def plot_phenotype_by_sex(phenotype):
shared_covars = np.load(f'{ukb}/traits/shared_covars/shared_covars.npy')
pheno_data = np.load(f'{ukb}/traits/phenotypes/{phenotype}.npy')
with open(f'{ukb}/traits/phenotypes/{phenotype}_unit.txt') as unit_file:
unit = next(unit_file).strip()
data = utils.merge_arrays(shared_covars, pheno_data)
data = data[:, [shared_covars.shape[1], 1]]
data = data[~np.any(np.isnan(data), axis=1), :]
if len(np.unique(data[:, 0])) < 2000:
plot_histogram(
data, f'{phenotype} ({unit})',
phenotype.capitalize() + ' x Sex distribution',
f'{ukb}/traits/phenotypes/{phenotype}_distribution_by_sex.png', {
1: 'male',
2: 'female'
})
else:
plot_1D_kde(
data, f'{phenotype} ({unit})',
phenotype.capitalize() + ' x Sex distribution',
f'{ukb}/traits/phenotypes/{phenotype}_distribution_by_sex.png', {
1: 'male',
2: 'female'
})
def samples_array_with_indicator(sample_fname):
'''
sample_fname - a file with the first line 'ID' followed by one sample per line (7 digit number)
(no negatives or missings)
'''
all_samples = get_all_samples()
with open(sample_fname) as samples_file:
samples = np.array([line.strip() for line in samples_file][1:],
dtype=int).reshape(-1, 1)
samples_indicator = np.concatenate((samples, samples), axis=1)
samples_merge = utils.merge_arrays(all_samples, samples_indicator)
assert samples_merge.shape[1] == 2
return samples_merge
def main(): # noqa: D103
parser = argparse.ArgumentParser()
parser.add_argument('outprefix')
parser.add_argument('pheno_data')
parser.add_argument('samples')
parser.add_argument('phenotype')
parser.add_argument('ethnicity')
parser.add_argument('--binary', default=False, action='store_true')
args = parser.parse_args()
with open(f'{args.outprefix}_README.txt', 'w') as readme:
today = datetime.datetime.now().strftime("%Y_%m_%d")
readme.write(f"Run date: {today}\n")
readme.write(
"Subsetting to samples with phenotype that passed sample_qc, "
f"as denoted by the file: {args.samples}\n")
readme.flush()
data = np.load(args.pheno_data)
with open(args.samples) as sample_file:
next(sample_file)
samples = np.array([int(sample.strip()) for sample in sample_file])
samples = samples.reshape(-1, 1)
data = utils.merge_arrays(samples, data)
readme.write(
"Standardizing covariates (subtracting mean, then dividing by standard deviation)\n"
)
readme.flush()
covariates = data[:, 2:]
standardized_covariates = \
(covariates - covariates.mean(axis=0))/covariates.std(axis=0)
if not args.binary:
ranks = rank_phenotypes(readme, data)
rin_ranks = inverse_normalize_ranks(readme, ranks)
transformed_data = np.concatenate(
(samples, rin_ranks.reshape(-1, 1), standardized_covariates),
axis=1)
else:
readme.write(
"Binary outcome is left untransformed (0=case, 1=control)\n")
phenotype = data[:, 1:2]
transformed_data = np.concatenate(
(samples, phenotype, standardized_covariates), axis=1)
np.save(f'{args.outprefix}.npy', transformed_data)
parser.add_argument('outfname')
args = parser.parse_args()
with open(args.samples_fname) as samples_file:
samples = np.array([line.strip() for line in samples_file][1:],
dtype=int).reshape(-1, 1)
imp_snp_samples_filepath = f'{ukb}/array_imputed/ukb46122_imp_chr1_v3_s487283.sample'
with open(imp_snp_samples_filepath) as imp_snp_samples_file:
imp_snp_samples = np.array([
line.split()[0] for line in imp_snp_samples_file
][2:],
dtype=int).reshape(-1, 1)
samples_indicator = np.concatenate((samples, samples), axis=1)
samples_merge = utils.merge_arrays(imp_snp_samples, samples_indicator)
