| name | bio-clinical-databases-acmg-classification |
| description | Applies ACMG/AMP 2015 framework with ClinGen SVI specifications, Tavtigian 2018/2020 Bayesian point system, Abou Tayoun 2018 PVS1 decision tree, Pejaver 2022 and Bergquist 2025 calibrated PP3/BP4 thresholds for REVEL/BayesDel/AlphaMissense, Brnich 2020 PS3/BS3 OddsPath, Walker 2023 SpliceAI splicing framework, and AMP/ASCO/CAP 2017 tumor tiers. Use when classifying germline variants P / LP / VUS / LB / B, applying VCEP-specific CSpec rules, computing Whiffin BS1, or assigning cancer Tier I-IV per Li 2017. |
| tool_type | python |
| primary_tool | requests |
Version Compatibility
Reference examples tested with: requests 2.31+, pandas 2.2+, AutoPVS1 (Xiang 2020), InterVar 2.2+, GeneBe 1.0+ (Stawiński 2024 Clin Genet). ACMG/AMP Bayesian point system is Tavtigian 2018 Genet Med / 2020 Hum Mutat. Pejaver 2022 AJHG PP3/BP4 calibrated thresholds. ClinGen Splicing Subgroup 2023 (Walker AJHG). v3.2 ACMG SF list (Miller 2023). The ACMG 2.0 framework is in development as of May 2026; not yet published.
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. VCEP-specific CSpec rules override default ACMG application; the authoritative directory is https://cspec.genome.network/cspec/ui/svi/all.
ACMG/AMP Variant Classification Framework
'Classify this variant per ACMG/AMP' -> Apply 28-criterion framework using Tavtigian point system; gate on ClinGen SVI specifications and VCEP-specific overrides; assign P / LP / VUS / LB / B classification with evidence trail.
- Python (automated): GeneBe API
https://api.genebe.net/cloud/api-public/v1/variant
- Python (rule-based): InterVar ->
python InterVar.py -i input.vcf -b hg38 --table_annovar table_annovar.pl
- Web tools: VarSome (commercial), Franklin/Genoox (commercial), ClinGen VCI (gold standard for SVI)
- Citation: Richards 2015 Genet Med 17:405 (original framework); Tavtigian 2020 Hum Mutat 41:1734 (point system)
The Tavtigian Bayesian Point System: The Engine Inside All Modern Classifiers
Richards 2015 specified 28 criteria with strength labels (Supporting / Moderate / Strong / Very Strong); combination rules produced P / LP / VUS / LB / B. Tavtigian 2018/2020 demonstrated this framework is mathematically a Bayesian classifier and proposed the naturally-scaled point system that every modern automated classifier implements:
| Strength | Points | Odds of pathogenicity |
|---|
| Supporting | 1 | 2.08:1 |
| Moderate | 2 | 4.33:1 |
| Strong | 4 | 18.7:1 |
| Very Strong | 8 | 350:1 |
Benign codes are negative-signed. Final classification:
| Sum of points | Category |
|---|
| >= 10 | Pathogenic |
| 6-9 | Likely Pathogenic |
| 0-5 | VUS |
| -1 to -6 | Likely Benign |
| <= -7 | Benign |
InterVar / GeneBe / VarSome / Franklin all implement Tavtigian point summation under the hood. Combinations never appearing in the 2015 combining rules (e.g., PVS1_VeryStrong + PM2_Supporting -> LP) emerge naturally from point arithmetic.
PVS1 Decision Tree (Abou Tayoun 2018 Hum Mutat 39:1517)
PVS1 is the most consequential code: pathogenic Very Strong (8 points) for predicted loss-of-function in a gene where LoF is established disease mechanism. The 2018 decision tree refined PVS1 from a binary into a graded code based on:
- Variant type: nonsense / frameshift / canonical +-1,2 splice / initiation codon / single-exon deletion / multi-exon deletion.
- NMD prediction: variant in 5'-most exon OR >50bp upstream of last exon-exon junction -> NMD-triggered. Else truncated protein.
- Critical region: removal of >10% of coding sequence OR removal of a critical functional domain.
- Alternative isoform: does the variant affect a transcript expressed in disease-relevant tissue?
Output strengths:
| Output | Original Strength |
|---|
| PVS1_VeryStrong | Strongest (Very Strong) |
| PVS1_Strong | Strong |
| PVS1_Moderate | Moderate |
| PVS1_Supporting | Supporting |
Subsumption rule (Abou Tayoun 2018): PVS1 + PP3 -> only PVS1 counts (PP3 is subsumed). Same for PVS1 + PM4.
