def main(args):
# Start Hail
hl.init(default_reference=args.default_ref_genome)
# Import unfiltered split MT
mt = get_mt_data(dataset=args.exome_cohort, part='unfiltered')
# Compute stratified sample_qc (biallelic and multi-allelic sites)
sample_qc_ht = compute_sample_qc(mt)
# Write HT with sample QC metrics
sample_qc_ht = sample_qc_ht.checkpoint(get_sample_qc_ht_path(
dataset=args.exome_cohort, part='high_conf_autosomes'),
overwrite=args.overwrite,
_read_if_exists=not args.overwrite)
# annotate sample population and platform qc info
pop_qc = hl.read_table(get_sample_qc_ht_path(part='population_qc'))
platform_qc = hl.read_table(get_sample_qc_ht_path(part='platform_pca'))
ann_expr = {
'qc_pop': pop_qc[sample_qc_ht.s].predicted_pop,
'qc_platform': platform_qc[sample_qc_ht.s].qc_platform
}
sample_qc_ht = sample_qc_ht.annotate(**ann_expr)
# Export HT to file
if args.write_to_file:
(sample_qc_ht.flatten().export(
f"{get_sample_qc_ht_path(dataset=args.exome_cohort, part='high_conf_autosomes')}.tsv.bgz"
))
# Apply stratified sample filters based on defined QC metrics
exome_qc_metrics = [
'n_snp', 'r_ti_tv', 'r_insertion_deletion', 'n_insertion',
'n_deletion', 'r_het_hom_var'
]
print('Computing stratified metrics filters...')
exome_pop_platform_filter_ht = compute_stratified_metrics_filter(
sample_qc_ht, exome_qc_metrics, ['qc_pop', 'qc_platform'])
exome_pop_platform_filter_ht = exome_pop_platform_filter_ht.checkpoint(
get_sample_qc_ht_path(dataset=args.exome_cohort,
part='stratified_metrics_filter'),
overwrite=args.overwrite,
_read_if_exists=not args.overwrite)
# Export HT to file
if args.write_to_file:
(exome_pop_platform_filter_ht.export(
f"{get_sample_qc_ht_path(dataset=args.exome_cohort, part='stratified_metrics_filter')}.tsv.bgz"
))
# Stop Hail
hl.stop()
print("Finished!")
def main(args):
# nfs_dir = 'file:///home/ubuntu/data'
hl.init(default_reference=args.default_reference)
logger.info("Importing data...")
# import unfiltered MT
mt = get_mt_data(dataset=args.exome_cohort, part='unfiltered')
# keep bi-allelic variants
mt = (mt
.filter_rows(bi_allelic_expr(mt), keep=True)
)
# read intervals for filtering variants (used mainly for exomes)
def _get_interval_table(interval: str) -> Union[None, hl.Table]:
return get_capture_interval_ht(name=interval,
reference=args.default_reference) if interval is not None else interval
ht = compute_mean_coverage(mt=mt,
normalization_contig=args.normalization_contig,
included_calling_intervals=_get_interval_table(args.interval_to_include),
excluded_calling_intervals=_get_interval_table(args.interval_to_exclude),
chr_x=args.chr_x,
chr_y=args.chr_y)
logger.info("Exporting data...")
# write HT
output_ht_path = get_sample_qc_ht_path(part='sex_chrom_coverage')
ht.write(output=output_ht_path,
overwrite=args.overwrite)
# export to file if true
if args.write_to_file:
(ht
.export(f'{output_ht_path}.tsv.bgz')
)
hl.stop()
print("Done!")
