
      "2024/08/05 10:54:51   - format_source  : https://www.biorxiv.org/content/10.1101/2022.07.15.500230v1.full\n",
      "2024/08/05 10:54:51   - format_cite_name  : GWAS-SSF v0.1\n",
      "2024/08/05 10:54:51   - format_separator  : \\t\n",
      "2024/08/05 10:54:51   - format_na  : #NA\n",
      "2024/08/05 10:54:51   - format_col_order  : chromosome,base_pair_location,effect_allele,other_allele,beta,odds_ratio,hazard_ratio,standard_error,effect_allele_frequency,p_value,neg_log_10_p_value,ci_upper,ci_lower,rsid,variant_id,info,ref_allele,n\n",
      "2024/08/05 10:54:51   - format_version  :  20230328\n",
      "2024/08/05 10:54:51  -ssf to gwaslab format dictionary:\n",
      "2024/08/05 10:54:51   - ssf keys: variant_id,rsid,chromosome,base_pair_location,other_allele,effect_allele,effect_allele_frequency,n,beta,standard_error,p_value,neg_log_10_p_value,info,odds_ratio,hazard_ratio,ci_lower,ci_upper\n",
      "2024/08/05 10:54:51   - gwaslab values: SNPID,rsID,CHR,POS,NEA,EA,EAF,N,BETA,SE,P,MLOG10P,INFO,OR,HR,OR_95L,OR_95U\n",
      "2024/08/05 10:54:51 Start to initialize gl.Sumstats from file :/local/home/ssigfstead/GWAS/gwas_summary_data/GCST90277450.tsv\n",
      "2024/08/05 10:54:53  -Reading columns          : beta,p_value,effect_allele,effect_allele_frequency,standard_error,base_pair_location,chromosome,other_allele\n",
      "2024/08/05 10:54:53  -Renaming columns to      : BETA,P,EA,EAF,SE,POS,CHR,NEA\n",
      "2024/08/05 10:54:53  -Current Dataframe shape : 5713469  x  8\n",
      "2024/08/05 10:54:53  -Initiating a status column: STATUS ...\n",
      "2024/08/05 10:54:53  #WARNING! Version of genomic coordinates is unknown...\n",
      "2024/08/05 10:54:53 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 10:54:53  -Current Dataframe shape : 5713469 x 9 ; Memory usage: 314.17 MB\n",
      "2024/08/05 10:54:53  -Reordering columns to    : CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 10:54:53 Finished reordering the columns.\n",
      "2024/08/05 10:54:53  -Column  : CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 10:54:53  -DType   : int64 int64 category category float64 float64 float64 float64 category\n",
      "2024/08/05 10:54:53  -Verified: T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 10:54:54  -Current Dataframe memory usage: 314.17 MB\n",
      "2024/08/05 10:54:54 Finished loading data successfully!\n"
     ]
    }
   ],
   "source": [
    "sumstats = fm.load_sumstats(\"GCST90277450.tsv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024/08/05 10:58:47 Start to check SNPID/rsID...v3.4.47\n",
      "2024/08/05 10:58:47  -Current Dataframe shape : 5713469 x 9 ; Memory usage: 314.17 MB\n",
      "2024/08/05 10:58:47 Finished checking SNPID/rsID.\n",
      "2024/08/05 10:58:47 Start to fix chromosome notation (CHR)...v3.4.47\n",
      "2024/08/05 10:58:47  -Current Dataframe shape : 5713469 x 9 ; Memory usage: 314.17 MB\n",
      "2024/08/05 10:58:47  -Checking CHR data type...\n",
      "2024/08/05 10:58:48  -Variants with standardized chromosome notation: 5713469\n",
      "2024/08/05 10:58:48  -All CHR are already fixed...\n",
      "2024/08/05 10:58:50 Finished fixing chromosome notation (CHR).\n",
      "2024/08/05 10:58:50 Start to fix basepair positions (POS)...v3.4.47\n",
      "2024/08/05 10:58:50  -Current Dataframe shape : 5713469 x 9 ; Memory usage: 319.62 MB\n",
      "2024/08/05 10:58:50  -Converting to Int64 data type ...\n",
      "2024/08/05 10:58:52  -Position bound:(0 , 250,000,000)\n",
      "2024/08/05 10:58:52  -Removed outliers: 0\n",
      "2024/08/05 10:58:52 Finished fixing basepair positions (POS).\n",
      "2024/08/05 10:58:52 Start to fix alleles (EA and NEA)...v3.4.47\n",
      "2024/08/05 10:58:52  -Current Dataframe shape : 5713469 x 9 ; Memory usage: 368.66 MB\n",
      "2024/08/05 10:58:52  -Converted all bases to string datatype and UPPERCASE.\n",
      "2024/08/05 10:58:53  -Variants with bad EA  : 0\n",
      "2024/08/05 10:58:53  -Variants with bad NEA : 0\n",
      "2024/08/05 10:58:53  -Variants with NA for EA or NEA: 0\n",
      "2024/08/05 10:58:53  -Variants with same EA and NEA: 0\n",
      "2024/08/05 10:58:53  -Detected 0 variants with alleles that contain bases other than A/C/T/G .\n",
      "2024/08/05 10:58:56 Finished fixing alleles (EA and NEA).\n",
      "2024/08/05 10:58:56 Start to perform sanity check for statistics...v3.4.47\n",
      "2024/08/05 10:58:56  -Current Dataframe shape : 5713469 x 9 ; Memory usage: 368.66 MB\n",
      "2024/08/05 10:58:56  -Comparison tolerance for floats: 1e-07\n",
      "2024/08/05 10:58:56  -Checking if -1e-07 < EAF < 1.0000001 ...\n",
      "2024/08/05 10:58:56  -Removed 0 variants with bad/na EAF.\n",
      "2024/08/05 10:58:56  -Checking if -1e-07 < P < 1.0000001 ...\n",
      "2024/08/05 10:58:56  -Removed 0 variants with bad/na P.\n",
      "2024/08/05 10:58:56  -Checking if -100.0000001 < BETA < 100.0000001 ...\n",
      "2024/08/05 10:58:57  -Removed 0 variants with bad/na BETA.\n",
      "2024/08/05 10:58:57  -Checking if -1e-07 < SE < inf ...\n",
      "2024/08/05 10:58:57  -Removed 0 variants with bad/na SE.\n",
      "2024/08/05 10:58:57  -Checking STATUS and converting STATUS to categories....\n",
      "2024/08/05 10:58:57  -Removed 0 variants with bad statistics in total.\n",
      "2024/08/05 10:58:57  -Data types for each column:\n",
      "2024/08/05 10:58:57  -Column  : CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 10:58:57  -DType   : Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 10:58:57  -Verified: T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 10:58:57 Finished sanity check for statistics.\n",
      "2024/08/05 10:58:57 Start to check data consistency across columns...v3.4.47\n",
      "2024/08/05 10:58:57  -Current Dataframe shape : 5713469 x 9 ; Memory usage: 346.87 MB\n",
      "2024/08/05 10:58:57  -Tolerance: 0.001 (Relative) and 0.001 (Absolute)\n",
      "2024/08/05 10:58:57  -SNPID/rsID not available...SKipping\n",
      "2024/08/05 10:58:57 Finished checking data consistency across columns.\n",
      "2024/08/05 10:58:57 Start to normalize indels...v3.4.47\n",
      "2024/08/05 10:58:57  -Current Dataframe shape : 5713469 x 9 ; Memory usage: 346.87 MB\n",
      "2024/08/05 10:58:58  -No available variants to normalize..\n",
      "2024/08/05 10:58:58 Finished normalizing variants successfully!\n",
      "2024/08/05 10:58:58 Start to sort the genome coordinates...v3.4.47\n",
      "2024/08/05 10:58:58  -Current Dataframe shape : 5713469 x 9 ; Memory usage: 346.87 MB\n",
      "2024/08/05 10:58:59 Finished sorting coordinates.\n",
      "2024/08/05 10:58:59 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 10:58:59  -Current Dataframe shape : 5713469 x 9 ; Memory usage: 303.28 MB\n",
      "2024/08/05 10:58:59  -Reordering columns to    : CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 10:58:59 Finished reordering the columns.\n",
      "2024/08/05 10:59:00 Start to check if NEA is aligned with reference sequence...v3.4.47\n",
      "2024/08/05 10:59:00  -Current Dataframe shape : 5713469 x 9 ; Memory usage: 303.28 MB\n",
      "2024/08/05 10:59:00  -Reference genome FASTA file: /local/home/ssigfstead/GWAS/reference_genomes/hg19.fa\n",
      "2024/08/05 10:59:00  -Loading fasta records:1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  20  19  22  21  \n",
      "2024/08/05 10:59:09  -Checking records\n",
      "2024/08/05 10:59:09    -Building numpy fasta records from dict\n",
      "2024/08/05 10:59:19    -Checking records for ( len(NEA) <= 4 and len(EA) <= 4 )\n",
      "2024/08/05 10:59:22    -Checking records for ( len(NEA) > 4 or len(EA) > 4 )\n",
      "2024/08/05 10:59:22  -Finished checking records\n",
      "2024/08/05 10:59:24  -Variants allele on given reference sequence :  4251448\n",
      "2024/08/05 10:59:24  -Variants flipped :  1462021\n",
      "2024/08/05 10:59:24   -Raw Matching rate :  100.00%\n",
      "2024/08/05 10:59:24  -Variants inferred reverse_complement :  0\n",
      "2024/08/05 10:59:24  -Variants inferred reverse_complement_flipped :  0\n",
      "2024/08/05 10:59:24  -Both allele on genome + unable to distinguish :  0\n",
      "2024/08/05 10:59:24  -Variants not on given reference sequence :  0\n",
      "2024/08/05 10:59:24 Finished checking if NEA is aligned with reference sequence.\n",
      "2024/08/05 10:59:24 Start to check SNPID/rsID...v3.4.47\n",
      "2024/08/05 10:59:24  -Current Dataframe shape : 5713469 x 9 ; Memory usage: 303.28 MB\n",
      "2024/08/05 10:59:32  -Filling 0 SNPID using CHR:POS...\n",
      "2024/08/05 10:59:32  -Filling 5713469 SNPID using CHR:POS:NEA:EA...\n",
      "2024/08/05 10:59:35  -Fixed 5713469 variants ID...\n",
      "2024/08/05 10:59:35 Finished checking SNPID/rsID.\n",
      "2024/08/05 10:59:35 Start to check SNPID/rsID...v3.4.47\n",
      "2024/08/05 10:59:35  -Current Dataframe shape : 5713469 x 10 ; Memory usage: 346.87 MB\n",
      "2024/08/05 10:59:35  -Checking SNPID data type...\n",
      "2024/08/05 10:59:35  -Checking if SNPID is CHR:POS:NEA:EA...(separator: - ,: , _)\n",
      "2024/08/05 10:59:38 Finished checking SNPID/rsID.\n",
      "2024/08/05 10:59:38 Start to fix chromosome notation (CHR)...v3.4.47\n",
      "2024/08/05 10:59:38  -Current Dataframe shape : 5713469 x 10 ; Memory usage: 346.87 MB\n",
      "2024/08/05 10:59:38  -Checking CHR data type...\n",
      "2024/08/05 10:59:38  -Variants with standardized chromosome notation: 5713469\n",
      "2024/08/05 10:59:39  -All CHR are already fixed...\n",
      "2024/08/05 10:59:41 Finished fixing chromosome notation (CHR).\n",
      "2024/08/05 10:59:41 Start to fix basepair positions (POS)...v3.4.47\n",
      "2024/08/05 10:59:41  -Current Dataframe shape : 5713469 x 10 ; Memory usage: 346.87 MB\n",
      "2024/08/05 10:59:41  -Converting to Int64 data type ...\n",
      "2024/08/05 10:59:42  -Position bound:(0 , 250,000,000)\n",
      "2024/08/05 10:59:43  -Removed outliers: 0\n",
      "2024/08/05 10:59:43 Finished fixing basepair positions (POS).\n",
      "2024/08/05 10:59:43 Start to fix alleles (EA and NEA)...v3.4.47\n",
      "2024/08/05 10:59:43  -Current Dataframe shape : 5713469 x 10 ; Memory usage: 390.46 MB\n",
      "2024/08/05 10:59:43  -Converted all bases to string datatype and UPPERCASE.\n",
      "2024/08/05 10:59:43  -Variants with bad EA  : 0\n",
      "2024/08/05 10:59:44  -Variants with bad NEA : 0\n",
      "2024/08/05 10:59:44  -Variants with NA for EA or NEA: 0\n",
      "2024/08/05 10:59:44  -Variants with same EA and NEA: 0\n",
      "2024/08/05 10:59:44  -Detected 0 variants with alleles that contain bases other than A/C/T/G .\n",
      "2024/08/05 10:59:46 Finished fixing alleles (EA and NEA).\n",
      "2024/08/05 10:59:46 Start to perform sanity check for statistics...v3.4.47\n",
      "2024/08/05 10:59:46  -Current Dataframe shape : 5713469 x 10 ; Memory usage: 390.46 MB\n",
      "2024/08/05 10:59:46  -Comparison tolerance for floats: 1e-07\n",
      "2024/08/05 10:59:46  -Checking if -1e-07 < EAF < 1.0000001 ...\n",
      "2024/08/05 10:59:47  -Removed 0 variants with bad/na EAF.\n",
      "2024/08/05 10:59:47  -Checking if -1e-07 < P < 1.0000001 ...\n",
      "2024/08/05 10:59:47  -Removed 0 variants with bad/na P.\n",
      "2024/08/05 10:59:47  -Checking if -100.0000001 < BETA < 100.0000001 ...\n",
      "2024/08/05 10:59:48  -Removed 0 variants with bad/na BETA.\n",
      "2024/08/05 10:59:48  -Checking if -1e-07 < SE < inf ...\n",
      "2024/08/05 10:59:48  -Removed 0 variants with bad/na SE.\n",
      "2024/08/05 10:59:48  -Checking STATUS and converting STATUS to categories....\n",
      "2024/08/05 10:59:48  -Removed 0 variants with bad statistics in total.\n",
      "2024/08/05 10:59:48  -Data types for each column:\n",
      "2024/08/05 10:59:48  -Column  : CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS   SNPID \n",
      "2024/08/05 10:59:48  -DType   : Int64 Int64 category category float32 float64 float64 float64 category string\n",
      "2024/08/05 10:59:48  -Verified: T     T     T        T        T       T       T       T       T        T     \n",
      "2024/08/05 10:59:48 Finished sanity check for statistics.\n",
      "2024/08/05 10:59:48 Start to normalize indels...v3.4.47\n",
      "2024/08/05 10:59:48  -Current Dataframe shape : 5713469 x 10 ; Memory usage: 390.46 MB\n",
      "2024/08/05 10:59:49  -No available variants to normalize..\n",
      "2024/08/05 10:59:49 Finished normalizing variants successfully!\n",
      "2024/08/05 10:59:49 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 10:59:49  -Current Dataframe shape : 5713469 x 10 ; Memory usage: 390.46 MB\n",
      "2024/08/05 10:59:49  -Reordering columns to    : SNPID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 10:59:49 Finished reordering the columns.\n",
      "2024/08/05 10:59:49 Start to check if NEA is aligned with reference sequence...v3.4.47\n",
      "2024/08/05 10:59:49  -Current Dataframe shape : 5713469 x 10 ; Memory usage: 390.46 MB\n",
      "2024/08/05 10:59:49  -Reference genome FASTA file: /local/home/ssigfstead/GWAS/reference_genomes/hg19.fa\n",
      "2024/08/05 10:59:49  -Loading fasta records:1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  20  19  22  21  \n",
      "2024/08/05 10:59:59  -Checking records\n",
      "2024/08/05 10:59:59    -Building numpy fasta records from dict\n",
      "2024/08/05 11:00:08    -Checking records for ( len(NEA) <= 4 and len(EA) <= 4 )\n",
      "2024/08/05 11:00:12    -Checking records for ( len(NEA) > 4 or len(EA) > 4 )\n",
      "2024/08/05 11:00:12  -Finished checking records\n",
      "2024/08/05 11:00:14  -Variants allele on given reference sequence :  4251448\n",
      "2024/08/05 11:00:14  -Variants flipped :  1462021\n",
      "2024/08/05 11:00:14   -Raw Matching rate :  100.00%\n",
      "2024/08/05 11:00:14  -Variants inferred reverse_complement :  0\n",
      "2024/08/05 11:00:14  -Variants inferred reverse_complement_flipped :  0\n",
      "2024/08/05 11:00:14  -Both allele on genome + unable to distinguish :  0\n",
      "2024/08/05 11:00:14  -Variants not on given reference sequence :  0\n",
      "2024/08/05 11:00:14 Finished checking if NEA is aligned with reference sequence.\n",
      "2024/08/05 11:00:14 Start to adjust statistics based on STATUS code...v3.4.47\n",
      "2024/08/05 11:00:14  -Current Dataframe shape : 5713469 x 10 ; Memory usage: 390.46 MB\n",
      "2024/08/05 11:00:15 Start to flip allele-specific stats for SNPs with status xxxxx[35]x: ALT->EA , REF->NEA ...v3.4.47\n",
      "2024/08/05 11:00:15  -Flipping 1462021 variants...\n",
      "2024/08/05 11:00:15  -Swapping column: NEA <=> EA...\n",
      "2024/08/05 11:00:16  -Flipping column: BETA = - BETA...\n",
      "2024/08/05 11:00:16  -Flipping column: EAF = 1 - EAF...\n",
      "2024/08/05 11:00:16  -Changed the status for flipped variants : xxxxx[35]x -> xxxxx[12]x\n",
      "2024/08/05 11:00:18 Finished adjusting statistics based on STATUS code.\n",
      "2024/08/05 11:00:18 Start to assign rsID by matching SNPID with CHR:POS:REF:ALT in the reference TSV...v3.4.47\n",
      "2024/08/05 11:00:18  -Current Dataframe shape : 5713469 x 10 ; Memory usage: 390.46 MB\n",
      "2024/08/05 11:00:18  -Number of threads/cores to use: 24\n",
      "2024/08/05 11:00:18  -Reference TSV: /local/home/ssigfstead/GWAS/SNP_rsid_references/1kg_dbsnp151_hg19_auto.txt.gz\n",
      "2024/08/05 11:00:18  -5713469 rsID could be possibly fixed...\n",
      "2024/08/05 11:00:19  -Setting block size:  5000000\n",
      "2024/08/05 11:00:19  -Loading block: 0   1   2   3   4   5   6   7   8   9   10   11   12   13   14   15   \n",
      "2024/08/05 11:02:46  -rsID annotation for 1486130 needed to be fixed!\n",
      "2024/08/05 11:02:46  -Annotated 4227339 rsID successfully!\n",
      "2024/08/05 11:02:46 Finished assign rsID using reference file.\n",
      "2024/08/05 11:02:46 Start to assign rsID using reference VCF...v3.4.47\n",
      "2024/08/05 11:02:46  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 390.46 MB\n",
      "2024/08/05 11:02:46  -Number of threads/cores to use: 24\n",
      "2024/08/05 11:02:46  -Reference VCF: /local/home/ssigfstead/GWAS/SNP_rsid_references/GCF_000001405.25\n",
      "2024/08/05 11:02:46  -Checking prefix for chromosomes in vcf files...\n",
      "2024/08/05 11:02:46  -No prefix for chromosomes in the VCF files.\n",
      "2024/08/05 11:02:46  -Assigning rsID based on CHR:POS and REF:ALT/ALT:REF...\n",
      "2024/08/05 11:02:50  -rsID Annotation for 1486130 need to be fixed!\n",
      "2024/08/05 11:02:50  -Annotated 0 rsID successfully!\n",
      "2024/08/05 11:02:50 Finished assign rsID using reference file.\n",
      "2024/08/05 11:02:50 Start to sort the genome coordinates...v3.4.47\n",
      "2024/08/05 11:02:50  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 390.46 MB\n",
      "2024/08/05 11:02:52 Finished sorting coordinates.\n",
      "2024/08/05 11:02:52 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:02:52  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 390.46 MB\n",
      "2024/08/05 11:02:52  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:02:52 Finished reordering the columns.\n",
      "2024/08/05 11:02:52 Start to check SNPID/rsID...v3.4.47\n",
      "2024/08/05 11:02:52  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 390.46 MB\n",
      "2024/08/05 11:02:52  -Checking rsID data type...\n",
      "2024/08/05 11:02:52  -Checking SNPID data type...\n",
      "2024/08/05 11:02:52  -Checking if SNPID is CHR:POS:NEA:EA...(separator: - ,: , _)\n",
      "2024/08/05 11:02:54  -Checking if rsID is rsxxxxxx...\n",
      "2024/08/05 11:02:57  -Checking if CHR:POS:NEA:EA is mixed in rsID column ...\n",
      "2024/08/05 11:02:58  -Number of CHR:POS:NEA:EA mixed in rsID column : 0\n",
      "2024/08/05 11:02:58  -Number of Unrecognized rsID : 1486130\n",
      "2024/08/05 11:02:58  -A look at the unrecognized rsID : {<NA>} ...\n",
      "2024/08/05 11:03:06  -Filling 0 SNPID using CHR:POS...\n",
      "2024/08/05 11:03:06  -Filling 5713469 SNPID using CHR:POS:NEA:EA...\n",
      "2024/08/05 11:03:08  -Fixed 0 variants ID...\n",