>15 VCEP-specific PVS1 trees exist as of 2024 (CDH1, ENIGMA BRCA1/2, FH LDLR/APOB/PCSK9, InSiGHT MMR, RASopathies, hearing loss, hypertrophic cardiomyopathy, Rett/Angelman, etc.). The automated implementation is AutoPVS1 (Xiang 2020).
Pejaver 2022 PP3/BP4 Calibrated Thresholds (the load-bearing 2024+ calibration)
Pejaver 2022 AJHG 109:2163 Bayesian-calibrated 13 missense predictors to PP3/BP4 strength levels using ClinVar P/B variants with leave-one-gene-out cross-validation.
| Predictor | BP4_Strong | BP4_Moderate | BP4_Supporting | PP3_Supporting | PP3_Moderate | PP3_Strong | Fails when |
|---|
| REVEL | <= 0.016 | <= 0.183 | <= 0.290 | >= 0.644 | >= 0.773 | >= 0.932 | Stacked with BayesDel/VEST4 (training overlap; double-counting) |
| BayesDel (no AF) | n/a | <= -0.36 | <= -0.18 | >= 0.13 | >= 0.27 | >= 0.50 | No BP4_Strong reached; combine no-AF version with PM2_Supporting |
| VEST4 | n/a | <= 0.302 | <= 0.449 | >= 0.764 | >= 0.861 | >= 0.965 | No BP4_Strong reached; indels (missense-trained); regulatory variants |
| MutPred2 | (Pejaver 2022) | -- | -- | -- | -- | -- | Genes with sparse MAVE training data |
| AlphaMissense | NOT ClinGen-endorsed | -- | -- | Use as supporting only | -- | NOT ClinGen-endorsed | Developer threshold 0.564 misapplied as PP3 |
The numbers to memorize: REVEL >= 0.932 = PP3_Strong; REVEL <= 0.016 = BP4_Strong (<= 0.003 BP4_VeryStrong); the 0.290-0.644 band is indeterminate (no criterion).
AlphaMissense calibration (Bergquist 2025 Genet Med 27:101402, ClinGen SVI; originally Bergquist et al. bioRxiv 2024.09.17): this ClinGen SVI calibration extends the graded PP3/BP4 options to AlphaMissense, reaching PP3_Strong and at least BP4_Moderate. Critical: the developer-recommended 0.564 threshold is NOT the calibrated PP3 threshold; verify the current ClinGen SVI recommendation for the exact score cutoffs before applying.
Do not stack predictors. REVEL, BayesDel, VEST4 share ClinVar/HGMD training data; using REVEL >= 0.773 AND BayesDel >= 0.27 to claim "two independent moderate hits" is double-counting. Pejaver 2022 explicitly recommends using ONE predictor per variant.
PM2_Supporting (ClinGen SVI 2020)
The original PM2 ("absent from controls") was over-weighted. SVI 2020 downgraded to PM2_Supporting (1 point, not 2). Mechanism: most rare variants are benign. Empirical recalibration showed ~6 variants per gene downgrade from LP to VUS when PM2 -> Supporting. Many 2017-2019 LP curations require re-classification post-SVI 2020 update.
PS3/BS3 Functional Evidence (Brnich 2020 Genome Med 12:3)
OddsPath framework; the four-step SOP:
- Define disease mechanism for the gene.
- Evaluate assay class (e.g., MAVE, biochemical, animal model).
- Evaluate specific assay instance (controls, replicate consistency).
- Apply per-variant.
OddsPath calibration mapping to ACMG strengths:
| OddsPath | Pathogenic strength | Benign strength |
|---|
| > 18.7 | Very Strong | n/a |
| 4.3 - 18.7 | Strong | -- |
| 2.1 - 4.3 | Moderate | -- |
| 1.2 - 2.1 | Supporting | (mirror) |
MAVEdb deep-mutational scans with >=11 controls (>=5 P/LP + >=5 B/LB) can yield up to PS3_Strong/BS3_Strong via OddsPath calibration. This is the entry point for MAVE/saturation-mutagenesis evidence into ACMG.
Default-Strong PS3 application is increasingly over-strengthening without OddsPath calibration; ClinGen SVI recommends moving toward PS3_Moderate as default unless OddsPath > 4.3.