def main(args):
# Start Hail
hl.init(default_reference=args.default_ref_genome)
# Import unfiltered split MT
mt = get_mt_data(dataset=args.exome_cohort, part='unfiltered')
# Compute stratified sample_qc (biallelic and multi-allelic sites)
sample_qc_ht = compute_sample_qc(mt)
# Write HT with sample QC metrics
output_path = (
f'{nfs_dir}/hail_data/sample_qc/chd_ukbb.sample_qc.high_conf.autosomes.cds.capture_intervals.rare_common.ht'
)
sample_qc_ht = sample_qc_ht.checkpoint(output_path,
overwrite=args.overwrite,
_read_if_exists=not args.overwrite)
# annotate sample population and platform qc info
pop_qc = hl.read_table(get_sample_qc_ht_path(part='population_qc'))
platform_qc = hl.read_table(get_sample_qc_ht_path(part='platform_pca'))
ann_expr = {
'qc_pop': pop_qc[sample_qc_ht.s].predicted_pop,
'qc_platform': platform_qc[sample_qc_ht.s].qc_platform
}
sample_qc_ht = sample_qc_ht.annotate(**ann_expr)
# Export HT to file
if args.write_to_file:
(sample_qc_ht.flatten().export(f"{output_path}.tsv.bgz"))
# Stop Hail
hl.stop()
print("Finished!")
def main(args):
# Start Hail
hl.init(default_reference=args.default_reference)
if not args.skip_filter_step:
logger.info("Importing data...")
# import unfiltered MT
mt = get_mt_data(dataset=args.exome_cohort, part='unfiltered')
# Read MT from 1kgenome and keep only locus defined in interval
mt_1kg = get_1kg_mt(args.default_reference)
# Joining dataset (inner join). Keep only 'GT' entry field
mt_joint = (mt.select_entries('GT').union_cols(
mt_1kg.select_entries('GT'), row_join_type='inner'))
logger.info(
"Filtering joint MT to bi-allelic, high-callrate, common SNPs...")
mt_joint = (mt_joint.filter_rows(
bi_allelic_expr(mt_joint)
& hl.is_snp(mt_joint.alleles[0], mt_joint.alleles[1])
& (hl.agg.mean(mt_joint.GT.n_alt_alleles()) / 2 > 0.001)
& (hl.agg.fraction(hl.is_defined(mt_joint.GT)) > 0.99)).
naive_coalesce(1000))
logger.info(
"Checkpoint: writing joint filtered MT before LD pruning...")
mt_joint = mt_joint.checkpoint(get_mt_checkpoint_path(
dataset=args.exome_cohort,
part='joint_1kg_high_callrate_common_snp_biallelic'),
overwrite=True)
logger.info(
f"Running ld_prune with r2 = {args.ld_prune_r2} on MT with {mt_joint.count_rows()} variants..."
)
# remove correlated variants
pruned_variant_table = hl.ld_prune(mt_joint.GT,
r2=args.ld_prune_r2,
bp_window_size=500000,
memory_per_core=512)
mt_joint = (mt_joint.filter_rows(
hl.is_defined(pruned_variant_table[mt_joint.row_key])))
logger.info("Writing filtered joint MT with variants in LD pruned...")
(mt_joint.write(get_qc_mt_path(
dataset=args.exome_cohort + '_1kg',
part='joint_high_callrate_common_snp_biallelic',
split=True,
ld_pruned=True),
overwrite=args.overwrite))
logger.info("Importing filtered joint MT...")
mt_joint = hl.read_matrix_table(
get_qc_mt_path(dataset=args.exome_cohort + '_1kg',
part='joint_high_callrate_common_snp_biallelic',
split=True,
ld_pruned=True))
logger.info(f"Running PCA with {mt_joint.count_rows()} variants...")
# run pca on merged dataset
eigenvalues, pc_scores, _ = hl.hwe_normalized_pca(mt_joint.GT,
k=args.n_pcs)
logger.info(f"Eigenvalues: {eigenvalues}") # TODO: save eigenvalues?
# Annotate PC array as independent fields.
pca_table = (pc_scores.annotate(
**
{'PC' + str(k + 1): pc_scores.scores[k]
for k in range(0, args.n_pcs)}).drop('scores'))
logger.info(f"Writing HT with PCA results...")