      "2024/08/05 11:03:08 Finished checking SNPID/rsID.\n",
      "2024/08/05 11:03:08 Start to check SNPID/rsID...v3.4.47\n",
      "2024/08/05 11:03:08  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 390.46 MB\n",
      "2024/08/05 11:03:08  -Checking rsID data type...\n",
      "2024/08/05 11:03:08  -Checking SNPID data type...\n",
      "2024/08/05 11:03:08  -Checking if SNPID is CHR:POS:NEA:EA...(separator: - ,: , _)\n",
      "2024/08/05 11:03:11  -Checking if rsID is rsxxxxxx...\n",
      "2024/08/05 11:03:13  -Checking if CHR:POS:NEA:EA is mixed in rsID column ...\n",
      "2024/08/05 11:03:14  -Number of CHR:POS:NEA:EA mixed in rsID column : 0\n",
      "2024/08/05 11:03:15  -Number of Unrecognized rsID : 1486130\n",
      "2024/08/05 11:03:15  -A look at the unrecognized rsID : {<NA>} ...\n",
      "2024/08/05 11:03:15 Finished checking SNPID/rsID.\n",
      "2024/08/05 11:03:15 Start to fix chromosome notation (CHR)...v3.4.47\n",
      "2024/08/05 11:03:15  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 390.46 MB\n",
      "2024/08/05 11:03:15  -Checking CHR data type...\n",
      "2024/08/05 11:03:16  -Variants with standardized chromosome notation: 5713469\n",
      "2024/08/05 11:03:16  -All CHR are already fixed...\n",
      "2024/08/05 11:03:18 Finished fixing chromosome notation (CHR).\n",
      "2024/08/05 11:03:18 Start to fix basepair positions (POS)...v3.4.47\n",
      "2024/08/05 11:03:18  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 390.46 MB\n",
      "2024/08/05 11:03:18  -Converting to Int64 data type ...\n",
      "2024/08/05 11:03:19  -Position bound:(0 , 250,000,000)\n",
      "2024/08/05 11:03:20  -Removed outliers: 0\n",
      "2024/08/05 11:03:20 Finished fixing basepair positions (POS).\n",
      "2024/08/05 11:03:20 Start to fix alleles (EA and NEA)...v3.4.47\n",
      "2024/08/05 11:03:20  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 434.05 MB\n",
      "2024/08/05 11:03:20  -Converted all bases to string datatype and UPPERCASE.\n",
      "2024/08/05 11:03:21  -Variants with bad EA  : 0\n",
      "2024/08/05 11:03:21  -Variants with bad NEA : 0\n",
      "2024/08/05 11:03:21  -Variants with NA for EA or NEA: 0\n",
      "2024/08/05 11:03:21  -Variants with same EA and NEA: 0\n",
      "2024/08/05 11:03:21  -Detected 0 variants with alleles that contain bases other than A/C/T/G .\n",
      "2024/08/05 11:03:24 Finished fixing alleles (EA and NEA).\n",
      "2024/08/05 11:03:24 Start to perform sanity check for statistics...v3.4.47\n",
      "2024/08/05 11:03:24  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 434.05 MB\n",
      "2024/08/05 11:03:24  -Comparison tolerance for floats: 1e-07\n",
      "2024/08/05 11:03:24  -Checking if -1e-07 < EAF < 1.0000001 ...\n",
      "2024/08/05 11:03:25  -Removed 0 variants with bad/na EAF.\n",
      "2024/08/05 11:03:25  -Checking if -1e-07 < P < 1.0000001 ...\n",
      "2024/08/05 11:03:26  -Removed 0 variants with bad/na P.\n",
      "2024/08/05 11:03:26  -Checking if -100.0000001 < BETA < 100.0000001 ...\n",
      "2024/08/05 11:03:26  -Removed 0 variants with bad/na BETA.\n",
      "2024/08/05 11:03:26  -Checking if -1e-07 < SE < inf ...\n",
      "2024/08/05 11:03:27  -Removed 0 variants with bad/na SE.\n",
      "2024/08/05 11:03:27  -Checking STATUS and converting STATUS to categories....\n",
      "2024/08/05 11:03:27  -Removed 0 variants with bad statistics in total.\n",
      "2024/08/05 11:03:27  -Data types for each column:\n",
      "2024/08/05 11:03:27  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:03:27  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:03:27  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:03:27 Finished sanity check for statistics.\n",
      "2024/08/05 11:03:28 Start to normalize indels...v3.4.47\n",
      "2024/08/05 11:03:28  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 434.05 MB\n",
      "2024/08/05 11:03:28  -No available variants to normalize..\n",
      "2024/08/05 11:03:28 Finished normalizing variants successfully!\n",
      "2024/08/05 11:03:28 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:03:28  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 434.05 MB\n",
      "2024/08/05 11:03:28  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:03:29 Finished reordering the columns.\n",
      "2024/08/05 11:03:29 Start to check if NEA is aligned with reference sequence...v3.4.47\n",
      "2024/08/05 11:03:29  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 434.05 MB\n",
      "2024/08/05 11:03:29  -Reference genome FASTA file: /local/home/ssigfstead/GWAS/reference_genomes/hg19.fa\n",
      "2024/08/05 11:03:29  -Loading fasta records:1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  20  19  22  21  \n",
      "2024/08/05 11:03:39  -Checking records\n",
      "2024/08/05 11:03:39    -Building numpy fasta records from dict\n",
      "2024/08/05 11:03:48    -Checking records for ( len(NEA) <= 4 and len(EA) <= 4 )\n",
      "2024/08/05 11:03:51    -Checking records for ( len(NEA) > 4 or len(EA) > 4 )\n",
      "2024/08/05 11:03:51  -Finished checking records\n",
      "2024/08/05 11:03:53  -Variants allele on given reference sequence :  5713469\n",
      "2024/08/05 11:03:53  -Variants flipped :  0\n",
      "2024/08/05 11:03:53   -Raw Matching rate :  100.00%\n",
      "2024/08/05 11:03:53  -Variants inferred reverse_complement :  0\n",
      "2024/08/05 11:03:53  -Variants inferred reverse_complement_flipped :  0\n",
      "2024/08/05 11:03:53  -Both allele on genome + unable to distinguish :  0\n",
      "2024/08/05 11:03:53  -Variants not on given reference sequence :  0\n",
      "2024/08/05 11:03:53 Finished checking if NEA is aligned with reference sequence.\n",
      "2024/08/05 11:03:53 Start to adjust statistics based on STATUS code...v3.4.47\n",
      "2024/08/05 11:03:53  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 434.05 MB\n",
      "2024/08/05 11:03:56  -No statistics have been changed.\n",
      "2024/08/05 11:03:56 Finished adjusting statistics based on STATUS code.\n",
      "2024/08/05 11:03:56 Start to assign rsID by matching SNPID with CHR:POS:REF:ALT in the reference TSV...v3.4.47\n",
      "2024/08/05 11:03:56  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 434.05 MB\n",
      "2024/08/05 11:03:56  -Number of threads/cores to use: 24\n",
      "2024/08/05 11:03:56  -Reference TSV: /local/home/ssigfstead/GWAS/SNP_rsid_references/1kg_dbsnp151_hg19_auto.txt.gz\n",
      "2024/08/05 11:03:57  -1486130 rsID could be possibly fixed...\n",
      "2024/08/05 11:03:57  -Setting block size:  5000000\n",
      "2024/08/05 11:03:57  -Loading block: 0   1   2   3   4   5   6   7   8   9   10   11   12   13   14   15   \n",
      "2024/08/05 11:06:39  -rsID annotation for 33093 needed to be fixed!\n",
      "2024/08/05 11:06:39  -Annotated 1453037 rsID successfully!\n",
      "2024/08/05 11:06:39 Finished assign rsID using reference file.\n",
      "2024/08/05 11:06:40 Start to assign rsID using reference VCF...v3.4.47\n",
      "2024/08/05 11:06:40  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 390.46 MB\n",
      "2024/08/05 11:06:40  -Number of threads/cores to use: 24\n",
      "2024/08/05 11:06:40  -Reference VCF: /local/home/ssigfstead/GWAS/SNP_rsid_references/GCF_000001405.25\n",
      "2024/08/05 11:06:40  -Checking prefix for chromosomes in vcf files...\n",
      "2024/08/05 11:06:40  -No prefix for chromosomes in the VCF files.\n",
      "2024/08/05 11:06:40  -Assigning rsID based on CHR:POS and REF:ALT/ALT:REF...\n",
      "2024/08/05 11:06:43  -rsID Annotation for 33093 need to be fixed!\n",
      "2024/08/05 11:06:43  -Annotated 0 rsID successfully!\n",
      "2024/08/05 11:06:43 Finished assign rsID using reference file.\n",
      "2024/08/05 11:06:43 Start to sort the genome coordinates...v3.4.47\n",
      "2024/08/05 11:06:43  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 390.46 MB\n",
      "2024/08/05 11:06:45 Finished sorting coordinates.\n",
      "2024/08/05 11:06:45 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:06:45  -Current Dataframe shape : 5713469 x 11 ; Memory usage: 390.46 MB\n",
      "2024/08/05 11:06:45  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:06:45 Finished reordering the columns.\n"
     ]
    }
   ],
   "source": [
    "fm.harmonize(sumstats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024/08/05 11:30:34 Start filtering values by condition: CHR==1.0 & POS>37178598.0 & POS<38178598.0\n",
      "2024/08/05 11:30:34  -Removing 5711491 variants not meeting the conditions: CHR==1.0 & POS>37178598.0 & POS<38178598.0\n",
      "2024/08/05 11:30:34 Finished filtering values.\n",
      "2024/08/05 11:30:35 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:35  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:35  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:35  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:35  -Overwrite mode:  False\n",
      "2024/08/05 11:30:35   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:35  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:35 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:35 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:35  -Current Dataframe shape : 1978 x 11 ; Memory usage: 20.08 MB\n",
      "2024/08/05 11:30:35  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:35 Finished reordering the columns.\n",
      "2024/08/05 11:30:35 Start filtering values by condition: CHR==1.0 & POS>72440273.0 & POS<73440273.0\n",
      "2024/08/05 11:30:35  -Removing 5712142 variants not meeting the conditions: CHR==1.0 & POS>72440273.0 & POS<73440273.0\n",
      "2024/08/05 11:30:35 Finished filtering values.\n",
      "2024/08/05 11:30:35 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:35  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:35  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:35  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:35  -Overwrite mode:  False\n",
      "2024/08/05 11:30:35   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:35  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:35 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:35 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:35  -Current Dataframe shape : 1327 x 11 ; Memory usage: 20.04 MB\n",
      "2024/08/05 11:30:35  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:35 Finished reordering the columns.\n",
      "2024/08/05 11:30:36 Start filtering values by condition: CHR==1.0 & POS>74971043.0 & POS<75971043.0\n",
      "2024/08/05 11:30:36  -Removing 5711668 variants not meeting the conditions: CHR==1.0 & POS>74971043.0 & POS<75971043.0\n",
      "2024/08/05 11:30:36 Finished filtering values.\n",
      "2024/08/05 11:30:36 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:36  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:36  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:36  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:36  -Overwrite mode:  False\n",
      "2024/08/05 11:30:36   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:36  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:36 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:36 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:36  -Current Dataframe shape : 1801 x 11 ; Memory usage: 20.07 MB\n",
      "2024/08/05 11:30:36  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:36 Finished reordering the columns.\n",
      "2024/08/05 11:30:36 Start filtering values by condition: CHR==2.0 & POS>212210934.0 & POS<213210934.0\n",
      "2024/08/05 11:30:36  -Removing 5710678 variants not meeting the conditions: CHR==2.0 & POS>212210934.0 & POS<213210934.0\n",
      "2024/08/05 11:30:36 Finished filtering values.\n",
      "2024/08/05 11:30:36 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:36  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:36  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:36  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:36  -Overwrite mode:  False\n",
      "2024/08/05 11:30:36   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:36  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:36 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:36 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:36  -Current Dataframe shape : 2791 x 11 ; Memory usage: 20.14 MB\n",
      "2024/08/05 11:30:36  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:36 Finished reordering the columns.\n",
      "2024/08/05 11:30:37 Start filtering values by condition: CHR==2.0 & POS>14962058.0 & POS<15962058.0\n",
      "2024/08/05 11:30:37  -Removing 5710998 variants not meeting the conditions: CHR==2.0 & POS>14962058.0 & POS<15962058.0\n",
      "2024/08/05 11:30:37 Finished filtering values.\n",
      "2024/08/05 11:30:37 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:37  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:37  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:37  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:37  -Overwrite mode:  False\n",
      "2024/08/05 11:30:37   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:37  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:37 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:37 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:37  -Current Dataframe shape : 2471 x 11 ; Memory usage: 20.12 MB\n",
      "2024/08/05 11:30:37  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:37 Finished reordering the columns.\n",
      "2024/08/05 11:30:37 Start filtering values by condition: CHR==3.0 & POS>48713637.0 & POS<49713637.0\n",
      "2024/08/05 11:30:37  -Removing 5712570 variants not meeting the conditions: CHR==3.0 & POS>48713637.0 & POS<49713637.0\n",
      "2024/08/05 11:30:37 Finished filtering values.\n",
      "2024/08/05 11:30:38 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:38  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:38  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:38  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:38  -Overwrite mode:  False\n",
      "2024/08/05 11:30:38   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:38  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:38 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:38 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:38  -Current Dataframe shape : 899 x 11 ; Memory usage: 20.01 MB\n",
      "2024/08/05 11:30:38  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:38 Finished reordering the columns.\n",
      "2024/08/05 11:30:38 Start filtering values by condition: CHR==5.0 & POS>103464585.0 & POS<104464585.0\n",
      "2024/08/05 11:30:38  -Removing 5711217 variants not meeting the conditions: CHR==5.0 & POS>103464585.0 & POS<104464585.0\n",
      "2024/08/05 11:30:38 Finished filtering values.\n",
      "2024/08/05 11:30:38 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:38  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:38  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:38  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:38  -Overwrite mode:  False\n",
      "2024/08/05 11:30:38   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:38  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:38 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:38 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:38  -Current Dataframe shape : 2252 x 11 ; Memory usage: 20.10 MB\n",
      "2024/08/05 11:30:38  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:38 Finished reordering the columns.\n",
      "2024/08/05 11:30:39 Start filtering values by condition: CHR==5.0 & POS>163987132.0 & POS<164987132.0\n",
      "2024/08/05 11:30:39  -Removing 5711394 variants not meeting the conditions: CHR==5.0 & POS>163987132.0 & POS<164987132.0\n",
      "2024/08/05 11:30:39 Finished filtering values.\n",
      "2024/08/05 11:30:39 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:39  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:39  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:39  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:39  -Overwrite mode:  False\n",
      "2024/08/05 11:30:39   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:39  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:39 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:39 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:39  -Current Dataframe shape : 2075 x 11 ; Memory usage: 20.09 MB\n",
      "2024/08/05 11:30:39  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:39 Finished reordering the columns.\n",
      "2024/08/05 11:30:39 Start filtering values by condition: CHR==5.0 & POS>61018394.0 & POS<62018394.0\n",
      "2024/08/05 11:30:39  -Removing 5711907 variants not meeting the conditions: CHR==5.0 & POS>61018394.0 & POS<62018394.0\n",
      "2024/08/05 11:30:39 Finished filtering values.\n",
      "2024/08/05 11:30:39 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:39  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:39  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:39  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:39  -Overwrite mode:  False\n",
      "2024/08/05 11:30:39   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:39  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:39 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:39 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:39  -Current Dataframe shape : 1562 x 11 ; Memory usage: 20.05 MB\n",
      "2024/08/05 11:30:39  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:39 Finished reordering the columns.\n",
      "2024/08/05 11:30:40 Start filtering values by condition: CHR==6.0 & POS>29063067.0 & POS<30063067.0\n",
      "2024/08/05 11:30:40  -Removing 5708013 variants not meeting the conditions: CHR==6.0 & POS>29063067.0 & POS<30063067.0\n",
      "2024/08/05 11:30:40 Finished filtering values.\n",
      "2024/08/05 11:30:40 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:40  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:40  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:40  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:40  -Overwrite mode:  False\n",