assert samples_merge.shape[1] == 2
sample_idx = ~np.isnan(samples_merge[:, 1])
strs = lfg.load_strs('first_pass',
f'{args.chrom}:{args.pos}-{args.pos}',
sample_idx,
details=False)
single_chrom_dosages = next(strs)[0]
summed_dosages = collections.defaultdict(lambda: np.zeros(np.sum(sample_idx)))
for len1, dosages1 in single_chrom_dosages.items():
#print(len1, dosages1)
for len2, dosages2 in single_chrom_dosages.items():
if len2 < len1:
continue
parser = argparse.ArgumentParser()
parser.add_argument('phenotype')
parser.add_argument('--conditional')
parser.add_argument('--binary', default=False, choices={'logistic', 'linear'})
args = parser.parse_args()
phenotype = args.phenotype
shared_covars = np.load(f'{ukb}/traits/shared_covars/shared_covars.npy')
subset_transformed_phenotype = np.load(f'{ukb}/traits/subset_transformed_phenotypes/white_brits/{phenotype}.npy')
# shared covars here aren't necessarily going to have exactly mean 0 and std 1
# because they were standardized before subsetting, but that's okay
data = utils.merge_arrays(
subset_transformed_phenotype,
shared_covars
)
data = np.concatenate((data[:, 0:1], data), axis=1)
col_names = ['FID', 'IID']
if not args.binary:
col_names.append(f'rin_{phenotype}')
else:
col_names.append(phenotype)
if args.binary == 'logistic':
# plink expects and ouptuts a 1=control, 2=case encoding
# instead of the 0=control, 1=case encoding we use elsewhere
# (from: https://www.cog-genomics.org/plink/2.0/input
# under the `--1` section)
data[:, 2] += 1
else:
today = datetime.datetime.now().strftime("%Y_%m_%d")
data_fname = f'{ukb}/main_dataset/extracted_data/{phenotype}_{args.phenotype_field_id}.txt'
readme.write(f"Run date: {today}\n")
readme.write(f"Loading phenotype {phenotype} from txt file "
f" {data_fname} \n")
data = np.genfromtxt(data_fname, skip_header=1, delimiter='\t')[:, 1:-1]
# drop first and last rows which because of the way data is extracted
# and then read by numpy are always nans
readme.write(f"Subsetting to samples at {args.samples}\n")
# load samples that have passed qc
samples = np.genfromtxt(args.samples, skip_header=1)
# subset to those samples
data = utils.merge_arrays(samples.reshape(-1, 1), data)
# number of sample with this phenotype at any assessment
num_samples = np.sum(np.any(~np.isnan(data[:, 1:]), axis=1))
# drop samples with categorical covars with less than this
# number of samples or fraction of samples
cat_drop_num = 50
cat_drop_frac = 0.001 #0.1%
covar_datas = []
reverse_covar_hashes = []
for covar in args.categorical_covars:
covar_name, covar_id = covar.split(',')
covar_fname = f'{ukb}/main_dataset/extracted_data/{covar_name}_{covar_id}.txt'
readme.write(
])
assert result.shape[0] == 1
pheno_data = np.load(pheno_datas_d[ethnicity])
bgen_samples = []
with open(f'{ukb}/microarray/ukb46122_hap_chr1_v2_s487314.sample') as samplefile:
for num, line in enumerate(samplefile):
if num <= 1:
# skip first two lines
continue
bgen_samples.append(line.split()[0])
assert len(bgen_samples) == 487409
samples_array = np.array(bgen_samples, dtype=float).reshape(-1, 1)
merged_arr = utils.merge_arrays(samples_array, pheno_data)
unfiltered_subset = ~np.isnan(merged_arr[:, 1])
n_samples = np.sum(unfiltered_subset)
vcf = cyvcf2.VCF(args.imputed_vcf)
found_rec = False
for record in vcf(f'{args.chrom}:{args.pos}-{args.pos}'):
if record.POS < args.pos:
continue
if record.INFO.get('PERIOD') is None:
continue
assert not found_rec
found_rec = True
trrecord = trh.HarmonizeRecord(vcfrecord=record, vcftype='beagle-hipstr')