# write as HT
output_ht_path = get_sample_qc_ht_path(dataset=args.exome_cohort,
part='joint_pca_1kg')
pca_table.write(output=output_ht_path)
if args.write_to_file:
(pca_table.export(f'{output_ht_path}.tsv.bgz'))
# Stop Hail
hl.stop()
print("Done!")
def main(args):
hl.init(default_reference=args.default_ref_genome)
if args.run_test_mode:
logger.info('Running pipeline on test data...')
mt = (get_mt_data(part='raw_chr20').sample_rows(0.1))
else:
logger.info(
'Running pipeline on MatrixTable wih adjusted genotypes...')
ds = args.exome_cohort
mt = hl.read_matrix_table(
get_qc_mt_path(dataset=ds,
part='unphase_adj_genotypes',
split=True))
# 1. Sample-QC filtering
if not args.skip_sample_qc_filtering:
logger.info('Applying per sample QC filtering...')
mt = apply_sample_qc_filtering(mt)
logger.info(
'Writing sample qc-filtered mt with rare variants (internal maf 0.01) to disk...'
)
mt = (mt.write(f'{hdfs_dir}/chd_ukbb.sample_qc_filtered.mt',
overwrite=True))
# 2. Variant-QC filtering
if not args.skip_variant_qc_filtering:
logger.info('Applying per variant QC filtering...')
if hl.hadoop_is_file(
f'{hdfs_dir}/chd_ukbb.sample_qc_filtered.mt/_SUCCESS'):
logger.info('Reading pre-existing sample qc-filtered MT...')
mt = hl.read_matrix_table(
f'{hdfs_dir}/chd_ukbb.sample_qc_filtered.mt')
mt = apply_variant_qc_filtering(mt)
# write hard filtered MT to disk
logger.info(
'Writing variant qc-filtered mt with rare variants (internal maf 0.01) to disk...'
)
mt = (mt.write(f'{hdfs_dir}/chd_ukbb.variant_qc_filtered.mt',
overwrite=True))
# 3. Annotate AFs
# allelic frequency cut-off
maf_cutoff = args.af_max_threshold
if not args.skip_af_filtering:
if hl.hadoop_is_file(
f'{hdfs_dir}/chd_ukbb.variant_qc_filtered.mt/_SUCCESS'):
logger.info(
'Reading pre-existing sample/variant qc-filtered MT...')
mt = hl.read_matrix_table(
f'{hdfs_dir}/chd_ukbb.variant_qc_filtered.mt')
# Annotate allelic frequencies from external source,
# and compute internal AF on samples passing QC
af_ht = get_af_annotation_ht()
mt = (mt.annotate_rows(**af_ht[mt.row_key]))
filter_expressions = [
af_filter_expr(mt, 'internal_af', af_cutoff=maf_cutoff),
af_filter_expr(mt, 'gnomad_genomes_af', af_cutoff=maf_cutoff),
af_filter_expr(mt, 'gnomAD_AF', af_cutoff=maf_cutoff),
af_filter_expr(mt, 'ger_af', af_cutoff=maf_cutoff),
af_filter_expr(mt, 'rumc_af', af_cutoff=maf_cutoff),
af_filter_expr(mt, 'bonn_af', af_cutoff=maf_cutoff)
]
mt = (mt.filter_rows(functools.reduce(operator.iand,
filter_expressions),
keep=True))
logger.info(
'Writing qc-filtered MT filtered to external maf with to disk...')
mt = (mt.write(f'{hdfs_dir}/chd_ukbb.qc_final.rare.mt',
overwrite=True))
# 4. ##### Burden Test ######
logger.info('Running burden test...')
if hl.hadoop_is_file(f'{hdfs_dir}/chd_ukbb.qc_final.rare.mt/_SUCCESS'):
logger.info(
'Reading pre-existing sample/variant qc-filtered MT with rare variants...'