      "2024/08/05 11:30:40   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:40  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:40 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:40 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:40  -Current Dataframe shape : 5456 x 11 ; Memory usage: 20.34 MB\n",
      "2024/08/05 11:30:40  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:40 Finished reordering the columns.\n",
      "2024/08/05 11:30:40 Start filtering values by condition: CHR==6.0 & POS>30065853.0 & POS<31065853.0\n",
      "2024/08/05 11:30:40  -Removing 5709474 variants not meeting the conditions: CHR==6.0 & POS>30065853.0 & POS<31065853.0\n",
      "2024/08/05 11:30:40 Finished filtering values.\n",
      "2024/08/05 11:30:40 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:40  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:40  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:40  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:40  -Overwrite mode:  False\n",
      "2024/08/05 11:30:40   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:40  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:40 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:40 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:40  -Current Dataframe shape : 3995 x 11 ; Memory usage: 20.23 MB\n",
      "2024/08/05 11:30:40  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:40 Finished reordering the columns.\n",
      "2024/08/05 11:30:41 Start filtering values by condition: CHR==6.0 & POS>27335435.0 & POS<28335435.0\n",
      "2024/08/05 11:30:41  -Removing 5711751 variants not meeting the conditions: CHR==6.0 & POS>27335435.0 & POS<28335435.0\n",
      "2024/08/05 11:30:41 Finished filtering values.\n",
      "2024/08/05 11:30:41 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:41  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:41  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:41  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:41  -Overwrite mode:  False\n",
      "2024/08/05 11:30:41   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:41  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:41 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:41 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:41  -Current Dataframe shape : 1718 x 11 ; Memory usage: 20.06 MB\n",
      "2024/08/05 11:30:41  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:41 Finished reordering the columns.\n",
      "2024/08/05 11:30:41 Start filtering values by condition: CHR==6.0 & POS>31416400.0 & POS<32416400.0\n",
      "2024/08/05 11:30:41  -Removing 5709854 variants not meeting the conditions: CHR==6.0 & POS>31416400.0 & POS<32416400.0\n",
      "2024/08/05 11:30:41 Finished filtering values.\n",
      "2024/08/05 11:30:42 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:42  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:42  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:42  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:42  -Overwrite mode:  False\n",
      "2024/08/05 11:30:42   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:42  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:42 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:42 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:42  -Current Dataframe shape : 3615 x 11 ; Memory usage: 20.20 MB\n",
      "2024/08/05 11:30:42  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:42 Finished reordering the columns.\n",
      "2024/08/05 11:30:42 Start filtering values by condition: CHR==7.0 & POS>11756548.0 & POS<12756548.0\n",
      "2024/08/05 11:30:42  -Removing 5709610 variants not meeting the conditions: CHR==7.0 & POS>11756548.0 & POS<12756548.0\n",
      "2024/08/05 11:30:42 Finished filtering values.\n",
      "2024/08/05 11:30:42 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:42  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:42  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:42  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:42  -Overwrite mode:  False\n",
      "2024/08/05 11:30:42   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:42  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:42 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:42 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:42  -Current Dataframe shape : 3859 x 11 ; Memory usage: 20.22 MB\n",
      "2024/08/05 11:30:42  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:42 Finished reordering the columns.\n",
      "2024/08/05 11:30:43 Start filtering values by condition: CHR==7.0 & POS>117085779.0 & POS<118085779.0\n",
      "2024/08/05 11:30:43  -Removing 5711826 variants not meeting the conditions: CHR==7.0 & POS>117085779.0 & POS<118085779.0\n",
      "2024/08/05 11:30:43 Finished filtering values.\n",
      "2024/08/05 11:30:43 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:43  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:43  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:43  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:43  -Overwrite mode:  False\n",
      "2024/08/05 11:30:43   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:43  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:43 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:43 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:43  -Current Dataframe shape : 1643 x 11 ; Memory usage: 20.06 MB\n",
      "2024/08/05 11:30:43  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:43 Finished reordering the columns.\n",
      "2024/08/05 11:30:43 Start filtering values by condition: CHR==8.0 & POS>73389570.0 & POS<74389570.0\n",
      "2024/08/05 11:30:43  -Removing 5711267 variants not meeting the conditions: CHR==8.0 & POS>73389570.0 & POS<74389570.0\n",
      "2024/08/05 11:30:43 Finished filtering values.\n",
      "2024/08/05 11:30:43 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:43  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:43  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:43  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:43  -Overwrite mode:  False\n",
      "2024/08/05 11:30:43   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:43  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:43 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:43 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:43  -Current Dataframe shape : 2202 x 11 ; Memory usage: 20.10 MB\n",
      "2024/08/05 11:30:43  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:43 Finished reordering the columns.\n",
      "2024/08/05 11:30:44 Start filtering values by condition: CHR==9.0 & POS>10645582.0 & POS<11645582.0\n",
      "2024/08/05 11:30:44  -Removing 5710083 variants not meeting the conditions: CHR==9.0 & POS>10645582.0 & POS<11645582.0\n",
      "2024/08/05 11:30:44 Finished filtering values.\n",
      "2024/08/05 11:30:44 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:44  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:44  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:44  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:44  -Overwrite mode:  False\n",
      "2024/08/05 11:30:44   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:44  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:44 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:44 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:44  -Current Dataframe shape : 3386 x 11 ; Memory usage: 20.19 MB\n",
      "2024/08/05 11:30:44  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:44 Finished reordering the columns.\n",
      "2024/08/05 11:30:44 Start filtering values by condition: CHR==9.0 & POS>97756309.0 & POS<98756309.0\n",
      "2024/08/05 11:30:45  -Removing 5711717 variants not meeting the conditions: CHR==9.0 & POS>97756309.0 & POS<98756309.0\n",
      "2024/08/05 11:30:45 Finished filtering values.\n",
      "2024/08/05 11:30:45 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:45  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:45  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:45  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:45  -Overwrite mode:  False\n",
      "2024/08/05 11:30:45   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:45  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:45 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:45 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:45  -Current Dataframe shape : 1752 x 11 ; Memory usage: 20.07 MB\n",
      "2024/08/05 11:30:45  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:45 Finished reordering the columns.\n",
      "2024/08/05 11:30:45 Start filtering values by condition: CHR==9.0 & POS>122165272.0 & POS<123165272.0\n",
      "2024/08/05 11:30:45  -Removing 5711793 variants not meeting the conditions: CHR==9.0 & POS>122165272.0 & POS<123165272.0\n",
      "2024/08/05 11:30:45 Finished filtering values.\n",
      "2024/08/05 11:30:45 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:45  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:45  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:45  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:45  -Overwrite mode:  False\n",
      "2024/08/05 11:30:45   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:45  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:45 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:45 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:45  -Current Dataframe shape : 1676 x 11 ; Memory usage: 20.06 MB\n",
      "2024/08/05 11:30:45  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:45 Finished reordering the columns.\n",
      "2024/08/05 11:30:46 Start filtering values by condition: CHR==10.0 & POS>105967853.0 & POS<106967853.0\n",
      "2024/08/05 11:30:46  -Removing 5711461 variants not meeting the conditions: CHR==10.0 & POS>105967853.0 & POS<106967853.0\n",
      "2024/08/05 11:30:46 Finished filtering values.\n",
      "2024/08/05 11:30:46 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:46  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:46  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:46  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:46  -Overwrite mode:  False\n",
      "2024/08/05 11:30:46   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:46  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:46 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:46 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:46  -Current Dataframe shape : 2008 x 11 ; Memory usage: 20.09 MB\n",
      "2024/08/05 11:30:46  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:46 Finished reordering the columns.\n",
      "2024/08/05 11:30:46 Start filtering values by condition: CHR==10.0 & POS>10382501.0 & POS<11382501.0\n",
      "2024/08/05 11:30:46  -Removing 5711222 variants not meeting the conditions: CHR==10.0 & POS>10382501.0 & POS<11382501.0\n",
      "2024/08/05 11:30:46 Finished filtering values.\n",
      "2024/08/05 11:30:46 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:46  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:46  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:46  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:46  -Overwrite mode:  False\n",
      "2024/08/05 11:30:46   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:46  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:46 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:46 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:46  -Current Dataframe shape : 2247 x 11 ; Memory usage: 20.10 MB\n",
      "2024/08/05 11:30:46  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:46 Finished reordering the columns.\n",
      "2024/08/05 11:30:47 Start filtering values by condition: CHR==11.0 & POS>112888674.0 & POS<113888674.0\n",
      "2024/08/05 11:30:47  -Removing 5711509 variants not meeting the conditions: CHR==11.0 & POS>112888674.0 & POS<113888674.0\n",
      "2024/08/05 11:30:47 Finished filtering values.\n",
      "2024/08/05 11:30:47 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:47  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:47  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:47  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:47  -Overwrite mode:  False\n",
      "2024/08/05 11:30:47   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:47  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:47 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:47 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:47  -Current Dataframe shape : 1960 x 11 ; Memory usage: 20.08 MB\n",
      "2024/08/05 11:30:47  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:47 Finished reordering the columns.\n",
      "2024/08/05 11:30:47 Start filtering values by condition: CHR==11.0 & POS>88216140.0 & POS<89216140.0\n",
      "2024/08/05 11:30:47  -Removing 5711766 variants not meeting the conditions: CHR==11.0 & POS>88216140.0 & POS<89216140.0\n",
      "2024/08/05 11:30:47 Finished filtering values.\n",
      "2024/08/05 11:30:48 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:48  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:48  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:48  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:48  -Overwrite mode:  False\n",
      "2024/08/05 11:30:48   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:48  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:48 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:48 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:48  -Current Dataframe shape : 1703 x 11 ; Memory usage: 20.06 MB\n",
      "2024/08/05 11:30:48  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:48 Finished reordering the columns.\n",
      "2024/08/05 11:30:48 Start filtering values by condition: CHR==11.0 & POS>32173019.0 & POS<33173019.0\n",
      "2024/08/05 11:30:48  -Removing 5711096 variants not meeting the conditions: CHR==11.0 & POS>32173019.0 & POS<33173019.0\n",
      "2024/08/05 11:30:48 Finished filtering values.\n",
      "2024/08/05 11:30:48 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:48  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:48  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:48  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:48  -Overwrite mode:  False\n",
      "2024/08/05 11:30:48   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:48  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:48 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:48 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:48  -Current Dataframe shape : 2373 x 11 ; Memory usage: 20.11 MB\n",
      "2024/08/05 11:30:48  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:48 Finished reordering the columns.\n",
      "2024/08/05 11:30:49 Start filtering values by condition: CHR==14.0 & POS>41574524.0 & POS<42574524.0\n",
      "2024/08/05 11:30:49  -Removing 5711553 variants not meeting the conditions: CHR==14.0 & POS>41574524.0 & POS<42574524.0\n",
      "2024/08/05 11:30:49 Finished filtering values.\n",
      "2024/08/05 11:30:49 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:49  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:49  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:49  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:49  -Overwrite mode:  False\n",
      "2024/08/05 11:30:49   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:49  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:49 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:49 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:49  -Current Dataframe shape : 1916 x 11 ; Memory usage: 20.08 MB\n",
      "2024/08/05 11:30:49  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:49 Finished reordering the columns.\n",
      "2024/08/05 11:30:49 Start filtering values by condition: CHR==14.0 & POS>68996425.0 & POS<69996425.0\n",
      "2024/08/05 11:30:49  -Removing 5711169 variants not meeting the conditions: CHR==14.0 & POS>68996425.0 & POS<69996425.0\n",
      "2024/08/05 11:30:49 Finished filtering values.\n",
      "2024/08/05 11:30:49 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:49  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:49  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:49  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:49  -Overwrite mode:  False\n",
      "2024/08/05 11:30:49   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:49  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:49 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:49 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:49  -Current Dataframe shape : 2300 x 11 ; Memory usage: 20.11 MB\n",
      "2024/08/05 11:30:49  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:49 Finished reordering the columns.\n",
      "2024/08/05 11:30:50 Start filtering values by condition: CHR==15.0 & POS>73510430.0 & POS<74510430.0\n",
      "2024/08/05 11:30:50  -Removing 5711939 variants not meeting the conditions: CHR==15.0 & POS>73510430.0 & POS<74510430.0\n",
      "2024/08/05 11:30:50 Finished filtering values.\n",
      "2024/08/05 11:30:50 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:50  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:50  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:50  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:50  -Overwrite mode:  False\n",
      "2024/08/05 11:30:50   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:50  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:50 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:50 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:50  -Current Dataframe shape : 1530 x 11 ; Memory usage: 20.05 MB\n",
      "2024/08/05 11:30:50  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:50 Finished reordering the columns.\n",
      "2024/08/05 11:30:50 Start filtering values by condition: CHR==16.0 & POS>21114009.0 & POS<22114009.0\n",
