| 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', , , , , , ,
, , , , , , , , ,
, , , , , , , , ,
, ,
, , , , , , ,
]
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())),
: ((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: 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
