Likelihood-based calibration improves the clinical utility of JAG1 functional data for variant classification

We translate JAG1 functional data obtained from a multiplexed assay of variant effects (MAVE) into ACMG/AMP-compatible log-likelihood evidence, improving variant interpretation in Alagille syndrome. This calibration enhances benign/pathogenic separation, increases classification rates, and enables reclassification of VUSs—demonstrating a scalable framework for integrating high-throughput functional data into clinical genomics.