      "2024/08/05 11:30:50  -Removing 5712807 variants not meeting the conditions: CHR==16.0 & POS>21114009.0 & POS<22114009.0\n",
      "2024/08/05 11:30:50 Finished filtering values.\n",
      "2024/08/05 11:30:50 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:50  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:50  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:50  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:50  -Overwrite mode:  False\n",
      "2024/08/05 11:30:50   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:50  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:50 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:50 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:50  -Current Dataframe shape : 662 x 11 ; Memory usage: 19.99 MB\n",
      "2024/08/05 11:30:50  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:50 Finished reordering the columns.\n",
      "2024/08/05 11:30:51 Start filtering values by condition: CHR==17.0 & POS>37719005.0 & POS<38719005.0\n",
      "2024/08/05 11:30:51  -Removing 5712352 variants not meeting the conditions: CHR==17.0 & POS>37719005.0 & POS<38719005.0\n",
      "2024/08/05 11:30:51 Finished filtering values.\n",
      "2024/08/05 11:30:51 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:51  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:51  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:51  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:51  -Overwrite mode:  False\n",
      "2024/08/05 11:30:51   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:51  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:51 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:51 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:51  -Current Dataframe shape : 1117 x 11 ; Memory usage: 20.02 MB\n",
      "2024/08/05 11:30:51  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:51 Finished reordering the columns.\n",
      "2024/08/05 11:30:51 Start filtering values by condition: CHR==18.0 & POS>50357663.0 & POS<51357663.0\n",
      "2024/08/05 11:30:51  -Removing 5710882 variants not meeting the conditions: CHR==18.0 & POS>50357663.0 & POS<51357663.0\n",
      "2024/08/05 11:30:51 Finished filtering values.\n",
      "2024/08/05 11:30:52 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:52  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:52  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:52  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:52  -Overwrite mode:  False\n",
      "2024/08/05 11:30:52   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:52  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:52 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:52 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:52  -Current Dataframe shape : 2587 x 11 ; Memory usage: 20.13 MB\n",
      "2024/08/05 11:30:52  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:52 Finished reordering the columns.\n",
      "2024/08/05 11:30:52 Start filtering values by condition: CHR==18.0 & POS>77063334.0 & POS<78063334.0\n",
      "2024/08/05 11:30:52  -Removing 5710690 variants not meeting the conditions: CHR==18.0 & POS>77063334.0 & POS<78063334.0\n",
      "2024/08/05 11:30:52 Finished filtering values.\n",
      "2024/08/05 11:30:52 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:52  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:52  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:52  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:52  -Overwrite mode:  False\n",
      "2024/08/05 11:30:52   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:52  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:52 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:52 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:52  -Current Dataframe shape : 2779 x 11 ; Memory usage: 20.14 MB\n",
      "2024/08/05 11:30:52  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:52 Finished reordering the columns.\n",
      "2024/08/05 11:30:53 Start filtering values by condition: CHR==18.0 & POS>52664693.0 & POS<53664693.0\n",
      "2024/08/05 11:30:53  -Removing 5712008 variants not meeting the conditions: CHR==18.0 & POS>52664693.0 & POS<53664693.0\n",
      "2024/08/05 11:30:53 Finished filtering values.\n",
      "2024/08/05 11:30:53 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:53  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:53  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:53  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:53  -Overwrite mode:  False\n",
      "2024/08/05 11:30:53   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:53  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:53 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:53 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:53  -Current Dataframe shape : 1461 x 11 ; Memory usage: 20.05 MB\n",
      "2024/08/05 11:30:53  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:53 Finished reordering the columns.\n",
      "2024/08/05 11:30:53 Start filtering values by condition: CHR==20.0 & POS>44188665.0 & POS<45188665.0\n",
      "2024/08/05 11:30:53  -Removing 5711656 variants not meeting the conditions: CHR==20.0 & POS>44188665.0 & POS<45188665.0\n",
      "2024/08/05 11:30:53 Finished filtering values.\n",
      "2024/08/05 11:30:53 Start filling data using existing columns...v3.4.47\n",
      "2024/08/05 11:30:53  -Column  : SNPID  rsID   CHR   POS   EA       NEA      EAF     BETA    SE      P       STATUS  \n",
      "2024/08/05 11:30:53  -DType   : string string Int64 Int64 category category float32 float64 float64 float64 category\n",
      "2024/08/05 11:30:53  -Verified: T      T      T     T     T        T        T       T       T       T       T       \n",
      "2024/08/05 11:30:53  -Overwrite mode:  False\n",
      "2024/08/05 11:30:53   -Skipping columns:  ['BETA']\n",
      "2024/08/05 11:30:53  -No available columns to fill. Skipping.\n",
      "2024/08/05 11:30:53 Finished filling data using existing columns.\n",
      "2024/08/05 11:30:53 Start to reorder the columns...v3.4.47\n",
      "2024/08/05 11:30:53  -Current Dataframe shape : 1813 x 11 ; Memory usage: 20.07 MB\n",
      "2024/08/05 11:30:53  -Reordering columns to    : SNPID,rsID,CHR,POS,EA,NEA,EAF,BETA,SE,P,STATUS\n",
      "2024/08/05 11:30:53 Finished reordering the columns.\n"
     ]
    }
   ],
   "source": [
    "loci = fm.get_loci(\"/local/home/ssigfstead/GWAS/filtered_snps_gwas_1_v2/filtered_snps_gwas_1_threshold=0.5.csv\", chr_col=\"chromosome\", pos_col=\"pos\", rsid_col=\"snp\")\n",
    "fm.output_loci_files(sumstats, loci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "print(\"Calculating LD and fine-mapping loci\")\n",
    "for locus in tqdm(loci):\n",
    "    fm.calculate_LD(locus)\n",
    "    fm.fine_map_locus(locus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "loci_orig = fm.get_loci(\"/local/home/ssigfstead/GWAS/filtered_snps_threshold=0.0.csv\", chr_col=\"chr\", pos_col=\"pos\", rsid_col=\"snp\")\n",
    "loci_orig_mapped = {}\n",
    "\n",
    "for locus in loci_orig:\n",
    "    loci_orig_mapped[locus[3]] = pd.read_csv(f\"/local/home/ssigfstead/GWAS/fine_mapping_results/locus_{locus[3]}_{locus[4]}_mapped.tsv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of loci mapped: 6\n",
      "Number of loci: 29\n"
     ]
    }
   ],
   "source": [
    "n_orig_mapped = 0\n",
    "mapped_orig_loci = []\n",
    "for rsid, locus in loci_orig_mapped.items():\n",
    "    if rsid in locus[locus[\"cs\"]>0][\"rsID\"].values:\n",
    "        n_orig_mapped += 1\n",
    "        mapped_orig_loci.append(rsid)\n",
    "\n",
    "print(f\"Number of loci mapped: {n_orig_mapped}\")\n",
    "print(f\"Number of loci: {len(loci_orig_mapped)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overlap = set(loci_mapped.keys()).intersection(set(loci_orig_mapped.keys()))\n",
    "len(overlap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sads = pd.read_csv(\"/local/home/ssigfstead/1000_genomes_csv_tsv_match/combined_result.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sads = pd.read_csv(\"/local/home/ssigfstead/1000genomes_as_csv/1000G.MAF_threshold=0.005.10_combined.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "locus_mapped_chosen = loci_mapped['rs6602453']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "locus_mapped_chosen = locus_mapped_chosen.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sads = sads.drop_duplicates(subset=[\"snp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "joined = pd.merge(locus_mapped_chosen, sads, left_on=\"rsID\", right_on=\"snp\", how='inner', validate='one_to_one')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "locus_with_sads = joined[['rsID', 'cs', 'pip', 'P', 'BETA', 'SE', 'SAD1', 'SAD9']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "locus_with_sads = locus_with_sads[locus_with_sads['cs'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "\n",
    "# Replace 'x_column' and 'y_column' with the actual column names in your dataframe\n",
    "x_column = 'pip'\n",
    "y_column = 'SAD9'\n",
    "y_column2 = 'SAD1'\n",
    "\n",
    "maxed = maxed = [max(abs(x[0]), abs(x[1])) for x in zip(locus_with_sads[y_column], locus_with_sads[y_column2])]\n",
    "\n",
    "# Calculate the correlation coefficient\n",
    "correlation, _ = pearsonr(locus_with_sads[x_column], maxed)#locus_with_sads[y_column].abs() + locus_with_sads[y_column2].abs())\n",
    "\n",
    "# Create the scatter plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(locus_with_sads[x_column], maxed)#locus_with_sads[y_column].abs() + locus_with_sads[y_column2].abs(), alpha=0.7)\n",
    "plt.title(f'Scatter Plot of {x_column} vs max of SAD scores\\nCorrelation: {correlation:.2f}')\n",
    "plt.xlabel(x_column)\n",
    "plt.ylabel(\"max of SAD scores\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(loci_mapped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "801 15.403846153846153\n"
     ]
    }
   ],
   "source": [
    "pst = 0\n",
    "for id_snp in loci_mapped:\n",
    "    locus = loci_mapped[id_snp]\n",
    "    locus = locus[locus['cs'] > 0]\n",
    "    pst += len(locus)\n",
    "print(pst, pst/len(loci_mapped))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "395 13.620689655172415\n"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "pri = 0\n",
    "for id_snp in loci_orig_mapped:\n",
    "    locus = loci_orig_mapped[id_snp]\n",
    "    locus = locus[locus['cs'] > 0]\n",
    "    pri += len(locus)\n",
    "print(pri, pri/len(loci_orig_mapped))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import susie_fine_mapping_utils as fm\n",
    "\n",
    "loci = fm.get_loci(\"/local/home/ssigfstead/GWAS/filtered_snps_threshold=0.0.csv\", chr_col=\"chr\", pos_col=\"pos\", rsid_col=\"snp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.0, 61018394.0, 62018394.0, 'rs3872822', 500000.0)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loci[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import paintor_utils as pu\n",
    "\n",
    "for locus in loci:\n",
    "    pu.reformat_locus(locus[3], locus[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(loci[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11.0, 88189953.0, 89189953.0, 'rs674437', 500000.0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loci[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import subprocess\n",
    "import susie_fine_mapping_utils as fm\n",
    "import os\n",
    "\n",
    "loci = fm.get_loci(\"/local/home/ssigfstead/GWAS/filtered_snps_threshold=0.0.csv\", chr_col=\"chr\", pos_col=\"pos\", rsid_col=\"snp\")\n",
    "\n",
    "for locus in loci:\n",
    "    subprocess.run(\n",
    "        [\n",
    "            \"python\",\n",
    "            pu.LD_SCRIPT,\n",
    "            \"--locus\",\n",
    "            f\"/local/home/ssigfstead/GWAS/PAINTOR_inputs/locus_{locus[3]}_{locus[4]}\",\n",
    "            \"--reference\",\n",
    "            f\"/local/home/ssigfstead/GWAS/1KG_genotypes/ALL.chr{int(locus[0])}.phase3_shapeit2_mvncall_integrated_v5b.20130502.genotypes.vcf.gz\",\n",
    "            \"--map\",\n",
    "            \"/local/home/ssigfstead/GWAS/1KG_genotypes/integrated_call_samples_v3.20130502.ALL.panel\",\n",
    "            \"--effect_allele\",\n",
    "            \"EA\",\n",
    "            \"--alt_allele\",\n",
    "            \"NEA\",\n",
    "            \"--population\",\n",
    "            \"EUR\",\n",
    "            \"--Zhead\",\n",
    "            \"ZSCORE\",\n",
    "            \"--out_name\",\n",
    "            f\"/local/home/ssigfstead/GWAS/PAINTOR_inputs/locus_{locus[3]}_{locus[4]}\",\n",
    "            \"--position\",\n",
    "            \"POS\"\n",
    "        ],\n",
    "        text=True,\n",
    "    )\n",
    "    os.rename(f\"/local/home/ssigfstead/GWAS/PAINTOR_inputs/locus_{locus[3]}_{locus[4]}.ld\", f\"/local/home/ssigfstead/GWAS/PAINTOR_inputs/locus_{locus[3]}_{locus[4]}.processed.ld\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/FANTOM5/brain_differentially_expressed_enhancers.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.CDS.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.exon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.gene.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.Selenocysteine.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.start_codon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.stop_codon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.transcript.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.UTR.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS11872.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS11877.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14464.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14717.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14718.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14803.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14815.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS15453.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS16302.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20221.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20226.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20231.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/TFBS/All.TFBS.bed\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/FANTOM5/brain_differentially_expressed_enhancers.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.CDS.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.exon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.gene.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.Selenocysteine.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.start_codon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.stop_codon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.transcript.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.UTR.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS11872.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS11877.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14464.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14717.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14718.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14803.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14815.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS15453.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS16302.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20221.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20226.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20231.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/TFBS/All.TFBS.bed\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/FANTOM5/brain_differentially_expressed_enhancers.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.CDS.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.exon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.gene.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.Selenocysteine.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.start_codon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.stop_codon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.transcript.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.UTR.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS11872.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS11877.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14464.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14717.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14718.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14803.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14815.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS15453.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS16302.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20221.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20226.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20231.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/TFBS/All.TFBS.bed\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/FANTOM5/brain_differentially_expressed_enhancers.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.CDS.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.exon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.gene.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.Selenocysteine.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.start_codon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.stop_codon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.transcript.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.UTR.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS11872.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS11877.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14464.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14717.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14718.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14803.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14815.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS15453.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS16302.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20221.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
