def preprocess(self, s):
print('Acor is preprocessing', s.name, 'with refpanel=',
self.params.refpanel)
print(self.params)
annots = [
pa.Annotation(paths.annotations + aname)
for aname in self.params.annot_chr.split(',')
]
for a in annots:
print('preprocessing', a.filestem())
for c in s.chromosomes:
if not os.path.exists(
a.conv_filename(c, full=self.params.fullconv)):
conv_command = [
'python', '-u', paths.code + 'acor/acor.py',
'--annot-chr', a.stem_chr,
'--bfile-chr', self.refpanel.bfile_chr] + \
(['-fullconv'] if self.params.fullconv else []) + \
['conv',
'--chroms', str(c)]
print(' '.join(conv_command))
outfilepath = a.filestem(c) + '.' + \
('full' if self.params.fullconv else '') + \
'convbsub_out'
bsub.submit(
conv_command,
outfilepath,
jobname=self.params.annot_chr.replace('/','_') + \
',conv,chr='+str(c))
def required_files(self, s):
annots = [
pa.Annotation(paths.annotations + aname)
for aname in self.params.annot_chr.split(',')
]
return [
a.conv_filename(c, full=self.params.fullconv)
for c in s.chromosomes for a in annots
]
def run(self, s, beta_num):
print('Acor is running', s.name, 'on beta', beta_num, 'with refpanel=',
self.params.refpanel)
print(self.params)
annots = [
pa.Annotation(paths.annotations + aname)
for aname in self.params.annot_chr.split(',')
]
cmd = [
'python', '-u', paths.code + 'acor/acor.py',
'--annot-chr', ' '.join([a.stem_chr for a in annots]),
'--bfile-chr', self.refpanel.bfile_chr] + \
(['-fullconv'] if self.params.fullconv else []) + \
['cor',
'--ldscores-chr', self.refpanel.bfile_chr,
'--sumstats', s.sumstats_filename(beta_num),
'--out', self.result_filename(s, beta_num),
self.params.kind] + \
(['-reg-var'] if self.params.regvar else []) + \
(['-noweights'] if not self.params.weights else []) + \
(['-biascorrect'] if self.params.biascorrect else []) + \
(['--maf-thresh', str(self.params.maf_thresh)] if self.params.maf_thresh > 0
else []) + \
['--chroms'] + [str(c) for c in s.chromosomes]
print(' '.join(cmd))
subprocess.call(cmd)
acorresults = pd.read_csv(self.result_filename(s, beta_num) +
'.results',
delim_whitespace=True,
header=0)
rowindex = np.where(
np.concatenate([a.names(s.chromosomes[-1])
for a in annots]) == self.params.coeff)[0][0]
if self.params.print == 'all':
estimate = acorresults['MU_EST'][rowindex]
elif self.params.print == 'num':
estimate = acorresults['TOP'][rowindex]
elif self.params.print == 'denom':
estimate = acorresults['BOTTOM'][rowindex]
stderr = acorresults['MU_STDERR'][rowindex]
pval = acorresults['MU_P'][rowindex]
return pd.DataFrame(columns=['ESTIMATE', 'STDERR', 'P'],
data=[[estimate, stderr, pval]])
def run(self, s, beta_num):
print('LDSC is running', s.name, 'on beta', beta_num, 'with refpanel=',
self.params.refpanel)
print(self.params)
annots = [
pa.Annotation(paths.annotations + aname)
for aname in self.params.annot_chr.split(',')
]
if self.params.constrain_intercept:
extra_args = ['--no-intercept']
else:
extra_args = []
cmd = [
'python', '-u', paths.foreign + 'ldsc/ldsc.py', '--h2',
s.sumstats_filename(beta_num), '--ref-ld-chr', ','.join(
[a.stem_chr
for a in annots]), '--w-ld-chr', self.refpanel.bfile_chr,
'--overlap-annot', '--print-coefficients', '--chisq-max', '999999',
'--frqfile-chr', self.refpanel.bfile_chr, '--out',
self.result_filename(s, beta_num)
] + extra_args
print(' '.join(cmd))
subprocess.call(cmd)
ldscresults = pd.read_csv(self.result_filename(s, beta_num) +
'.results',
delim_whitespace=True,
header=0)
rowindex = np.where(
np.concatenate([a.names(22)
for a in annots]) == self.params.coeff)[0][0]
estimate = ldscresults['Prop._h2'][rowindex]
stderr = ldscresults['Prop._h2_std_error'][rowindex]
pval = ldscresults['Enrichment_p'][rowindex]
return pd.DataFrame(columns=['ESTIMATE', 'STDERR', 'P'],
data=[[estimate, stderr, pval]])
def preprocess(self, s):
print('LDSC is preprocessing', s.name, 'with refpanel=',
self.params.refpanel)
print(self.params)
for annot_chr in self.params.annot_chr.split(','):
a = pa.Annotation(paths.annotations + annot_chr)
