bio-clinical-databases-variant-prioritization

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 (2024)	Deep learning trio caller	Most accurate as of 2024-2026

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

Strong 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 = [
    # Cardiomyopathies
    'ACTA2', 'ACTC1', 'COL3A1', 'DES', 'FBN1', 'FLNC', 'GLA', 'LMNA', 'MYBPC3',
    'MYH11', 'MYH7', 'MYL2', 'MYL3', 'PRKAG2', 'PKP2', 'RBM20', 'SCN5A', 'SMAD3',
    'TGFBR1', 'TGFBR2', 'TMEM43', 'TNNI3', 'TNNT2', 'TPM1', 'TTN',
    # CALM v3.2 additions (calmodulinopathies)
    'CALM1', 'CALM2', 'CALM3',
    # Arrhythmias and channelopathies
    'CACNA1S', 'KCNH2', 'KCNQ1', 'RYR1', 'RYR2',
    # Vascular
    'ACVRL1', 'ENG',
    # Cancer predisposition
    'APC', 'ATM', 'BAP1', 'BMPR1A', 'BRCA1', 'BRCA2', 'BRIP1', 'CDH1', 'CDKN2A',
    'CHEK2', 'GREM1', 'HOXB13', 'MAX', 'MEN1', , , , ,
    , , , , , , , , ,
    , , , , , , , , ,
    , ,
    
    , , , , , , , , 
]

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	Lynch / Brett 2025 Nat Med
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

# Quality + population frequency filter (apply first)
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  # e.g., [proband, mother, father]
    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())),
            : ((samples, v.gt_depths.tolist())),
            : gnomad,
            : v.INFO.get(, ).split()[]  v.INFO.get()  
        })
     pd.DataFrame(rows)


 ():
    
    is_dnv = []
     _, row  df.iterrows():
        gts = row[]
         gts[mother] ==   gts[father] ==   gts[proband]  (, ):
            
            
             row[][mother] >=   row[][father] >= :
                is_dnv.append()
                
        is_dnv.append()
    df[] = is_dnv
     df


 ():
    
    het_in_proband = df[df[].apply( gts: gts[proband] == )]
    candidate_genes = []
     gene  het_in_proband[gene_col].unique():
         pd.isna(gene):
            
        gene_variants = het_in_proband[het_in_proband[gene_col] == gene]
        
        maternal_het = gene_variants[gene_variants[].apply(
             gts: gts[mother] ==   gts[father] == )]
        paternal_het = gene_variants[gene_variants[].apply(
             gts: gts[father] ==   gts[mother] == )]
         (maternal_het) >=   (paternal_het) >= :
            candidate_genes.append(gene)
    df[] = df[gene_col].isin(candidate_genes)
     df


 ():
    
    df[] = (
        df[gene_col].isin(acmg_sf_genes) &
        df[clnsig_col].astype()..contains(, na=)
    )
     df


 ():
    
    rank = {: , : , : ,
            : , : , : }
    min_rank = rank[min_validity]
    df_merged = df.merge(validity_table, on=gene_col, how=)
    df_merged[] = df_merged[].(rank).fillna()
    df_merged[] = df_merged[] >= min_rank
     df_merged


 ():
    
     (
            
            )

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: Median VUS reclassification cycle ~5 years for actively-curated genes (Harrison 2017 follow-up).
• 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	Median 5 years for active genes; up to 10 for orphan	Harrison 2017 follow-up
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 accelerates Mendelian patient diagnosis directly from the 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.
• Strong A et al. 2017. Gene-disease validity framework. AJHG 100:895.
• Whiffin N et al. 2017. Using high-resolution variant frequencies to empower clinical genome interpretation. Genet Med 19:1151.
• Lynch F, Brett T et al. 2025. BabyScreen+ implementation results from genomic newborn screening. Nat Med.
• Yauy K et al. 2022. Genome Alert! Genet Med 24:S1098. (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