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sum_stats.py
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"""
A bunch of mehtod for parsing and handling GWAS summary statistics
"""
__updated__ = '2017-01-12'
import kgenome
import scipy as sp
import h5py
from scipy import stats
import random
import getopt
import os, sys
import pandas
from itertools import izip
valid_nts = set(['A', 'T', 'C', 'G'])
lc_2_cap_map = {'a':'A', 'c':'C', 'g':'G', 't':'T'}
opp_strand_dict = {'A':'T', 'G':'C', 'T':'A', 'C':'G'}
ok_nts = set([('A', 'G'), ('G', 'A'), ('A', 'C'), ('C', 'A'), ('G', 'T'), ('T', 'G'), ('C', 'T'), ('T', 'C')])
file_format_maps = {'SS1':{'header':['snpid', 'hg18chr', 'bp', 'a1', 'a2', 'or', 'se', 'pval', 'info', 'ngt', 'CEUaf'],
'column_map':{'delimiter':'','sid':0, 'pval':7, 'odds_ratio':5, 'nt1':3, 'nt1':4,
'stat_type':'OR', 'nt_type':'CAPS', 'weights_type':'AVAIL', }},
'SS2':{'header':['hg19chrc', 'snpid', 'a1', 'a2', 'bp', 'info', 'or', 'se', 'p', 'ngt'],
'column_map':{'delimiter':'','sid':1, 'pval':8, 'odds_ratio':6, 'nt1':3, 'nt1':4,
'stat_type':'OR', 'nt_type':'CAPS', 'weights_type':'AVAIL', }},
'SSGAC1':['MarkerName', 'Effect_Allele', 'Other_Allele', 'EAF', 'Beta', 'SE', 'Pvalue'],
'SSGAC2':['MarkerName', 'Effect_Allele', 'Other_Allele', 'EAF', 'OR', 'SE', 'Pvalue'],
'CHIC':['SNP', 'CHR', 'BP', 'A1', 'A2', 'FREQ_A1', 'EFFECT_A1', 'SE', 'P'],
'GCAN':['chromosome', 'position', 'SNP', 'reference_allele', 'other_allele', 'eaf', 'OR',
'OR_se', 'OR_95L', 'OR_95U', 'z', 'p_sanger', '_-log10_p-value', 'q_statistic',
'q_p-value', 'i2', 'n_studies', 'n_samples', 'effects'],
'TESLOVICH':['MarkerName', 'Allele1', 'Allele2', 'Weight', 'GC.Zscore', 'GC.Pvalue', 'Overall', 'Direction'],
'GIANT1':['MarkerName', 'Allele1', 'Allele2', 'FreqAllele1HapMapCEU', 'b', 'se', 'p', 'N'],
'GIANT1b':['MarkerName', 'Allele1', 'Allele2', 'Freq.Allele1.HapMapCEU', 'b', 'SE', 'p', 'N'],
'GIANT1c':['MarkerName', 'Chr', 'Pos', 'Allele1', 'Allele2', 'FreqAllele1HapMapCEU', 'b', 'se', 'p', 'N'],
'GIANT2':['SNP', 'A1', 'A2', 'Freq1.Hapmap', 'b', 'se', 'p', 'N'],
'MAGIC':['snp', 'effect_allele', 'other_allele', 'maf', 'effect', 'stderr', 'pvalue'],
'CARDIoGRAM':['SNP', 'chr_pos_(b36)', 'reference_allele', 'other_allele', 'ref_allele_frequency', 'pvalue', 'het_pvalue', 'log_odds', 'log_odds_se', 'N_case', 'N_control', 'model'],
'DIAGRAM':['SNP', 'CHROMOSOME', 'POSITION', 'RISK_ALLELE', 'OTHER_ALLELE', 'P_VALUE', 'OR', 'OR_95L', 'OR_95U', 'N_CASES', 'N_CONTROLS'],
'TAG':['CHR', 'SNP', 'BP', 'A1', 'A2', 'FRQ_A', 'FRQ_U', 'INFO', 'OR', 'SE', 'P'],
'CD':['CHR', 'SNP', 'BP', 'A1', 'A2', 'FRQ_A_5956', 'FRQ_U_14927', 'INFO', 'OR', 'SE', 'P', 'Direction', 'HetISqt', 'HetPVa'],
'UC':['CHR', 'SNP', 'BP', 'A1', 'A2', 'FRQ_A_6968', 'FRQ_U_20464', 'INFO', 'OR', 'SE', 'P', 'Direction', 'HetISqt', 'HetPVa'],
'IBD': ['CHR', 'SNP', 'BP', 'A1', 'A2', 'FRQ_A_12882', 'FRQ_U_21770', 'INFO', 'OR', 'SE', 'P', 'Direction', 'HetISqt', 'HetPVa'],
'GEFOS':['chromosome', 'position', 'rs_number', 'reference_allele', 'other_allele', 'eaf', 'beta', 'se', 'beta_95L', 'beta_95U', 'z', 'p-value', '_-log10_p-value', 'q_statistic', 'q_p-value', 'i2', 'n_studies', 'n_samples', 'effects'],
'RA':['SNPID', 'Chr', 'Position(hg19)', 'A1', 'A2', 'OR(A1)', 'OR_95%CIlow', 'OR_95%CIup', 'P-val'],
'ASTHMA':['Chr', 'rs', 'position', 'Allele_1', 'Allele_2', 'freq_all_1_min', 'freq_all_1_max', 'OR_fix', 'ORl_fix', 'ORu_fix', 'P_fix'],
