From Pathogenicity to Mechanism: A Variant Interpretation Framework for Monogenic Epilepsy

ABSTRACT

Pathogenicity predictors exceed AUROC 0.97 on expert‐curated ClinVar, yet the monogenic‐epilepsy variant‐of‐uncertain‐significance backlog persists because pathogenicity alone provides no direction‐of‐effect, no scalable mechanism‐to‐treatment mapping, and no evidence that updates as ClinVar grows. SeizeVar couples a consensus pathogenicity head (random forest plus ESM‐2 LoRA cross‐attention) to a gain‐versus‐loss‐of‐function mechanism classifier and a deterministic sodium‐channel mechanism‐direction rule. The framework was trained on a 49‐gene epilepsy panel ( n  = 4576 labelled variants) and evaluated on six pairwise‐disjoint held‐out cohorts ( n  = 11 274) plus an external functional cohort with patch‐clamp/TEVC labels (T2, n  = 415). SeizeVar's mechanism head reaches honest leave‐one‐gene‐out AUROC = 0.736 at 100% panel coverage and matches the proteome‐wide specialist LoGoFunc on fair full‐coverage comparison (0.770 vs. 0.760), whereas general‐purpose pathogenicity predictors are mechanism‐blind (AUROC ≤ 0.62). Applied to 29 293 epilepsy VUS, the pipeline returned 4708 consensus Likely‐Pathogenic candidates, of which 1500 sodium‐channel variants received predicted mechanism‐direction labels (679 LoF‐leaning, 821 GoF‐leaning). By integrating pathogenicity, mechanism, and dynamic evidence on a shared probability scale, SeizeVar produces a mechanism‐annotated prioritisation list to support—not replace—expert variant curation; the outputs are computational predictions and a natural next step is prospective clinical validation. Reclassified‐VUS is released as a community benchmark for reclassification‐aware evaluation.