if not args.zero_one_neg_nan:
# cols: id, date first reported
data = load_date_data_field(data_fname)
# cols: id, is_case
data[:, 1] = ~np.isnan(data[:, 1])
else:
# cols: id, is_case
data = load_0_1_neg_nan_field(data_fname)
readme.write(f"Subsetting to samples at {args.samples}\n")
# load samples that have passed qc
samples = np.genfromtxt(args.samples, skip_header=1)
# TODO test this
# subset to those samples
data = utils.merge_arrays(samples.reshape(-1, 1), data)
year_of_birth = np.genfromtxt(
f'{ukb}/main_dataset/extracted_data/year_of_birth_34.txt',
delimiter='\t',
skip_header=1)[:, 1:-1]
month_of_birth = np.genfromtxt(
f'{ukb}/main_dataset/extracted_data/month_of_birth_52.txt',
delimiter='\t',
skip_header=1)[:, 1:-1]
# cols: id, is case, year of birth, month of birth
data = utils.merge_arrays(utils.merge_arrays(data, year_of_birth),
month_of_birth)
missing_birth = np.isnan(data[:, 2]) | np.isnan(data[:, 3])
assert np.sum(missing_birth) < 100
def generate_figure(assoc_results_fname, pheno_data_fname, chrom, pos,
phenotype, dosage_fraction_threshold, unit, binary,
publication):
assert bool(unit) or binary
assert 0 <= dosage_fraction_threshold <= 1
if not binary:
y_axis_label = 'Mean ' + phenotype.replace('_', ' ') + f' ({unit})'
else:
y_axis_label = 'Fraction ' + phenotype.replace('_', ' ') + ' cases'
figure = bokeh.plotting.figure(
width=600,
height=600,
y_axis_label=y_axis_label,
x_axis_label='Sum of allele lengths (repeat copies)')
figure.grid.grid_line_color = None
figure.background_fill_color = None
figure.border_fill_color = None
figure.toolbar_location = None
figure.title.text_font_size = '18px'
figure.axis.axis_label_text_font_size = '18px'
figure.axis.major_label_text_font_size = '14px'
if not binary:
stat_name = 'mean'
else:
stat_name = 'fraction'
def fix_header(header):
def fix_header_helper(_):
part1 = header.rpartition('0.05_significance_CI')
fix1 = part1[0] + 'foo' + part1[2]
part2 = fix1.rpartition('5e-8_significance_CI')
fix2 = part2[0] + 'bar' + part2[2]
return fix2.split('\t')
return fix_header_helper
with open(assoc_results_fname) as tsv:
header = tsv.readline().strip()
result = pl.scan_csv(
assoc_results_fname,
sep='\t',
dtypes={
'locus_filtered': str
},
skip_rows=1,
has_header=False,
with_column_names=fix_header(header)).filter(
(pl.col('chrom') == chrom)
& (pl.col('pos') == pos)).collect().select(
[ # have to collect first due to some sort of bug
'motif', '0.05_significance_CI', '5e-8_significance_CI',
f'{stat_name}_{phenotype}_per_single_dosage',
'total_subset_dosage_per_summed_gt'
])
assert result.shape[0] == 1
pheno_data = np.load(pheno_data_fname)
bgen_samples = sample_utils.get_all_samples()
assert len(bgen_samples) == 487409
samples_array = np.array(bgen_samples, dtype=float).reshape(-1, 1)
merged_arr = utils.merge_arrays(samples_array, pheno_data)
unfiltered_subset = ~np.isnan(merged_arr[:, 1])
n_samples = np.sum(unfiltered_subset)
subset_summed_dosage_fractions = {
float(allele): val
for allele, val in ast.literal_eval(
result['total_subset_dosage_per_summed_gt'].to_numpy()[0]).items()
}
total_dosage = np.sum(list(subset_summed_dosage_fractions.values()))
subset_summed_dosage_fractions = {
key: val / total_dosage
for key, val in subset_summed_dosage_fractions.items()
}
alleles = list(subset_summed_dosage_fractions.keys())
alleles_copy = alleles.copy()
for allele in alleles_copy:
if subset_summed_dosage_fractions[allele] < dosage_fraction_threshold:
alleles.remove(allele)
alleles = sorted(alleles)