'ICBP': ['ID', 'Analysis', 'ID', 'SNP', 'ID', 'P-value', 'Rank', 'Plot', 'data', 'Chr', 'ID', 'Chr', 'Position', 'Submitted', 'SNP', 'ID', 'ss2rs', 'rs2genome', 'Allele1', 'Allele2', 'Minor', 'allele', 'pHWE', 'Call', 'Rate', 'Effect', 'SE', 'R-Squared', 'Coded', 'Allele', 'Sample', 'size', 'Bin', 'ID'],
'GLC': ['SNP_hg18', 'SNP_hg19', 'rsid', 'A1', 'A2', 'beta', 'se', 'N', 'P-value', 'Freq.A1.1000G.EUR'],
}
def _get_format_(header):
for k,v in headers:
if v==header:
return k
raise Exception('File format unknown')
column_maps = {
'SS1':{'delimiter':'','sid':0, 'pval':5, 'odds_ratio':7, 'nt1':3, 'nt1':4,
'stat_type':'OR', 'nt_type':'CAPS', 'weights_type':'AVAIL', },
'SS2':{'delimiter':'','sid':1, 'pval':8, 'odds_ratio':6, 'nt1':3, 'nt1':4,
'stat_type':'OR', 'nt_type':'CAPS', 'weights_type':'AVAIL', }
}
def _parse_sum_stats_file_(f, column_map, sid_map, chrom_dict, print_mod_size=100000):
line_i = 0
for line in f:
line_i += 1
l = line.split(column_map['delimiter'])
sid = l[column_map['sid']]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[column_map['pval']])
chrom_dict[chrom]['ps'].append(pval)
if column_map['stat_type']=='OR':
raw_beta = -sp.log(float(l[column_map['odds_ratio']]))
elif column_map['stat_type']=='LOR':
raw_beta = float(l[column_map['log_odds_ratio']])
elif column_map['stat_type']=='Z':
raw_beta = float(l[column_map['z_stat']])
if random.random() > 0.5:
if column_map['nt_type']=='CAPS':
nt = [l[column_map['nt1']], l[column_map['nt2']]]
elif column_map['nt_type']=='LOWERCAPS':
nt = [lc_2_cap_map[l[column_map['nt1']]], lc_2_cap_map[l[column_map['nt2']]]]
else:
if column_map['nt_type']=='CAPS':
nt = [l[column_map['nt2']], l[column_map['nt1']]]
elif column_map['nt_type']=='LOWERCAPS':
nt = [lc_2_cap_map[l[column_map['nt2']]], lc_2_cap_map[l[column_map['nt1']]]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
if column_map['weights_type']=='FROM_Z_SCORE':
weight = z ** 2 / ((raw_beta ** 2) * 2 * eur_maf * (1 - eur_maf))
elif column_map['weights_type']=='AVAIL':
weight = l[column_map['weight']]
chrom_dict[chrom]['weights'].append(weight)
if line_i % print_mod_size == 0:
print line_i
def parse_sum_stats(filename,
comb_hdf5_file,
ss_id,
KGpath,
bimfile=None,):
"""
"""
h5f = h5py.File(comb_hdf5_file)
if bimfile != None:
print 'Parsing SNP list'
valid_sids = set()
print 'Parsing bim file: %s' % bimfile
with open(bimfile) as f:
for line in f:
l = line.split()
valid_sids.add(l[1])
print len(valid_sids)
chrom_dict = {}
print 'Retrieving 1K genomes positions..'
sids = []
with open(filename) as f:
line = f.next()
header = line.split()
file_format = _get_format_(header)
column_map = column_maps[file_format]
for line in f:
l = line.split(column_map['delimiter'])
sids.append(l[column_map['sid']])
sid_map = kgenome.get_sid_pos_map(sids, KGpath)
if len(sid_map) > 0:
raise Exception('Unable to find SNP IDs')
print 'Parsing the file: %s' % filename
with open(filename) as f:
line = f.next()
header = line.split()
file_format = _get_format_(header)
_parse_sum_stats_file_(f, column_maps[file_format], sid_map, chrom_dict)
elif header == ['hg19chrc', 'snpid', 'a1', 'a2', 'bp', 'info', 'or', 'se', 'p', 'ngt']:
for line in f:
line_i += 1
l = line.split()
sid = l[1]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[8])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = -sp.log(float(l[6]))
if random.random() > 0.5:
nt = [l[2], l[3]]
else:
nt = [l[3], l[2]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = z ** 2 / ((raw_beta ** 2) * 2 * eur_maf * (1 - eur_maf))
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == ['snpid', 'hg18chr', 'bp', 'a1', 'a2', 'zscore', 'pval', 'CEUmaf']:
for line in f:
line_i += 1
l = line.split()
sid = l[0]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[6])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = float(l[5])
if random.random() > 0.5:
nt = [l[3], l[4]]
else:
nt = [l[4], l[3]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = z ** 2 / ((raw_beta ** 2) * 2 * eur_maf * (1 - eur_maf))
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == ['SNP', 'CHR', 'BP', 'A1', 'A2', 'OR', 'SE', 'P', 'INFO', 'EUR_FRQ']:
for line in f:
line_i += 1
l = line.split()
sid = l[0]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[7])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = -sp.log(float(l[5]))
if random.random() > 0.5:
nt = [l[3], l[4]]
else:
nt = [l[4], l[3]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = z ** 2 / ((raw_beta ** 2) * 2 * eur_maf * (1 - eur_maf))
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == ['Chromosome', 'Position', 'MarkerName', 'Effect_allele', 'Non_Effect_allele', 'Beta', 'SE', 'Pvalue']:
for line in f:
line_i += 1
l = line.split()
sid = l[2]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[7])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = float(l[5])
if random.random() > 0.5:
nt = [l[3], l[4]]
else:
nt = [l[4], l[3]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = z ** 2 / ((raw_beta ** 2) * 2 * eur_maf * (1 - eur_maf))
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == headers['SSGAC1']:
for line in f:
line_i += 1
l = line.split()
sid = l[0]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[6])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = float(l[4])
if random.random() > 0.5:
nt = [l[1], l[2]]
else:
nt = [l[2], l[1]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = z ** 2 / ((raw_beta ** 2) * 2 * eur_maf * (1 - eur_maf))
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == headers['SSGAC2']:
for line in f:
line_i += 1
l = line.split()
sid = l[0]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[6])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = sp.log(float(l[4]))
if random.random() > 0.5:
nt = [l[1], l[2]]
else:
nt = [l[2], l[1]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = z ** 2 / ((raw_beta ** 2) * 2 * eur_maf * (1 - eur_maf))
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == headers['CHIC']:
for line in f:
line_i += 1
l = line.split()
sid = l[0]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[8])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = float(l[8])
if random.random() > 0.5:
nt = [l[3], l[4]]
else:
nt = [l[4], l[3]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = z ** 2 / ((raw_beta ** 2) * 2 * eur_maf * (1 - eur_maf))
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == headers['GCAN']:
for line in f:
line_i += 1
l = line.split()
sid = l[2]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[11])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = -sp.log(float(l[6]))
if random.random() > 0.5:
nt = [l[3], l[4]]
else:
nt = [l[4], l[3]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = z ** 2 / ((raw_beta ** 2) * 2 * eur_maf * (1 - eur_maf))
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == headers['TESLOVICH']:
for line in f:
line_i += 1
l = line.split()
sid = l[0]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[5])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = float(l[4])
if random.random() > 0.5:
nt = [lc_2_cap_map[l[1]], lc_2_cap_map[l[2]]]
else:
nt = [lc_2_cap_map[l[2]], lc_2_cap_map[l[1]]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
chrom_dict[chrom]['weights'].append(int(float(l[3])))