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      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14803.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14815.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS15453.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS16302.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20221.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20226.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20231.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/TFBS/All.TFBS.bed\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/FANTOM5/brain_differentially_expressed_enhancers.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.CDS.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.exon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.gene.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.Selenocysteine.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.start_codon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.stop_codon.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.transcript.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/GeneElements_Gencode/GenCode.UTR.hg19\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS11872.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS11877.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14464.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14717.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14718.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14803.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS14815.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS15453.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS16302.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20221.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20226.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/Maurano_Science2012_DHS/fBrain-DS20231.hg19.hotspot.twopass.fdr0.05.merge.bed\n",
      "\n",
      "/local/home/ssigfstead/GWAS/PAINTOR_annotations/Functional_Annotations/TFBS/All.TFBS.bed\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "import susie_fine_mapping_utils as fm\n",
    "import os\n",
    "import paintor_utils as pu\n",
    "\n",
    "loci = fm.get_loci(\"/local/home/ssigfstead/GWAS/filtered_snps_threshold=0.0.csv\", chr_col=\"chr\", pos_col=\"pos\", rsid_col=\"snp\")\n",
    "\n",
    "for locus in loci:\n",
    "    subprocess.run(\n",
    "        [\n",
    "            \"python\",\n",
    "            pu.OVERLAP_SCRIPT,\n",
    "            \"--input\",\n",
    "            \"/local/home/ssigfstead/GWAS/PAINTOR_annotations/mdd_annotations\",\n",
    "            \"--locus\",\n",
    "            f\"/local/home/ssigfstead/GWAS/PAINTOR_inputs/locus_{locus[3]}_{locus[4]}.processed\",\n",
    "            \"--out\",\n",
    "            f\"/local/home/ssigfstead/GWAS/PAINTOR_inputs/locus_{locus[3]}_{locus[4]}.processed.annotations\",\n",
    "            \"--chr\",\n",
    "            \"CHR\",\n",
    "            \"--pos\",\n",
    "            \"POS\",\n",
    "        ],\n",
    "        text=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running PAINTOR in MCMC mode\n",
      "Number of chains: 5\n",
      "Samples per chain: 50000\n",
      "Burn in: 5000\n",
      "Proportion of LD variance kept when performing truncated SVD for estimating N*h2g: 0.95\n",
      "Model annotations: brain_differentially_expressed_enhancers.bed GenCode.CDS.hg19 GenCode.exon.hg19 GenCode.gene.hg19 GenCode.Selenocysteine.hg19 GenCode.start_codon.hg19 GenCode.stop_codon.hg19 \n",
      "**********\n",
      "Reading in files for: locus_rs3872822_500000.0.processed\n",
      "Reading in files for: locus_rs200949_500000.0.processed\n",
      "Reading in files for: locus_rs4404022_500000.0.processed\n",
      "Reading in files for: locus_rs4801157_500000.0.processed\n",
      "Reading in files for: locus_rs138218528_500000.0.processed\n",
      "Reading in files for: locus_rs7502539_500000.0.processed\n",
      "Reading in files for: locus_rs215852_500000.0.processed\n",
      "Reading in files for: locus_rs10752206_500000.0.processed\n",
      "Reading in files for: locus_rs10765821_500000.0.processed\n",
      "Reading in files for: locus_rs9262120_500000.0.processed\n",
      "Reading in files for: locus_rs10512249_500000.0.processed\n",
      "Reading in files for: locus_rs10789340_500000.0.processed\n",
      "Reading in files for: locus_rs71367544_500000.0.processed\n",
      "Reading in files for: locus_rs1950834_500000.0.processed\n",
      "Reading in files for: locus_rs13174273_500000.0.processed\n",
      "Reading in files for: locus_rs537160_500000.0.processed\n",
      "Reading in files for: locus_rs12377572_500000.0.processed\n",
      "Reading in files for: locus_rs3896224_500000.0.processed\n",
      "Reading in files for: locus_rs30266_500000.0.processed\n",
      "Reading in files for: locus_rs75329615_500000.0.processed\n",
      "Reading in files for: locus_rs111333124_500000.0.processed\n",
      "Reading in files for: locus_rs4245154_500000.0.processed\n",
      "Reading in files for: locus_rs10818400_500000.0.processed\n",
      "Reading in files for: locus_rs35538328_500000.0.processed\n",
      "Reading in files for: locus_rs3800847_500000.0.processed\n",
      "Reading in files for: locus_rs1504746_500000.0.processed\n",
      "Reading in files for: locus_rs3863241_500000.0.processed\n",
      "Reading in files for: locus_rs17508548_500000.0.processed\n",
      "Reading in files for: locus_rs2369818_500000.0.processed\n",
      "**********\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enrichment estimates at iteration 1 is :\n",
      "7.71824\n",
      "      0\n",
      "      0\n",
      "      0\n",
      "      0\n",
      "      0\n",
      "      0\n",
      "      0\n",
      "Average log bayes factor: 569.88\n",
      "\n",
      "Enrichment estimates at iteration 2 is :\n",
      " 6.71815\n",
      " 17.7089\n",
      "0.504301\n",
      "-0.39801\n",
      "0.137108\n",
      "       0\n",
      " 13.5314\n",
      " 12.9058\n",
      "Average log bayes factor: 543.922\n",
      "\n",
      "Enrichment estimates at iteration 3 is :\n",
      " 6.34729\n",
      " 17.7089\n",
      " 1.12673\n",
      "-0.57141\n",
      "0.102345\n",
      "       0\n",
      " 13.5314\n",
      " 12.9058\n",
      "Average log bayes factor: 489.52\n",
      "\n",
      "Enrichment estimates at iteration 4 is :\n",
      "  6.14029\n",
      "  17.7089\n",
      "  1.39361\n",
      "-0.393298\n",
      "0.0329609\n",
      "        0\n",
      "  13.5314\n",
      "  12.9058\n",
      "Average log bayes factor: 440.727\n",
      "\n",
      "Enrichment estimates at iteration 5 is :\n",
      "  5.98393\n",
      "  17.7089\n",
      "  1.09668\n",
      "-0.238965\n",
      "0.0103021\n",
      "        0\n",
      "  13.5314\n",
      "  12.9058\n",
      "Average log bayes factor: 381.545\n",
      "\n",
      "Enrichment estimates at iteration 6 is :\n",
      "    5.85995\n",
      "    17.7089\n",
      "    1.14862\n",
      "-0.00216829\n",
      " -0.0468079\n",
      "          0\n",
      "    13.5314\n",
      "    12.9058\n",
      "Average log bayes factor: 376.266\n",
      "\n",
      "Enrichment estimates at iteration 7 is :\n",
      "  5.81158\n",
      "  17.7089\n",
      "  1.26747\n",
      " 0.085626\n",
      "-0.021479\n",
      "        0\n",
      "  13.5314\n",
      "  12.9058\n",
      "Average log bayes factor: 342.642\n",
      "\n",
      "Enrichment estimates at iteration 8 is :\n",
      "  5.78775\n",
      "  17.7089\n",
      "  2.29245\n",
      "0.0733675\n",
      "-0.091967\n",
      "        0\n",
      "  13.5314\n",
      "  12.9058\n",
      "Average log bayes factor: 306.343\n",
      "\n",
      "Enrichment estimates at iteration 9 is :\n",
      "    5.6741\n",
      "   17.7089\n",
      "   2.19094\n",
      "   0.16159\n",
      "-0.0880025\n",
      "         0\n",
      "   13.5314\n",
      "   12.9058\n",
      "Average log bayes factor: 285.184\n",
      "\n",
      "Enrichment estimates at iteration 10 is :\n",
      "   5.6126\n",
      "  17.7089\n",
      "  1.53582\n",
      " 0.213656\n",
      "-0.114056\n",
      "        0\n",
      "  13.5314\n",
      "  12.9058\n",
      "Average log bayes factor: 241.877\n",
      "\n",
      "Enrichment estimates at iteration 11 is :\n",
      "  5.55101\n",
      "  17.7089\n",
      "  1.60774\n",
      "0.0739441\n",
      "-0.075373\n",
      "        0\n",
      "  13.5314\n",
      "  12.9058\n",
      "Average log bayes factor: 213.589\n",
      "\n",
      "Enrichment estimates at iteration 12 is :\n",
      "   5.44624\n",
      "   17.7089\n",
      "   1.21946\n",
      "  0.274334\n",
      "-0.0624491\n",
      "         0\n",
      "   13.5314\n",
      "   12.9058\n",
      "Average log bayes factor: 161.275\n",
      "\n",
      "Enrichment estimates at iteration 13 is :\n",
      "   5.43333\n",
      "   17.7089\n",
      "  0.777316\n",
      "  0.412017\n",
      "-0.0600776\n",
      "         0\n",
      "   13.5314\n",
      "   12.9058\n",
      "Average log bayes factor: 103.368\n",
      "\n",
      "Enrichment estimates at iteration 14 is :\n",
      "   5.43911\n",
      "   17.7089\n",
      "   1.24718\n",
      "  0.300851\n",
      "-0.0988885\n",
      "         0\n",
      "   13.5314\n",
      "   12.9058\n",
      "Average log bayes factor: 97.0858\n",
      "\n",
      "Enrichment estimates at iteration 15 is :\n",
      "  5.41075\n",
      "  17.7089\n",
      "  1.99319\n",
      " 0.392349\n",
      "-0.138235\n",
      "        0\n",
      "  13.5314\n",
      "  12.9058\n",
      "Average log bayes factor: 57.5929\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CompletedProcess(args=['/local/home/ssigfstead/tools/PAINTOR_V3.0/PAINTOR', '-in', '/local/home/ssigfstead/GWAS/PAINTOR_inputs/', '-input', '/local/home/ssigfstead/GWAS/PAINTOR_inputs/loci.txt', '-Zhead', 'ZSCORE', '-LDname', 'ld', '-annotations', 'brain_differentially_expressed_enhancers.bed,GenCode.CDS.hg19,GenCode.exon.hg19,GenCode.gene.hg19,GenCode.Selenocysteine.hg19,GenCode.start_codon.hg19,GenCode.stop_codon.hg19', 'GenCode.transcript.hg19,GenCode.UTR.hg19,fBrain-DS11872.hotspot.twopass.fdr0.05.merge.bed,fBrain-DS11877.hotspot.twopass.fdr0.05.merge.bed,fBrain-DS14464.hotspot.twopass.fdr0.05.merge.bed,fBrain-DS14717.hotspot.twopass.fdr0.05.merge.bed,fBrain-DS14718.hotspot.twopass.fdr0.05.merge.bed,fBrain-DS14803.hotspot.twopass.fdr0.05.merge.bed,fBrain-DS14815.hotspot.twopass.fdr0.05.merge.bed,fBrain-DS15453.hotspot.twopass.fdr0.05.merge.bed,fBrain-DS16302.hotspot.twopass.fdr0.05.merge.bed,fBrain-DS20221.hg19.hotspot.twopass.fdr0.05.merge.bed,fBrain-DS20226.hg19.hotspot.twopass.fdr0.05.merge.bed,fBrain-DS20231.hg19.hotspot.twopass.fdr0.05.merge.bed,All.TFBS.bed', '-out', '/local/home/ssigfstead/GWAS/PAINTOR_outputs', '-mcmc'], returncode=0)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import subprocess\n",
    "import susie_fine_mapping_utils as fm\n",
    "import os\n",
    "import paintor_utils as pu\n",
    "\n",
    "loci = fm.get_loci(\"/local/home/ssigfstead/GWAS/filtered_snps_threshold=0.0.csv\", chr_col=\"chr\", pos_col=\"pos\", rsid_col=\"snp\")\n",
    "\n",
    "with open(\"/local/home/ssigfstead/GWAS/PAINTOR_inputs/loci.txt\", \"w\") as f:\n",
    "    for locus in loci:\n",
    "        f.write(f\"locus_{locus[3]}_{locus[4]}.processed\\n\")\n",
    "\n",
    "subprocess.run(\n",
    "    [\n",
    "        pu.PAINTOR_SCRIPT,\n",
    "        \"-in\",\n",
    "        \"/local/home/ssigfstead/GWAS/PAINTOR_inputs/\",\n",
    "        \"-input\",\n",
    "        \"/local/home/ssigfstead/GWAS/PAINTOR_inputs/loci.txt\",\n",
    "        \"-Zhead\",\n",
    "        \"ZSCORE\",\n",
    "        \"-LDname\",\n",
    "        \"ld\",\n",
    "        \"-annotations\",\n",
    "        \"brain_differentially_expressed_enhancers.bed,\"\n",
    "        \"GenCode.CDS.hg19,\"\n",
    "        \"GenCode.exon.hg19,\"\n",
    "        \"GenCode.gene.hg19,\"\n",
    "        \"GenCode.Selenocysteine.hg19,\"\n",
    "        \"GenCode.start_codon.hg19,\"\n",
    "        \"GenCode.stop_codon.hg19\",\n",
    "        \"GenCode.transcript.hg19,\"\n",
    "        \"GenCode.UTR.hg19,\"\n",
    "        \"fBrain-DS11872.hotspot.twopass.fdr0.05.merge.bed,\"\n",
    "        \"fBrain-DS11877.hotspot.twopass.fdr0.05.merge.bed,\"\n",
    "        \"fBrain-DS14464.hotspot.twopass.fdr0.05.merge.bed,\"\n",
    "        \"fBrain-DS14717.hotspot.twopass.fdr0.05.merge.bed,\"\n",
    "        \"fBrain-DS14718.hotspot.twopass.fdr0.05.merge.bed,\"\n",
    "        \"fBrain-DS14803.hotspot.twopass.fdr0.05.merge.bed,\"\n",
    "        \"fBrain-DS14815.hotspot.twopass.fdr0.05.merge.bed,\"\n",
    "        \"fBrain-DS15453.hotspot.twopass.fdr0.05.merge.bed,\"\n",
    "        \"fBrain-DS16302.hotspot.twopass.fdr0.05.merge.bed,\"\n",
    "        \"fBrain-DS20221.hg19.hotspot.twopass.fdr0.05.merge.bed,\"\n",
    "        \"fBrain-DS20226.hg19.hotspot.twopass.fdr0.05.merge.bed,\"\n",
    "        \"fBrain-DS20231.hg19.hotspot.twopass.fdr0.05.merge.bed,\"\n",
    "        \"All.TFBS.bed\",\n",
    "        \"-out\",\n",
    "        \"/local/home/ssigfstead/GWAS/PAINTOR_outputs\",\n",
    "        \"-mcmc\",\n",
    "    ],\n",
    "    text=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rpy2.situation:cffi mode is CFFI_MODE.ANY\n",
      "INFO:rpy2.situation:R home found: /usr/lib/R\n",
      "INFO:rpy2.situation:R library path: /usr/lib/R/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/default-java/lib/server\n",
      "INFO:rpy2.situation:LD_LIBRARY_PATH: /usr/lib/R/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/default-java/lib/server\n",
      "INFO:rpy2.rinterface_lib.embedded:Default options to initialize R: rpy2, --quiet, --no-save\n",
      "INFO:rpy2.rinterface_lib.embedded:R is already initialized. No need to initialize.\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "import susie_fine_mapping_utils as fm\n",
    "import os\n",
    "import paintor_utils as pu\n",
    "import pandas as pd\n",
    "\n",
    "loci = fm.get_loci(\"/local/home/ssigfstead/GWAS/filtered_snps_threshold=0.5.csv\", chr_col=\"chr\", pos_col=\"pos\", rsid_col=\"snp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "locus = loci[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sads = pd.read_csv(\"/local/home/ssigfstead/GWAS/1000_genomes_csv_tsv_match/combined_result_sorted.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sads = sads.drop_duplicates(subset=[\"snp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df = pd.read_csv(f\"/local/home/ssigfstead/GWAS/PAINTOR_outputs/locus_{locus[3]}_{locus[4]}.processed.results\", sep=\" \")\n",
    "sads_df = pd.merge(results_df, sads, left_on=\"rsID\", right_on=\"snp\", how='inner', validate='one_to_one', )\n",
    "#calculate 95% credible set\n",
    "# results_df = results_df.sort_values(\"Posterior_Prob\", ascending=False)\n",
    "# results_df[\"cumsum\"] = results_df[\"Posterior_Prob\"].cumsum()\n",
    "# credible_set = results_df[results_df[\"cumsum\"] <= 0.95]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>SNPID</th>\n",
       "      <th>rsID</th>\n",
       "      <th>CHR</th>\n",
       "      <th>POS</th>\n",
       "      <th>EA</th>\n",
       "      <th>NEA</th>\n",
       "      <th>EAF</th>\n",
       "      <th>BETA</th>\n",
       "      <th>SE</th>\n",
       "      <th>P</th>\n",
       "      <th>...</th>\n",
       "      <th>pos</th>\n",
       "      <th>ref</th>\n",
       "      <th>snp</th>\n",
       "      <th>beta</th>\n",
       "      <th>standard_error</th>\n",
       "      <th>effect_allele_frequency</th>\n",
       "      <th>p_value</th>\n",
       "      <th>average_SAD</th>\n",
       "      <th>abs_average_SAD</th>\n",
       "      <th>ranking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7:117036505:T:C</td>\n",
       "      <td>rs77831922</td>\n",
       "      <td>7</td>\n",
       "      <td>117036505</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>0.893932</td>\n",
       "      <td>0.008478</td>\n",
       "      <td>0.004519</td>\n",
       "      <td>0.060659</td>\n",
       "      <td>...</td>\n",
       "      <td>117036505.0</td>\n",
       "      <td>T</td>\n",
       "      <td>rs77831922</td>\n",
       "      <td>0.008478</td>\n",
       "      <td>0.004519</td>\n",
       "      <td>0.893932</td>\n",
       "      <td>0.060659</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>21892807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7:117040117:A:G</td>\n",
       "      <td>rs2188554</td>\n",
       "      <td>7</td>\n",
       "      <td>117040117</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0.888101</td>\n",
       "      <td>-0.001726</td>\n",
       "      <td>0.003191</td>\n",
       "      <td>0.588729</td>\n",
       "      <td>...</td>\n",
       "      <td>117040117.0</td>\n",
       "      <td>A</td>\n",
       "      <td>rs2188554</td>\n",
       "      <td>-0.001726</td>\n",
       "      <td>0.003191</td>\n",
       "      <td>0.888101</td>\n",
       "      <td>0.588729</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>3977358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7:117041069:C:T</td>\n",
       "      <td>rs7805063</td>\n",
       "      <td>7</td>\n",
       "      <td>117041069</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.891128</td>\n",
       "      <td>0.002302</td>\n",
       "      <td>0.002543</td>\n",
       "      <td>0.365423</td>\n",
       "      <td>...</td>\n",
       "      <td>117041069.0</td>\n",
       "      <td>C</td>\n",
       "      <td>rs7805063</td>\n",
       "      <td>0.002302</td>\n",
       "      <td>0.002543</td>\n",
       "      <td>0.891128</td>\n",
       "      <td>0.365423</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>24490846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7:117041941:G:A</td>\n",
