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Select related individuals for exclusion based on output from KING
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#!/usr/bin/python | |
# Run KING to generate sample QC and IBD summary stats | |
# ./king_1.9 -b <data>.bed --bysample --prefix <prefix_for_output> | |
# ./king_1.9 -b <data>.bed --kinship --related --degree 2 --prefix <prefix_for_output> | |
# | |
# Run this script to create a list of exclusions (member of pair with least data will be excluded) | |
# python king.py --prefix <prefix_for_output> --out <output_file_name> | |
# Add error if no samples in kinship file. | |
import sys | |
import os | |
import pandas as pd | |
import numpy as np | |
import argparse | |
def get_options(): | |
parser = argparse.ArgumentParser() | |
parser.add_argument('--prefix', action='store', dest='prefix', required=True, | |
help='File prefix to KING files') | |
parser.add_argument('--out', action='store', dest='out_path', required=True, | |
help='Directory path for output file') | |
args = parser.parse_args() | |
return args | |
def main(): | |
# define input/output | |
options = get_options() | |
# read kin0 file | |
related_file = options.prefix + '.kin0' | |
related = pd.read_table(related_file) | |
# read bysample file | |
qc_file = options.prefix + 'bySample.txt' | |
qc = pd.read_table(qc_file, sep=' ') | |
qc_dict = dict(zip(qc.IID, qc.Missing)) | |
# compare missing rates | |
results = [] | |
for fid1,id1,fid2,id2 in zip(related.FID1, related.ID1, related.FID2, related.ID2): | |
results.append((fid1,id1) if qc_dict[id1] >= qc_dict[id2] else (fid2,id2)) | |
# write exclusion file | |
out_file = options.out_path + '/fail_ibd.list' | |
pd.DataFrame(results).to_csv(out_file, sep=' ', header=False, index=False) | |
if __name__=='__main__': | |
main() |
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