Version Compatibility

Reference examples tested with: pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Variant Prioritization

"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.

• Python: pandas for multi-criteria filtering with ACMG/AMP classification logic

Basic Filtering Pipeline

Goal: 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.

python
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

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.

python
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

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.

python
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

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.

python
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

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.

python
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