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.
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.
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.
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.
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.
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