for c in range(1, 23):
if not os.path.exists(a.ldscores_filename(c)):
ldscores_command = [
'python', '-u', paths.foreign + 'ldsc/ldsc.py', '--l2',
'--ld-wind-cm',
str(self.params.ld_window), '--bfile',
self.refpanel.bfile(c), '--annot',
a.annot_filename(c), '--out',
a.filestem(c)
]
print(' '.join(ldscores_command))
outfilepath = a.filestem(c) + '.ldscoresbsub_out'
bsub.submit(
ldscores_command,
outfilepath,
jobname=self.params.annot_chr.replace('/','_') + \
',ldcores,chr='+str(c))
def write(folder, args, name, background_names, mux, muy, z, corr_thresh=0.8):
print('STORYTELLING for ', name, 'z=', z)
refpanel = gd.Dataset(args.bfile_reg_chr)
annot = [ga.Annotation(a) for a in args.sannot_chr
if name in ga.Annotation(a).names(22, RV=True)][0]
backgroundannots = [ga.Annotation(a) for a in args.background_sannot_chr]
print('focal annotation columns:', annot.names(22, True))
print('background annotations:', background_names)
# get refpanel snp metadata
print('re-reading snps')
snps = pd.concat([refpanel.bim_df(c) for c in args.chroms], axis=0)
# read sumstats
print('re-reading sumstats')
ss = pd.concat([
pd.read_csv(args.pss_chr+str(c)+'.pss.gz', sep='\t', usecols=['N','Winv_ahat_I'])
for c in args.chroms])
snps['ahat'] = ss['Winv_ahat_I']
snps['N'] = ss['N']
del ss
# read annotations
print('re-reading background annotations')
for a in backgroundannots:
mynames = [n for n in a.names(22, RV=True) if '.R' in n] #names of annotations
snps = pd.concat([snps,
pd.concat([a.RV_df(c)[mynames] for c in args.chroms], axis=0)
], axis=1)
print('reading focal annotation')
snps = pd.concat([snps,
pd.concat([annot.RV_df(c)[name] for c in args.chroms], axis=0)
], axis=1)
print('residualizing background out of focal')
A = snps[background_names]
snps['chi2'] = snps.N * snps.ahat**2
snps['Rv'] = snps[name]
snps['ahat_resid'] = snps.ahat - A.values.dot(muy)
snps['Rv_resid'] = snps.Rv - A.values.dot(mux)
snps['typed'] = snps.ahat_resid.notnull()
snps = snps[snps.typed].reset_index(drop=True)
snps['significant'] = snps.chi2 > 29.716785
print(snps.significant.sum(), 'genome-wide significant SNPs')
print('searching for good windows')
# get endpoints of windows
stride = 20
windowsize_in_strides = 5
windowsize = stride * windowsize_in_strides
# find all starting points of windows containing GWAS-sig SNPs
starts = np.concatenate([
[ int(i/stride)*stride - k*stride
for k in range(0, windowsize_in_strides)]
for i in np.where(snps.significant)[0]])
starts = np.array(sorted(list(set(starts))))
# compute corresponding endpoints
ends = starts + windowsize
# truncate any windows that extend past the ends of the genome
starts = starts[ends < len(snps)]
ends = ends[ends < len(snps)]
ends = ends[starts >= 0]
starts = starts[starts >= 0]
print(len(starts), 'windows with GWAS hits found')
# compute correlations
numbers = pd.DataFrame(
np.array([[i,j,
np.max(snps.iloc[i:j].chi2),
np.corrcoef(snps.iloc[i:j].Rv_resid, snps.iloc[i:j].ahat_resid)[0,1]]
for i,j in zip(starts, ends)]),
columns=['start','end','maxchi2','corr'])
# keep only cases with strong correlations in the right direction
numbers = numbers[numbers['corr']**2 >= corr_thresh]
numbers = numbers[np.sign(numbers['corr']) == np.sign(z)]
print(len(numbers), 'windows with GWAS hits and squared correlation with Rv >=',
corr_thresh, 'in the right direction')
for i,j in zip(numbers.start, numbers.end):
i = int(i); j = int(j)
print('saving', i,j)
c = snps.iloc[i].CHR
start = snps.iloc[i].BP
end = snps.iloc[j].BP
plt.figure()
plt.scatter(snps.iloc[i:j].Rv_resid,
snps.iloc[i:j].ahat_resid * np.sqrt(snps.iloc[i:j].N))
plt.title('chr{}:{}-{}'.format(c, start, end))
plt.xlabel(r'residual $Rv$')
plt.ylabel(r'residual $Z$')
filename = '{}/chr{}:{}-{}.pdf'.format(folder, c, start, end)
fs.makedir_for_file(filename)
plt.savefig(filename)
plt.close()
def required_files(self, s):
a = pa.Annotation(paths.annotations + self.params.sannot_chr)
return [a.filestem(c) + '.testprocess' for c in s.chromosomes]
def main(args):
print('initializing...')