if line_i % 100000 == 0:
print line_i
elif header == headers['GIANT1'] or header == headers['GIANT1b'] or header == headers['GIANT2']:
for line in f:
line_i += 1
l = line.split()
sid = l[0]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[6])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = float(l[4])
if random.random() > 0.5:
nt = [l[1], l[2]]
else:
nt = [l[2], l[1]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
chrom_dict[chrom]['weights'].append(int(float(l[7])))
if line_i % 100000 == 0:
print line_i
elif header == headers['GIANT1c']:
for line in f:
line_i += 1
l = line.split()
sid = l[0]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[8])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = float(l[6])
if random.random() > 0.5:
nt = [l[3], l[4]]
else:
nt = [l[4], l[3]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
chrom_dict[chrom]['weights'].append(int(float(l[9])))
if line_i % 100000 == 0:
print line_i
elif header == headers['MAGIC']:
for line in f:
line_i += 1
l = line.split()
sid = l[0]
d = sid_map.get(sid, None)
eur_maf = float(l[3])
if d is not None and eur_maf > 0:
raw_beta = float(l[4])
if raw_beta == 0:
continue
pos = d['pos']
chrom = d['chrom']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[6])
chrom_dict[chrom]['ps'].append(pval)
if random.random() > 0.5:
nt = [lc_2_cap_map[l[1]], lc_2_cap_map[l[2]]]
else:
nt = [lc_2_cap_map[l[2]], lc_2_cap_map[l[1]]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = z ** 2 / ((raw_beta ** 2) * 2 * eur_maf * (1 - eur_maf))
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == headers['CARDIoGRAM']:
for line in f:
line_i += 1
l = line.split()
sid = l[0]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[5])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = float(l[7])
if random.random() > 0.5:
nt = [l[2], l[3]]
else:
nt = [l[3], l[2]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = float(l[9]) + float(l[10])
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == headers['DIAGRAM']:
for line in f:
line_i += 1
l = line.split()
sid = l[0]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[5])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = sp.log(float(l[6]))
if random.random() > 0.5:
nt = [l[3], l[4]]
else:
nt = [l[4], l[3]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = float(l[9]) + float(l[10])
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == headers['TAG']:
for line in f:
line_i += 1
l = line.split()
sid = l[1]
d = sid_map.get(sid, None)
if d is not None:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[10])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = float(l[8])
if random.random() > 0.5:
nt = [l[3], l[4]]
else:
nt = [l[4], l[3]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = z ** 2 / ((raw_beta ** 2) * 2 * eur_maf * (1 - eur_maf))
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == headers['CD'] or header == headers['UC'] or header == headers['IBD']:
# ['CHR', 'SNP', 'BP', 'A1', 'A2', 'FRQ_A_12882', 'FRQ_U_21770', 'INFO', 'OR', 'SE', 'P', 'Direction', 'HetISqt', 'HetPVa']
for line in f:
line_i += 1
l = line.split()
sid = l[1]
info_score = float(l[7])
d = sid_map.get(sid, None)
if d is not None and info_score > 0.8:
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[10])
chrom_dict[chrom]['ps'].append(pval)
raw_beta = sp.log(float(l[8]))
if random.random() > 0.5:
nt = [l[3], l[4]]
else:
nt = [l[4], l[3]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
direction = l[11]
num_studies = len(direction) - direction.count('?')
