Filter and prioritize variants by pathogenicity, population frequency, and clinical evidence for rare disease analysis. Use when identifying candidate disease-causing variants from exome or genome sequencing.
Reference examples tested with: pandas 2.2+
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"Prioritize candidate disease variants from my exome data" → Filter and rank variants by pathogenicity scores, population frequency, inheritance pattern, and clinical evidence to identify candidate disease-causing mutations.
pandas for multi-criteria filtering with ACMG/AMP classification logicGoal: Filter variants to retain rare, potentially pathogenic candidates for rare disease analysis.
Approach: Apply gnomAD population frequency and ClinVar significance filters, retaining pathogenic, VUS, and unannotated variants.
import pandas as pd
def prioritize_variants(df, gnomad_af_col='gnomad_af', clinvar_col='clinvar_sig'):
'''Basic variant prioritization pipeline
Filters:
1. Rare in population (gnomAD AF < 0.01)
2. Pathogenic/likely pathogenic in ClinVar OR VUS with low AF
'''
# Filter rare variants (ACMG PM2: AF < 1%)
rare = df[df[gnomad_af_col].isna() | (df[gnomad_af_col] < 0.01)]
# Prioritize by ClinVar
pathogenic_terms = ['Pathogenic', 'Likely_pathogenic', 'Pathogenic/Likely_pathogenic']
prioritized = rare[
rare[clinvar_col].isin(pathogenic_terms) |
rare[clinvar_col].isna() | # No ClinVar = needs review
(rare[clinvar_col] == 'Uncertain_significance')
]
return prioritized
Goal: Score variants using ACMG-style evidence criteria for pathogenicity assessment.
Approach: Evaluate PM2 (population rarity) and PVS1 (loss-of-function) evidence, then compute a weighted priority score.
def acmg_filter(df):
'''Apply ACMG-style filtering criteria
Strong pathogenic evidence:
- PVS1: Null variant in gene where LOF is disease mechanism
- PS1: Same amino acid change as established pathogenic
- PS3: Functional studies support damaging effect
Moderate evidence:
- PM1: Located in mutational hot spot
- PM2: Absent/rare in population databases (AF < 0.01)
- PM5: Novel missense at position of known pathogenic
'''
# PM2: Rare in gnomAD
df['pm2'] = df['gnomad_af'].isna() | (df['gnomad_af'] < 0.01)
# PVS1: Loss of function variants
lof_consequences = ['frameshift', 'stop_gained', 'splice_donor', 'splice_acceptor']
df['pvs1'] = df['consequence'].isin(lof_consequences)
# Score based on evidence
df['priority_score'] = df['pm2'].astype(int) + df['pvs1'].astype(int) * 2
return df.sort_values('priority_score', ascending=False)
Goal: Prioritize variants using aggregated evidence from ClinVar, gnomAD, CADD, and REVEL in a single query.
Approach: Fetch annotations via myvariant.info, then compute a composite priority score weighting clinical, population, and computational evidence.
import myvariant
def annotate_and_prioritize(variants):
'''Annotate variants and apply prioritization'''
mv = myvariant.MyVariantInfo()
# Fetch annotations
results = mv.getvariants(
variants,
fields=[
'clinvar.clinical_significance',
'clinvar.review_status',
'gnomad_exome.af.af',
'cadd.phred',
'dbnsfp.revel.score'
]
)
records = []
for r in results:
clinvar = r.get('clinvar', {})
gnomad = r.get('gnomad_exome', {})
cadd = r.get('cadd', {})
revel = r.get('dbnsfp', {}).get('revel', {})
records.append({
'variant': r.get('query'),
'clinvar_sig': clinvar.get('clinical_significance'),
'clinvar_stars': clinvar.get('review_status'),
'gnomad_af': gnomad.get('af', {}).get('af'),
'cadd_phred': cadd.get('phred'),
'revel_score': revel.get('score') if isinstance(revel, dict) else None
})
df = pd.DataFrame(records)
return prioritize_with_scores(df)
def prioritize_with_scores(df):
'''Apply multi-evidence prioritization'''
# Computational predictions
# CADD phred > 20 suggests deleteriousness
# REVEL > 0.5 suggests pathogenicity
df['cadd_deleterious'] = df['cadd_phred'].fillna(0) > 20
df['revel_pathogenic'] = df['revel_score'].fillna(0) > 0.5
# Rare in population
df['is_rare'] = df['gnomad_af'].isna() | (df['gnomad_af'] < 0.01)
# ClinVar pathogenic
pathogenic = ['Pathogenic', 'Likely_pathogenic']
df['clinvar_pathogenic'] = df['clinvar_sig'].apply(
lambda x: any(p in str(x) for p in pathogenic) if pd.notna(x) else False
)
# Priority score
df['priority'] = (
df['clinvar_pathogenic'].astype(int) * 10 +
df['is_rare'].astype(int) * 3 +
df['cadd_deleterious'].astype(int) * 2 +
df['revel_pathogenic'].astype(int) * 2
)
return df.sort_values('priority', ascending=False)
Goal: Filter variants by expected inheritance pattern (autosomal dominant, recessive, or X-linked).
Approach: Select heterozygous ultra-rare variants for AD, or homozygous plus compound heterozygous candidates for AR.
def filter_by_inheritance(df, inheritance='AD'):
'''Filter variants by inheritance pattern
AD: Autosomal dominant - heterozygous variants
AR: Autosomal recessive - homozygous or compound het
XL: X-linked
'''
if inheritance == 'AD':
# Dominant: heterozygous, rare
return df[(df['zygosity'] == 'HET') & (df['gnomad_af'] < 0.0001)]
elif inheritance == 'AR':
# Recessive: homozygous or two variants in same gene
hom = df[df['zygosity'] == 'HOM']
# Find genes with 2+ het variants (compound het candidates)
het = df[df['zygosity'] == 'HET']
compound_genes = het['gene'].value_counts()
compound_genes = compound_genes[compound_genes >= 2].index
compound_het = het[het['gene'].isin(compound_genes)]
return pd.concat([hom, compound_het])
return df
Goal: Assign clinical interpretation tiers (1-4) for structured reporting of prioritized variants.
Approach: Combine ClinVar pathogenicity, population rarity, and computational predictions to classify into strong, potential, uncertain, or benign tiers.
def assign_tiers(df):
'''Assign clinical interpretation tiers
Tier 1: Strong pathogenic evidence
Tier 2: Potential pathogenic
Tier 3: Uncertain significance
Tier 4: Likely benign
'''
def get_tier(row):
if row['clinvar_pathogenic'] and row['is_rare']:
return 1
elif row['is_rare'] and (row['cadd_deleterious'] or row['revel_pathogenic']):
return 2
elif row['is_rare']:
return 3
else:
return 4
df['tier'] = df.apply(get_tier, axis=1)
return df