mean_per_dosage = {
float(allele): val
for allele, val in ast.literal_eval(
result[f'{stat_name}_{phenotype}_per_single_dosage'].to_numpy()
[0]).items()
}
ci5e_2 = {
float(allele): val
for allele, val in ast.literal_eval(
result['0.05_significance_CI'].to_numpy()[0]).items()
}
ci5e_8 = {
float(allele): val
for allele, val in ast.literal_eval(
result['5e-8_significance_CI'].to_numpy()[0]).items()
}
y_min = min(ci5e_8[allele][0] for allele in alleles)
y_max = max(ci5e_8[allele][1] for allele in alleles)
figure.varea(alleles, [ci5e_2[allele][1] for allele in alleles],
[ci5e_8[allele][1] for allele in alleles],
color="red",
alpha=0.2,
legend_label='1 - 5e-8 Confidence Interval')
figure.varea(alleles, [ci5e_2[allele][0] for allele in alleles],
[ci5e_2[allele][1] for allele in alleles],
color="red",
alpha=0.4,
legend_label='0.95 Confidence Interval')
figure.varea(alleles, [ci5e_8[allele][0] for allele in alleles],
[ci5e_2[allele][0] for allele in alleles],
color="red",
alpha=0.2)
figure.line(alleles, [mean_per_dosage[allele] for allele in alleles],
line_width=2,
color="black")
figure.circle(alleles, [mean_per_dosage[allele] for allele in alleles],
color="black",
size=6,
legend_label='mean')
figure.legend.label_text_font_size = '10px'
figure.y_range = bokeh.models.Range1d(y_min - 0.05 * (y_max - y_min),
y_max + 0.05 * (y_max - y_min))
figure.add_layout(
bokeh.models.Title(text=f'STR {chrom}:{pos}',
align="center",
text_font_size='18px'), "above")
figure.add_layout(
bokeh.models.Title(text=phenotype.replace('_', ' ').capitalize() +
" vs genotype",
align="center",
text_font_size='18px'), "above")
if not publication:
figure.add_layout(
bokeh.models.Title(
text="Phenotype values are unadjusted for covariates",
align="center"), "below")
figure.add_layout(
bokeh.models.Title(
text=
"People contribute to each genotype based on their prob. of having that genotype",
align="center"), "below")
figure.add_layout(
bokeh.models.Title(text="Only considers tested individuals",
align="center"), "below")
figure.add_layout(
bokeh.models.Title(
text=
f"Genotypes with dosages less than {100*dosage_fraction_threshold}% of the population are omitted",
align="center"), "below")
return figure
def main():
# do an association test of the combined dosage of the listed alleles vs the listed phenotypes in each ethnicity
parser = argparse.ArgumentParser()
#parser.add_argument('chrom', type=int)
#parser.add_argument('pos', type=int)
parser.add_argument('var_file')
#parser.add_argument('--phenotypes', nargs='+')
#parser.add_argument('--alleles', type=int, nargs='+') #allele indicies, i.e. ths SNP is present in alleles 0 (ref), 2 (2nd alt) and 5
args = parser.parse_args()
print(
'str\tvariant\tethnicity\tvar frequency\tstr var r2\tphenotype\tstr p-val\tvar p-val\tstr p-val conditioning on var'
)
scovs = np.load(f'{ukb}/traits/shared_covars/shared_covars.npy')
variants = pl.read_csv(args.var_file, sep='\t')
for i in range(variants.shape[0]):
chrom = variants[i, 'chrom']
pos = variants[i, 'pos']
str_ = f'{chrom}:{pos}'
var = next(
cyvcf2.VCF(
f'{ukb}/str_imputed/runs/first_pass/vcfs/annotated_strs/chr{chrom}.vcf.gz'
)(str_))
alleles = [int(num) for num in variants[i, 'alleles'].split(',')]
name = variants[i, 'name']
phenotypes = variants[i, 'phenos'].split(',')
for phenotype in phenotypes:
for ethnicity in ('white_brits', 'black', 'south_asian', 'chinese',
'irish', 'white_other'):
#ethnicity
total_samp_idx = sample_utils.get_samples_idx_ethnicity(
ethnicity)
