Documentation / Screening / Scoring Components
Screening Scoring Components
Every in-scope variant is evaluated across seven evidence dimensions. The component values are combined according to the active clinical profile, then selected patient-context signals are added before the final review tier is assigned.
Folklore shows the component values so reviewers can understand the direction of prioritization. The exact internal weighting and calibration are proprietary. Component and total values are prioritization scores, not probabilities of pathogenicity or clinical benefit.
Evidence Dimensions
| Component | What It Measures | Primary Inputs |
|---|---|---|
| Gene Constraint | How intolerant the gene is to loss-of-function or missense variation, interpreted in the context of the variant consequence. | pLI, LOEUF, missense constraint |
| Deleteriousness | Whether multiple in-silico signals support a damaging protein or splicing effect. | BayesDel_noAF, SpliceAI, AlphaMissense, DANN, SIFT, MetaSVM, PhyloP, GERP |
| Phenotype Relevance | Patient-to-gene HPO overlap in diagnostic cases, or a conservative gene-disease association signal when no patient HPO terms are supplied. | Patient HPO terms and gene HPO annotations |
| Dosage Sensitivity | Whether the predicted consequence is compatible with available gene haploinsufficiency evidence. | ClinGen dosage curation and consequence |
| Consequence | The predicted severity and coding relevance of the annotated consequence. | VEP consequence and transcript biotype |
| Compound-Heterozygous Potential | Whether a qualifying heterozygous coding or splice-relevant variant has a same-gene partner or an upstream candidate flag. | Genotype, consequence, same-gene variants |
| Age Relevance | How the gene fits the patient age and the selected panel annotation. | Patient age, panel age relevance, curated fallback categories |
Deleteriousness Is an Ensemble Signal
The current engine uses eight protein, splice, meta-prediction, and conservation inputs. BayesDel_noAF is the primary calibrated signal, with other predictors providing complementary coverage. This ensemble contributes to screening priority and must not be counted again as independent ACMG evidence without reviewing the Variant Analysis evidence trace.
Phenotype and No-Phenotype Behavior
When patient HPO terms are present, the phenotype component measures direct overlap with gene HPO annotations and the Diagnostic profile gives phenotype greater influence. Without patient HPO terms, the component uses a deliberately capped gene-disease association signal. Non-coding consequences are discounted so a heavily annotated gene does not dominate solely because it has many known phenotypes.
Compound-Heterozygous Boundary
The component identifies candidate pairs among qualifying heterozygous coding or splice-relevant variants in the same gene. It does not prove that the variants are in trans, establish biallelic pathogenicity, or replace Family Analysis phasing.
Missing Data
Missing annotations use conservative defaults. Missing BayesDel is handled by redistributing its contribution across the remaining predictor slots; other absent predictor values do not receive the same per-field redistribution. Scores from variants with different annotation completeness should therefore be compared with care.
Clinical Boundary
A high component value explains why a variant moved upward in the review queue. It does not change the ACMG class, confirm a molecular diagnosis, or determine whether a finding is reportable.