Select related individuals for exclusion based on output from KING

king.py

#!/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()