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

Multiplexed assays of variant effects (MAVEs) represent a powerful approach to providing functional information for variants at scale. To harness the full utility of these systems, assay readout must be translated into a language that is accommodating to the clinical genomics community. We previously performed a MAVE to characterize variants in JAG1, the primary cause of the autosomal-dominant, multisystemic disease Alagille syndrome. Using this existing dataset, we calculated log-likelihood ratios of pathogenicity for each variant score, allowing for direct translation of variant data into recognized evidence weights utilized by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) in clinical variant classification. Calibration resulted in improved separation of known benign and pathogenic variants and increased the classification rate of abnormal missense variants from 486 to 610, providing clear binning for strong (n = 1), moderate (n = 340), and supporting (n = 269) evidence toward pathogenicity. Retrospective application of this evidence to a cohort of 29 individuals with a JAG1 variant of uncertain significance (VUS) identified from clinical diagnostic sequencing yielded nine (31%) variants with abnormal data meeting supporting (n = 3) and moderate (n = 6) weight for pathogenicity, of which six (21%) were upgraded to likely pathogenic or pathogenic. Calibration of MAVE data to comply with the ACMG/AMP variant classification framework improves the diagnostic yield for JAG1 variants for Alagille syndrome. Moreover, our results support the broader application of these models to additional MAVEs, suggesting that they are likely to strongly impact variant classification across genes associated with disease.