ClinGen SVI Splicing Subgroup 2023 (Walker AJHG 110:1046)
SpliceAI is the recommended primary splicing tool. Calibrated thresholds:
| SpliceAI DS_max | Strength (Walker 2023: computational splice codes applied at Supporting weight) |
|---|
| >= 0.2 | PP3_Supporting (minimum threshold for ANY splicing PP3) |
| 0.1 - 0.2 | Indeterminate (no criterion) |
| <= 0.1 | BP4_Supporting |
SpliceAI prediction alone does NOT reach PP3_Strong; strength escalation requires experimental/RNA splicing evidence (PS3) or the repurposed PVS1 route.
SpliceVault / 300K-RNA (Dawes 2023 Nat Genet 55:324): does NOT predict whether a variant is splice-altering; predicts WHAT the aberrant transcript will be (which exon skips, which cryptic site activates). 96% sensitivity for exon-skipping; 86% for cryptic site activation in 140 clinical RNA-tested cases. Critical for PVS1 application to splice variants because PVS1 depends on whether the aberrant transcript triggers NMD.
Pangolin (Zeng 2022 Genome Biol 23:103): SpliceAI improvement for cryptic donor sites; not yet ClinGen-endorsed but increasingly used as tiebreaker.
BS1 / BA1 (Whiffin Max-Credible-AF)
BA1 default: AF > 5% in non-bottleneck group per ClinGen SVI; VCEP-specific overrides (Hearing Loss VCEP uses 0.5% AR).
BS1 gene-specific: (prevalence x heterogeneity x allelic-contribution) / (penetrance x 2) from Whiffin 2017 Genet Med 19:1151. Compare against gnomAD grpmax_faf95.
See clinical-databases/gnomad-frequencies for FAF95 details.
ClinGen VCEP CSpec Hierarchy
| Layer | Authority | Application |
|---|
| Generic ACMG/AMP 2015 | Richards 2015 | Default fallback |
| ClinGen SVI specifications | SVI Working Group | Overrides generic for all genes (PM2 -> Supporting; AutoPVS1 trees; etc.) |
| VCEP-specific CSpec | Gene/disease-specific expert panel | Overrides SVI for that gene-disease |
ClinGen VCEP CSpec authoritative registry: https://cspec.genome.network/cspec/ui/svi/all. ~80-90 VCEPs as of 2025. Examples:
- Hearing Loss VCEP: PM2 -> supporting default; PS3 thresholds upgraded for OTOF; BA1 lowered to 0.5% AR.
- ENIGMA BRCA1/2 VCEP: gene-specific PVS1 trees with NMD escape rules; PS4 case-control thresholds.
- Inherited Cardiac Conditions VCEP: gene-specific PS4 (5+ unrelated probands for PS4_Supporting).
Apply VCEP CSpec when one exists. Generic ACMG with no VCEP awareness is unreliable for many genes.
Cancer Somatic Framework (Li 2017 J Mol Diagn 19:4)
AMP/ASCO/CAP somatic variant interpretation; four tiers:
| Tier | Definition | Action |
|---|
| Tier I-A | FDA-approved drug for same tumor type with this biomarker | On-label therapy |
| Tier I-B | Professional guidelines (NCCN, ESMO) | Standard-of-care |
| Tier II-C | FDA drug in different tumor type (off-label) | Basket trials |
| Tier II-D | Preclinical / investigational | Research |
| Tier III | VUS-somatic | Watch list |
| Tier IV | Benign-somatic | Filter out |
Knowledgebases: OncoKB (MSKCC; Chakravarty 2017), CIViC (Griffith 2017 Nat Genet 49:170), CGI (Tamborero 2018), JAX-CKB, COSMIC. OncoKB Levels (1-4 therapeutic) map to AMP tiers loosely.
The Variant Interpretation for Cancer Consortium (VICC) Meta-Knowledgebase standards (2024-2025) harmonize across knowledgebases. ClinGen Somatic VCEPs are emerging (started 2022).
