Improving the precision and accuracy of variant classification in clinical genetic testing requires further specification and stratification of the American College of Medical Genetics/Association of Molecular Pathology (ACMG/AMP) criteria. While the ClinGen Bayesian framework enables quantitative evidence calibration for selected criteria, standardised tools to optimise evidence thresholds and refine ACMG/AMP criteria remain underdeveloped. To address this need, we developed BayesQuantify, an R package that provides a unified tool for quantifying evidence strength for the ACMG/AMP criteria based on the Bayesian framework. BayesQuantify accepts a variant classification file as input and automatically calculates the odds of pathogenicity for each evidence strength, incorporating a user-provided prior probability of pathogenicity. Through bootstrapping, BayesQuantify generates thresholds by aligning the 95% lower bound of positive likelihood ratio/local positive likelihood ratio with the odds of pathogenicity for different evidence strengths. Three independent datasets derived from ClinVar, HGMD and gnomAD were used to evaluate the utility of BayesQuantify. BayesQuantify supports the calibration of both categorical and continuous ACMG/AMP evidence. Specifically, we replicated the PP3/BP4 thresholds for four computational tools recommended by ClinGen. Our analysis also indicated that the PM2 criterion can reach 'supporting,' or 'moderate,' evidence, varying by prior probability. Importantly, we established thresholds for supporting, moderate and strong evidence for in-silico tools, thereby expanding the application of PP3/BP4 criteria for missense variants in the PTEN gene. BayesQuantify is a user-friendly tool that enhances the flexibility and reproducibility of ACMG/AMP criteria refinement, thus improving the accuracy and consistency of variant classification. The package is freely available at https://github.com/liusihan/BayesQuantify.
Liu et al. (2025) studied this question.