def test_betaprod(self):
x = ps.betaprod('test/parse_test/test.betaprod')
self.assertEqual(list(x['SNP']), ['rs1', 'rs2', 'rs3'])
self.assertEqual(list(x['N1']), [100, 100, 100])
self.assertEqual(list(x['N2']), [5, 5, 5])
self.assertEqual(list(x.columns),
['SNP', 'N1', 'BETAHAT1', 'N2', 'BETAHAT2'])
self.assertEqual(list(x['BETAHAT1']), [-1, 1, -1])
self.assertEqual(list(x['BETAHAT2']), [1, -1, 1])
def sumstats(args):
'''
Wrapper function for estmating
1. h2 / partitioned h2
2. genetic covariance / correlation
3. LD Score regression intercept
from reference panel LD and GWAS summary statistics.
'''
# open output files
log = logger(args.out + ".log")
log.log(args)
# read .chisq or betaprod
try:
if args.sumstats_h2:
sumstats = ps.chisq(args.sumstats_h2)
elif args.sumstats_intercept:
sumstats = ps.chisq(args.sumstats_intercept)
elif args.sumstats_gencor:
sumstats = ps.betaprod(args.sumstats_gencor)
except ValueError as e:
log.log('Error parsing summary statistics.')
raise e
log_msg = 'Read summary statistics for {N} SNPs.'
log.log(log_msg.format(N=len(sumstats)))
# read reference panel LD Scores and .M
try:
if args.ref_ld:
ref_ldscores = ps.ldscore(args.ref_ld)
M_annot = ps.M(args.ref_ld)
elif args.ref_ld_chr:
ref_ldscores = ps.ldscore(args.ref_ld_chr, 22)
M_annot = ps.M(args.ref_ld_chr, 22)
except ValueError as e:
log.log('Error parsing reference LD.')
raise e
if np.any(ref_ldscores.iloc[:,
1:len(ref_ldscores.columns)].var(axis=0) == 0):
raise ValueError('Zero-variance LD Score. Possibly an empty column?')
log_msg = 'Read reference panel LD Scores for {N} SNPs.'
log.log(log_msg.format(N=len(ref_ldscores)))
# read regression SNP LD Scores
try:
if args.regression_snp_ld:
w_ldscores = ps.ldscore(args.regression_snp_ld)
elif args.regression_snp_ld_chr:
w_ldscores = ps.ldscore(args.regression_snp_ld_chr, 22)
except ValueError as e:
log.log('Error parsing regression SNP LD')
raise e
w_ldscores.columns = ['SNP', 'LD_weights'
] #to keep the column names from being the same
log_msg = 'Read LD Scores for {N} SNPs to be retained for regression.'
log.log(log_msg.format(N=len(w_ldscores)))
# merge with reference panel LD Scores
sumstats = pd.merge(sumstats, ref_ldscores, how="inner", on="SNP")
log_msg = 'After merging with reference panel LD, {N} SNPs remain.'
log.log(log_msg.format(N=len(sumstats)))
# merge with regression SNP LD Scores
sumstats = pd.merge(sumstats, w_ldscores, how="inner", on="SNP")
log_msg = 'After merging with regression SNP LD, {N} SNPs remain.'
log.log(log_msg.format(N=len(sumstats)))
# this has to be here, because pandas will modify duplicate column names on merge
### TODO still not quite satisfactory -- what if user accidentally submits
# the same file for ref_ld and w_ld?
# maybe don't merge, but just get the row numbers to prevent modification of colnames??
ref_ld_colnames = ref_ldscores.columns[1:len(ref_ldscores.columns)]
w_ld_colname = sumstats.columns[-1]
del (ref_ldscores)
del (w_ldscores)
err_msg = 'No SNPs retained for analysis after filtering on {C} {P} {F}.'
log_msg = 'After filtering on {C} {P} {F}, {N} SNPs remain.'
