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0_split_ld_reference.py
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0_split_ld_reference.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Splits the LD reference panel into multiple subfiles, to greatly speed up
# GCTA-cojo. Each subfile is a 3-Mb window, so that for any top_loci variant,
# we can use a subfile that has the top_loci variant +- 1 Mb.
import pandas as pd
import os
import subprocess as sp
import argparse
def main():
# Parse args
args = parse_args()
window_size = int(3e6)
window_spacing = int(1e6)
chrom_lengths = pd.read_csv('configs/grch38_chrom_lengths.tsv', sep='\t')
# Loop through each chromosome and use plink to split the ref panel into
# overlapping windows
for index, row in chrom_lengths.iterrows():
chr_ld_path = args.path.format(chrom=row['chrom'])
print(chr_ld_path)
window_start = int(0)
while (window_start + window_size - window_spacing) < row['length']:
# Define window and output path for subfile of main LD file
window_end = window_start + window_size
out_ld_path = chr_ld_path + '.{:d}_{:d}'.format(window_start, window_end)
print("chr_ld_path:" + chr_ld_path)
print("out_ld_path:" + out_ld_path)
# plink requires a file to define the range to extract
# We don't want a temp file, so we use bash process substitution <(...)
range_str = ' '.join([str(row['chrom']), str(window_start), str(window_end), 'range1'])
cmd = '/bin/bash 0_plink_extract.sh {} {} \'{}\''.format(chr_ld_path, out_ld_path, range_str)
print(cmd)
# Run plink
os.system(cmd)
cp = sp.run(cmd, shell=True, stderr=sp.STDOUT)
if cp.returncode != 0:
print('Failed on plink command:\n{}'.format(cmd))
#return cp.returncode
window_start = window_start + window_spacing
return 0
def parse_args():
""" Load command line args """
parser = argparse.ArgumentParser()
parser.add_argument('--path',
metavar="<string>",
help='Path to LD reference; {chrom} in place of each chromosome name',
type=str,
required=True)
args = parser.parse_args()
return args
if __name__ == '__main__':
main()