       "      <td>rs73480763</td>\n",
       "      <td>7</td>\n",
       "      <td>117041941</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.890759</td>\n",
       "      <td>0.003134</td>\n",
       "      <td>0.004508</td>\n",
       "      <td>0.486988</td>\n",
       "      <td>...</td>\n",
       "      <td>117041941.0</td>\n",
       "      <td>G</td>\n",
       "      <td>rs73480763</td>\n",
       "      <td>0.003134</td>\n",
       "      <td>0.004508</td>\n",
       "      <td>0.890759</td>\n",
       "      <td>0.486988</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>16596278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7:117044221:G:T</td>\n",
       "      <td>rs4730781</td>\n",
       "      <td>7</td>\n",
       "      <td>117044221</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>0.106454</td>\n",
       "      <td>0.004764</td>\n",
       "      <td>0.003649</td>\n",
       "      <td>0.191645</td>\n",
       "      <td>...</td>\n",
       "      <td>117044221.0</td>\n",
       "      <td>T</td>\n",
       "      <td>rs4730781</td>\n",
       "      <td>-0.004764</td>\n",
       "      <td>0.003649</td>\n",
       "      <td>0.893546</td>\n",
       "      <td>0.191645</td>\n",
       "      <td>-0.000036</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>7131140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1520</th>\n",
       "      <td>7:118032952:A:C</td>\n",
       "      <td>rs7809552</td>\n",
       "      <td>7</td>\n",
       "      <td>118032952</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>0.107217</td>\n",
       "      <td>0.007254</td>\n",
       "      <td>0.002489</td>\n",
       "      <td>0.003564</td>\n",
       "      <td>...</td>\n",
       "      <td>118032952.0</td>\n",
       "      <td>C</td>\n",
       "      <td>rs7809552</td>\n",
       "      <td>-0.007254</td>\n",
       "      <td>0.002489</td>\n",
       "      <td>0.892783</td>\n",
       "      <td>0.003564</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>16675970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1521</th>\n",
       "      <td>7:118033164:C:T</td>\n",
       "      <td>rs76404389</td>\n",
       "      <td>7</td>\n",
       "      <td>118033164</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>0.893809</td>\n",
       "      <td>-0.005996</td>\n",
       "      <td>0.005471</td>\n",
       "      <td>0.273077</td>\n",
       "      <td>...</td>\n",
       "      <td>118033164.0</td>\n",
       "      <td>C</td>\n",
       "      <td>rs76404389</td>\n",
       "      <td>-0.005996</td>\n",
       "      <td>0.005471</td>\n",
       "      <td>0.893809</td>\n",
       "      <td>0.273077</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>23187037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1522</th>\n",
       "      <td>7:118033367:A:G</td>\n",
       "      <td>rs112917343</td>\n",
       "      <td>7</td>\n",
       "      <td>118033367</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0.893739</td>\n",
       "      <td>-0.005956</td>\n",
       "      <td>0.005470</td>\n",
       "      <td>0.276232</td>\n",
       "      <td>...</td>\n",
       "      <td>118033367.0</td>\n",
       "      <td>A</td>\n",
       "      <td>rs112917343</td>\n",
       "      <td>-0.005956</td>\n",
       "      <td>0.005470</td>\n",
       "      <td>0.893739</td>\n",
       "      <td>0.276232</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>18059612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>7:118033790:A:G</td>\n",
       "      <td>rs6954272</td>\n",
       "      <td>7</td>\n",
       "      <td>118033790</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>0.106893</td>\n",
       "      <td>0.007368</td>\n",
       "      <td>0.002488</td>\n",
       "      <td>0.003066</td>\n",
       "      <td>...</td>\n",
       "      <td>118033790.0</td>\n",
       "      <td>G</td>\n",
       "      <td>rs6954272</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>0.002488</td>\n",
       "      <td>0.893107</td>\n",
       "      <td>0.003066</td>\n",
       "      <td>-0.000023</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>10147430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1524</th>\n",
       "      <td>7:118034281:G:A</td>\n",
       "      <td>rs6466655</td>\n",
       "      <td>7</td>\n",
       "      <td>118034281</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>0.106998</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.002489</td>\n",
       "      <td>0.003595</td>\n",
       "      <td>...</td>\n",
       "      <td>118034281.0</td>\n",
       "      <td>A</td>\n",
       "      <td>rs6466655</td>\n",
       "      <td>-0.007246</td>\n",
       "      <td>0.002489</td>\n",
       "      <td>0.893002</td>\n",
       "      <td>0.003595</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>3684802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1525 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                SNPID         rsID  CHR        POS EA NEA       EAF      BETA  \\\n",
       "0     7:117036505:T:C   rs77831922    7  117036505  C   T  0.893932  0.008478   \n",
       "1     7:117040117:A:G    rs2188554    7  117040117  G   A  0.888101 -0.001726   \n",
       "2     7:117041069:C:T    rs7805063    7  117041069  T   C  0.891128  0.002302   \n",
       "3     7:117041941:G:A   rs73480763    7  117041941  A   G  0.890759  0.003134   \n",
       "4     7:117044221:G:T    rs4730781    7  117044221  T   G  0.106454  0.004764   \n",
       "...               ...          ...  ...        ... ..  ..       ...       ...   \n",
       "1520  7:118032952:A:C    rs7809552    7  118032952  C   A  0.107217  0.007254   \n",
       "1521  7:118033164:C:T   rs76404389    7  118033164  T   C  0.893809 -0.005996   \n",
       "1522  7:118033367:A:G  rs112917343    7  118033367  G   A  0.893739 -0.005956   \n",
       "1523  7:118033790:A:G    rs6954272    7  118033790  G   A  0.106893  0.007368   \n",
       "1524  7:118034281:G:A    rs6466655    7  118034281  A   G  0.106998  0.007246   \n",
       "\n",
       "            SE         P  ...          pos  ref          snp      beta  \\\n",
       "0     0.004519  0.060659  ...  117036505.0    T   rs77831922  0.008478   \n",
       "1     0.003191  0.588729  ...  117040117.0    A    rs2188554 -0.001726   \n",
       "2     0.002543  0.365423  ...  117041069.0    C    rs7805063  0.002302   \n",
       "3     0.004508  0.486988  ...  117041941.0    G   rs73480763  0.003134   \n",
       "4     0.003649  0.191645  ...  117044221.0    T    rs4730781 -0.004764   \n",
       "...        ...       ...  ...          ...  ...          ...       ...   \n",
       "1520  0.002489  0.003564  ...  118032952.0    C    rs7809552 -0.007254   \n",
       "1521  0.005471  0.273077  ...  118033164.0    C   rs76404389 -0.005996   \n",
       "1522  0.005470  0.276232  ...  118033367.0    A  rs112917343 -0.005956   \n",
       "1523  0.002488  0.003066  ...  118033790.0    G    rs6954272 -0.007368   \n",
       "1524  0.002489  0.003595  ...  118034281.0    A    rs6466655 -0.007246   \n",
       "\n",
       "      standard_error effect_allele_frequency   p_value  average_SAD  \\\n",
       "0           0.004519                0.893932  0.060659     0.000004   \n",
       "1           0.003191                0.888101  0.588729     0.000067   \n",
       "2           0.002543                0.891128  0.365423    -0.000002   \n",
       "3           0.004508                0.890759  0.486988    -0.000010   \n",
       "4           0.003649                0.893546  0.191645    -0.000036   \n",
       "...              ...                     ...       ...          ...   \n",
       "1520        0.002489                0.892783  0.003564    -0.000009   \n",
       "1521        0.005471                0.893809  0.273077    -0.000003   \n",
       "1522        0.005470                0.893739  0.276232    -0.000008   \n",
       "1523        0.002488                0.893107  0.003066    -0.000023   \n",
       "1524        0.002489                0.893002  0.003595     0.000072   \n",
       "\n",
       "     abs_average_SAD   ranking  \n",
       "0           0.000004  21892807  \n",
       "1           0.000067   3977358  \n",
       "2           0.000002  24490846  \n",
       "3           0.000010  16596278  \n",
       "4           0.000036   7131140  \n",
       "...              ...       ...  \n",
       "1520        0.000009  16675970  \n",
       "1521        0.000003  23187037  \n",
       "1522        0.000008  18059612  \n",
       "1523        0.000023  10147430  \n",
       "1524        0.000072   3684802  \n",
       "\n",
       "[1525 rows x 27 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sads_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "mapped_locus_df = sads_df\n",
    "\n",
    "# ld = pd.read_csv(f\"/local/home/ssigfstead/GWAS/PAINTOR_inputs/locus_{locus[3]}_{locus[4]}.processed.ld\", sep=\" \", header=None)\n",
    "\n",
    "import numpy as np\n",
    "fig ,axes = plt.subplots(nrows=2,sharex=True,figsize=(15,15),height_ratios=(1,1))\n",
    "df = mapped_locus_df.copy(deep=True)\n",
    "df[\"MLOG10P\"] = -np.log10(df[\"P\"])\n",
    "col_to_plot = \"MLOG10P\"\n",
    "p=axes[0].scatter(df[\"POS\"],df[col_to_plot],c=df[\"SAD9\"].abs(), cmap=\"YlOrRd\")\n",
    "\n",
    "# axes[0].scatter(df.loc[df[\"cumsum\"] <= 0.95,\"POS\"],df.loc[df[\"cumsum\"] <= 0.95,col_to_plot],\n",
    "#         marker='o',s=40,c=\"None\",edgecolors='red',label=\"Variants in credible sets\")\n",
    "\n",
    "plt.colorbar( p , label=\"SAD9\")\n",
    "axes[0].set_xlabel(\"position\")\n",
    "axes[0].set_xlim((locus[1], locus[2]))\n",
    "axes[0].set_ylabel(col_to_plot)\n",
    "axes[0].legend()\n",
    "\n",
    "p=axes[1].scatter(df[\"POS\"],df[\"Posterior_Prob\"],c=df[\"SAD9\"].abs(), cmap=\"YlGnBu\")\n",
    "\n",
    "# axes[1].scatter(df.loc[df[\"cumsum\"] <= 0.95,\"POS\"],df.loc[df[\"cumsum\"] <= 0.95,\"Posterior_Prob\"],\n",
    "#         marker='o',s=40,c=\"None\",edgecolors='red',label=\"Variants in credible sets\")\n",
    "\n",
    "plt.colorbar( p , label=\"SAD9\")\n",
    "axes[1].set_xlabel(\"position\")\n",
    "axes[1].set_xlim((locus[1], locus[2]))\n",
    "axes[1].set_ylabel(\"PIP\")\n",
    "axes[1].legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "our_loci = fm.get_loci(\"/local/home/ssigfstead/GWAS/filtered_snps_threshold=0.5.csv\", chr_col=\"chr\", pos_col=\"pos\", rsid_col=\"snp\")\n",
    "orig_loci = fm.get_loci(\"/local/home/ssigfstead/GWAS/filtered_snps_threshold=0.0.csv\", chr_col=\"chr\", pos_col=\"pos\", rsid_col=\"snp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "locus = our_loci[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df = pd.read_csv(f\"/local/home/ssigfstead/GWAS/PAINTOR_outputs/locus_{locus[3]}_{locus[4]}.processed.results\", sep=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df = pd.read_csv(f\"/local/home/ssigfstead/GWAS/PAINTOR_outputs/locus_rs674437_500000.0.processed.results\", sep=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sads = pd.read_csv(\"/local/home/ssigfstead/GWAS/1000_genomes_csv_tsv_match/combined_result_sorted.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "MergeError",
     "evalue": "Merge keys are not unique in right dataset; not a one-to-one merge",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMergeError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[33], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m lead_probs\u001b[38;5;241m.\u001b[39mappend(results_df\u001b[38;5;241m.\u001b[39mloc[results_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mP\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39midxmin()][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPosterior_Prob\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m      3\u001b[0m all_probs\u001b[38;5;241m.\u001b[39mextend(results_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPosterior_Prob\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues)\n\u001b[0;32m----> 4\u001b[0m sads_df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmerge\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresults_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msads\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mleft_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrsID\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msnp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minner\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mone_to_one\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/gwas_lovro/lib/python3.9/site-packages/pandas/core/reshape/merge.py:170\u001b[0m, in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m    155\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _cross_merge(\n\u001b[1;32m    156\u001b[0m         left_df,\n\u001b[1;32m    157\u001b[0m         right_df,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    167\u001b[0m         copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[1;32m    168\u001b[0m     )\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     op \u001b[38;5;241m=\u001b[39m \u001b[43m_MergeOperation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[43m        \u001b[49m\u001b[43mleft_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    172\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    173\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    174\u001b[0m \u001b[43m        \u001b[49m\u001b[43mon\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mon\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    175\u001b[0m \u001b[43m        \u001b[49m\u001b[43mleft_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mleft_on\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    176\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mright_on\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    177\u001b[0m \u001b[43m        \u001b[49m\u001b[43mleft_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mleft_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    178\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mright_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    179\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    180\u001b[0m \u001b[43m        \u001b[49m\u001b[43msuffixes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msuffixes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    181\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindicator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindicator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    182\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    183\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    184\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result(copy\u001b[38;5;241m=\u001b[39mcopy)\n",
      "File \u001b[0;32m~/anaconda3/envs/gwas_lovro/lib/python3.9/site-packages/pandas/core/reshape/merge.py:813\u001b[0m, in \u001b[0;36m_MergeOperation.__init__\u001b[0;34m(self, left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, indicator, validate)\u001b[0m\n\u001b[1;32m    809\u001b[0m \u001b[38;5;66;03m# If argument passed to validate,\u001b[39;00m\n\u001b[1;32m    810\u001b[0m \u001b[38;5;66;03m# check if columns specified as unique\u001b[39;00m\n\u001b[1;32m    811\u001b[0m \u001b[38;5;66;03m# are in fact unique.\u001b[39;00m\n\u001b[1;32m    812\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m validate \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 813\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_validate_kwd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalidate\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/gwas_lovro/lib/python3.9/site-packages/pandas/core/reshape/merge.py:1657\u001b[0m, in \u001b[0;36m_MergeOperation._validate_validate_kwd\u001b[0;34m(self, validate)\u001b[0m\n\u001b[1;32m   1653\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m MergeError(\n\u001b[1;32m   1654\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMerge keys are not unique in left dataset; not a one-to-one merge\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1655\u001b[0m         )\n\u001b[1;32m   1656\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m right_unique:\n\u001b[0;32m-> 1657\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m MergeError(\n\u001b[1;32m   1658\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMerge keys are not unique in right dataset; not a one-to-one merge\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1659\u001b[0m         )\n\u001b[1;32m   1661\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m validate \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mone_to_many\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1:m\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m   1662\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m left_unique:\n",
      "\u001b[0;31mMergeError\u001b[0m: Merge keys are not unique in right dataset; not a one-to-one merge"
     ]
    }
   ],
   "source": [
    "highest_probs.append(results_df[\"Posterior_Prob\"].max())\n",
    "lead_probs.append(results_df.loc[results_df[\"P\"].idxmin()][\"Posterior_Prob\"])\n",
    "all_probs.extend(results_df[\"Posterior_Prob\"].values)\n",
    "sads_df = pd.merge(results_df, sads, left_on=\"rsID\", right_on=\"snp\", how='inner', validate='one_to_one')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/52 [00:55<?, ?it/s]\n"
     ]
    },
    {
     "ename": "MergeError",