import gzip, gc, time
import numpy as np
import pandas as pd
import gprim.annotation as ga
import gprim.dataset as gd
import ypy.memo as memo
# basic initialization
mhc_bp = [25684587, 35455756]
refpanel = gd.Dataset(args.bfile_chr)
annots = [ga.Annotation(annot) for annot in args.sannot_chr]
# read in ld blocks, remove MHC, read SNPs to print
ldblocks = pd.read_csv(args.ld_blocks,
delim_whitespace=True,
header=None,
names=['chr', 'start', 'end'])
mhcblocks = (ldblocks.chr == 'chr6') & \
(ldblocks.end > mhc_bp[0]) & \
(ldblocks.start < mhc_bp[1])
ldblocks = ldblocks[~mhcblocks]
print(len(ldblocks), 'loci after removing MHC')
print_snps = pd.read_csv(args.print_snps, header=None, names=['SNP'])
print_snps['printsnp'] = True
print(len(print_snps), 'print snps')
# process annotations
for annot in annots:
t0 = time.time()
for c in args.chroms:
print(time.time() - t0, ': loading chr', c, 'of', args.chroms)
# get refpanel snp metadata for this chromosome
snps = refpanel.bim_df(c)
snps = ga.smart_merge(snps, refpanel.frq_df(c)[['SNP', 'MAF']])
print(len(snps), 'snps in refpanel', len(snps.columns),
'columns, including metadata')
# read in annotation
print('reading annot', annot.filestem())
names = annot.names(c) # names of annotations
namesR = [n + '.R' for n in names] # names of results
a = annot.sannot_df(c)
if 'SNP' in a.columns:
print(
'not a thinannot => doing full reconciliation of snps and allele coding'
)
snps = ga.reconciled_to(snps, a, names, missing_val=0)
else:
print(
'detected thinannot, so assuming that annotation is synched to refpanel'
)
snps = pd.concat([snps, a[names]], axis=1)
# add information on which snps to print
print('merging in print_snps')
snps = pd.merge(snps, print_snps, how='left', on='SNP')
snps.printsnp.fillna(False, inplace=True)
snps.printsnp.astype(bool)
# put on per-normalized-genotype scale
if args.alpha != -1:
print('scaling by maf according to alpha=', args.alpha)
snps[names] = snps[names].values*\
np.power(2*snps.MAF.values*(1-snps.MAF.values),
(1.+args.alpha)/2)[:,None]
# make room for RV and make sure annotation values are treated as floats
snps = pd.concat([
snps,
pd.DataFrame(np.zeros(snps[names].shape), columns=namesR)
],
axis=1)
snps[names] = snps[names].astype(float)
# compute simple statistics about annotation
print('computing basic statistics and writing')
info = pd.DataFrame(columns=[
'M', 'M_5_50', 'sqnorm', 'sqnorm_5_50', 'supp', 'supp_5_50'
])
info['name'] = names
info.set_index('name', inplace=True)
info['M'] = len(snps)
info['sqnorm'] = np.linalg.norm(snps[names], axis=0)**2
info['supp'] = np.linalg.norm(snps[names], ord=0, axis=0)
M_5_50 = (snps.MAF >= 0.05).values
info['M_5_50'] = M_5_50.sum()
info['sqnorm_5_50'] = np.linalg.norm(snps.loc[M_5_50, names],
axis=0)**2
info['supp_5_50'] = np.linalg.norm(snps.loc[M_5_50, names],
ord=0,
axis=0)
info.to_csv(annot.info_filename(c), sep='\t')
# process ldblocks one by one
for ldblock, X, meta, ind in refpanel.block_data(ldblocks,
c,
meta=snps):
if meta.printsnp.sum() == 0:
print('no print-snps in this block')
continue
print(meta.printsnp.sum(), 'print-snps')
if (meta[names] == 0).values.all():
print('annotations are all 0 in this block')
snps.loc[ind, namesR] = 0
else:
mask = meta.printsnp.values
V = meta[names].values
XV = X.dot(V)
snps.loc[ind[mask], namesR] = \
X[:,mask].T.dot(XV[:,-len(names):]) / X.shape[0]
# write
print('writing output')
with gzip.open(annot.RV_filename(c), 'w') as f:
snps.loc[snps.printsnp, ['SNP', 'A1', 'A2'] + names +
namesR].to_csv(f, index=False, sep='\t')
del snps
memo.reset()
gc.collect()
print('done')
def annotation(self):
return pa.Annotation(paths.annotations + self.params.annot_chr)
def required_files(self, s):
annots = [
pa.Annotation(paths.annotations + aname)
for aname in self.params.annot_chr.split(',')
]
return [a.ldscores_filename(c) for c in range(1, 23) for a in annots]