Variant Prioritization

Basic Filtering Pipeline

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

ACMG-Style Filtering

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)

Multi-Database Prioritization

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)

Inheritance-Based Filtering

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

Output Priority 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

Related Skills

• clinvar-lookup - ClinVar pathogenicity queries

• gnomad-frequencies - Population frequency filtering

• variant-calling/clinical-interpretation - ACMG classification

• variant-calling/filtering-best-practices - Quality filtering