     "evalue": "Merge keys are not unique in right dataset; not a one-to-one merge",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMergeError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 15\u001b[0m\n\u001b[1;32m     13\u001b[0m lead_probs\u001b[38;5;241m.\u001b[39mappend(results_df\u001b[38;5;241m.\u001b[39mloc[results_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mP\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39midxmin()][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPosterior_Prob\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m     14\u001b[0m all_probs\u001b[38;5;241m.\u001b[39mextend(results_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPosterior_Prob\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues)\n\u001b[0;32m---> 15\u001b[0m sads_df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmerge\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresults_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msads\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mleft_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrsID\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msnp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minner\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mone_to_one\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     16\u001b[0m sads_dfs\u001b[38;5;241m.\u001b[39mappend(sads_df)\n",
      "File \u001b[0;32m~/anaconda3/envs/gwas_lovro/lib/python3.9/site-packages/pandas/core/reshape/merge.py:170\u001b[0m, in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m    155\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _cross_merge(\n\u001b[1;32m    156\u001b[0m         left_df,\n\u001b[1;32m    157\u001b[0m         right_df,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    167\u001b[0m         copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[1;32m    168\u001b[0m     )\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 170\u001b[0m     op \u001b[38;5;241m=\u001b[39m \u001b[43m_MergeOperation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    171\u001b[0m \u001b[43m        \u001b[49m\u001b[43mleft_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    172\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    173\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    174\u001b[0m \u001b[43m        \u001b[49m\u001b[43mon\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mon\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    175\u001b[0m \u001b[43m        \u001b[49m\u001b[43mleft_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mleft_on\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    176\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright_on\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mright_on\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    177\u001b[0m \u001b[43m        \u001b[49m\u001b[43mleft_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mleft_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    178\u001b[0m \u001b[43m        \u001b[49m\u001b[43mright_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mright_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    179\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    180\u001b[0m \u001b[43m        \u001b[49m\u001b[43msuffixes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msuffixes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    181\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindicator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindicator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    182\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    183\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    184\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result(copy\u001b[38;5;241m=\u001b[39mcopy)\n",
      "File \u001b[0;32m~/anaconda3/envs/gwas_lovro/lib/python3.9/site-packages/pandas/core/reshape/merge.py:813\u001b[0m, in \u001b[0;36m_MergeOperation.__init__\u001b[0;34m(self, left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, indicator, validate)\u001b[0m\n\u001b[1;32m    809\u001b[0m \u001b[38;5;66;03m# If argument passed to validate,\u001b[39;00m\n\u001b[1;32m    810\u001b[0m \u001b[38;5;66;03m# check if columns specified as unique\u001b[39;00m\n\u001b[1;32m    811\u001b[0m \u001b[38;5;66;03m# are in fact unique.\u001b[39;00m\n\u001b[1;32m    812\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m validate \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 813\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_validate_kwd\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalidate\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/gwas_lovro/lib/python3.9/site-packages/pandas/core/reshape/merge.py:1657\u001b[0m, in \u001b[0;36m_MergeOperation._validate_validate_kwd\u001b[0;34m(self, validate)\u001b[0m\n\u001b[1;32m   1653\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m MergeError(\n\u001b[1;32m   1654\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMerge keys are not unique in left dataset; not a one-to-one merge\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1655\u001b[0m         )\n\u001b[1;32m   1656\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m right_unique:\n\u001b[0;32m-> 1657\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m MergeError(\n\u001b[1;32m   1658\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMerge keys are not unique in right dataset; not a one-to-one merge\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1659\u001b[0m         )\n\u001b[1;32m   1661\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m validate \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mone_to_many\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1:m\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m   1662\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m left_unique:\n",
      "\u001b[0;31mMergeError\u001b[0m: Merge keys are not unique in right dataset; not a one-to-one merge"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "lead_probs = []\n",
    "highest_probs = []\n",
    "all_probs = []\n",
    "all_sad1 = []\n",
    "all_sad9 = []\n",
    "all_probs_in_sad = []\n",
    "sads_dfs = []\n",
    "for locus in tqdm(our_loci):\n",
    "    results_df = pd.read_csv(f\"/local/home/ssigfstead/GWAS/PAINTOR_outputs/locus_{locus[3]}_{locus[4]}.processed.results\", sep=\" \")\n",
    "    highest_probs.append(results_df[\"Posterior_Prob\"].max())\n",
    "    lead_probs.append(results_df.loc[results_df[\"P\"].idxmin()][\"Posterior_Prob\"])\n",
    "    all_probs.extend(results_df[\"Posterior_Prob\"].values)\n",
    "    sads_df = pd.merge(results_df, sads, left_on=\"rsID\", right_on=\"snp\", how='inner', validate='one_to_one')\n",
    "    sads_dfs.append(sads_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_probs_in_sad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sads_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[32], line 6\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstats\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pearsonr\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# Calculate the correlation coefficient\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m correlation, _ \u001b[38;5;241m=\u001b[39m pearsonr(\u001b[43msads_df\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPosterior_Prob\u001b[39m\u001b[38;5;124m\"\u001b[39m], sads_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSAD1\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mabs())\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Create the scatter plot\u001b[39;00m\n\u001b[1;32m      9\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sads_df' is not defined"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "\n",
    "# Calculate the correlation coefficient\n",
    "correlation, _ = pearsonr(sads_df[\"Posterior_Prob\"], sads_df[\"SAD1\"].abs())\n",
    "\n",
    "# Create the scatter plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(sads_df[\"Posterior_Prob\"],  sads_df[\"SAD1\"].abs(), alpha=0.7)\n",
    "plt.title(f'Scatter Plot of PIP vs SAD9\\nCorrelation: {correlation:.2f}')\n",
    "plt.xlabel(\"PIP\")\n",
    "plt.ylabel(\"SAD9\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11143861538461541 0.303461923076923 0.005086988791644877\n"
     ]
    }
   ],
   "source": [
    "print(sum(lead_probs)/len(lead_probs), sum(highest_probs)/len(highest_probs), sum(all_probs)/len(all_probs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 29/29 [02:41<00:00,  5.58s/it]\n"
     ]
    }
   ],
   "source": [
    "lead_probs = []\n",
    "highest_probs = []\n",
    "all_probs = []\n",
    "all_sad1 = []\n",
    "all_sad9 = []\n",
    "all_probs_in_sad = []\n",
    "for locus in tqdm(orig_loci):\n",
    "    results_df = pd.read_csv(f\"/local/home/ssigfstead/GWAS/PAINTOR_outputs/locus_{locus[3]}_{locus[4]}.processed.results\", sep=\" \")\n",
    "    highest_probs.append(results_df[\"Posterior_Prob\"].max())\n",
    "    lead_probs.append(results_df.loc[results_df[\"P\"].idxmin()][\"Posterior_Prob\"])\n",
    "    all_probs.extend(results_df[\"Posterior_Prob\"].values)\n",
    "    all_sad1.extend(sads[sads[\"snp\"].isin(results_df[\"rsID\"])][\"SAD1\"].values)\n",
    "    all_sad9.extend(sads[sads[\"snp\"].isin(results_df[\"rsID\"])][\"SAD9\"].values)\n",
    "    all_probs_in_sad.extend(results_df[results_df[\"rsID\"].isin(sads[\"snp\"])][\"Posterior_Prob\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " (7.5e-06, 1.484e-05),\n",
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       " (6.6e-06, 1.365e-05),\n",
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       " (1.48e-05, 5.1e-06),\n",
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       " (-7e-06, -1.28e-05),\n",
       " (1.675e-05, 3.04e-06),\n",
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       " (-1e-05, -9.7e-06),\n",
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       " (5.84e-06, 1.38e-05),\n",
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       " (-1.14e-05, -8.1e-06),\n",
       " (-9.66e-06, -9.83e-06),\n",
       " (-8.2e-06, -1.127e-05),\n",
       " (8e-06, 1.14e-05),\n",
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       " (-6.56e-06, -1.28e-05),\n",
       " (1.204e-05, 7.3e-06),\n",
       " (-6.26e-06, -1.3e-05),\n",
       " (1.02e-05, 9.06e-06),\n",
       " (8.94e-06, 1.03e-05),\n",
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       " (9.83e-06, 9.36e-06),\n",
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       " (-8e-06, -1.11e-05),\n",
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       " (1.2e-05, 7e-06),\n",
       " (9e-06, 9.95e-06),\n",
       " (-8.2e-06, -1.07e-05),\n",
       " (9.66e-06, 9.2e-06),\n",
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       " (-1.216e-05, -6.6e-06),\n",
       " (-8.05e-06, -1.07e-05),\n",
       " (8.94e-06, 9.8e-06),\n",
       " (5.84e-06, 1.28e-05),\n",
       " (5.25e-06, 1.33e-05),\n",
       " (9e-06, 9.5e-06),\n",
       " (1.16e-05, 6.85e-06),\n",
       " (1.216e-05, 6.2e-06),\n",
       " (-8.2e-06, -1.01e-05),\n",
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       " (9.36e-06, 8.76e-06),\n",
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       " (8.3e-06, 9.8e-06),\n",
       " (1.013e-05, 7.9e-06),\n",
       " (9.6e-06, 8.4e-06),\n",
       " (9.06e-06, 8.9e-06),\n",
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       " (-1.1e-05, -6.85e-06),\n",
       " (-1.21e-05, -5.7e-06),\n",
       " (1.3e-05, 4.77e-06),\n",
       " (8.64e-06, 9.06e-06),\n",
       " (-8.64e-06, -9.06e-06),\n",
       " (-6.9e-06, -1.07e-05),\n",
       " (-8.76e-06, -8.8e-06),\n",
       " (-1.097e-05, -6.56e-06),\n",
       " (5.6e-06, 1.186e-05),\n",
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       " (-8.9e-06, -8.5e-06),\n",
       " (1.15e-05, 5.9e-06),\n",
       " (-8e-06, -9.4e-06),\n",
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       " (-7.3e-06, -9.95e-06),\n",
       " (6.44e-06, 1.08e-05),\n",
       " (-9.9e-06, -7.3e-06),\n",
       " (9.54e-06, 7.6e-06),\n",
       " (7.33e-06, 9.8e-06),\n",
       " (-4.06e-05, 2.35e-05),\n",
       " (-1.305e-05, -3.93e-06),\n",
       " (3.576e-05, -1.88e-05),\n",
       " (1.02e-05, 6.74e-06),\n",
       " (9.06e-06, 7.87e-06),\n",
       " (8.46e-06, 8.46e-06),\n",
       " (-1.16e-05, -5.3e-06),\n",
       " (7.9e-06, 9e-06),\n",
       " (-8e-06, -8.9e-06),\n",
       " (-1.705e-05, 2e-07),\n",
       " (-1.115e-05, -5.7e-06),\n",
       " (-1.59e-05, -9e-07),\n",
       " (1.12e-05, 5.6e-06),\n",
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       " (1.734e-05, -6e-07),\n",
       " (-1.144e-05, -5.3e-06),\n",
       " (8.64e-06, 8.1e-06),\n",
       " (8.3e-06, 8.4e-06),\n",
       " (4.65e-06, 1.204e-05),\n",
       " (9.2e-06, 7.4e-06),\n",
       " (8.6e-06, 8e-06),\n",
       " (1.44e-05, 2.15e-06),\n",
       " (1.9e-06, 1.454e-05),\n",
       " (9.83e-06, 6.6e-06),\n",
       " (-4.7e-06, -1.17e-05),\n",
       " (1.37e-05, 2.7e-06),\n",
       " (-9.95e-06, -6.4e-06),\n",
       " (-6.8e-06, -9.54e-06),\n",
       " (9.6e-06, 6.7e-06),\n",
       " (-7.75e-06, -8.46e-06),\n",
       " (1e-05, 6.2e-06),\n",
       " (-1.08e-05, -5.36e-06),\n",
       " (9.06e-06, 7.03e-06),\n",
       " (-8e-07, -1.526e-05),\n",
       " (2.7e-06, 1.335e-05),\n",
       " (4.8e-06, 1.12e-05),\n",
       " (8.9e-06, 7.1e-06),\n",
       " (1e-05, 5.96e-06),\n",
       " (-9.95e-06, -6e-06),\n",
       " (-6.1e-06, -9.83e-06),\n",
       " (-4.77e-06, -1.115e-05),\n",
       " (8.46e-06, 7.45e-06),\n",
       " (-1e-05, -5.9e-06),\n",
       " (9.9e-06, 5.96e-06),\n",
       " (4.3e-06, 1.156e-05),\n",
       " (9.36e-06, 6.5e-06),\n",
       " (-6.14e-06, -9.66e-06),\n",
       " (7.75e-06, 8e-06),\n",
       " (-3.1e-06, -1.264e-05),\n",
       " (-1.705e-05, 1.4e-06),\n",
       " (5.66e-06, 9.9e-06),\n",
       " (-6.14e-06, -9.4e-06),\n",
       " (4.05e-06, 1.144e-05),\n",
       " (1.04e-05, 5.07e-06),\n",
       " (-8.34e-06, -7.1e-06),\n",
       " (-1.067e-05, -4.77e-06),\n",
       " (-3.7e-06, -1.17e-05),\n",
       " (5.6e-06, 9.8e-06),\n",
       " (1.12e-05, 4.2e-06),\n",
       " (2.15e-06, 1.323e-05),\n",
       " (3.8e-06, 1.156e-05),\n",
       " (9.06e-06, 6.3e-06),\n",
       " (-7.3e-06, -8e-06),\n",
       " (1.01e-05, 5.07e-06),\n",
       " (-5.36e-06, -9.8e-06),\n",
       " (-7.75e-06, -7.3e-06),\n",
       " (7.15e-06, 7.9e-06),\n",
       " (5.36e-06, 9.66e-06),\n",
       " (4.1e-06, 1.09e-05),\n",
       " (-7.9e-06, -7.1e-06),\n",
       " (6.2e-06, 8.8e-06),\n",
       " (-6.85e-06, -8.05e-06),\n",
       " (1.925e-05, -4.4e-06),\n",
       " (-9.5e-07, -1.377e-05),\n",
       " (-3.2e-06, -1.144e-05),\n",
       " (-7.6e-06, -7.03e-06),\n",
       " (-2.15e-06, -1.246e-05),\n",
       " (-1.13e-05, -3.3e-06),\n",
       " (-6.14e-06, -8.46e-06),\n",
       " (-7.6e-06, -6.9e-06),\n",
       " (-7.15e-06, -7.33e-06),\n",
       " (1.41e-05, 3.6e-07),\n",
       " (6.74e-06, 7.7e-06),\n",
       " (-8.05e-06, -6.3e-06),\n",
       " (-1.234e-05, -1.97e-06),\n",
       " (-7.8e-06, -6.5e-06),\n",
       " (-1.72e-05, 3.04e-06),\n",
       " (-5.7e-06, 1.98e-05),\n",
       " (1.043e-05, 3.64e-06),\n",
       " (1.25e-06, -1.526e-05),\n",
       " (-4.9e-06, -9e-06),\n",
       " (1.37e-05, 2e-07),\n",
       " (-9.5e-06, -4.4e-06),\n",
       " (5.54e-06, 8.34e-06),\n",
       " (-5e-06, 1.884e-05),\n",
       " (8e-06, 5.84e-06),\n",
       " (-6.3e-06, -7.5e-06),\n",
       " (-5e-06, -8.8e-06),\n",
       " (-7.33e-06, -6.44e-06),\n",
       " (-9.06e-06, -4.7e-06),\n",
       " (-1.097e-05, -2.74e-06),\n",
       " (-8.9e-06, -4.77e-06),\n",
       " (8e-06, 5.66e-06),\n",
       " (-1.28e-05, -8e-07),\n",
       " (5.7e-06, 7.87e-06),\n",
       " (7.1e-06, 6.44e-06),\n",
       " (4.53e-06, 9e-06),\n",
       " (9.83e-06, 3.7e-06),\n",
       " (6.5e-06, 7e-06),\n",
       " (-5.5e-06, -8e-06),\n",
       " (-1.4e-05, 5.4e-07),\n",
       " (-5.07e-06, -8.34e-06),\n",
       " (6.8e-06, 6.6e-06),\n",
       " (4.8e-06, 8.6e-06),\n",
       " (-8.4e-06, -5e-06),\n",
       " (-7.4e-06, -5.96e-06),\n",
       " (1.466e-05, -1.3e-06),\n",
       " (6.6e-06, 6.74e-06),\n",
       " (-6.7e-06, -6.56e-06),\n",
       " (-7.1e-06, -6.14e-06),\n",
       " (9.66e-06, 3.5e-06),\n",
       " (-8.46e-06, -4.7e-06),\n",
       " (6.56e-06, 6.6e-06),\n",
       " (7e-06, 6.1e-06),\n",
       " (1.2e-06, 1.19e-05),\n",
       " (4.35e-06, 8.7e-06),\n",
       " (-1.323e-05, 2e-07),\n",
       " (-9.9e-06, -3.1e-06),\n",
       " (-8.46e-06, -4.53e-06),\n",
       " (-9.5e-06, -3.46e-06),\n",
       " (-1.01e-05, -2.86e-06),\n",
       " (1.9e-06, -1.484e-05),\n",
       " (2.5e-06, 1.04e-05),\n",
       " (-7.2e-06, -5.7e-06),\n",
       " (-8.2e-06, -4.65e-06),\n",
       " (-1.25e-06, 1.41e-05),\n",
       " (-7e-06, -5.84e-06),\n",
       " (-1.46e-05, 1.8e-06),\n",
       " ...]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(all_sad1, all_sad9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation coefficient\n",
    "from functools import reduce\n",
    "\n",
    "summed = [abs(x[0]) + abs(x[1]) for x in zip(all_sad1, all_sad9)]\n",
    "maxed = [max(abs(x[0]), abs(x[1])) for x in zip(all_sad1, all_sad9)]\n",
    "\n",
    "correlation, _ = pearsonr(all_probs_in_sad, list(map(abs, maxed)))\n",
    "\n",
    "# Create the scatter plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(all_probs_in_sad,  list(map(abs, maxed)), alpha=0.7)\n",
    "plt.title(f'Scatter Plot of PIP vs max of SADs\\nCorrelation: {correlation:.2f}')\n",
    "plt.xlabel(\"PIP\")\n",
    "plt.ylabel(\"max of SADs\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13443255172413796 0.2739108965517241 0.004752667453372376\n"
     ]
    }
   ],
   "source": [
    "print(sum(lead_probs)/len(lead_probs), sum(highest_probs)/len(highest_probs), sum(all_probs)/len(all_probs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'results_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mresults_df\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPosterior_Prob\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmax()\n\u001b[1;32m      3\u001b[0m mapped_locus_df \u001b[38;5;241m=\u001b[39m results_df\n\u001b[1;32m      5\u001b[0m ld \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/local/home/ssigfstead/GWAS/PAINTOR_inputs/locus_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlocus[\u001b[38;5;241m3\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlocus[\u001b[38;5;241m4\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.processed.ld\u001b[39m\u001b[38;5;124m\"\u001b[39m, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m, header\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'results_df' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "results_df[\"Posterior_Prob\"].max()\n",
