def compute_sample_qc(mt: hl.MatrixTable) -> hl.Table:
"""
Perform sample QC on the raw split matrix table using `compute_stratified_sample_qc`.
:return: Table containing sample QC metrics
:rtype: hl.Table
"""
logger.info("Computing sample QC")
# mt = mt.select_entries("GT")
# Remove centromeres and telomeres incase they were included
mt = filter_low_conf_regions(
mt,
filter_lcr=
True, # TODO: include also decoy and low coverage exome regions
filter_segdup=True)
# filter to autosomes
mt = filter_to_autosomes(mt)
# filter telomeres/centromes
mt = remove_telomeres_centromes(mt)
# filter coding variants
# mt = filter_cds_regions(mt)
sample_qc_ht = compute_stratified_sample_qc(mt,
strata={
"bi_allelic":
bi_allelic_expr(mt),
"multi_allelic":
~bi_allelic_expr(mt),
},
tmp_ht_prefix=None,
gt_expr=None)
# Remove annotations that cannot be computed from the sparse format
# sample_qc_ht = sample_qc_ht.annotate(
# **{
# x: sample_qc_ht[x].drop(
# "n_called", "n_not_called", "n_filtered", "call_rate"
# )
# for x in sample_qc_ht.row_value
# }
# )
return sample_qc_ht.repartition(100)
def compute_callrate_mt(
mt: hl.MatrixTable,
intervals_ht: hl.Table,
bi_allelic_only: bool = True,
autosomes_only: bool = True,
match: bool = True,
) -> hl.MatrixTable:
"""
Computes a sample/interval MT with each entry containing the call rate for that sample/interval.
This can be used as input for imputing exome sequencing platforms.
.. note::
The input interval HT should have a key of type Interval.
The resulting table will have a key of the same type as the `intervals_ht` table and
contain an `interval_info` field containing all non-key fields of the `intervals_ht`.
:param match:
:param mt: Input MT
:param intervals_ht: Table containing the intervals. This table has to be keyed by locus.
:param bi_allelic_only: If set, only bi-allelic sites are used for the computation
:param autosomes_only: If set, only autosomal intervals are used.
:param matches: If set, returns all intervals in intervals_ht that overlap the locus in the input MT.
:return: Callrate MT
"""
logger.info("Computing call rate MatrixTable")
if len(intervals_ht.key) != 1 or not isinstance(
intervals_ht.key[0], hl.expr.IntervalExpression):
logger.warning(
f"Call rate matrix computation expects `intervals_ht` with a key of type Interval. "
f"Found: {intervals_ht.key}")
if autosomes_only:
callrate_mt = filter_to_autosomes(mt)
if bi_allelic_only:
callrate_mt = callrate_mt.filter_rows(bi_allelic_expr(callrate_mt))
intervals_ht = intervals_ht.annotate(_interval_key=intervals_ht.key)
callrate_mt = callrate_mt.annotate_rows(_interval_key=intervals_ht.index(
callrate_mt.locus, all_matches=match)._interval_key)
if match:
callrate_mt = callrate_mt.explode_rows("_interval_key")
callrate_mt = callrate_mt.filter_rows(
hl.is_defined(callrate_mt._interval_key.interval))
callrate_mt = callrate_mt.select_entries(
GT=hl.or_missing(hl.is_defined(callrate_mt.GT), hl.struct()))
callrate_mt = callrate_mt.group_rows_by(
**callrate_mt._interval_key).aggregate(
callrate=hl.agg.fraction(hl.is_defined(callrate_mt.GT)))
intervals_ht = intervals_ht.drop("_interval_key")
callrate_mt = callrate_mt.annotate_rows(interval_info=hl.struct(
**intervals_ht[callrate_mt.row_key]))
return callrate_mt
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_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_reference)
if not args.skip_filter_step:
logger.info("Importing data...")
# import unfiltered MT
mt = hl.read_matrix_table(
get_qc_mt_path(dataset=args.exome_cohort,
part='unphase_adj_genotypes',
split=True))
# filter to samples passing QC filters
logger.info(
"Filtering MT to samples passing QC filters (hard filters, relatedness, european ancestries)..."
)
sample_qc_ht = hl.read_table(get_sample_qc_ht_path(part='final_qc'))
sample_qc_ht = (sample_qc_ht.filter(sample_qc_ht.pass_filters))
mt = (mt.filter_cols(hl.is_defined(sample_qc_ht[mt.col_key])))
logger.info(
"Filtering joint MT to bi-allelic, high-callrate, common SNPs...")
maf = args.maf_threshold
mt = (mt.filter_rows(
bi_allelic_expr(mt) & hl.is_snp(mt.alleles[0], mt.alleles[1])
& (hl.agg.mean(mt.GT.n_alt_alleles()) / 2 > maf)
& (hl.agg.fraction(hl.is_defined(mt.GT)) > 0.99)).naive_coalesce(
500))
logger.info("Checkpoint: writing filtered MT before LD pruning...")
mt = mt.checkpoint(get_mt_checkpoint_path(
dataset=args.exome_cohort,
part='high_callrate_common_snp_biallelic'),
overwrite=args.overwrite)
logger.info(
f"Running ld_prune with r2 = {args.ld_prune_r2} on MT with {mt.count_rows()} variants..."