weight = num_studies
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == headers['GEFOS']:
for line in f:
line_i += 1
l = line.split()
sid = l[2]
d = sid_map.get(sid, None)
eur_maf = float(l[5])
if d is not None and eur_maf > 0 and eur_maf < 1:
raw_beta = float(l[6])
if raw_beta == 0:
continue
pos = d['pos']
chrom = d['chrom']
# eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[11])
chrom_dict[chrom]['ps'].append(pval)
if random.random() > 0.5:
nt = [l[3], l[4]]
else:
nt = [l[4], l[3]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = z ** 2 / ((raw_beta ** 2) * 2 * eur_maf * (1 - eur_maf))
# weight = (z/raw_beta)**2
# weight = float(l[16]) # Number of studies used.
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
# 'RA':['SNPID','Chr','Position(hg19)','A1','A2','OR(A1)','OR_95%CIlow','OR_95%CIup','P-val'],
# 'ASTHMA':['Chr', 'rs', 'position', 'Allele_1', 'Allele_2', 'freq_all_1_min', 'freq_all_1_max', 'OR_fix', 'ORl_fix', 'ORu_fix', 'P_fix'],
elif header == headers['RA']:
for line in f:
line_i += 1
l = line.split()
sid = l[0]
d = sid_map.get(sid, None)
if d is not None:
raw_beta = sp.log(float(l[5]))
eur_maf = d['eur_maf']
if raw_beta == 0 or eur_maf == 0:
continue
pos = d['pos']
chrom = d['chrom']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
pval = float(l[8])
chrom_dict[chrom]['ps'].append(pval)
if random.random() > 0.5:
nt = [l[3], l[4]]
else:
nt = [l[4], l[3]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = z ** 2 / ((raw_beta ** 2) * 2 * eur_maf * (1 - eur_maf))
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == headers['ASTHMA']:
for line in f:
line_i += 1
l = line.split()
sid = l[1]
d = sid_map.get(sid, None)
if d is not None:
eur_maf = d['eur_maf']
odd_rat = float(l[7])
pval = float(l[10])
if odd_rat == 0 or pval == 0 or eur_maf == 0:
continue
raw_beta = sp.log(odd_rat)
pos = d['pos']
chrom = d['chrom']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
chrom_dict[chrom]['ps'].append(pval)
if random.random() > 0.5:
nt = [l[3], l[4]]
else:
nt = [l[4], l[3]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = z ** 2 / ((raw_beta ** 2) * 2 * eur_maf * (1 - eur_maf))
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
elif header == headers['ICBP']:
for line in f:
line_i += 1
l = line.split()
sid = l[2]
d = sid_map.get(sid, None)
coded_allele = l[16]
if d is not None and coded_allele in valid_nts:
# raw_beta = sp.log(float(l[7]))
pval = float(l[3])
if pval == 0:
continue
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
chrom_dict[chrom]['ps'].append(pval)
# if random.random()>0.5:
nt = [l[11], l[12]]
sign = 1
# else:
# sign = -1
if coded_allele == nt[1] or opp_strand_dict[coded_allele] == nt[1]:
nt = [l[12], l[11]]
sign = -1 # *sign