)
mt = hl.read_matrix_table(f'{hdfs_dir}/chd_ukbb.qc_final.rare.mt')
## Add VEP-annotated fields
vep_ht = get_vep_annotation_ht()
mt = (mt.annotate_rows(LoF=vep_ht[mt.row_key].vep.LoF,
Consequence=vep_ht[mt.row_key].vep.Consequence,
DOMAINS=vep_ht[mt.row_key].vep.DOMAINS,
SYMBOL=vep_ht[mt.row_key].vep.SYMBOL))
## Filter to bi-allelic variants
if args.filter_biallelic:
logger.info('Running burden test on biallelic variants...')
mt = mt.filter_rows(bi_allelic_expr(mt))
## Filter to variants within protein domain(s)
if args.filter_protein_domain:
logger.info(
'Running burden test on variants within protein domain(s)...')
mt = mt.filter_rows(vep_protein_domain_filter_expr(mt.DOMAINS),
keep=True)
## Add cases/controls sample annotations
tb_sample = get_sample_meta_data()
mt = (mt.annotate_cols(**tb_sample[mt.s]))
mt = (mt.filter_cols(mt['phe.is_case'] | mt['phe.is_control']))
## Annotate pathogenic scores
ht_scores = get_vep_scores_ht()
mt = mt.annotate_rows(**ht_scores[mt.row_key])
## Classify variant into (major) consequence groups
score_expr_ann = {
'hcLOF': mt.LoF == 'HC',
'syn': mt.Consequence == 'synonymous_variant',
'miss': mt.Consequence == 'missense_variant'
}
# Update dict expr annotations with combinations of variant consequences categories
score_expr_ann.update({
'missC': (hl.sum([(mt['vep.MVP_score'] >= MVP_THRESHOLD),
(mt['vep.REVEL_score'] >= REVEL_THRESHOLD),
(mt['vep.CADD_PHRED'] >= CADD_THRESHOLD)]) >= 2)
& score_expr_ann.get('miss')
})
score_expr_ann.update({
'hcLOF_missC':
score_expr_ann.get('hcLOF') | score_expr_ann.get('missC')
})
mt = (mt.annotate_rows(csq_group=score_expr_ann))
# Transmute csq_group and convert dict to set where the group is defined
# (easier to explode and grouping later)
mt = (mt.transmute_rows(csq_group=hl.set(
hl.filter(lambda x: mt.csq_group.get(x), mt.csq_group.keys()))))
mt = (mt.filter_rows(hl.len(mt.csq_group) > 0))
# Explode nested csq_group before grouping
mt = (mt.explode_rows(mt.csq_group))
# print('Number of samples/variants: ')
# print(mt.count())
# Group mt by gene/csq_group.
mt_grouped = (mt.group_rows_by(mt['SYMBOL'], mt['csq_group']).aggregate(
hets=hl.agg.any(mt.GT.is_het()),
homs=hl.agg.any(mt.GT.is_hom_var()),
chets=hl.agg.count_where(mt.GT.is_het()) >= 2,
homs_chets=(hl.agg.count_where(mt.GT.is_het()) >= 2) |
(hl.agg.any(mt.GT.is_hom_var()))).repartition(100).persist())
mts = []
if args.homs:
# select homs genotypes.
mt_homs = (mt_grouped.select_entries(
mac=mt_grouped.homs).annotate_rows(agg_genotype='homs'))
mts.append(mt_homs)
if args.chets:
# select compound hets (chets) genotypes.
mt_chets = (mt_grouped.select_entries(
mac=mt_grouped.chets).annotate_rows(agg_genotype='chets'))
mts.append(mt_chets)
if args.homs_chets:
# select chets and/or homs genotypes.
mt_homs_chets = (mt_grouped.select_entries(
mac=mt_grouped.homs_chets).annotate_rows(
agg_genotype='homs_chets'))
mts.append(mt_homs_chets)
if args.hets:
# select hets genotypes
mt_hets = (mt_grouped.select_entries(
mac=mt_grouped.hets).annotate_rows(agg_genotype='hets'))
mts.append(mt_hets)
## Joint MatrixTables
mt_grouped = hl.MatrixTable.union_rows(*mts)
# Generate table of counts
tb_gene = (mt_grouped.annotate_rows(
n_cases=hl.agg.filter(mt_grouped['phe.is_case'],
hl.agg.sum(mt_grouped.mac)),
n_syndromic=hl.agg.filter(mt_grouped['phe.is_syndromic'],
hl.agg.sum(mt_grouped.mac)),
n_nonsyndromic=hl.agg.filter(mt_grouped['phe.is_nonsyndromic'],
hl.agg.sum(mt_grouped.mac)),
n_controls=hl.agg.filter(mt_grouped['phe.is_control'],
hl.agg.sum(mt_grouped.mac)),
n_total_cases=hl.agg.filter(mt_grouped['phe.is_case'], hl.agg.count()),
n_total_syndromic=hl.agg.filter(mt_grouped['phe.is_syndromic'],
hl.agg.count()),
n_total_nonsyndromic=hl.agg.filter(mt_grouped['phe.is_nonsyndromic'],
hl.agg.count()),
n_total_controls=hl.agg.filter(mt_grouped['phe.is_control'],
hl.agg.count())).rows())
# run fet stratified by proband type
analysis = ['all_cases', 'syndromic', 'nonsyndromic']
tbs = []
for proband in analysis:
logger.info(f'Running test for {proband}...')
