BayesDel_noAF Score Thresholds for ACMG PP3 and BP4

BayesDel_noAF with ClinGen SVI Calibration

Folklore 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.

BayesDel contributes computational evidence inside Variant Analysis. It does not compare the patient's HPO profile or rank genes by phenotype; that separate step is documented under HPO Phenotype Matching.

Review the SIFT score threshold and interpretation limits separately. Folklore can display SIFT for clinical context, but it does not add another PP3 or BP4 contribution alongside BayesDel_noAF.

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.

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.

PP3 Splice Evidence (SpliceAI)

Independently of the BayesDel missense assessment, SpliceAI provides splice-specific evidence. The current production intervals are 0.2 to below 0.5 for Supporting, 0.5 to below 0.8 for Moderate and 0.8 or higher for Strong. Consequence, transcript, disease-association and mutual-exclusion guards determine whether an interval is eligible.

PP3_splice is excluded when PVS1 applies to the same predicted loss-of-function mechanism. Pure missense variants with BayesDel data but no VEP splice consequence do not receive a second computational splice criterion. See SpliceAI for the full eligibility rules.

BP4 Benign Evidence (BayesDel)

Low BayesDel scores provide evidence that a variant is computationally predicted to be benign. Two evidence strength levels are calibrated:

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.

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.