# else:
# assert coded_allele==nt[0] or opp_strand_dict[coded_allele]==nt[0]
chrom_dict[chrom]['nts'].append(nt)
z = sign * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = float(l[17])
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
# 'GLC': ['SNP_hg18', 'SNP_hg19', 'rsid', 'A1', 'A2', 'beta', 'se', 'N', 'P-value', 'Freq.A1.1000G.EUR'],
elif header == headers['GLC']:
for line in f:
line_i += 1
l = line.split()
sid = l[2]
d = sid_map.get(sid, None)
if d is not None :
raw_beta = float(l[5])
pval = float(l[8])
pos = d['pos']
chrom = d['chrom']
eur_maf = d['eur_maf']
if not chrom in chrom_dict.keys():
chrom_dict[chrom] = {'ps':[], 'zs':[], 'nts': [], 'sids': [],
'positions': [], 'eur_maf':[], 'weights':[]}
chrom_dict[chrom]['sids'].append(sid)
chrom_dict[chrom]['positions'].append(pos)
chrom_dict[chrom]['eur_maf'].append(eur_maf)
chrom_dict[chrom]['ps'].append(pval)
if random.random() > 0.5:
nt = [lc_2_cap_map[l[3]], lc_2_cap_map[l[4]]]
else:
nt = [lc_2_cap_map[l[4]], lc_2_cap_map[l[3]]]
raw_beta = -raw_beta
chrom_dict[chrom]['nts'].append(nt)
z = sp.sign(raw_beta) * stats.norm.ppf(pval / 2.0)
chrom_dict[chrom]['zs'].append(z)
weight = float(l[7])
chrom_dict[chrom]['weights'].append(weight)
if line_i % 100000 == 0:
print line_i
else:
raise Exception('Wrong or unknown file format')
assert sp.all(sp.isreal(chrom_dict[1]['zs'])), 'WTF?'
print 'SS file loaded, now sorting and storing in HDF5 file.'
assert not ss_id in h5f.keys(), 'Summary stats with this name are already in the HDF5 file?'
ssg = h5f.create_group(ss_id)
num_snps = 0
for chrom in chrom_dict.keys():
print 'Parsed summary stats for %d SNPs on chromosome %d' % (len(chrom_dict[chrom]['positions']), chrom)
sl = zip(chrom_dict[chrom]['positions'], chrom_dict[chrom]['sids'], chrom_dict[chrom]['nts'],
chrom_dict[chrom]['ps'], chrom_dict[chrom]['zs'], chrom_dict[chrom]['eur_maf'],
chrom_dict[chrom]['weights'])
sl.sort()
ps = []
nts = []
sids = []
positions = []
zs = []
eur_mafs = []
weights = []
prev_pos = -1
for pos, sid, nt, p, z, eur_maf, weight in sl:
if pos == prev_pos:
print 'duplicated position %d' % pos
continue
else:
prev_pos = pos
ps.append(p)
nts.append(nt)
sids.append(sid)
positions.append(pos)
zs.append(z)
eur_mafs.append(eur_maf)
weights.append(weight)
g = ssg.create_group('chrom_%d' % chrom)
g.create_dataset('ps', data=sp.array(ps))
g.create_dataset('nts', data=nts)
g.create_dataset('sids', data=sids)
g.create_dataset('eur_mafs', data=eur_mafs)
g.create_dataset('positions', data=positions)
g.create_dataset('zs', data=zs)
g.create_dataset('weights', data=weights)
num_snps += len(sids)
h5f.flush()
print 'In all, %d SNPs parsed from summary statistics file.' % num_snps
def coordinate_sum_stats(comb_hdf5_file, coord_hdf5_file, filter_ambiguous_nts=True,
ss_labs=None, weight_min=0, weight_max_diff=1,