)
# remove correlated variants
pruned_variant_table = hl.ld_prune(mt.GT,
r2=args.ld_prune_r2,
bp_window_size=500000,
memory_per_core=512)
mt = (mt.filter_rows(hl.is_defined(pruned_variant_table[mt.row_key])))
logger.info("Writing filtered MT with ld-pruned variants...")
(mt.write(get_qc_mt_path(dataset=args.exome_cohort,
part='high_callrate_common_snp_biallelic',
split=True,
ld_pruned=True),
overwrite=args.overwrite))
logger.info("Importing filtered ld-pruned MT...")
mt = hl.read_matrix_table(
get_qc_mt_path(dataset=args.exome_cohort,
part='high_callrate_common_snp_biallelic',
split=True,
ld_pruned=True))
logger.info(f"Running PCA on {mt.count_rows()} variants...")
# run pca on merged dataset
eigenvalues, pc_scores, _ = hl.hwe_normalized_pca(mt.GT, k=args.n_pcs)
logger.info(f"Eigenvalues: {eigenvalues}")
# Annotate eigenvalues as global field
pc_scores = (pc_scores.annotate_globals(**{'eigenvalues': 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 = args.output_ht
pca_table = (pca_table.checkpoint(output=output_ht_path,
overwrite=args.overwrite))
if args.write_to_file:
(pca_table.export(f'{output_ht_path}.tsv.bgz'))
# Stop Hail
hl.stop()
print("PCA pipeline finalised...")
def main(args):
## Init Hail
hl.init(default_reference=args.default_ref_genome)
## Import unfiltered MT with adjusted genotypes
ds = args.exome_cohort
mt = hl.read_matrix_table(get_qc_mt_path(dataset=ds,
part='unphase_adj_genotypes',
split=True))
## 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)
)
## Parse geneset
geneset = parse_geneset(args.geneset_file)
## Filter to geneset
mt = (mt
.filter_rows(hl.set(geneset).contains(mt.SYMBOL))
.checkpoint(f'{nfs_tmp}/tmp.mt',
overwrite=True)
)
## Sample-QC filtering
if args.apply_sample_qc_filtering:
logger.info('Applying per sample QC filtering...')
mt = apply_sample_qc_filtering(mt)
logger.info('Writing sample qc-filtered MT to disk...')
mt = (mt
.checkpoint(f'{hdfs_dir}/chd_ukbb.sample_qc_filtered.mt',
overwrite=True)
)
## Variant-QC filtering
if args.apply_variant_qc_filtering:
logger.info('Applying per variant QC filtering...')
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
.checkpoint(f'{hdfs_dir}/chd_ukbb.variant_qc_filtered.mt',
overwrite=True)
)
## Filtering by AFs
# allelic frequency cut-off
maf_cutoff = args.af_max_threshold
if args.apply_af_filtering:
# 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 AF-filtered MT to disk...')
mt = (mt
.checkpoint(f'{hdfs_dir}/chd_ukbb.qc_final.rare.mt',
overwrite=True)
)
## 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))
## Generate blind sample IDs
mt = mt.add_col_index()
mt = (mt
.annotate_cols(BIID=hl.str('BLIND_ID_') + hl.str(mt.col_idx))
)
## 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])
## Annotate variants ID
mt = annotate_variant_id(mt)
# annotate samples
ann_expr = {'n_het_cases': hl.agg.filter(mt.GT.is_het() & mt['phe.is_case'], hl.agg.count()),
'n_hom_cases': hl.agg.filter(mt.GT.is_hom_var() & mt['phe.is_case'], hl.agg.count()),
'n_het_syndromic': hl.agg.filter(mt.GT.is_het() & mt['phe.is_syndromic'], hl.agg.count()),
'n_hom_syndromic': hl.agg.filter(mt.GT.is_hom_var() & mt['phe.is_syndromic'], hl.agg.count()),
'n_het_nonsyndromic': hl.agg.filter(mt.GT.is_het() & mt['phe.is_nonsyndromic'], hl.agg.count()),
'n_hom_nonsyndromic': hl.agg.filter(mt.GT.is_hom_var() & mt['phe.is_nonsyndromic'], hl.agg.count()),
'n_het_controls': hl.agg.filter(mt.GT.is_het() & ~mt['phe.is_case'], hl.agg.count()),
'n_hom_controls': hl.agg.filter(mt.GT.is_hom_var() & ~mt['phe.is_case'], hl.agg.count()),
'het_case_ids': hl.agg.filter(mt.GT.is_het() & mt['phe.is_case'],
hl.delimit(hl.agg.collect_as_set(mt.BIID), '|')),
'hom_case_ids': hl.agg.filter(mt.GT.is_hom_var() & mt['phe.is_case'],
hl.delimit(hl.agg.collect_as_set(mt.BIID), '|')),
'het_control_ids': hl.agg.filter(mt.GT.is_het() & ~mt['phe.is_case'],
hl.delimit(hl.agg.collect_as_set(mt.BIID), '|')),
'hom_control_ids': hl.agg.filter(mt.GT.is_hom_var() & ~mt['phe.is_case'],
hl.delimit(hl.agg.collect_as_set(mt.BIID), '|'))
}
ht = (mt
.annotate_rows(**ann_expr)
.rows()
.key_by()
.select(*list(['vid', 'Consequence', 'SYMBOL', 'internal_af', 'gnomAD_AF', 'vep.MVP_score', 'vep.REVEL_score',
'vep.MPC_score', 'vep.CADD_PHRED']) + list(ann_expr.keys()))
)
# export results
(ht
.export(args.output_file)
)
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()