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bio-clinical-databases-variant-prioritization Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinical-databases/acmg-classification.
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name bio-clinical-databases-variant-prioritization description Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinical-databases/acmg-classification. tool_type python primary_tool pandas
Version Compatibility
Reference examples tested with: pandas 2.2+, cyvcf2 0.30+, pyhgvs 0.12+, Exomiser 14.0+ (Smedley 2015), Phen2Gene 1.2+ (Zhao 2020), DeNovoGear 1.1.1+ (Ramu 2013), WhatsHap 2.0+ (Patterson 2015), HPO 2024+ (Human Phenotype Ontology). ACMG Secondary Findings list is v3.2 (Miller 2023): 81 genes.
Before using code patterns, verify installed versions match. If versions differ:
Python: pip show <package> then help(module.function) to check signatures
CLI: <tool> --version
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. Phenotype-driven prioritization REQUIRES high-quality HPO terms; without rich phenotypic input Exomiser/AMELIE degrade significantly.
Rare-Disease Variant Prioritization Pipeline 'Prioritize candidate disease-causing variants from this trio exome' -> Filter to rare + functional + inheritance-consistent variants; rank by phenotype concordance; flag ACMG SF v3.2 incidental findings; report tiers with classification logic deferred to acmg-classification.
Python (filtering pipeline): pandas + cyvcf2 + myvariant.info aggregation
CLI (phenotype-driven ranking): exomiser --analysis hiPHIVE-prioritised.yml
Python (de novo calling): DeNovoGear / Triodenovo / PossibleDeNovo
CLI (compound het phasing): whatshap phase --indels for singletons; trio-based for families
Python (HPO concordance): Phen2Gene / AMELIE / Phenolyzer
VCEP curations: https://cspec.genome.network/cspec/ui/svi/all
Pipeline Architecture: The Standard Rare-Disease Funnel Typical trio exome enters as 40,000-100,000 variants per individual; reaches diagnostic candidate list of 1-10 variants through cascading filters:
Stage Filter Variant count (typical trio) Raw joint-called -- 100k-150k QC filter (PASS, depth, GQ, missingness) GATK best practices + Hail QC 80k-120k Population frequency gnomAD grpmax_faf95 < 0.0001 (or disease-specific Whiffin max-credible-AF) 5k-15k Functional consequence Coding / splice / regulatory 1k-3k Inheritance pattern de novo / AR-hom / AR-compoundhet / X-linked / mosaic 50-500 Phenotype concordance Exomiser hiPHIVE / Phen2Gene / AMELIE score 5-50 ACMG classification Defer to acmg-classification 1-10 ACMG SF v3.2 cross-check Miller 2023 (81 genes) Separate output
Inheritance-Based Filtering Pattern Filter De novo (DNV) Variant in proband, absent in both parents; needs trio Autosomal recessive; homozygous Hom-alt in proband; het in both parents Autosomal recessive; compound het Two het variants in same gene on opposite alleles X-linked recessive Male proband hemizygous; carrier mother het X-linked dominant Het in affected; consider XCI skewing in females Mitochondrial heteroplasmy mtDNA variant present at varying heteroplasmy across tissues Mosaic Sub-clonal VAF in proband; absent in inherited transmissions
De Novo Calling: Trio Analysis Goal: Identify variants present in proband but absent in both parents with high specificity.
Approach: Use specialized DNV callers; supplement with manual IGV inspection.