Decision Tree by Variant Type
| Variant type | Recommended workflow |
|---|
| Predicted LoF in known LoF-mechanism gene | AutoPVS1 decision tree -> PVS1_VeryStrong/Strong/Moderate/Supporting; check VCEP-specific PVS1 |
| Missense in known missense-pathogenic gene | Apply Pejaver 2022 PP3/BP4 calibrated thresholds; ONE predictor only |
| Splice variant | SpliceAI DS_max + SpliceVault for aberrant-transcript prediction; PP3_Supporting if DS_max >=0.2 (strength escalation needs RNA/experimental evidence) |
| Synonymous | SpliceAI for cryptic splice effect; synVep / PrimateAI synonymous extension |
| Variant in ACMG SF v3.2 gene | Apply full classification; flag P/LP for opt-in disclosure |
| Cancer somatic variant | AMP/ASCO/CAP 2017 Tier I-IV; cross-check OncoKB / CIViC |
| Variant in Limited gene-disease validity | ClinGen Strong/Definitive required for clinical action |
| Functional evidence available | Brnich 2020 PS3/BS3 OddsPath framework |
| Family segregation | PP1 / BS4 LOD score per Biesecker 2024 |
| In-trans observations (AR) | PM3 with ClinGen tabular scoring system |
| HGVS-c on alternative transcript | Re-evaluate on MANE Select |
Standard Workflow: ACMG Classification
Goal: Apply ACMG/AMP framework to a candidate variant with proper SVI specifications and VCEP overrides.
Approach: Pull aggregated evidence; apply Pejaver-calibrated in-silico thresholds; check VCEP-specific CSpec; sum Tavtigian points.
import requests
import pandas as pd
REVEL_THRESHOLDS = {
'BP4_VeryStrong': (-float('inf'), 0.003),
'BP4_Strong': (0.003, 0.016),
'BP4_Moderate': (0.016, 0.183),
'BP4_Supporting': (0.183, 0.290),
'PP3_Supporting': (0.644, 0.773),
'PP3_Moderate': (0.773, 0.932),
'PP3_Strong': (0.932, float('inf'))
}
STRENGTH_POINTS = {
'PVS1_VeryStrong': 8, 'PVS1_Strong': 4, 'PVS1_Moderate': 2, 'PVS1_Supporting': 1,
'PS1': 4, 'PS2': 4, 'PS3': 4, 'PS3_Moderate': 2, 'PS3_Supporting': 1, 'PS4': 4,
'PM1': 2, 'PM2_Supporting': 1, 'PM3': 2, 'PM3_Strong': 4, 'PM3_VeryStrong': 8,
'PM4': 2, 'PM5': 2, 'PM6': 2,
'PP1': 1, 'PP1_Moderate': 2, 'PP1_Strong': 4,
'PP2': 1, 'PP3_Supporting': 1, 'PP3_Moderate': 2, 'PP3_Strong': 4, 'PP4': 1, 'PP5': 1,
'BA1': -100,
'BS1': -4, 'BS2': -4, 'BS3': -4, 'BS3_Moderate': -2, 'BS3_Supporting': -1, 'BS4': -4,
'BP1': -1, 'BP2': -1, 'BP3': -1,
'BP4_Supporting': -1, 'BP4_Moderate': -2, 'BP4_Strong': -4, 'BP4_VeryStrong': -8,
'BP5': -1, 'BP6': -1, 'BP7': -1
}
def classify_revel_pp3_bp4(revel_score):
'''Map REVEL score to PP3/BP4 strength per Pejaver 2022.'''
if revel_score is None:
return None
for code, (lo, hi) in REVEL_THRESHOLDS.items():
if lo <= revel_score < hi:
return code
return None
def classify_alphamissense_supporting_only(am_score):
'''AlphaMissense is currently supporting-only; ClinGen has not endorsed PP3 calibration.
Cheng 2023 developer threshold 0.564 is NOT the Pejaver-style PP3 calibration.
'''
if am_score is None:
return None
if am_score >= 0.7:
return 'PP3_Supporting'
if am_score <= 0.2:
return 'BP4_Supporting'
return None
def spliceai_to_acmg(ds_max):
'''Walker 2023 SVI Splicing Subgroup framework.
Computational SpliceAI codes are applied at Supporting weight.
SpliceAI >= 0.20 -> PP3_Supporting (minimum for ANY splicing PP3).
SpliceAI <= 0.1 -> BP4_Supporting.
Prediction alone does not reach PP3_Strong; escalation needs RNA/experimental evidence.