loop = ['1', '2'] if args.sumstats_gencor else ['']
var_to_arg = {
'infomax': args.info_max,
'infomin': args.info_min,
'maf': args.maf
}
var_to_cname = {'infomax': 'INFO', 'infomin': 'INFO', 'maf': 'MAF'}
var_to_pred = {
'infomax': lambda x: x < args.info_max,
'infomin': lambda x: x > args.info_min,
'maf': lambda x: x > args.maf
}
var_to_predstr = {'infomax': '<', 'infomin': '>', 'maf': '>'}
for v in var_to_arg.keys():
arg = var_to_arg[v]
pred = var_to_pred[v]
pred_str = var_to_predstr[v]
for p in loop:
cname = var_to_cname[v] + p
if arg is not None:
sumstats = ps.filter_df(sumstats, cname, pred)
snp_count = len(sumstats)
if snp_count == 0:
raise ValueError(err_msg.format(C=cname, F=arg,
P=pred_str))
else:
log.log(
log_msg.format(C=cname, F=arg, N=snp_count,
P=pred_str))
# LD Score regression intercept
if args.sumstats_intercept:
log.log('Estimating LD Score regression intercept')
# filter out large-effect loci
max_N = np.max(sumstats['N'])
max_chisq = max(0.01 * max_N, 20)
sumstats = sumstats[sumstats['CHISQ'] < max_chisq]
log_msg = 'After filtering on chi^2 < {C}, {N} SNPs remain.'
snp_count = len(sumstats)
if snp_count == 0:
raise ValueError(log_msg.format(C=max_chisq, N='no'))
else:
log.log(log_msg.format(C=max_chisq, N=len(sumstats)))
snp_count = len(sumstats)
n_annot = len(ref_ld_colnames)
ref_ld = np.matrix(sumstats[ref_ld_colnames]).reshape(
(snp_count, n_annot))
w_ld = np.matrix(sumstats[w_ld_colname]).reshape((snp_count, 1))
M_annot = np.matrix(M_annot).reshape((1, n_annot))
chisq = np.matrix(sumstats.CHISQ).reshape((snp_count, 1))
N = np.matrix(sumstats.N).reshape((snp_count, 1))
del sumstats
h2hat = jk.Hsq(chisq, ref_ld, w_ld, N, M_annot, args.num_blocks)
log.log(_print_intercept(h2hat))
return h2hat
# LD Score regression to estimate h2
elif args.sumstats_h2:
log.log('Estimating heritability.')
snp_count = len(sumstats)
n_annot = len(ref_ld_colnames)
ref_ld = np.matrix(sumstats[ref_ld_colnames]).reshape(
(snp_count, n_annot))
w_ld = np.matrix(sumstats[w_ld_colname]).reshape((snp_count, 1))
M_annot = np.matrix(M_annot).reshape((1, n_annot))
chisq = np.matrix(sumstats.CHISQ).reshape((snp_count, 1))
N = np.matrix(sumstats.N).reshape((snp_count, 1))
del sumstats
h2hat = jk.Hsq(chisq, ref_ld, w_ld, N, M_annot, args.num_blocks)
log.log(_print_hsq(h2hat, ref_ld_colnames))
return [M_annot, h2hat]
# LD Score regression to estimate genetic correlation
elif args.sumstats_gencor:
log.log('Estimating genetic correlation.')
snp_count = len(sumstats)
n_annot = len(ref_ld_colnames)
ref_ld = np.matrix(sumstats[ref_ld_colnames]).reshape(
(snp_count, n_annot))
w_ld = np.matrix(sumstats[w_ld_colname]).reshape((snp_count, 1))
M_annot = np.matrix(M_annot).reshape((1, n_annot))
betahat1 = np.matrix(sumstats.BETAHAT1).reshape((snp_count, 1))
betahat2 = np.matrix(sumstats.BETAHAT2).reshape((snp_count, 1))
N1 = np.matrix(sumstats.N1).reshape((snp_count, 1))
N2 = np.matrix(sumstats.N2).reshape((snp_count, 1))
del sumstats
gchat = jk.Gencor(betahat1, betahat2, ref_ld, w_ld, N1, N2, M_annot,
args.overlap, args.rho, args.num_blocks)
log.log('\n')
log.log('Heritability of first phenotype')
log.log('-------------------------------')
log.log(_print_hsq(gchat.hsq1, ref_ld_colnames))
log.log('\n')
log.log('Heritability of second phenotype')
log.log('--------------------------------')
log.log(_print_hsq(gchat.hsq2, ref_ld_colnames))
log.log('\n')
log.log('Genetic Covariance')
log.log('------------------')
log.log(_print_gencov(gchat.gencov, ref_ld_colnames))
log.log('\n')
log.log('Genetic Correlation')
log.log('-------------------')
log.log(_print_gencor(gchat))
return [M_annot, gchat]
def sumstats(args):
'''
Wrapper function for estmating
1. h2 / partitioned h2
2. genetic covariance / correlation
3. LD Score regression intercept
from reference panel LD and GWAS summary statistics.