Tool Approach Use case DeNovoGear (Ramu 2013 Nat Methods )Bayesian, considers parent-of-origin Standard for trio WES Triodenovo (Wei 2015)Bayesian + family-aware Alternative GATK PossibleDeNovo annotation Hard filter Quick prefilter; not standalone DeNovoCNN (2022)Deep learning trio caller Most accurate as of 2022-2026
False-DNV rate: ~10-30% without manual IGV inspection; concentrated in:
Tandem repeat regions (DNM rate inflated)
Heterozygous parent with low coverage
Mosaic parents (parental mosaicism transmitted to >1 offspring)
Mapping errors in segmental duplications
Phenotype-Driven Prioritization Tool Approach Performance (typical benchmark) Fails when Exomiser (Smedley 2015 Nat Protoc )hiPHIVE: phenotype + interactome + sequence damage 74% top-1; 94% top-5 (Cipriani 2020) Sparse HPO (< 5 specific terms); novel-disease gene Phen2Gene (Zhao 2020 NARGAB )HPO-to-gene mapping; faster than Exomiser Similar top-5 Phenotype-only filtering insufficient AMELIE (Birgmeier 2020 Sci Transl Med )Literature-mining + phenotype Best when literature is rich New / rare disease without literature; specific patient HPO unmatched Phenolyzer (Yang 2015 Nat Methods )Phenotype-based gene scoring Legacy Modern multi-feature tools (Exomiser, AMELIE) preferred GADO (Deelen 2019 Nat Commun )Gene Network-based; HPO-free option When HPO is sparse Phenotype-rich cases where Exomiser hiPHIVE wins CADA (Peng 2021)Cross-species gene prioritization Animal model integration Genes without orthologs; rare-disease without animal model
Critical requirement: all phenotype-driven tools degrade significantly with sparse HPO terms. Capture 5-10 specific HPO terms; avoid generic "intellectual disability" alone.
ClinGen Gene-Disease Validity: Mandatory Gating Strande et al. 2017 AJHG + ClinGen ongoing curation: Limited / Moderate / Strong / Definitive evidence per gene-disease pair.
Category When to apply Definitive Strong literature evidence + functional / population genetic evidence Strong -- Moderate -- Limited Single case report or weak segregation Disputed Contradicting evidence No Known Disease Relationship Gene not associated with the queried disease
Many commercial panels include genes with only Limited validity. ClinGen-curated https://search.clinicalgenome.org/kb/gene-validity is the authoritative directory.
ACMG Secondary Findings v3.2 (Miller 2023 Genet Med 25:100866) 81 genes for opt-in/opt-out reporting on clinical exome/genome. Growth: 56 -> 59 -> 73 -> 78 -> 81. v3.2 additions: CALM1, CALM2, CALM3 (calmodulinopathy; long QT / CPVT; high actionability via beta-blockade + ICD).
Inclusion criteria: ClinGen Strong or Definitive gene-disease validity + ClinGen ADWG actionability scoring.
ACMG_SF_V3_2_GENES = [
'ACTA2' , 'ACTC1' , 'BAG3' , 'COL3A1' , 'DES' , 'FBN1' , 'FLNC' , 'GLA' , 'LMNA' , 'MYBPC3' ,
'MYH11' , 'MYH7' , 'MYL2' , 'MYL3' , 'PRKAG2' , 'PKP2' , 'RBM20' , 'SCN5A' , 'SMAD3' ,
'TGFBR1' , 'TGFBR2' , 'TMEM43' , 'TNNC1' , 'TNNI3' , 'TNNT2' , 'TPM1' , 'TTN' ,
'CALM1' , 'CALM2' , 'CALM3' ,
'CACNA1S' , 'KCNH2' , 'KCNQ1' , 'RYR1' , 'RYR2' ,
'ACVRL1' , 'ENG' ,
'APC' , 'ATM' , 'BAP1' , 'BMPR1A' , 'BRCA1' , 'BRCA2' , 'BRIP1' , 'CDH1' , 'CDKN2A' ,
'CHEK2' , 'GREM1' , 'HOXB13' , 'MAX' , 'MEN1' , 'MLH1' , 'MSH2' , 'MSH6' , 'MUTYH' ,
'NF2' , 'PALB2' , 'PMS2' , 'PTEN' , 'RAD51C' , 'RAD51D' , 'RB1' , 'RET' , 'SDHAF2' ,
'SDHB' , 'SDHC' , 'SDHD' , 'SMAD4' , 'STK11' , 'TMEM127' , 'TP53' , 'TSC1' , 'TSC2' ,
'VHL' , 'WT1' ,
'FH' , 'GAA' , 'HFE' , 'HNF1A' , 'LDLR' , 'OTC' , 'PCSK9' , 'TTR'
]
Decision Tree by Scenario Scenario Recommended path Why Trio WES, suspected Mendelian Full pipeline with DeNovoGear + Exomiser + HPO Standard rare-disease workflow Singleton WES WhatsHap read-based phasing + AR-hom + AR-compoundhet candidates Compound het hard without trio Suspected mosaic Lower VAF threshold (2-30%); deep coverage (>200x) Standard tools miss mosaic Long-read genome Add SV calling + STR repeat expansion SVs miss in short-read Newborn screening (BabyScreen+) 605-gene Mendelian panel with current ACMG SF v3.2 Lunke 2025 Nat Med 31:4236 Cancer predisposition ClinGen Hereditary Cancer VCEPs + ACMG SF cancer subset Use VCEP CSpec Cardiomyopathy / arrhythmia ClinGen HCM / DCM / LQT VCEPs Strict gene-disease validity Population screening ACMG SF v3.2 (81 genes) opt-in/opt-out Miller 2023
Standard Pipeline Workflow Goal: From a trio joint-called VCF, output ranked candidate variants with inheritance pattern, phenotype concordance, and ACMG SF flags.