'''
if ds_max is None:
return None
if ds_max >= 0.20:
return 'PP3_Supporting'
if ds_max <= 0.1:
return 'BP4_Supporting'
return None
def tavtigian_classify(criteria_assigned):
'''Sum Tavtigian points and classify P / LP / VUS / LB / B.
criteria_assigned: list of criterion strings (e.g., ['PVS1_VeryStrong', 'PM2_Supporting'])
'''
points = sum(STRENGTH_POINTS.get(c, 0) for c in criteria_assigned)
if any(c == 'BA1' for c in criteria_assigned):
return {'classification': 'Benign', 'points': points, 'rationale': 'BA1 standalone'}
if points >= 10:
category = 'Pathogenic'
elif points >= 6:
category = 'Likely Pathogenic'
elif points >= 0:
category = 'VUS'
elif points >= -6:
category = 'Likely Benign'
else:
category = 'Benign'
return {'classification': category, 'points': points, 'criteria': criteria_assigned}
def genebe_classify(hgvs):
'''Query GeneBe API (Stawiński 2024) for automated ACMG classification.
GeneBe is open-source, Tavtigian-point-system-based, and performs comparably to
VarSome (which is commercial, 82% ACMG criteria auto-application rate).
'''
r = requests.get(f'https://api.genebe.net/cloud/api-public/v1/variant',
params={'variant': hgvs, 'genome': 'hg38'},
timeout=30)
r.raise_for_status()
return r.json()
def whiffin_max_credible_af(prevalence, max_allelic_contribution=1.0,
max_genetic_contribution=1.0, penetrance=1.0):
'''Compute gene-specific BS1 max-credible-AF (Whiffin 2017 Genet Med).
Returns: max-credible per-allele frequency under dominant inheritance.
For autosomal recessive, transform appropriately.
'''
return (prevalence * max_genetic_contribution * max_allelic_contribution) / (penetrance * 2)
def apply_bs1_ba1(grpmax_faf95, max_credible_af, ba1_threshold=0.05):
'''Apply ClinGen SVI BS1/BA1 from gnomAD grpmax FAF95.'''
if grpmax_faf95 is None or grpmax_faf95 == 0.0:
return 'PM2_Supporting'
if grpmax_faf95 > ba1_threshold:
return 'BA1'
if grpmax_faf95 > max_credible_af:
return 'BS1'
return None
Per-Operation Failure Modes
1. Stacking REVEL + BayesDel + VEST4 as independent evidence
- Trigger: Apply PP3 from multiple predictors.
- Mechanism: Predictors share training data; double-counting.
- Symptom: Inflated PP3 strength; over-classified LP.
- Fix: Use ONE predictor per variant (Pejaver 2022).
2. AlphaMissense PP3_Strong with developer 0.564 threshold
- Trigger: Apply AlphaMissense >0.564 -> PP3_Strong.
- Mechanism: 0.564 is the developer-recommended likely-pathogenic threshold, NOT a calibrated PP3 cutoff; use the ClinGen SVI calibration (Bergquist 2025) score thresholds instead.
- Symptom: Over-application of PP3.
- Fix: Use AlphaMissense as supporting evidence only; defer to Pejaver-calibrated REVEL.
3. PVS1 applied to nonsense variant in GoF gene
- Trigger: Nonsense variant in SCN5A reported as PVS1 for LQT3.
- Mechanism: SCN5A has both LoF (Brugada) and GoF (LQT3) mechanisms. PVS1 should NOT apply if LoF is not the established mechanism.
- Symptom: Wrong classification; clinical action mis-directed.
- Fix: Check ClinGen gene-disease mechanism; apply PVS1 only when LoF is established.
4. Generic ACMG instead of VCEP CSpec
- Trigger: Apply default ACMG to a variant in a gene with VCEP-specific CSpec.
- Mechanism: VCEP CSpec overrides for the gene; e.g., Hearing Loss VCEP PM2 default = supporting, BA1 = 0.5%.
- Symptom: Wrong strength applied; misclassification.
- Fix: Check
https://cspec.genome.network/cspec/ui/svi/all for active VCEP; apply gene-specific CSpec.
5. PM2 at Moderate (pre-2020 SVI)
- Trigger: Apply PM2 = Moderate (2 points) per 2015 rules.
- Mechanism: SVI 2020 downgraded to PM2_Supporting (1 point).
- Symptom: ~6 variants per gene over-strengthened LP.
- Fix: Use PM2_Supporting per current SVI.
6. PS3 default Strong without OddsPath
- Trigger: Apply PS3 = Strong without OddsPath calibration.
- Mechanism: Default PS3 = Strong over-strengthens; Brnich 2020 SOP requires OddsPath > 4.3 for Strong.
