{"id":1063,"date":"2025-10-03T04:58:09","date_gmt":"2025-10-03T04:58:09","guid":{"rendered":"http:\/\/sebigec.es\/blog\/?guid=46e13c50ba8e67c208bc1332561f7ac7"},"modified":"2025-12-26T05:51:05","modified_gmt":"2025-12-26T05:51:05","slug":"calibration-and-refinement-of-acmg-amp-criteria-for-variant-classification-with-bayesquantify","status":"publish","type":"post","link":"https:\/\/sebigec.es\/blog\/index.php\/2025\/10\/03\/calibration-and-refinement-of-acmg-amp-criteria-for-variant-classification-with-bayesquantify\/","title":{"rendered":"Calibration and refinement of ACMG\/AMP criteria for variant classification with BayesQuantify"},"content":{"rendered":"\n<sec><st>Background<\/st>\n<p>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.<\/p>\n<\/sec>\n<sec><st>Methods<\/st>\n<p>To address this need, we developed <I>BayesQuantify<\/I>, an R package that provides a unified tool for quantifying evidence strength for the ACMG\/AMP criteria based on the Bayesian framework. <I>BayesQuantify<\/I> 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, <I>BayesQuantify<\/I> 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 <I>BayesQuantify<\/I>.<\/p>\n<\/sec>\n<sec><st>Results<\/st>\n<p>  <I>BayesQuantify<\/I> 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 &lsquo;supporting,&rsquo; or &lsquo;moderate,&rsquo; 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 <I>PTEN<\/I> gene.<\/p>\n<\/sec>\n<sec><st>Conclusion<\/st>\n<p>  <I>BayesQuantify<\/I> 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 <A HREF=\"https:\/\/github.com\/liusihan\/BayesQuantify\">https:\/\/github.com\/liusihan\/BayesQuantify<\/A>.<\/p>\n<\/sec>\n","protected":false},"excerpt":{"rendered":"<p>Background<br \/>\nImproving 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&#8230;.<\/p>\n","protected":false},"author":225,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6,14],"tags":[83],"class_list":["post-1063","post","type-post","status-publish","format-standard","hentry","category-articulos","category-jmg","tag-jmedgenet"],"_links":{"self":[{"href":"https:\/\/sebigec.es\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1063","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sebigec.es\/blog\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sebigec.es\/blog\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sebigec.es\/blog\/index.php\/wp-json\/wp\/v2\/users\/225"}],"replies":[{"embeddable":true,"href":"https:\/\/sebigec.es\/blog\/index.php\/wp-json\/wp\/v2\/comments?post=1063"}],"version-history":[{"count":2,"href":"https:\/\/sebigec.es\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1063\/revisions"}],"predecessor-version":[{"id":4734,"href":"https:\/\/sebigec.es\/blog\/index.php\/wp-json\/wp\/v2\/posts\/1063\/revisions\/4734"}],"wp:attachment":[{"href":"https:\/\/sebigec.es\/blog\/index.php\/wp-json\/wp\/v2\/media?parent=1063"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sebigec.es\/blog\/index.php\/wp-json\/wp\/v2\/categories?post=1063"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sebigec.es\/blog\/index.php\/wp-json\/wp\/v2\/tags?post=1063"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}