Documentation / Computational Predictors / Consensus Calculation
PP3/BP4 Evidence Calculation
BayesDel_noAF with ClinGen SVI Calibration
Helix Insight uses BayesDel_noAF as the single computational tool for determining PP3 (computational evidence supports a deleterious effect) and BP4 (computational evidence suggests no impact). BayesDel is a meta-predictor that integrates deleteriousness scores from multiple tools into a single calibrated score. The "_noAF" variant explicitly excludes allele frequency from its model, which is critical: since the ACMG framework already has frequency-based criteria (PM2, BA1, BS1), using a predictor that includes frequency would double-count the same evidence.
The evidence strength thresholds were calibrated by the ClinGen Sequence Variant Interpretation (SVI) Working Group against clinical truth sets (Pejaver et al. 2022). These thresholds map BayesDel_noAF score ranges directly to ACMG evidence strength levels, enabling evidence strength modulation -- a feature not possible with simpler binary (damaging/benign) approaches.
Two Independent Paths
PP3 and BP4 are evaluated through two independent paths that capture different biological mechanisms. These paths do not overlap: a given variant is assessed through the missense path, the splice path, or both, depending on the available data.
| Path | Tool | Applies To | Evidence |
|---|---|---|---|
| Missense | BayesDel_noAF | Variants with BayesDel score available | PP3 (Strong / Moderate / Supporting) or BP4 (Moderate / Supporting) |
| Splice | SpliceAI | Variants with SpliceAI score available | PP3_splice (Supporting) |
PP3 Pathogenic Evidence (BayesDel)
The ClinGen SVI calibration provides three evidence strength levels for pathogenic computational evidence. Higher BayesDel scores produce stronger evidence. This graduated approach reflects the clinical reality that a variant with a very high pathogenicity score provides more compelling evidence than one near the minimum threshold.
| BayesDel_noAF Score | Evidence Strength | Criteria Label | Bayesian Points |
|---|---|---|---|
| >= 0.518 | Strong | PP3_Strong | +4 |
| 0.290 -- 0.517 | Moderate | PP3_Moderate | +2 |
| 0.130 -- 0.289 | Supporting | PP3 | +1 |
PP3 Splice Evidence (SpliceAI)
Independently of the BayesDel missense assessment, SpliceAI provides splice-specific evidence. When the maximum SpliceAI delta score is 0.2 or above, PP3_splice is triggered as Supporting pathogenic evidence (+1 Bayesian point). This threshold follows ClinGen SVI 2023 recommendations (Walker et al., 2023).
PP3_splice is excluded when PVS1 (loss-of-function) applies to the same variant. This prevents double-counting splice disruption that already contributed to the Very Strong PVS1 criterion. See SpliceAI for full details.
BP4 Benign Evidence (BayesDel)
Low BayesDel scores provide evidence that a variant is computationally predicted to be benign. Two evidence strength levels are calibrated:
| BayesDel_noAF Score | Evidence Strength | Criteria Label | Bayesian Points |
|---|---|---|---|
| <= -0.361 | Moderate | BP4_Moderate | -2 |
| -0.360 -- -0.181 | Supporting | BP4 | -1 |
BP4 at any level requires that SpliceAI max score is below 0.1 or absent. A variant cannot receive computational benign evidence if there is any predicted splice impact.
PM1 + PP3 Point-Sum Cap
The ClinGen SVI Working Group recommends that the combined evidence from PM1 (variant in a functional domain) and PP3 (computational prediction) should not exceed Strong equivalent (4 Bayesian points). This prevents over-counting evidence when a variant is both in a known functional domain and computationally predicted to be damaging -- since these two observations are not fully independent.
In practice, PM1 contributes Moderate evidence (2 points). When PM1 is triggered and BayesDel reaches the Strong threshold (>= 0.518), PP3 is automatically downgraded from Strong (4 points) to Moderate (2 points), keeping the combined total at 4 points. When PM1 is not triggered, PP3_Strong is applied at full strength.
| Scenario | PM1 | PP3 | Combined Points |
|---|---|---|---|
| No functional domain, BayesDel >= 0.518 | -- (0 pts) | PP3_Strong (4 pts) | 4 |
| Pfam domain, BayesDel >= 0.518 | PM1 (2 pts) | PP3_Moderate (2 pts, capped) | 4 |
| Pfam domain, BayesDel 0.290-0.517 | PM1 (2 pts) | PP3_Moderate (2 pts) | 4 |
| Pfam domain, BayesDel 0.130-0.289 | PM1 (2 pts) | PP3 (1 pt) | 3 |
Indeterminate Zone
BayesDel_noAF scores between -0.180 and 0.129 fall in the indeterminate zone -- neither PP3 nor BP4 is applied. This is intentional: variants in this range do not have sufficient computational signal to contribute evidence in either direction. Approximately 20-30% of rare missense variants fall in this zone (Stenton et al. 2024), which prevents computational evidence from being over-applied.
Expected Evidence Yield
For a typical case with approximately 75 rare missense variants in disease genes, the expected distribution is approximately: 1 variant receiving PP3_Strong, 3-5 receiving PP3_Moderate, 3-5 receiving PP3_Supporting, 41-49 receiving BP4, and 17-19 in the indeterminate zone (Stenton et al. 2024). PP3_Strong is rare enough to avoid excessive reclassification of VUS variants.
Why Not a Multi-Predictor Consensus
Some variant classification systems use a weighted consensus across multiple individual predictors (SIFT, PolyPhen, CADD, etc.) to determine PP3/BP4. Helix Insight uses BayesDel_noAF as a single calibrated tool instead, for three reasons: it has ClinGen SVI-calibrated thresholds directly mapping to ACMG evidence strength levels; it excludes allele frequency, avoiding circular reasoning with PM2/BA1/BS1; and it provides evidence strength modulation (Supporting, Moderate, Strong) which a binary consensus approach cannot. The individual predictors (SIFT, AlphaMissense, MetaSVM, DANN, PhyloP, GERP) remain displayed in the results for additional clinical context.
ClinGen SVI calibration: Pejaver V, et al. Am J Hum Genet. 2022;109(12):2163-2177. PMID: 36413997
Evidence yield: Stenton SL, et al. Genet Med. 2024;26(11):101213. PMID: 39030733
ClinGen SVI splice: Walker LC, et al. Am J Hum Genet. 2023;110(7):1046-1067. PMID: 37352859
BayesDel original: Feng BJ. Hum Mutat. 2017;38(3):243-251.