- Symptom: PS3-driven over-classification.
- Fix: Apply Brnich 2020 four-step OddsPath; default move to PS3_Moderate without OddsPath > 4.3.
7. Synonymous treated as no impact
- Trigger: Filter out synonymous variants from classification pipeline.
- Mechanism: Synonymous can disrupt splicing; SpliceAI captures this.
- Symptom: Pathogenic splice-disrupting synonymous missed.
- Fix: Always run SpliceAI on synonymous variants in disease genes; PP3_Supporting if DS_max >= 0.2.
8. ClinVar P + ClinGen Limited validity
- Trigger: Report variant P in gene with Limited gene-disease validity.
- Mechanism: ClinVar P is variant-level; gene-disease validity is upstream.
- Symptom: Mis-attribution to a non-disease gene.
- Fix: Apply ClinGen gene-disease validity gate (Moderate+ for clinical action); for Limited genes, require VCEP curation.
9. Variant on wrong transcript
- Trigger: HGVS-c on alt transcript; functional impact different on MANE Select.
- Mechanism: Tissue-specific isoform considerations; MANE Select 2024+ is clinical standard.
- Symptom: Wrong consequence prediction.
- Fix: Re-evaluate on MANE Select transcript; cross-check with VEP
--mane_select.
Reconciliation: When Tools Disagree
| Pattern | Likely cause | Action |
|---|
| GeneBe LP vs VarSome P | Different VCEP-specific application | Check VCEP CSpec; apply gene-specific rules |
| ClinVar P vs my classification VUS | Submission stale OR my evidence incomplete | Re-curate with current evidence; check ClinVar star + freshness |
| REVEL PP3_Strong vs SpliceAI BP4 | Variant has missense impact but no splice impact | Apply ONE predictor; if splice-altering, PVS1 trumps |
| PVS1 applies but ClinGen Limited validity | Variant-level vs gene-disease tension | Treat as candidate; require VCEP or strong functional evidence |
| ClinGen VCI vs automated tool | VCI is gold standard for expert curation | Trust VCI; automated tools approximate |
| AlphaMissense 0.564 dev call vs calibrated strength | Developer threshold not calibrated | Use the ClinGen SVI calibrated cutoffs (Bergquist 2025) |
Quantitative Thresholds and Conventions
| Threshold | Convention | Source |
|---|
| Tavtigian P | >= 10 points | Tavtigian 2020 |
| Tavtigian LP | 6-9 points | Tavtigian 2020 |
| Tavtigian VUS | 0-5 points | Tavtigian 2020 |
| Tavtigian LB | -1 to -6 | Tavtigian 2020 |
| Tavtigian B | <= -7 | Tavtigian 2020 |
| REVEL PP3_Strong | >= 0.932 | Pejaver 2022 |
| REVEL BP4_Strong | <= 0.016 | Pejaver 2022 |
| SpliceAI PP3 (Supporting) | >= 0.2 | Walker 2023 |
| SpliceAI BP4 (Supporting) | <= 0.1 | Walker 2023 |
| BA1 default | grpmax_faf95 > 5% | ClinGen SVI |
| BS1 | grpmax_faf95 > gene-specific max-credible-AF | Whiffin 2017 |
| PM2 -> PM2_Supporting | Always (post-SVI 2020) | SVI 2020 |
| PS3 OddsPath Strong | > 4.3 | Brnich 2020 |
| PVS1 LoF mechanism check | Required (do not apply if GoF) | Abou Tayoun 2018 |
| ACMG SF v3.2 | 81 genes | Miller 2023 |
| Cancer Tier I-A | FDA drug + same tumor + this biomarker | Li 2017 |
Common Errors
| Symptom | Cause | Solution |
|---|
| Over-application of PP3 | Multiple predictors stacked | ONE predictor only |
| AlphaMissense PP3_Strong from dev threshold | 0.564 not calibrated | Use Pejaver-style REVEL |
| LP variant in gene with Limited validity | No gene-disease gate | Apply ClinGen gene-disease validity |
| PVS1 in GoF gene | Wrong mechanism | Check ClinGen gene-disease mechanism |
| Non-VCEP rule for VCEP-covered gene | Generic ACMG | Apply VCEP CSpec |
| PM2 = Moderate | Pre-SVI 2020 | Use PM2_Supporting |
| PS3 = Strong default | No OddsPath | Apply Brnich 2020 OddsPath |
Anticipated Reviewer Pushback
| Pushback | Standard response |
|---|
| "Why Tavtigian point system?" | Every modern automated classifier implements it (InterVar, GeneBe, VarSome, Franklin). The 2015 combining rules are subsumed; many P/LP combinations only emerge from points. |