colCases = None
colTotalCases = None
colControls = 'n_controls'
colTotalControls = 'n_total_controls'
if proband == 'all_cases':
colCases = 'n_cases'
colTotalCases = 'n_total_cases'
if proband == 'syndromic':
colCases = 'n_syndromic'
colTotalCases = 'n_total_syndromic'
if proband == 'nonsyndromic':
colCases = 'n_nonsyndromic'
colTotalCases = 'n_total_nonsyndromic'
tb_fet = compute_fisher_exact(tb=tb_gene,
n_cases_col=colCases,
n_control_col=colControls,
total_cases_col=colTotalCases,
total_controls_col=colTotalControls,
correct_total_counts=True,
root_col_name='fet',
extra_fields={
'analysis': proband,
'maf': maf_cutoff
})
# filter out zero-count genes
tb_fet = (tb_fet.filter(
hl.sum([tb_fet[colCases], tb_fet[colControls]]) > 0, keep=True))
tbs.append(tb_fet)
tb_final = hl.Table.union(*tbs)
tb_final.describe()
# export results
date = current_date()
run_hash = str(uuid.uuid4())[:6]
output_path = f'{args.output_dir}/{date}/{args.exome_cohort}.fet_burden.{run_hash}.ht'
tb_final = (tb_final.checkpoint(output=output_path))
if args.write_to_file:
# write table to disk as TSV file
(tb_final.export(f'{output_path}.tsv'))
hl.stop()
def main(args):
# Start Hail
hl.init(default_reference=args.default_ref_genome)
# Import raw split MT
mt = (get_mt_data(dataset=args.exome_cohort, part='raw',
split=True).select_cols())
ht = (mt.cols().key_by('s'))
# Annotate samples filters
sample_qc_filters = {}
# 1. Add sample hard filters annotation expr
sample_qc_hard_filters_ht = hl.read_table(
get_sample_qc_ht_path(dataset=args.exome_cohort, part='hard_filters'))
sample_qc_filters.update(
{'hard_filters': sample_qc_hard_filters_ht[ht.s]['hard_filters']})
# 2. Add population qc filters annotation expr
sample_qc_pop_ht = hl.read_table(
get_sample_qc_ht_path(dataset=args.exome_cohort, part='population_qc'))
sample_qc_filters.update(
{'predicted_pop': sample_qc_pop_ht[ht.s]['predicted_pop']})
# 3. Add relatedness filters annotation expr
related_samples_to_drop = get_related_samples_to_drop()
related_samples = hl.set(
related_samples_to_drop.aggregate(
hl.agg.collect_as_set(related_samples_to_drop.node.id)))
sample_qc_filters.update({'is_related': related_samples.contains(ht.s)})
# 4. Add stratified sample qc (population/platform) annotation expr
sample_qc_pop_platform_filters_ht = hl.read_table(
get_sample_qc_ht_path(dataset=args.exome_cohort,
part='stratified_metrics_filter'))
sample_qc_filters.update({
'pop_platform_filters':
sample_qc_pop_platform_filters_ht[ht.s]['pop_platform_filters']
})
ht = (ht.annotate(**sample_qc_filters))
# Final sample qc filter joint expression
final_sample_qc_ann_expr = {
'pass_filters':
hl.cond((hl.len(ht.hard_filters) == 0) &
(hl.len(ht.pop_platform_filters) == 0) &
(ht.predicted_pop == 'EUR') & ~ht.is_related, True, False)
}
ht = (ht.annotate(**final_sample_qc_ann_expr))
logger.info('Writing final sample qc HT to disk...')
