bio-clinical-databases-variant-prioritization

## 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