Approach: Cascading filters with QC, population frequency, functional consequence, inheritance, phenotype.
from cyvcf2 import VCF
import pandas as pd
from pathlib import Path
def filter_qc_and_frequency (vcf_path, max_grpmax_faf95=0.0001 , min_dp=10 , min_gq=20 ):
'''Stage 1: QC + frequency filter. Reduces 100k -> ~5-15k variants.'''
vcf = VCF(vcf_path)
samples = vcf.samples
rows = []
for v in vcf:
if v.FILTER is not None :
continue
if min (v.gt_depths) < min_dp:
continue
if v.QUAL is not None and v.QUAL < min_gq:
continue
gnomad = (v.INFO.get('grpmax_faf95' ) or v.INFO.get('AF_grpmax' ) or
v.INFO.get('AF_popmax' ) or 0 )
if gnomad > max_grpmax_faf95:
continue
rows.append({
'chrom' : v.CHROM, 'pos' : v.POS, 'ref' : v.REF, 'alt' : v.ALT[0 ],
'genotypes' : dict (zip (samples, v.gt_types.tolist())),
'depth' : dict (zip (samples, v.gt_depths.tolist())),
'gnomad_faf95' : gnomad,
'consequence' : v.INFO.get('CSQ' , '' ).split('|' )[1 ] if v.INFO.get('CSQ' ) else None
})
return pd.DataFrame(rows)
def call_de_novo (df, proband, mother, father ):
'''Stage 2: identify DNV candidates: hom-ref both parents, het/hom-alt proband.
Implements Mendelian-violation logic; supplement with DeNovoGear or DeNovoCNN
for production (this implementation has 10-30% false-positive rate without IGV).
'''
is_dnv = []
for _, row in df.iterrows():
gts = row['genotypes' ]
if gts[mother] == 0 and gts[father] == 0 and gts[proband] in (1 , 3 ):
if row['depth' ][mother] >= 10 and row['depth' ][father] >= 10 :
is_dnv.append(True )
continue
is_dnv.append(False )
df['is_de_novo_candidate' ] = is_dnv
return df
def call_compound_het (df, proband, mother, father, gene_col='gene' ):
'''Stage 3: identify compound het: two het variants in same gene, one from each parent.
Trio phasing is gold standard; singletons require WhatsHap read-based phasing.
'''
het_in_proband = df[df['genotypes' ].apply(lambda gts: gts[proband] == 1 )]
candidate_genes = []
for gene in het_in_proband[gene_col].unique():
if pd.isna(gene):
continue
gene_variants = het_in_proband[het_in_proband[gene_col] == gene]
maternal_het = gene_variants[gene_variants['genotypes' ].apply(
lambda gts: gts[mother] == 1 and gts[father] == 0 )]
paternal_het = gene_variants[gene_variants['genotypes' ].apply(
lambda gts: gts[father] == 1 and gts[mother] == 0 )]
if len (maternal_het) >= 1 and len (paternal_het) >= 1 :
candidate_genes.append(gene)
df['is_compound_het_candidate' ] = df[gene_col].isin(candidate_genes)
return df
def flag_acmg_sf (df, acmg_sf_genes, gene_col='gene' , clnsig_col='clinvar_sig' ):
'''Stage: flag ACMG Secondary Findings (Miller 2023 v3.2; 81 genes).