output_path_ht = get_sample_qc_ht_path(dataset=args.exome_cohort,
part='final_qc')
ht = ht.checkpoint(output_path_ht, overwrite=args.overwrite)
# Export final sample QC annotations to file
if args.write_to_file:
(ht.export(f'{output_path_ht}.tsv.bgz'))
## Release final unphase MT with adjusted genotypes filtered
mt = unphase_mt(mt)
mt = annotate_adj(mt)
mt = mt.filter_entries(mt.adj).select_entries('GT', 'DP', 'GQ', 'adj')
logger.info('Writing unphase MT with adjusted genotypes to disk...')
# write MT
mt.write(get_qc_mt_path(dataset=args.exome_cohort,
part='unphase_adj_genotypes',
split=True),
overwrite=args.overwrite)
# Stop Hail
hl.stop()
print("Finished!")
def main(args):
hl.init(default_reference=args.default_ref_genome)
if args.run_test_mode:
logger.info('Running pipeline on test data...')
mt = (get_mt_data(part='raw_chr20').sample_rows(0.1))
else:
logger.info(
'Running pipeline on MatrixTable wih adjusted genotypes...')
ds = args.exome_cohort
mt = hl.read_matrix_table(
get_qc_mt_path(dataset=ds,
part='unphase_adj_genotypes',
split=True))
# 1. Sample-QC filtering
if not args.skip_sample_qc_filtering:
logger.info('Applying per sample QC filtering...')
mt = apply_sample_qc_filtering(mt)
logger.info(
'Writing sample qc-filtered mt with rare variants (internal maf 0.01) to disk...'
)
mt = (mt.write(f'{hdfs_dir}/chd_ukbb.sample_qc_filtered.mt',
overwrite=True))
# 2. Variant-QC filtering
if not args.skip_variant_qc_filtering:
logger.info('Applying per variant QC filtering...')
if hl.hadoop_is_file(
f'{hdfs_dir}/chd_ukbb.sample_qc_filtered.mt/_SUCCESS'):
logger.info('Reading pre-existing sample qc-filtered MT...')
mt = hl.read_matrix_table(
f'{hdfs_dir}/chd_ukbb.sample_qc_filtered.mt')
mt = apply_variant_qc_filtering(mt)
# write hard filtered MT to disk
logger.info(
'Writing variant qc-filtered mt with rare variants (internal maf 0.01) to disk...'
)
mt = (mt.write(f'{hdfs_dir}/chd_ukbb.variant_qc_filtered.mt',
overwrite=True))
# 3. Annotate AFs
# allelic frequency cut-off
maf_cutoff = args.af_max_threshold
if not args.skip_af_filtering:
if hl.hadoop_is_file(
f'{hdfs_dir}/chd_ukbb.variant_qc_filtered.mt/_SUCCESS'):
logger.info(
'Reading pre-existing sample/variant qc-filtered MT...')
mt = hl.read_matrix_table(
f'{hdfs_dir}/chd_ukbb.variant_qc_filtered.mt')
# Annotate allelic frequencies from external source,
# and compute internal AF on samples passing QC
af_ht = get_af_annotation_ht()
mt = (mt.annotate_rows(**af_ht[mt.row_key]))
filter_expressions = [
af_filter_expr(mt, 'internal_af', af_cutoff=maf_cutoff),
af_filter_expr(mt, 'gnomad_genomes_af', af_cutoff=maf_cutoff),
af_filter_expr(mt, 'gnomAD_AF', af_cutoff=maf_cutoff),
af_filter_expr(mt, 'ger_af', af_cutoff=maf_cutoff),
af_filter_expr(mt, 'rumc_af', af_cutoff=maf_cutoff),
af_filter_expr(mt, 'bonn_af', af_cutoff=maf_cutoff)
]
mt = (mt.filter_rows(functools.reduce(operator.iand,
filter_expressions),
keep=True))
logger.info(
f'Writing sample/variant QCed MT with rare variants at maf: {args.af_max_threshold}.'