'''
# open output files
log = logger(args.out + ".log")
log.log(args)
# read .chisq or betaprod
try:
if args.sumstats_h2:
sumstats = ps.chisq(args.sumstats_h2)
elif args.sumstats_intercept:
sumstats = ps.chisq(args.sumstats_intercept)
elif args.sumstats_gencor:
sumstats = ps.betaprod(args.sumstats_gencor)
except ValueError as e:
log.log('Error parsing summary statistics.')
raise e
log_msg = 'Read summary statistics for {N} SNPs.'
log.log(log_msg.format(N=len(sumstats)))
# read reference panel LD Scores and .M
try:
if args.ref_ld:
ref_ldscores = ps.ldscore(args.ref_ld)
M_annot = ps.M(args.ref_ld)
elif args.ref_ld_chr:
ref_ldscores = ps.ldscore(args.ref_ld_chr,22)
M_annot = ps.M(args.ref_ld_chr, 22)
except ValueError as e:
log.log('Error parsing reference LD.')
raise e
if np.any(ref_ldscores.iloc[:,1:len(ref_ldscores.columns)].var(axis=0) == 0):
raise ValueError('Zero-variance LD Score. Possibly an empty column?')
log_msg = 'Read reference panel LD Scores for {N} SNPs.'
log.log(log_msg.format(N=len(ref_ldscores)))
# read regression SNP LD Scores
try:
if args.regression_snp_ld:
w_ldscores = ps.ldscore(args.regression_snp_ld)
elif args.regression_snp_ld_chr:
w_ldscores = ps.ldscore(args.regression_snp_ld_chr, 22)
except ValueError as e:
log.log('Error parsing regression SNP LD')
raise e
w_ldscores.columns = ['SNP','LD_weights'] #to keep the column names from being the same
log_msg = 'Read LD Scores for {N} SNPs to be retained for regression.'
log.log(log_msg.format(N=len(w_ldscores)))
# merge with reference panel LD Scores
sumstats = pd.merge(sumstats, ref_ldscores, how="inner", on="SNP")
log_msg = 'After merging with reference panel LD, {N} SNPs remain.'
log.log(log_msg.format(N=len(sumstats)))
# merge with regression SNP LD Scores
sumstats = pd.merge(sumstats, w_ldscores, how="inner", on="SNP")
log_msg = 'After merging with regression SNP LD, {N} SNPs remain.'
log.log(log_msg.format(N=len(sumstats)))
# this has to be here, because pandas will modify duplicate column names on merge
### TODO still not quite satisfactory -- what if user accidentally submits
# the same file for ref_ld and w_ld?
# maybe don't merge, but just get the row numbers to prevent modification of colnames??
ref_ld_colnames = ref_ldscores.columns[1:len(ref_ldscores.columns)]
w_ld_colname = sumstats.columns[-1]
del(ref_ldscores); del(w_ldscores)
err_msg = 'No SNPs retained for analysis after filtering on {C} {P} {F}.'
log_msg = 'After filtering on {C} {P} {F}, {N} SNPs remain.'