total_var_gts = var_dosage_gts(var, total_samp_idx, alleles)
total_str_gts = str_dosage_gts(var, total_samp_idx)
var_freq = np.sum(total_var_gts) / (2 * total_var_gts.shape[0])
corr = np.corrcoef(total_var_gts, total_str_gts)
assert corr.shape == (2, 2)
str_var_r2 = corr[0, 1]**2
#ethnicity,pheno
pcovs = np.load(
f'{ukb}/traits/subset_transformed_phenotypes/{ethnicity}/{phenotype}.npy'
)
samps = sample_utils.get_ordered_samples_phenotype(
ethnicity, phenotype).reshape(-1, 1)
covs = python_array_utils.merge_arrays(
python_array_utils.merge_arrays(samps, pcovs), scovs)
outcomes = covs[:, 1]
covs = covs[:, 2:]
samp_idx = sample_utils.get_samples_idx_phenotype(
ethnicity, phenotype)
var_gts = standardize(var_dosage_gts(var, samp_idx, alleles))
str_gts = standardize(str_dosage_gts(var, samp_idx))
str_best_guess_gts = trh.HarmonizeRecord(
vcfrecord=var,
vcftype='beagle-hipstr').GetGenotypeIndicies()[
samp_idx, :-1]
str_p = OLS(
outcomes,
np.hstack((covs, np.ones((covs.shape[0], 1)),
str_gts.reshape(-1, 1)))).fit().pvalues[-1]
if np.all(var_gts == 0) or np.all(var_gts == 2):
var_p = 1
str_cond_p = str_p
else:
var_p = OLS(
outcomes,
np.hstack((covs, np.ones((covs.shape[0], 1)),
var_gts.reshape(-1, 1)))).fit().pvalues[-1]
str_cond_p = OLS(
outcomes,
np.hstack((covs, np.ones(
(covs.shape[0], 1)), var_gts.reshape(-1, 1),
str_gts.reshape(-1, 1)))).fit().pvalues[-1]
print(
f'{str_}\t{name}\t{ethnicity}\t{var_freq:.3g}\t{str_var_r2:.3g}\t{phenotype}\t{str_p:.3g}\t{var_p:.3g}\t{str_cond_p:.3g}'
)
scovs = np.load(f'{ukb}/traits/shared_covars/shared_covars.npy')
print(
'phenotype\tethnicity\tcompound coeff single test (s.d.)\tA coeff (s.d.)\tTA coeff (s.d.)\tCA coeff (s.d.)'
)
for phenotype in ('mean_platelet_volume', 'platelet_distribution_width',
'platelet_count'):
for ethnicity in ('white_brits', 'black', 'south_asian', 'chinese',
'irish', 'white_other'):
print(f'{phenotype}\t{ethnicity}', end='')
pcovs = np.load(
f'{ukb}/traits/subset_transformed_phenotypes/{ethnicity}/{phenotype}.npy'
)
samps = sample_utils.get_ordered_samples_phenotype(
ethnicity, phenotype).reshape(-1, 1)
covs = python_array_utils.merge_arrays(
python_array_utils.merge_arrays(samps, pcovs), scovs)
outcomes = covs[:, 1]
covs = covs[:, 2:]
samp_idx = sample_utils.get_samples_idx_phenotype(ethnicity, phenotype)
itr = load_PACSIN2.get_gt_itr(samp_idx)
next(itr) # skip details
compound, a, ta, ca = [next(itr)[0] for _ in range(4)]
compound, a, ta, ca = [
np.sum([
len_ * np.sum(dosages, axis=1)
for len_, dosages in dosage_dict.items()
],
axis=0).reshape(-1, 1)
for dosage_dict in (compound, a, ta, ca)
def perform_regional_gwas_helper(outfile,
pheno_and_covars_fname,
shared_covars_fname,
untransformed_phenotypes_fname,
get_genotype_iter,
phenotype,
binary,
region,
runtype,
conditional_covars_fname=None):
outfile.write("chrom\tpos\talleles\tlocus_filtered\t"
f"p_{phenotype}\tcoeff_{phenotype}\t")
if binary != 'logistic':
outfile.write(f'se_{phenotype}\tR^2\t')
else:
outfile.write("unused_col\tunused_col\t")
outfile.flush()
n_loci = 0
batch_time = 0
batch_size = 50
total_time = 0
pheno_specific_covars = np.load(pheno_and_covars_fname)
shared_covars = np.load(shared_covars_fname)
covars = utils.merge_arrays(pheno_specific_covars, shared_covars)
if conditional_covars_fname:
gt_covars = np.load(conditional_covars_fname)
covars = utils.merge_arrays(covars, gt_covars)
# order samples according to order in genetics files
bgen_samples = sample_utils.get_all_samples()
assert len(bgen_samples) == 487409