| "Why ONE predictor and not REVEL + BayesDel?" | Pejaver 2022 explicit recommendation; predictors share training data. |
| "AlphaMissense PP3_Strong?" | ClinGen SVI calibrated AlphaMissense to graded PP3/BP4 (Bergquist 2025); use the calibrated cutoffs, not the developer 0.564 threshold. |
| "PVS1 for nonsense in SCN5A LQT3" | LQT3 is GoF; LoF mechanism not established; PVS1 does not apply. |
| "Generic ACMG vs VCEP" | VCEP CSpec overrides generic; we check cspec.genome.network for active VCEP. |
| "Splice variant PP3 from SpliceAI" | Walker 2023 SVI Splicing Subgroup: DS_max >= 0.2 applies PP3 at Supporting weight; prediction alone does not reach PP3_Strong (needs RNA/experimental evidence). |
| "PM2 Moderate or Supporting?" | SVI 2020 downgraded to Supporting; we use Supporting for all classification post-2020. |
References
- Richards S et al. 2015. Standards and guidelines for the interpretation of sequence variants. Genet Med 17:405. (Original ACMG/AMP)
- Tavtigian SV et al. 2018. Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. Genet Med 20:1054.
- Tavtigian SV et al. 2020. Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Hum Mutat 41:1734.
- Abou Tayoun AN et al. 2018. Recommendations for interpreting the loss of function PVS1 ACMG/AMP variant criterion. Hum Mutat 39:1517.
- Pejaver V et al. 2022. Calibration of computational tools for missense variant pathogenicity classification. Am J Hum Genet 109:2163.
- Bergquist T et al. 2025. Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria. Genet Med 27:101402.
- Brnich SE et al. 2020. Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. Genome Med 12:3.
- Walker LC et al. 2023. ClinGen SVI Splicing Subgroup recommendations. Am J Hum Genet 110:1046.
- Biesecker LG et al. 2024. ClinGen guidance for use of the PP1/BS4 co-segregation and PP4 phenotype specificity criteria for sequence variant pathogenicity classification. Am J Hum Genet 111:24. (PP1/BS4 co-segregation)
- Cheng J et al. 2023. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381:eadg7492.
- Jaganathan K et al. 2019. Predicting splicing from primary sequence with deep learning. Cell 176:535. (SpliceAI)
- Zeng T et al. 2022. Predicting RNA splicing from DNA sequence using Pangolin. Genome Biol 23:103.
- Dawes R et al. 2023. SpliceVault predicts the precise nature of variant-associated mis-splicing. Nat Genet 55:324.
- Whiffin N et al. 2017. Using high-resolution variant frequencies to empower clinical genome interpretation. Genet Med 19:1151.
- Li MM et al. 2017. Standards and guidelines for the interpretation and reporting of sequence variants in cancer. J Mol Diagn 19:4. (AMP/ASCO/CAP)
- Miller DT et al. 2023. ACMG SF v3.2 list. Genet Med 25:100866.
- Stawiński P, Płoski R. 2024. Genebe.net: implementation and validation of an automatic ACMG variant pathogenicity criteria assignment. Clin Genet 106:119.
- Kopanos C et al. 2019. VarSome: the human genomic variant search engine. Bioinformatics 35:1978.
- Li Q, Wang K. 2017. InterVar: clinical interpretation of genetic variants. Am J Hum Genet 100:267.
- Xiang J et al. 2020. AutoPVS1 -- automated PVS1 decision-tree implementation (verify exact venue/year against the published code/release).
- ClinGen CSpec Registry:
https://cspec.genome.network/cspec/ui/svi/all
- ClinGen VCI (Variant Curation Interface):
https://curation.clinicalgenome.org/
Related Skills
- clinical-databases/variant-prioritization - Rare-disease pipeline (filters variants; this skill classifies)
- clinical-databases/clinvar-lookup - ClinVar evidence aggregation
- clinical-databases/gnomad-frequencies - BS1/BA1 with Whiffin FAF95
- clinical-databases/myvariant-queries - Aggregated annotation pull
- variant-calling/clinical-interpretation - Clinical reporting workflow