loop = ['1','2'] if args.sumstats_gencor else ['']
var_to_arg = {'infomax': args.info_max, 'infomin': args.info_min, 'maf': args.maf}
var_to_cname = {'infomax': 'INFO', 'infomin': 'INFO', 'maf': 'MAF'}
var_to_pred = {'infomax': lambda x: x < args.info_max,
'infomin': lambda x: x > args.info_min,
'maf': lambda x: x > args.maf}
var_to_predstr = {'infomax': '<', 'infomin': '>', 'maf': '>'}
for v in var_to_arg.keys():
arg = var_to_arg[v]; pred = var_to_pred[v]; pred_str = var_to_predstr[v]
for p in loop:
cname = var_to_cname[v] + p;
if arg is not None:
sumstats = ps.filter_df(sumstats, cname, pred)
snp_count = len(sumstats)
if snp_count == 0:
raise ValueError(err_msg.format(C=cname, F=arg, P=pred_str))
else:
log.log(log_msg.format(C=cname, F=arg, N=snp_count, P=pred_str))
# LD Score regression intercept
if args.sumstats_intercept:
log.log('Estimating LD Score regression intercept')
# filter out large-effect loci
max_N = np.max(sumstats['N'])
max_chisq = max(0.01*max_N, 20)
sumstats = sumstats[sumstats['CHISQ'] < max_chisq]
log_msg = 'After filtering on chi^2 < {C}, {N} SNPs remain.'
snp_count = len(sumstats)
if snp_count == 0:
raise ValueError(log_msg.format(C=max_chisq, N='no'))
else:
log.log(log_msg.format(C=max_chisq, N=len(sumstats)))
snp_count = len(sumstats); n_annot = len(ref_ld_colnames)
ref_ld = np.matrix(sumstats[ref_ld_colnames]).reshape((snp_count, n_annot))
w_ld = np.matrix(sumstats[w_ld_colname]).reshape((snp_count, 1))
M_annot = np.matrix(M_annot).reshape((1, n_annot))
chisq = np.matrix(sumstats.CHISQ).reshape((snp_count, 1))
N = np.matrix(sumstats.N).reshape((snp_count,1))
del sumstats
h2hat = jk.Hsq(chisq, ref_ld, w_ld, N, M_annot, args.num_blocks)
log.log(_print_intercept(h2hat))
return h2hat
# LD Score regression to estimate h2
elif args.sumstats_h2:
log.log('Estimating heritability.')
snp_count = len(sumstats); n_annot = len(ref_ld_colnames)
ref_ld = np.matrix(sumstats[ref_ld_colnames]).reshape((snp_count, n_annot))
w_ld = np.matrix(sumstats[w_ld_colname]).reshape((snp_count, 1))
M_annot = np.matrix(M_annot).reshape((1, n_annot))
chisq = np.matrix(sumstats.CHISQ).reshape((snp_count, 1))
N = np.matrix(sumstats.N).reshape((snp_count,1))
del sumstats
h2hat = jk.Hsq(chisq, ref_ld, w_ld, N, M_annot, args.num_blocks)
log.log(_print_hsq(h2hat, ref_ld_colnames))
return [M_annot,h2hat]
# LD Score regression to estimate genetic correlation
elif args.sumstats_gencor:
log.log('Estimating genetic correlation.')
snp_count = len(sumstats); n_annot = len(ref_ld_colnames)
ref_ld = np.matrix(sumstats[ref_ld_colnames]).reshape((snp_count, n_annot))
w_ld = np.matrix(sumstats[w_ld_colname]).reshape((snp_count, 1))
M_annot = np.matrix(M_annot).reshape((1, n_annot))
betahat1 = np.matrix(sumstats.BETAHAT1).reshape((snp_count, 1))
betahat2 = np.matrix(sumstats.BETAHAT2).reshape((snp_count, 1))
N1 = np.matrix(sumstats.N1).reshape((snp_count,1))
N2 = np.matrix(sumstats.N2).reshape((snp_count,1))
del sumstats
gchat = jk.Gencor(betahat1, betahat2, ref_ld, w_ld, N1, N2, M_annot, args.overlap,
args.rho, args.num_blocks)
log.log( '\n' )
log.log( 'Heritability of first phenotype' )
log.log( '-------------------------------' )
log.log( _print_hsq(gchat.hsq1, ref_ld_colnames) )
log.log( '\n' )
log.log( 'Heritability of second phenotype' )
log.log( '--------------------------------' )
log.log( _print_hsq(gchat.hsq2, ref_ld_colnames) )
log.log( '\n' )
log.log( 'Genetic Covariance' )
log.log( '------------------' )
log.log( _print_gencov(gchat.gencov, ref_ld_colnames) )
log.log( '\n' )
log.log( 'Genetic Correlation' )
log.log( '-------------------' )
log.log( _print_gencor(gchat) )
return [M_annot,gchat]