    "mapped_locus_df = results_df\n",
    "\n",
    "ld = pd.read_csv(f\"/local/home/ssigfstead/GWAS/PAINTOR_inputs/locus_{locus[3]}_{locus[4]}.processed.ld\", sep=\" \", header=None)\n",
    "sad1 = sads[sads[\"snp\"].isin(mapped_locus_df[\"rsID\"])][\"SAD1\"]\n",
    "\n",
    "import numpy as np\n",
    "fig ,axes = plt.subplots(nrows=2,sharex=True,figsize=(15,15),height_ratios=(1,1))\n",
    "df = mapped_locus_df.copy(deep=True)\n",
    "df[\"MLOG10P\"] = -np.log10(df[\"P\"])\n",
    "col_to_plot = \"MLOG10P\"\n",
    "p=axes[0].scatter(df[\"POS\"],df[col_to_plot],c=ld[df[\"P\"].idxmin()]**2)\n",
    "\n",
    "# axes[0].scatter(df.loc[df[\"cumsum\"] <= 0.95,\"POS\"],df.loc[df[\"cumsum\"] <= 0.95,col_to_plot],\n",
    "#         marker='o',s=40,c=\"None\",edgecolors='red',label=\"Variants in credible sets\")\n",
    "\n",
    "plt.colorbar( p , label=\"Rsq with the lead variant\")\n",
    "axes[0].set_xlabel(\"position\")\n",
    "axes[0].set_xlim((locus[1], locus[2]))\n",
    "axes[0].set_ylabel(col_to_plot)\n",
    "axes[0].legend()\n",
    "\n",
    "p=axes[1].scatter(df[\"POS\"],df[\"Posterior_Prob\"],c=df[\"BETA\"].abs(), cmap=\"YlOrRd\")\n",
    "\n",
    "# axes[1].scatter(df.loc[df[\"cumsum\"] <= 0.95,\"POS\"],df.loc[df[\"cumsum\"] <= 0.95,\"Posterior_Prob\"],\n",
    "#         marker='o',s=40,c=\"None\",edgecolors='red',label=\"Variants in credible sets\")\n",
    "\n",
    "plt.colorbar( p , label=\"|BETA|\")\n",
    "axes[1].set_xlabel(\"position\")\n",
    "axes[1].set_xlim((locus[1], locus[2]))\n",
    "axes[1].set_ylabel(\"PIP\")\n",
    "axes[1].legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:rpy2.situation:cffi mode is CFFI_MODE.ANY\n",
      "INFO:rpy2.situation:R home found: /usr/lib/R\n",
      "INFO:rpy2.situation:R library path: /usr/lib/R/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/default-java/lib/server\n",
      "INFO:rpy2.situation:LD_LIBRARY_PATH: /usr/lib/R/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/default-java/lib/server\n",
      "INFO:rpy2.rinterface_lib.embedded:Default options to initialize R: rpy2, --quiet, --no-save\n",
      "INFO:rpy2.rinterface_lib.embedded:R is already initialized. No need to initialize.\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "import susie_fine_mapping_utils as fm\n",
    "import os\n",
    "import paintor_utils as pu\n",
    "import pandas as pd\n",
    "\n",
    "our_loci = fm.get_loci(\"/local/home/ssigfstead/GWAS/filtered_snps_threshold=0.5.csv\", chr_col=\"chr\", pos_col=\"pos\", rsid_col=\"snp\")\n",
    "orig_loci = fm.get_loci(\"/local/home/ssigfstead/GWAS/filtered_snps_threshold=0.0.csv\", chr_col=\"chr\", pos_col=\"pos\", rsid_col=\"snp\")\n",
    "\n",
    "common_loci_indices = set([locus[3] for locus in our_loci]).intersection(set([locus[3] for locus in orig_loci]))\n",
    "our_loci_indices = set([locus[3] for locus in our_loci]).difference(set([locus[3] for locus in orig_loci]))\n",
    "discarded_loci_indices = set([locus[3] for locus in orig_loci]).difference(set([locus[3] for locus in our_loci]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47, 5, 24)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(our_loci_indices), len(common_loci_indices), len(discarded_loci_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "our_loci_lead_probs = []\n",
    "our_loci_highest_probs = []\n",
    "our_n_above_1 = []\n",
    "for locus_index in our_loci_indices:\n",
    "    results_df = pd.read_csv(f\"/local/home/ssigfstead/GWAS/PAINTOR_outputs/locus_{locus_index}_500000.0.processed.results\", sep=\" \")\n",
    "    our_loci_lead_probs.append(results_df.loc[results_df[\"P\"].idxmin()][\"Posterior_Prob\"])\n",
    "    our_loci_highest_probs.append(results_df[\"Posterior_Prob\"].max())\n",
    "    our_n_above_1.append(len(results_df[results_df[\"Posterior_Prob\"] > 0.01]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_loci_lead_probs = []\n",
    "common_loci_highest_probs = []\n",
    "common_n_above_1 = []\n",
    "for locus_index in common_loci_indices:\n",
    "    results_df = pd.read_csv(f\"/local/home/ssigfstead/GWAS/PAINTOR_outputs/locus_{locus_index}_500000.0.processed.results\", sep=\" \")\n",
    "    common_loci_lead_probs.append(results_df.loc[results_df[\"P\"].idxmin()][\"Posterior_Prob\"])\n",
    "    common_loci_highest_probs.append(results_df[\"Posterior_Prob\"].max())\n",
    "    common_n_above_1.append(len(results_df[results_df[\"Posterior_Prob\"] > 0.01]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "orig_loci_lead_probs = []\n",
    "orig_loci_highest_probs = []\n",
    "orig_n_above_1 = []\n",
    "for locus_index in discarded_loci_indices:\n",
    "    results_df = pd.read_csv(f\"/local/home/ssigfstead/GWAS/PAINTOR_outputs/locus_{locus_index}_500000.0.processed.results\", sep=\" \")\n",
    "    orig_loci_lead_probs.append(results_df.loc[results_df[\"P\"].idxmin()][\"Posterior_Prob\"])\n",
    "    orig_loci_highest_probs.append(results_df[\"Posterior_Prob\"].max())\n",
    "    orig_n_above_1.append(len(results_df[results_df[\"Posterior_Prob\"] > 0.01]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist([our_loci_lead_probs, orig_loci_lead_probs, common_loci_lead_probs], bins=10, label=[\"Our loci\", \"Orig loci\", \"Common loci\"])\n",
    "plt.legend()\n",
    "plt.title(\"Lead probabilities\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ionx8fPT++++rZ8+eioyMVHx8vDZu3Khjx45p6tSpjr42m03vvPOO0/Z169bVypUrJUk5OTmy2Wx644031LNnT/n6+mr16tUVqmPfvn3q06eP/Pz81KBBA40ePVpnz5516rN8+XK1adNGdrtdYWFhGjdu3FVruxm4/Xs+5syZo1q1aunxxx+vUP9Zs2Zp+vTp7i7jilw9j1ldzkN6at0A4G75+flKT0/XzJkzy3w3SWhoqIYNG6Y33nhDL774oku3hk6ZMkXPP/+8OnXqJF9f32v2LyoqUlxcnGJjY7Vz506dPHlSv/71rzVu3DhHsFm2bJkmTpyo2bNnKz4+XgUFBdq2bZtLz7cmcmv42L17t1544QXt2bOnwhOenJysiRMnOh4XFhYqIiLCnWUBAGqQrKwsWZalmJiYctfHxMTo9OnTOnXqlIKDgyu83wkTJugXv/hFhfu//vrrunDhgl577TX5+/tLkpYsWaKBAwdqzpw5CgkJ0TPPPKNJkyYpKSnJsV3Xrl0rPEZN5dbTLv/7v/+rkydPKjIyUrVq1VKtWrV09OhRTZo0SVFRUeVuY7fbFRgY6LQAAHAt7v4tmi5durjUPzMzUx06dHAED0nq3r27SktLdfDgQZ08eVJff/21+vbt69Y6awK3ho/hw4frs88+U0ZGhmMJDw/X5MmTlZ6e7s6hAAA3qebNm8tmsykzM7Pc9ZmZmapXr54aNWok6fvrKn4aVMr7Bd8fhwh34Ovqr8zl8HH27FlHsJCk7OxsZWRk6Msvv1SDBg3Utm1bp6V27doKDQ1Vy5Yt3V07AOAm1KBBA/Xv318vvviizp8/77QuLy9Pq1ev1tChQx2n/xs1aqTjx487+mRlZencuXM3XEdMTIw+/fRTFRUVOdq2bdsmLy8vtWzZUgEBAYqKitKmTZtueKyaxuXwsWvXLnXq1EmdOnWSJE2cOFGdOnXSU0895fbiAAAoz5IlS1RcXKy4uDht3bpVubm5SktLU//+/XXrrbdq5syZjr59+vTRkiVLtHfvXu3atUuPPfaYW26jHTZsmHx9fZWQkKD9+/dr8+bNGj9+vIYPH66QkBBJUmpqqp5//nktWrRIWVlZ2rNnjxYvXnzDY3s6ly847dWrl0vn2XJyclwdAgBQxar7HXMtWrTQrl27lJKSoiFDhig/P1+hoaEaPHiwUlJSnL7j4/nnn9eoUaPUo0cPhYeH64UXXtDu3btvuIY6deooPT1dSUlJ6tq1q+rUqaMHHnhA8+fPd/RJSEjQhQsXtGDBAv3+979Xw4YN9eCDD97w2J7OZrn7ip0bVFhYqKCgIBUUFFTKxaeeesuqp9YNoHq7cOGCsrOzFR0dXaHbS3Fzu9r7xZXPb35YDgAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABjl8terAwBuAqlBhscrMDpcTk6OoqOjtXfvXnXs2PG692Oz2bRu3ToNHjzYLXW5e3/VFUc+AAAeKTc3V4888ojCw8Pl4+OjJk2aKCkpSd9+++01t42IiNDx48fVtm1bA5VW3PHjxxUfH1/VZVQ6wgcAwOMcOXJEXbp0UVZWltasWaPDhw/rpZde0qZNmxQbG6v8/Pwrbnvx4kV5e3srNDRUtWpVrxMAoaGhstvtVV1GpSN8AAA8TmJionx8fPT++++rZ8+eioyMVHx8vDZu3Khjx45p6tSpjr5RUVGaMWOGRowYocDAQI0ePVo5OTmy2WzKyMhw9Fu/fr1atGghX19f9e7dW6tWrZLNZtOZM2cqXNe+ffvUp08f+fn5qUGDBho9erTOnj3r1Gf58uVq06aN7Ha7wsLCNG7cOMc6m82md95553pfFo9B+AAAeJT8/Hylp6dr7Nix8vPzc1oXGhqqYcOG6Y033tCPf7R93rx56tChg/bu3atp06aV2Wd2drYefPBBDR48WJ9++qnGjBnjFGAqoqioSHFxcapXr5527typN998Uxs3bnQKF8uWLVNiYqJGjx6tffv2af369WrevLmLr4Dnq17HmwAAuIasrCxZlqWYmJhy18fExOj06dM6deqUgoODJUl9+vTRpEmTHH1ycnKctnn55ZfVsmVLzZ07V5LUsmVL7d+/XzNnzqxwXa+//rouXLig1157Tf7+/pKkJUuWaODAgZozZ45CQkL0zDPPaNKkSUpKSnJs17Vr1wqPUVMQPgAAHunHRzaupUuXLlddf/DgwTIhoFu3bi7Vk5mZqQ4dOjiChyR1795dpaWlOnjwoGw2m77++mv17dvXpf3WRJx2AQB4lObNm8tmsykzM7Pc9ZmZmapXr54aNWrkaPtxIKgqPz1FdDMjfAAAPEqDBg3Uv39/vfjiizp//rzTury8PK1evVpDhw6VzWar8D5btmypXbt2ObXt3LnTpbpiYmL06aefqqioyNG2bds2eXl5qWXLlgoICFBUVJQ2bdrk0n5rIsIHAMDjLFmyRMXFxYqLi9PWrVuVm5urtLQ09e/fX7feeqtL12pI0pgxY/T555/rySef1KFDh/S3v/1NK1eulKQKh5hhw4bJ19dXCQkJ2r9/vzZv3qzx48dr+PDhCgkJkSSlpqbq+eef16JFi5SVlaU9e/Zo8eLFLtVaE3DNBwCgLMPfOOqqFi1aaNeuXUpJSdGQIUOUn5+v0NBQDR48WCkpKapfv75L+4uOjtZbb72lSZMm6YUXXlBsbKymTp2q3/72txX+3o06deooPT1dSUlJ6tq1q+rUqaMHHnhA8+fPd/RJSEjQhQsXtGDBAv3+979Xw4YN9eCDD7pUa01A+AAAeKQmTZo4jk5czU/vbJG+/+6Pn16wOmjQIA0aNMjxeObMmWrcuLF8fX2vuO+f7qNdu3b68MMPr1rPmDFjNGbMmArtr6YifAAAIOnFF19U165d1aBBA23btk1z5851+o4OuA/hAwAAff/9Ic8884zy8/MVGRmpSZMmKTk5uarLqpEIHwAASFqwYIEWLFhQ1WXcFLjbBQAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGcastAKCMdqvaGR1vX8I+o+PVJFFRUZowYYImTJhQLfdXHo58AAA8Ul5ensaPH6+mTZvKbrcrIiJCAwcO5Fdjb9DOnTs1evToSh2DIx8AAI+Tk5Oj7t27q27dupo7d67atWunS5cuKT09XYmJifr888+rukSP1ahRo0ofgyMfAACPM3bsWNlsNn3yySd64IEHdNttt6lNmzaaOHGiduzY4ej35Zdf6r777tMtt9yiwMBADRkyRCdOnHCsT01NVceOHbV8+XJFRkbqlltu0dixY1VSUqLnnntOoaGhCg4O1syZM53Gt9lsevnll3XvvfeqTp06iomJ0fbt23X48GH16tVL/v7+uuOOO/TFF184bbds2TI1a9ZMPj4+atmypf7yl7+U2e+f//xn3X///apTp45atGih9evXu/TaXOs5S9I///lPde3aVb6+vmrYsKHuv/9+x7qoqCgtXLjQpTFdRfgAAHiU/Px8paWlKTExUf7+/mXW161bV5JUWlqq++67T/n5+fr3v/+tDz74QEeOHNHQoUOd+n/xxRd67733lJaWpjVr1ujVV1/VgAED9NVXX+nf//635syZoz/+8Y/6z3/+47TdjBkzNGLECGVkZKhVq1Z6+OGHNWbMGCUnJ2vXrl2yLMvph+nWrVunpKQkTZo0Sfv379eYMWM0atQobd682Wm/06dP15AhQ/TZZ5/pnnvu0bBhw5Sfn1+h16Yiz/ndd9/V/fffr3vuuUd79+7Vpk2b1K1btwrt31047QIA8CiHDx+WZVlq1arVVftt2rRJ+/btU3Z2tiIiIiRJr732mtq0aaOdO3eqa9eukr7/wF6+fLkCAgLUunVr9e7dWwcPHtSGDRvk5eWlli1bas6cOdq8ebNuv/12x/5HjRqlIUOGSJKefPJJxcbGatq0aYqLi5MkJSUladSoUY7+8+bN08iRIzV27FhJchylmTdvnnr37u3oN3LkSP3qV7+SJD377LNatGiRPvnkE919993XfG0q8pxnzpyphx56SNOnT3ds16FDh2vu25048gEA8CiWZVWoX2ZmpiIiIhwfwpLUunVr1a1bV5mZmY62qKgoBQQEOB6HhISodevW8vLycmo7efKk0/7bt2/vtF6S2rVr59R24cIFFRYWOurp3r270z66d+/uVMtP9+vv76/AwMAyY9/Ic87IyFDfvn0rtL/KQvgAAHiUFi1ayGazue2i0tq1azs9ttls5baVlpZecTubzXbFtp9udz31uLqPq/Hz83Pbvq4X4QMA4FHq16+vuLg4LV26VEVFRWXWnzlzRpIUExOj3Nxc5ebmOtYdOHBAZ86cUevWrU2V6xATE6Nt27Y5tW3bts2ttVTkObdv377Kb0d2OXxs3bpVAwcOVHh4uGw2m9555x3HukuXLunJJ59Uu3bt5O/vr/DwcI0YMUJff/21O2sGANzkli5dqpKSEnXr1k1///vflZWVpczMTC1atEixsbGSpH79+qldu3YaNmyY9uzZo08++UQjRoxQz5491aVLF+M1T548WStXrtSyZcuUlZWl+fPn6+2339bvf/97t41RkeeckpKiNWvWKCUlRZmZmdq3b5/mzJnjthoqwuULTouKitShQwc98sgj+sUvfuG07ty5c9qzZ4+mTZumDh066PTp00pKStKgQYO0a9cutxUNAKhc1f0bR5s2bao9e/Zo5syZmjRpko4fP65GjRqpc+fOWrZsmaTvT1f84x//0Pjx43XnnXfKy8tLd999txYvXlwlNQ8ePFgvvPCC5s2bp6SkJEVHR2vFihXq1auX28aoyHPu1auX3nzzTc2YMUOzZ89WYGCg7rzzTrfVUKE6rYpeuVPexjab1q1bp8GDB1+xz86dO9WtWzcdPXpUkZGR19xnYWGhgoKCVFBQoMDAwOst7YqiprzrUv+c2QPcXsP18NS6AVRvFy5cUHZ2tqKjo+Xr61vV5aCau9r7xZXP70q/1bagoEA2m81x3/VPFRcXq7i42PH4h6uCAQBAzVSpF5xeuHBBTz75pH71q19dMQXNmjVLQUFBjuXHtwcBAICap9LCx6VLlzRkyBBZluU4/1ae5ORkFRQUOJYfX6ELAABqnko57fJD8Dh69Kg+/PDDq577sdvtstvtlVEGAACohtwePn4IHllZWdq8ebMaNGjg7iEAAG52A/ce4CbirveJy+Hj7NmzOnz4sONxdna2MjIyVL9+fYWFhenBBx/Unj179K9//UslJSXKy8uT9P2Xwvj4+LilaACAe3h7e0uSLl68WC2++RLV28WLFyX93/vmerkcPnbt2uX0AzgTJ06UJCUkJCg1NdXx078dO3Z02m7z5s1uvZcZAHDjatWqpTp16ujUqVOqXbu20++ZAD9WWlqqU6dOqU6dOqpV68ZOnLi8da9eva562IVDdwDgOWw2m8LCwpSdna2jR49WdTmo5ry8vBQZGen43ZrrVenf8wEAqN58fHzUokULxyF14Ep8fHzccnSM8AEAkJeXF99wCmM4uQcAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIxyOXxs3bpVAwcOVHh4uGw2m9555x2n9ZZl6amnnlJYWJj8/PzUr18/ZWVluateAADg4VwOH0VFRerQoYOWLl1a7vrnnntOixYt0ksvvaT//Oc/8vf3V1xcnC5cuHDDxQIAAM9Xy9UN4uPjFR8fX+46y7K0cOFC/fGPf9R9990nSXrttdcUEhKid955Rw899NCNVQsAADyeW6/5yM7OVl5envr16+doCwoK0u23367t27eXu01xcbEKCwudFgAAUHO5NXzk5eVJkkJCQpzaQ0JCHOt+atasWQoKCnIsERER7iwJAABUM1V+t0tycrIKCgocS25ublWXBAAAKpFbw0doaKgk6cSJE07tJ06ccKz7KbvdrsDAQKcFAADUXG4NH9HR0QoNDdWmTZscbYWFhfrPf/6j2NhYdw4FAAA8lMt3u5w9e1aHDx92PM7OzlZGRobq16+vyMhITZgwQc8884xatGih6OhoTZs2TeHh4Ro8eLA76wYAAB7K5fCxa9cu9e7d2/F44sSJkqSEhAStXLlSTzzxhIqKijR69GidOXNGP//5z5WWliZfX1/3VQ0AADyWy+GjV69esizriuttNpuefvppPf300zdUGAAAqJmq/G4XAABwcyF8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKLeHj5KSEk2bNk3R0dHy8/NTs2bNNGPGDFmW5e6hAACAB6rl7h3OmTNHy5Yt06pVq9SmTRvt2rVLo0aNUlBQkB5//HF3DwcAADyM28PHxx9/rPvuu08DBgyQJEVFRWnNmjX65JNP3D0UAADwQG4/7XLHHXdo06ZNOnTokCTp008/1UcffaT4+Phy+xcXF6uwsNBpAQAANZfbj3xMmTJFhYWFatWqlby9vVVSUqKZM2dq2LBh5fafNWuWpk+f7u4y3Cc1yMX+BZVTBwAANYTbj3z87W9/0+rVq/X6669rz549WrVqlebNm6dVq1aV2z85OVkFBQWOJTc3190lAQCAasTtRz4mT56sKVOm6KGHHpIktWvXTkePHtWsWbOUkJBQpr/dbpfdbnd3GQAAoJpy+5GPc+fOycvLebfe3t4qLS1191AAAMADuf3Ix8CBAzVz5kxFRkaqTZs22rt3r+bPn69HHnnE3UMBAAAP5PbwsXjxYk2bNk1jx47VyZMnFR4erjFjxuipp55y91AAAMADuT18BAQEaOHChVq4cKG7dw0AAGoAftsFAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEbVquoCUElSg1zsX1A5dXioqCnvutQ/Z/aASqoEAGoejnwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAoyolfBw7dkz/8z//owYNGsjPz0/t2rXTrl27KmMoAADgYWq5e4enT59W9+7d1bt3b7333ntq1KiRsrKyVK9ePXcPBQAAPJDbw8ecOXMUERGhFStWONqio6Ov2L+4uFjFxcWOx4WFhe4uCQAAVCNuP+2yfv16denSRb/85S8VHBysTp066ZVXXrli/1mzZikoKMixREREuLskAABQjbg9fBw5ckTLli1TixYtlJ6ert/+9rd6/PHHtWrVqnL7Jycnq6CgwLHk5ua6uyQAAFCNuP20S2lpqbp06aJnn31WktSpUyft379fL730khISEsr0t9vtstvt7i4DAABUU24/8hEWFqbWrVs7tcXExOjLL79091AAAMADuT18dO/eXQcPHnRqO3TokJo0aeLuoQAAgAdye/j43e9+px07dujZZ5/V4cOH9frrr+tPf/qTEhMT3T0UAADwQG4PH127dtW6deu0Zs0atW3bVjNmzNDChQs1bNgwdw8FAAA8kNsvOJWke++9V/fee29l7BoAAHg4ftsFAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRlR4+Zs+eLZvNpgkTJlT2UAAAwANUavjYuXOnXn75ZbVv374yhwEAAB6k0sLH2bNnNWzYML3yyiuqV69eZQ0DAAA8TKWFj8TERA0YMED9+vW7ar/i4mIVFhY6LQAAoOaqVRk7Xbt2rfbs2aOdO3des++sWbM0ffr0yigDAABUQ24/8pGbm6ukpCStXr1avr6+1+yfnJysgoICx5Kbm+vukgAAQDXi9iMfu3fv1smTJ/Wzn/3M0VZSUqKtW7dqyZIlKi4ulre3t2Od3W6X3W53dxkAAKCacnv46Nu3r/bt2+fUNmrUKLVq1UpPPvmkU/AAAAA3H7eHj4CAALVt29apzd/fXw0aNCjTDgAAbj58wykAADCqUu52+aktW7aYGAYAAHgAjnwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwKhaVV0AaraoKe+61D9n9oBKqgQAUF1w5AMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8AAGAU4QMAABhF+AAAAEYRPgAAgFGEDwAAYBThAwAAGEX4AAAARhE+AACAUYQPAABglNvDx6xZs9S1a1cFBAQoODhYgwcP1sGDB909DAAA8FBuDx///ve/lZiYqB07duiDDz7QpUuXdNddd6moqMjdQwEAAA9Uy907TEtLc3q8cuVKBQcHa/fu3brzzjvdPRwAAPAwbg8fP1VQUCBJql+/frnri4uLVVxc7HhcWFhY2SUBAIAqVKnho7S0VBMmTFD37t3Vtm3bcvvMmjVL06dPr8wyjGq3qp3L2+xL2FcJlXio1CAX+xdUTh2uqiZ1R01516X+Ob4PuzYAr7cTl1/v2QMqpQ5XeWrdnorXu6xKvdslMTFR+/fv19q1a6/YJzk5WQUFBY4lNze3MksCAABVrNKOfIwbN07/+te/tHXrVjVu3PiK/ex2u+x2e2WVAQAAqhm3hw/LsjR+/HitW7dOW7ZsUXR0tLuHAAAAHszt4SMxMVGvv/66/vGPfyggIEB5eXmSpKCgIPn5+bl7OAAA4GHcfs3HsmXLVFBQoF69eiksLMyxvPHGG+4eCgAAeKBKOe0CAABwJfy2CwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMqlXVBaB6aLeqncvb7EvYVwmVuIa6gZtH1JR3XeqfM3tAJVVSyVKDrmObAvfXUYk48gEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIwifAAAAKMIHwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwqtLCx9KlSxUVFSVfX1/dfvvt+uSTTyprKAAA4EEqJXy88cYbmjhxolJSUrRnzx516NBBcXFxOnnyZGUMBwAAPEilhI/58+frN7/5jUaNGqXWrVvrpZdeUp06dbR8+fLKGA4AAHiQWu7e4cWLF7V7924lJyc72ry8vNSvXz9t3769TP/i4mIVFxc7HhcUFEiSCgsL3V2aJKm0+JxL/Qttlkv9S86XuNRfqthzpe7yUbezyq5blfR36bLi6lG3y693NXn9qNusSv+7lKrF3+YPr7dlVaB+y82OHTtmSbI+/vhjp/bJkydb3bp1K9M/JSXFksTCwsLCwsJSA5bc3NxrZgW3H/lwVXJysiZOnOh4XFpaqvz8fDVo0EA2m60KK/NshYWFioiIUG5urgIDA6u6nJse81H9MCfVD3NS/bgyJ5Zl6bvvvlN4ePg19+v28NGwYUN5e3vrxIkTTu0nTpxQaGhomf52u112u92prW7duu4u66YVGBjIH3E1wnxUP8xJ9cOcVD8VnZOgoKAK7c/tF5z6+Pioc+fO2rRpk6OttLRUmzZtUmxsrLuHAwAAHqZSTrtMnDhRCQkJ6tKli7p166aFCxeqqKhIo0aNqozhAACAB6mU8DF06FCdOnVKTz31lPLy8tSxY0elpaUpJCSkMoZDOex2u1JSUsqc0kLVYD6qH+ak+mFOqp/KmhObZVXknhgAAAD34LddAACAUYQPAABgFOEDAAAYRfgAAABGET4AAIBRhA8PtXTpUkVFRcnX11e33367Pvnkkyv2feWVV9SjRw/Vq1dP9erVU79+/a7aH9fHlTn5sbVr18pms2nw4MGVW+BNyNU5OXPmjBITExUWFia73a7bbrtNGzZsMFTtzcHVOVm4cKFatmwpPz8/RURE6He/+50uXLhgqNqab+vWrRo4cKDCw8Nls9n0zjvvXHObLVu26Gc/+5nsdruaN2+ulStXuj6we35ODiatXbvW8vHxsZYvX27997//tX7zm99YdevWtU6cOFFu/4cffthaunSptXfvXiszM9MaOXKkFRQUZH311VeGK6+5XJ2TH2RnZ1u33nqr1aNHD+u+++4zU+xNwtU5KS4utrp06WLdc8891kcffWRlZ2dbW7ZssTIyMgxXXnO5OierV6+27Ha7tXr1ais7O9tKT0+3wsLCrN/97neGK6+5NmzYYE2dOtV6++23LUnWunXrrtr/yJEjVp06dayJEydaBw4csBYvXmx5e3tbaWlpLo1L+PBA3bp1sxITEx2PS0pKrPDwcGvWrFkV2v7y5ctWQECAtWrVqsoq8aZzPXNy+fJl64477rD+/Oc/WwkJCYQPN3N1TpYtW2Y1bdrUunjxoqkSbzquzkliYqLVp08fp7aJEyda3bt3r9Q6b1YVCR9PPPGE1aZNG6e2oUOHWnFxcS6NxWkXD3Px4kXt3r1b/fr1c7R5eXmpX79+2r59e4X2ce7cOV26dEn169evrDJvKtc7J08//bSCg4P16KOPmijzpnI9c7J+/XrFxsYqMTFRISEhatu2rZ599lmVlJSYKrtGu545ueOOO7R7927HqZkjR45ow4YNuueee4zUjLK2b9/uNIeSFBcXV+HPnx9Uytero/J88803KikpKfNV9SEhIfr8888rtI8nn3xS4eHhZd5AuD7XMycfffSRXn31VWVkZBio8OZzPXNy5MgRffjhhxo2bJg2bNigw4cPa+zYsbp06ZJSUlJMlF2jXc+cPPzww/rmm2/085//XJZl6fLly3rsscf0hz/8wUTJKEdeXl65c1hYWKjz58/Lz8+vQvvhyMdNZvbs2Vq7dq3WrVsnX1/fqi7npvTdd99p+PDheuWVV9SwYcOqLgf/v9LSUgUHB+tPf/qTOnfurKFDh2rq1Kl66aWXqrq0m9aWLVv07LPP6sUXX9SePXv09ttv691339WMGTOqujTcII58eJiGDRvK29tbJ06ccGo/ceKEQkNDr7rtvHnzNHv2bG3cuFHt27evzDJvKq7OyRdffKGcnBwNHDjQ0VZaWipJqlWrlg4ePKhmzZpVbtE13PX8nYSFhal27dry9vZ2tMXExCgvL08XL16Uj49PpdZc013PnEybNk3Dhw/Xr3/9a0lSu3btVFRUpNGjR2vq1Kny8uLfz6aFhoaWO4eBgYEVPuohceTD4/j4+Khz587atGmTo620tFSbNm1SbGzsFbd77rnnNGPGDKWlpalLly4mSr1puDonrVq10r59+5SRkeFYBg0apN69eysjI0MREREmy6+RrufvpHv37jp8+LAjCErSoUOHFBYWRvBwg+uZk3PnzpUJGD+EQ4vfRK0SsbGxTnMoSR988MFVP3/K5dq1sKgO1q5da9ntdmvlypXWgQMHrNGjR1t169a18vLyLMuyrOHDh1tTpkxx9J89e7bl4+NjvfXWW9bx48cdy3fffVdVT6HGcXVOfoq7XdzP1Tn58ssvrYCAAGvcuHHWwYMHrX/9619WcHCw9cwzz1TVU6hxXJ2TlJQUKyAgwFqzZo115MgR6/3337eaNWtmDRkypKqeQo3z3XffWXv37rX27t1rSbLmz59v7d271zp69KhlWZY1ZcoUa/jw4Y7+P9xqO3nyZCszM9NaunQpt9reTBYvXmxFRkZaPj4+Vrdu3awdO3Y41vXs2dNKSEhwPG7SpIklqcySkpJivvAazJU5+SnCR+VwdU4+/vhj6/bbb7fsdrvVtGlTa+bMmdbly5cNV12zuTInly5dslJTU61mzZpZvr6+VkREhDV27Fjr9OnT5guvoTZv3lzu58MP85CQkGD17NmzzDYdO3a0fHx8rKZNm1orVqxweVybZXHsCgAAmMM1HwAAwCjCBwAAMIrwAQAAjCJ8AAAAowgfAADAKMIHAAAwivABAACMInwAAACjCB8AAMAowgcAADCK8AEAAIz6/wDwsOJQcaHSuAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([our_loci_highest_probs, orig_loci_highest_probs, common_loci_highest_probs], bins=10, label=[\"Our loci\", \"Orig loci\", \"Common loci\"])\n",
    "plt.legend()\n",
    "plt.title(\"Highest probabilities\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([our_n_above_1, orig_n_above_1, common_n_above_1], bins=10, label=[\"Our loci\", \"Orig loci\", \"Common loci\"])\n",
    "plt.legend()\n",
    "plt.title(\"Number of variants with PIP > 0.01\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gwas_lovro",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}