samples_array = np.array(bgen_samples, dtype=float).reshape(-1, 1)
merge = utils.merge_arrays(samples_array, covars)
unfiltered_samples = ~np.isnan(merge[:, 1])
outcome = merge[unfiltered_samples, 1].copy()
covars = merge[unfiltered_samples, :]
covars = (covars - np.mean(covars, axis=0)) / np.std(covars, axis=0)
covars[:, 1] = 1 # reuse the column that was the outcome as the intercept
ori_phenotypes = np.load(untransformed_phenotypes_fname)
ori_phenotypes = utils.merge_arrays(samples_array, ori_phenotypes)[:, 1]
ori_phenotypes = ori_phenotypes[unfiltered_samples]
# first yield is special
genotype_iter = get_genotype_iter(unfiltered_samples)
extra_detail_fields = next(genotype_iter)
outfile.write('\t'.join(extra_detail_fields) + '\t')
if not binary:
stat = 'mean'
else:
stat = 'fraction'
outfile.write(f'{stat}_{phenotype}_per_single_dosage\t'
'0.05_significance_CI\t'
'5e-8_significance_CI')
if runtype == 'strs':
outfile.write('\ttotal_subset_dosage_per_summed_gt\t'
f'{stat}_{phenotype}_per_paired_dosage\t'
'0.05_significance_CI\t'
'5e-8_significance_CI')
outfile.write('\n')
outfile.flush()
start_time = time.time()
for dosage_gts, unique_alleles, chrom, pos, locus_filtered, locus_details in genotype_iter:
assert len(locus_details) == len(extra_detail_fields)
covars[:,
0] = np.nan # reuse the column that was the ids as the genotypes
n_loci += 1
allele_names = ','.join(list(unique_alleles.astype(str)))
outfile.write(f"{chrom}\t{pos}\t{allele_names}\t")
if locus_filtered:
outfile.write(f'{locus_filtered}\t1\tnan\tnan\tnan\t')
outfile.write('\t'.join(locus_details))
if runtype == 'strs':
outfile.write('\tnan' * 6 + '\n')
else:
outfile.write('\tnan' * 3 + '\n')
outfile.flush()
continue
else:
outfile.write('False\t')
if runtype == 'strs':
gts = np.sum([
_len * np.sum(dosages, axis=1)
for _len, dosages in dosage_gts.items()
],
axis=0)
else:
gts = dosage_gts[:, 1] + 2 * dosage_gts[:, 2]
std = np.std(gts)
gts = (gts - np.mean(gts)) / np.std(gts)
covars[:, 0] = gts
if not binary or binary == 'linear':
#do da regression
model = OLS(
outcome,
covars,
missing='drop',
)
reg_result = model.fit()
pval = reg_result.pvalues[0]
coef = reg_result.params[0]
se = reg_result.bse[0]
rsquared = reg_result.rsquared
outfile.write(f"{pval:.2e}\t{coef/std}\t{se/std}\t{rsquared}\t")
else:
model = sm.GLM(outcome,
covars,
missing='drop',
family=sm.families.Binomial())
reg_result = model.fit()
pval = reg_result.pvalues[0]
coef = reg_result.params[0]
outfile.write(f'{pval:.2e}\t{coef/std}\tnan\tnan\t')
outfile.write('\t'.join(locus_details) + '\t')
if runtype == 'strs':
single_dosages = {}
paired_dosages = {}
for len1 in unique_alleles:
for len2 in unique_alleles:
if len1 > len2:
continue
if len1 != len2:
dosages = (
dosage_gts[len1][:, 0] * dosage_gts[len2][:, 1] +
dosage_gts[len1][:, 1] * dosage_gts[len2][:, 0])
else:
dosages = dosage_gts[len1][:, 0] * dosage_gts[len1][:,
1]
if np.sum(dosages) <= 0:
continue
summed_len = round(len1 + len2, 2)
if summed_len not in single_dosages:
single_dosages[summed_len] = dosages
else:
single_dosages[summed_len] += dosages
minlen = min(len1, len2)
maxlen = max(len1, len2)
paired_dosages[(minlen, maxlen)] = dosages
single_dosage_stat = {}
single_dosage_95_CI = {}
single_dosage_GWAS_CI = {}
paired_dosage_stat = {}
paired_dosage_95_CI = {}
paired_dosage_GWAS_CI = {}
if not binary:
for _len, dosages in single_dosages.items():
if len(np.unique(ori_phenotypes[dosages != 0])) <= 1:
continue
mean_stats = statsmodels.stats.weightstats.DescrStatsW(