Only P/LP variants in SF genes are reportable as secondary findings.
'''
df['is_acmg_sf_candidate' ] = (
df[gene_col].isin(acmg_sf_genes) &
df[clnsig_col].astype(str ).str .contains('athogenic' , na=False )
)
return df
def filter_by_clingen_validity (df, validity_table, gene_col='gene' ,
min_validity='Moderate' ):
'''Gate on ClinGen gene-disease validity. Limited or Disputed -> low confidence.
validity_table: DataFrame from `https://search.clinicalgenome.org/kb/gene-validity`
'''
rank = {'No Known Disease Relationship' : 0 , 'Disputed' : 0 , 'Limited' : 1 ,
'Moderate' : 2 , 'Strong' : 3 , 'Definitive' : 4 }
min_rank = rank[min_validity]
df_merged = df.merge(validity_table, on=gene_col, how='left' )
df_merged['validity_rank' ] = df_merged['gene_validity' ].map (rank).fillna(0 )
df_merged['pass_validity' ] = df_merged['validity_rank' ] >= min_rank
return df_merged
def phenotype_score_with_exomiser_yml (yml_path, vcf_path, hpo_terms, output_dir ):
'''Emit Exomiser command for phenotype-driven ranking.
HPO terms (e.g., HP:0001250 for seizures) must be SPECIFIC.
Sparse generic HPO degrades Exomiser hiPHIVE accuracy significantly.
'''
return (f'java -jar exomiser-cli-14.0.0.jar --analysis {yml_path} '
f'--vcf {vcf_path} --hpo {"," .join(hpo_terms)} '
f'--output-dir {output_dir} ' )
Per-Operation Failure Modes 1. De novo with false-positive rate 10-30%
Trigger: Report DNV candidates from Mendelian-violation analysis without IGV inspection.
Mechanism: Tandem-repeat regions, low-coverage parents, parental mosaicism, mapping errors in segmental duplications all produce false DNVs.
Symptom: 10-30% of reported DNVs are artifacts.
Fix: Use DeNovoGear / DeNovoCNN (Bayesian frameworks); manually inspect candidates in IGV; check parental coverage at site.
2. Compound het without phasing
Trigger: Report two hets in same gene as compound het without confirming phase.
Mechanism: Trans (compound het) vs cis (same chromosome) is critical for AR mechanism.
Symptom: False-positive compound het when both variants are in cis.
Fix: Trio phasing if available; WhatsHap read-based phasing for variants within ~500 bp; consider long-read for broader phasing.
3. Limited-validity gene reported as diagnostic
Trigger: Gene appears on commercial panel; variant labeled disease-causing.
Mechanism: Commercial panels often include Limited or Disputed validity genes.
Symptom: False-positive diagnostic report.
Fix: Cross-check ClinGen gene-disease validity; reject Limited / Disputed without VCEP curation.
4. Sparse HPO terms degrading Exomiser
Trigger: Submit Exomiser with single generic HPO (e.g., HP:0001250 "Seizure" only).
Mechanism: Phenotype-driven prioritization relies on HPO-to-gene network; sparse terms reduce discriminative power.
Symptom: Top-5 rank includes implausible genes; correct diagnosis sub-rank.
Fix: Capture 5-10 specific HPO terms (e.g., "infantile spasms with hypsarrhythmia", "facial dysmorphism with hypertelorism").
5. ACMG SF v3.1 used instead of v3.2
Trigger: Pipeline reports SF based on 78-gene v3.1 list; misses CALM1/2/3 calmodulinopathies.
Mechanism: v3.2 (Miller 2023) added CALM1, CALM2, CALM3.