)
mt = (mt.write(f'{hdfs_dir}/chd_ukbb.qc_final.rare.mt',
overwrite=True))
# 4. ##### Run gene-set burden logistic regression ######
logger.info('Running gene-set burden logistic regression test...')
if hl.hadoop_is_file(f'{hdfs_dir}/chd_ukbb.qc_final.rare.mt/_SUCCESS'):
logger.info(
'Reading pre-existing sample/variant qc-filtered MT with rare variants...'
)
mt = hl.read_matrix_table(f'{hdfs_dir}/chd_ukbb.qc_final.rare.mt')
## Add VEP-annotated fields
vep_ht = get_vep_annotation_ht()
mt = (mt.annotate_rows(LoF=vep_ht[mt.row_key].vep.LoF,
Consequence=vep_ht[mt.row_key].vep.Consequence,
DOMAINS=vep_ht[mt.row_key].vep.DOMAINS,
SYMBOL=vep_ht[mt.row_key].vep.SYMBOL))
## Filter to bi-allelic variants
if args.filter_biallelic:
logger.info('Running burden test on biallelic variants...')
mt = mt.filter_rows(bi_allelic_expr(mt))
## Filter to variants within protein domain(s)
if args.filter_protein_domain:
logger.info(
'Running burden test on variants within protein domain(s)...')
mt = mt.filter_rows(vep_protein_domain_filter_expr(mt.DOMAINS),
keep=True)
## Annotate pathogenic scores
ht_scores = get_vep_scores_ht()
mt = mt.annotate_rows(**ht_scores[mt.row_key])
## Classify variant into (major) consequence groups
score_expr_ann = {
'hcLOF': mt.LoF == 'HC',
'syn': mt.Consequence == 'synonymous_variant',
'miss': mt.Consequence == 'missense_variant'
}
# Update dict expr annotations with combinations of variant consequences categories
score_expr_ann.update({
'missC': (hl.sum([(mt['vep.MVP_score'] >= MVP_THRESHOLD),
(mt['vep.REVEL_score'] >= REVEL_THRESHOLD),
(mt['vep.CADD_PHRED'] >= CADD_THRESHOLD)]) >= 2)
& score_expr_ann.get('miss')
})
score_expr_ann.update({
'hcLOF_missC':
score_expr_ann.get('hcLOF') | score_expr_ann.get('missC')
})
mt = (mt.annotate_rows(csq_group=score_expr_ann))
# Transmute csq_group and convert dict to set where the group is defined
# (easier to explode and grouping later)
mt = (mt.transmute_rows(csq_group=hl.set(
hl.filter(lambda x: mt.csq_group.get(x), mt.csq_group.keys()))))
mt = (mt.filter_rows(hl.len(mt.csq_group) > 0))
# Explode nested csq_group and gene clusters before grouping
mt = (mt.explode_rows(mt.csq_group))
# First-step aggregation:
# Generate a sample per gene/variant_type (binary) matrix aggregating genotypes as follow:
#
# a) entry: hets
# b) entry: homs
# c) entry: chets (compound hets)
mt_grouped = (mt.group_rows_by(mt['SYMBOL'], mt['csq_group']).aggregate(
hets=hl.agg.any(mt.GT.is_het()),
homs=hl.agg.any(mt.GT.is_hom_var()),
chets=hl.agg.count_where(
mt.GT.is_het()) >= 2).repartition(100).persist())
# Import/generate gene clusters
clusters = hl.import_table(args.set_file,
no_header=True,
delimiter="\t",
min_partitions=50,
impute=False)
clusters = generate_clusters_map(clusters)
# Annotate gene-set info
mt_grouped = (mt_grouped.annotate_rows(**clusters[mt_grouped.SYMBOL]))
# Explode nested csq_group before grouping
mt_grouped = (mt_grouped.explode_rows(mt_grouped.cluster_id))
# filter rows with defined consequence and gene-set name
mt_grouped = (mt_grouped.filter_rows(
hl.is_defined(mt_grouped.csq_group)
& hl.is_defined(mt_grouped.cluster_id)))
# 2. Second-step aggregation
# Generate a sample per gene-sets/variant type matrix aggregating genotypes as follow:
# if dominant -> sum hets (default)
# if recessive -> sum (homs)
# if recessive (a) -> sum (chets)
# if recessive (b) -> sum (chets and/or homs)
mts = []
if args.homs:
# Group mt by gene-sets/csq_group aggregating homs genotypes.
mt_homs = (mt_grouped.group_rows_by(
mt_grouped.csq_group, mt_grouped.cluster_id).aggregate(
mac=hl.int(hl.agg.sum(mt_grouped.homs))).repartition(
100).persist().annotate_rows(agg_genotype='homs'))
mts.append(mt_homs)
if args.chets:
# Group mt by gene-sets/csq_group aggregating compound hets (chets) genotypes.
mt_chets = (mt_grouped.group_rows_by(
mt_grouped.csq_group, mt_grouped.cluster_id).aggregate(
mac=hl.int(hl.agg.sum(mt_grouped.chets))).repartition(
100).persist().annotate_rows(agg_genotype='chets'))
mts.append(mt_chets)
if args.homs_chets:
# Group mt by gene-sets/csq_group aggregating chets and/or homs genotypes.
mt_homs_chets = (mt_grouped.group_rows_by(
mt_grouped.csq_group, mt_grouped.cluster_id).aggregate(mac=hl.int(
hl.agg.count_where(mt_grouped.chets
| mt_grouped.homs))).repartition(100).
persist().annotate_rows(agg_genotype='homs_chets'))
mts.append(mt_homs_chets)
if args.hets:
# Group mt by gene-sets/csq_group aggregating hets genotypes (default)
mt_hets = (mt_grouped.group_rows_by(
mt_grouped.csq_group, mt_grouped.cluster_id).aggregate(
mac=hl.int(hl.agg.sum(mt_grouped.hets))).repartition(
100).persist().annotate_rows(agg_genotype='hets'))
mts.append(mt_hets)
## Joint MatrixTables
mt_joint = hl.MatrixTable.union_rows(*mts)
## Add samples annotations
# annotate sample covs
covariates = hl.read_table(
f'{nfs_dir}/hail_data/sample_qc/chd_ukbb.sample_covariates.ht')
mt_joint = (mt_joint.annotate_cols(**covariates[mt_joint.s]))
# annotate case/control phenotype info
tb_sample = get_sample_meta_data()
mt_joint = (mt_joint.annotate_cols(**tb_sample[mt_joint.s]))
mt_joint = (mt_joint.filter_cols(mt_joint['phe.is_case']
| mt_joint['phe.is_control']))
## Run logistic regression stratified by proband type
analysis = ['all_cases', 'syndromic', 'nonsyndromic']
tbs = []
covs = ['sex', 'PC1', 'PC2', 'PC3', 'PC4', 'PC5']
for proband in analysis:
logger.info(f'Running burden test for {proband}...')
mt_tmp = hl.MatrixTable
if proband == 'all_cases':
mt_tmp = mt_joint
if proband == 'syndromic':
mt_tmp = mt_joint.filter_cols(~mt_joint['phe.is_nonsyndromic'])
if proband == 'nonsyndromic':
mt_tmp = mt_joint.filter_cols(~mt_joint['phe.is_syndromic'])
tb_logreg = logistic_regression(mt=mt_tmp,
x_expr='mac',
response='phe.is_case',
covs=covs,
pass_through=['agg_genotype'],
extra_fields={
'analysis': proband,
'maf': maf_cutoff,
'covs': '|'.join(covs)
})
tbs.append(tb_logreg)
tb_final = hl.Table.union(*tbs)
# export results
date = current_date()
run_hash = str(uuid.uuid4())[:6]
output_path = f'{args.output_dir}/{date}/{args.exome_cohort}.logreg_burden.{run_hash}.ht'
tb_final = (tb_final.checkpoint(output=output_path))
if args.write_to_file:
# write table to disk as TSV file
(tb_final.export(f'{output_path}.tsv'))
hl.stop()