ori_phenotypes, weights=dosages)
single_dosage_stat[_len] = mean_stats.mean
single_dosage_95_CI[_len] = mean_stats.tconfint_mean()
single_dosage_GWAS_CI[_len] = mean_stats.tconfint_mean(
5e-8)
for _len, dosages in paired_dosages.items():
if len(np.unique(ori_phenotypes[dosages != 0])) <= 1:
continue
mean_stats = statsmodels.stats.weightstats.DescrStatsW(
ori_phenotypes, weights=dosages)
paired_dosage_stat[_len] = mean_stats.mean
paired_dosage_95_CI[_len] = mean_stats.tconfint_mean()
paired_dosage_GWAS_CI[_len] = mean_stats.tconfint_mean(
5e-8)
else:
for _len, dosages in single_dosages.items():
if not np.any(dosages != 0):
continue
p, lower, upper = weighted_binom_conf.weighted_binom_conf(
dosages, ori_phenotypes, 0.05)
single_dosage_stat[_len] = p
single_dosage_95_CI[_len] = (lower, upper)
_, lower_gwas, upper_gwas = weighted_binom_conf.weighted_binom_conf(
dosages, ori_phenotypes, 5e-8)
single_dosage_GWAS_CI[_len] = (lower_gwas, upper_gwas)
for _len, dosages in paired_dosages.items():
if not np.any(dosages != 0):
continue
p, lower, upper = weighted_binom_conf.weighted_binom_conf(
dosages, ori_phenotypes, 0.05)
paired_dosage_stat[_len] = p
paired_dosage_95_CI[_len] = (lower, upper)
_, lower_gwas, upper_gwas = weighted_binom_conf.weighted_binom_conf(
dosages, ori_phenotypes, 5e-8)
paired_dosage_GWAS_CI[_len] = (lower_gwas, upper_gwas)
outfile.write(
load_and_filter_genotypes.dict_str(single_dosage_stat) + '\t')
outfile.write(
load_and_filter_genotypes.dict_str(single_dosage_95_CI) + '\t')
outfile.write(
load_and_filter_genotypes.dict_str(single_dosage_GWAS_CI) +
'\t')
outfile.write(
load_and_filter_genotypes.dict_str(
{key: np.sum(arr)
for key, arr in single_dosages.items()}) + '\t')
outfile.write(
load_and_filter_genotypes.dict_str(paired_dosage_stat) + '\t')
outfile.write(
load_and_filter_genotypes.dict_str(paired_dosage_95_CI) + '\t')
outfile.write(
load_and_filter_genotypes.dict_str(paired_dosage_GWAS_CI) +
'\n')
else:
single_dosage_stat = {}
single_dosage_95_CI = {}
single_dosage_GWAS_CI = {}
if not binary:
for alt_count in range(3):
mean_stats = statsmodels.stats.weightstats.DescrStatsW(
ori_phenotypes, weights=dosage_gts[:, alt_count])
single_dosage_stat[alt_count] = mean_stats.mean
single_dosage_95_CI[alt_count] = mean_stats.tconfint_mean()
single_dosage_GWAS_CI[
alt_count] = mean_stats.tconfint_mean(5e-8)
else:
for alt_count in range(3):
p, lower, upper = weighted_binom_conf.weighted_binom_conf(
dosage_gts[:, alt_count], ori_phenotypes, 0.05)
single_dosage_stat[alt_count] = p
single_dosage_95_CI[alt_count] = (lower, upper)
_, lower_gwas, upper_gwas = weighted_binom_conf.weighted_binom_conf(
dosage_gts[:, alt_count], ori_phenotypes, 5e-8)
single_dosage_GWAS_CI[alt_count] = (lower_gwas, upper_gwas)
outfile.write(
load_and_filter_genotypes.dict_str(single_dosage_stat) + '\t')
outfile.write(
load_and_filter_genotypes.dict_str(single_dosage_95_CI) + '\t')
outfile.write(
load_and_filter_genotypes.dict_str(single_dosage_GWAS_CI) +
'\n')
outfile.flush()
duration = time.time() - start_time
total_time += duration
batch_time += duration
if n_loci % batch_size == 0:
print(
f"time/locus (last {batch_size}): "
f"{batch_time/batch_size}s\n"
f"time/locus ({n_loci} total loci): {total_time/n_loci}s\n",
flush=True)
batch_time = 0
start_time = time.time()
if n_loci > 0:
print(
f"Done.\nTotal loci: {n_loci}\nTotal time: {total_time}s\ntime/locus: {total_time/n_loci}s\n",
flush=True)
else:
print(f"No variants found in the region {region}\n", flush=True)
