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

Likelihood-based calibration of JAG1 MAVE data enhances variant classification, increasing pathogenic variant detection from 486 to 610 and supporting clinical decision-making.

• Why it matters: Accurate interpretation of genetic variants is crucial for diagnosing diseases like Alagille syndrome, but current methods often lack standardized, scalable functional evidence. Improving the translation of assay data into clinical evidence can significantly impact diagnostic accuracy and patient management.
• What they did: The study applied a likelihood-based calibration to existing JAG1 MAVE data, converting variant scores into ACMG/AMP-compatible evidence weights, and validated this approach with clinical cohorts.
• The result: This calibration improved the separation of benign and pathogenic variants, upgraded 21% of uncertain variants to likely pathogenic or pathogenic, and demonstrated potential for broader application across disease-associated genes.