Symptom: Misses calmodulinopathy SF; high-actionability long-QT/CPVT not flagged.
Fix: Use Miller 2023 v3.2 list (81 genes); re-run prior cohorts.
6. Mosaic variants below standard VAF threshold
Trigger: Filter at VAF >= 30% on standard pipeline.
Mechanism: Mosaic variants frequently 2-30% VAF; below threshold filters them out.
Symptom: Mosaic disease missed (e.g., Proteus syndrome PIK3CA, McCune-Albright GNAS).
Fix: For suspected mosaic disorders, deep coverage (>= 200x); VAF threshold 2-5%; sample affected tissue when possible.
7. ClinVar P variant in Limited-validity gene
Trigger: Variant labeled P in ClinVar; gene-disease validity is Limited.
Mechanism: ClinVar P is variant-level assertion; gene-disease validity is the upstream question.
Symptom: Reported P variant in non-disease-associated gene.
Fix: Apply ClinGen gene-disease validity gate BEFORE variant-level interpretation.
8. VUS reclassification gaps
Trigger: VUS labeled 2017 still in active diagnostic report 2025.
Mechanism: VUS are reclassified as evidence accrues in actively-curated genes; a one-time classification has an expiry date.
Symptom: Stale classifications drive incorrect clinical decisions.
Fix: Annual VUS re-review for active diagnostic variants; tools like Genome Alert! (Yauy 2022) automate detection of monthly ClinVar changes.
9. Inheritance pattern assumed wrong
Trigger: Assume AD inheritance for a gene with variable expressivity / incomplete penetrance.
Mechanism: AD genes can have AR variants in functionally significant compound het pattern.
Symptom: Miss AR mechanism in mostly-AD gene.
Fix: Allow multi-inheritance candidate generation; cross-check ClinGen gene-disease inheritance.
Reconciliation: When Sources Disagree Pattern Likely cause Action Exomiser ranks low; ClinVar says P Sparse or wrong HPO terms; rare disease in atypical gene Re-run with full HPO; manual review ClinVar P + ClinGen Limited validity Variant-level vs gene-disease tension Treat as candidate; require VCEP curation or functional evidence DeNovoGear high posterior; trio coverage uneven Parental mosaicism or mapping error IGV review; consider parent-of-origin testing Compound het in phasing-ambiguous gene Distance > 500 bp; can't phase from reads Trio phasing; long-read confirmation SF gene with V3.1 list; missing CALM Miller 2023 v3.2 update Re-run with v3.2 (81 genes) Phenotype tool disagrees with clinical Tool-specific phenotype model; literature gap Cross-check with AMELIE for literature-mining alternative Mosaic suspected but standard pipeline negative VAF below 30% threshold Deep targeted sequencing or affected tissue
Quantitative Thresholds and Conventions Threshold Convention Source Rare-disease frequency filter grpmax_faf95 < 0.0001 ClinGen SVI Recessive disease filter grpmax_faf95 < 0.005 ClinGen SVI Whiffin gene-specific max-credible-AF Computed per gene + disease Whiffin 2017 DNV minimum parental coverage >= 10x both parents Standard DNV manual IGV review Required for all reportable DNVs Standard Compound het phasing <= 500 bp read-based; trio gold standard WhatsHap Exomiser top-1 diagnostic rank 74%; top-5 94% (with rich HPO) Cipriani 2020 ACMG SF v3.2 genes 81 (Miller 2023) Miller 2023 Genet Med VUS reclassification cycle Reassess as evidence accrues; ClinGen recommends periodic re-review convention Mosaic VAF threshold 2-30% Convention ClinGen gene-disease validity gate Moderate or Strong minimum for diagnostic reporting ClinGen SVI