outlier_thres=0, sd_thres=0, iq_range=None, min_maf=0):
"""
Coordinate multiple summary statistics
"""
h5f = h5py.File(comb_hdf5_file)
oh5f = h5py.File(coord_hdf5_file)
if ss_labs is None:
sums_ids = h5f.keys()
sums_ids = [x.encode('UTF8') for x in sums_ids]
else:
all_sums_ids = h5f.keys()
all_sums_ids = [x.encode('UTF8') for x in all_sums_ids]
sums_ids = []
for ss_lab in all_sums_ids:
if ss_lab in ss_labs:
sums_ids.append(ss_lab)
print 'Coordinating summary statistics: ' + ' '.join(sums_ids)
oh5f.create_dataset('sums_ids', data=sums_ids)
for chrom in range(1, 23):
chrom_str = 'chrom_%d' % chrom
common_sids = set(h5f[sums_ids[0]][chrom_str]['sids'][...])
for sums_id in sums_ids[1:]:
chr_g = h5f[sums_id][chrom_str]
sids = chr_g['sids'][...]
print len(sids)
common_sids = common_sids.intersection(sids)
num_sids = len(common_sids)
common_sids = sp.array(list(common_sids))
print 'Found %d SNPs in common on chromosome %d' % (num_sids, chrom)
# Use order and information from first summary stats dataset.
# Store the OK sids in the common_sids, which we'll update accordingly..
chr_g = h5f[sums_ids[0]][chrom_str]
sids = chr_g['sids'][...]
sids_map = sp.in1d(sids, common_sids)
filtered_sids = sids[sids_map]
assert len(filtered_sids) == len(common_sids), 'WTF?'
common_sids = filtered_sids # To ensure that they are ordered by position, etc.
if filter_ambiguous_nts:
# Filter SNPs with ambiguous NTs
ok_sids = set()
for sums_id in sums_ids:
chr_g = h5f[sums_id][chrom_str]
sids1 = chr_g['sids'][...]
sids_map = sp.in1d(sids1, common_sids)
nts1 = chr_g['nts'][...][sids_map]
sids1 = sids1[sids_map]
assert len(sids1) == len(common_sids), 'WTF?'
for sid, nt in izip(sids1, nts1):
nt_tuple = tuple(nt)
if nt_tuple in ok_nts:
ok_sids.add(sid)
print "%d SNPs were found with ambiguous or weird nucleotides in some dataset." % (len(sids) - len(ok_sids))
# If ambiguous SNPs found, then update the SNP map
if len(ok_sids) < len(common_sids):
chr_g = h5f[sums_ids[0]][chrom_str]
sids = chr_g['sids'][...]
sids_map = sp.in1d(sids, sp.array(list(ok_sids)))
common_sids = sids[sids_map] # To ensure that they are ordered by the order in the first sum stats
if weight_min > 0 or weight_max_diff < 1 or outlier_thres > 0 or sd_thres > 0 or min_maf > 0 or iq_range is not None:
# Filtering SNPs with weight differences.
# Calculating the relative weights
n_snps = len(common_sids)
n_sums = len(sums_ids)
rel_weights_mat = sp.zeros((n_snps, n_sums))
weights_filter = sp.ones((n_snps), dtype='bool')
for s_i, sums_id in enumerate(sums_ids):
chr_g = h5f[sums_id][chrom_str]
sids1 = chr_g['sids'][...]
sids_map1 = sp.in1d(sids1, common_sids)
weights = chr_g['weights'][...][sids_map1]
eur_mafs = chr_g['eur_mafs'][...][sids_map1]
# print weights
sids1 = sids1[sids_map1]