Common Errors Symptom Cause Solution Too many candidate variants (>50) Frequency filter too loose Tighten to grpmax_faf95 < 0.0001 (dominant) or 0.005 (recessive) No DNV candidates in obvious DNV phenotype False-negative DNV calling DeNovoGear / DeNovoCNN; check parental sample swap Compound het in gene known AD only Phasing not validated Confirm phase via trio or long-read Exomiser top hit unrelated to phenotype HPO too generic or wrong Add specific HPO; check ontology version Mosaic disease missed VAF threshold too high Deep coverage; affected tissue sampling; VAF 2-5% SF gene match flagged but variant benign Wrong variant classification Apply ACMG framework via acmg-classification skill Genotype-phenotype discordance Locus heterogeneity OR multi-gene contribution Run digenic / oligogenic analysis tools
Anticipated Reviewer Pushback Pushback Standard response "Why grpmax_faf95 instead of AF?" grpmax_faf95 is the Whiffin 2017 ClinGen-recommended frequency; excludes bottleneck groups; per ACMG SVI specifications. "Compound het without phase confirmation" Trio phased; if singleton, WhatsHap read-based for variants within 500 bp; long-read otherwise. "DNV call without IGV review?" All reportable DNVs underwent IGV inspection; we report posterior probability + parental coverage. "ClinGen Limited validity gene" Excluded per gate; we require Moderate or higher for reportable diagnostic candidates. "Why ACMG SF v3.2 not v3.1?" v3.2 (Miller 2023) added CALM1/2/3 calmodulinopathies (high actionability). We use current. "Phenotype-driven prioritization with single HPO term?" We submit 5-10 specific HPO terms; sparse input degrades Exomiser. "ACMG classification logic?" Variant prioritization (this skill) outputs candidates; ACMG classification (PVS1 / PP3 / BS1 / etc.) is in acmg-classification skill. "Why not VarSome / Franklin automated ACMG?" We report aggregated annotations via myvariant.info; ACMG classification per acmg-classification skill using Tavtigian point system + Pejaver 2022 calibration.
References
Richards S et al. 2015. Standards and guidelines for the interpretation of sequence variants. Genet Med 17:405. (ACMG/AMP)
Miller DT et al. 2023. ACMG SF v3.2 list for reporting of secondary findings in clinical exome and genome sequencing. Genet Med 25:100866.
Smedley D et al. 2015. Next-generation diagnostics and disease-gene discovery with the Exomiser. Nat Protoc 10:2004.
Zhao M et al. 2020. Phen2Gene: rapid phenotype-driven gene prioritization for rare diseases. NARGAB 2:lqaa032.
Birgmeier J et al. 2020. AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature. Sci Transl Med 12:eaau9113.
Cipriani V et al. 2020. An improved phenotype-driven tool for rare Mendelian variant prioritization. Genes 11:460.
Ramu A et al. 2013. DeNovoGear: de novo indel and point mutation discovery and phasing. Nat Methods 10:985.
Patterson M et al. 2015. WhatsHap: weighted haplotype assembly for future-generation sequencing reads. J Comput Biol 22:498.
Strande NT et al. 2017. Evaluating the clinical validity of gene-disease associations: an evidence-based framework developed by ClinGen. AJHG 100:895.
Whiffin N et al. 2017. Using high-resolution variant frequencies to empower clinical genome interpretation. Genet Med 19:1151.
Lunke S et al. 2025. Feasibility, acceptability and clinical outcomes of the BabyScreen+ genomic newborn screening study. Nat Med 31:4236.
Yauy K et al. 2022. Genome Alert! Genet Med 24:1316. (VUS reclassification monitoring)
ClinGen gene-disease validity: https://search.clinicalgenome.org/kb/gene-validity
HPO: https://hpo.jax.org/
ACMG SF v3.2 supplement: https://www.gimjournal.org/article/S1098-3600(23)00879-1/fulltext
Related Skills
clinical-databases/acmg-classification - PVS1 / PP3 / BS1 / PM2 calibration and Tavtigian point system
clinical-databases/clinvar-lookup - Variant pathogenicity database query
clinical-databases/gnomad-frequencies - Population frequency filtering
clinical-databases/myvariant-queries - Aggregated annotation
clinical-databases/pharmacogenomics - PGx variant handling
variant-calling/clinical-interpretation - Clinical reporting workflow
variant-calling/filtering-best-practices - Upstream QC
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