Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Issue 10 (30th August 2020)

• Record Type: Journal Article
• Authors: Tavtigian, Sean V.; Harrison, Steven M.; Boucher, Kenneth M.; Biesecker, Leslie G.
• Is Part Of: Human mutation. Volume 41:Issue 10(2020)
• Page Start: 1734
• Page End: 1737
• Publication Date: 2020-08-30
• Subjects: ACMG -- Bayesian framework -- medical genetics -- points‐based classification system -- scoring metric -- unclassified variants -- variant classification -- variants of uncertain significance -- VUS; Human chromosome abnormalities -- Periodicals; Mutation (Biology) -- Periodicals; 616.04205
• DOI: 10.1002/humu.24088
• Languages: English
• ISSNs: 1059-7794

Abstract

Abstract: Recently, we demonstrated that the qualitative American College of Medical Genetics and Genomics/Association for Medical Pathology (ACMG/AMP) guidelines for evaluation of Mendelian disease gene variants are fundamentally compatible with a quantitative Bayesian formulation. Here, we show that the underlying ACMG/AMP "strength of evidence categories" can be abstracted into a point system. These points are proportional to Log(odds), are additive, and produce a system that recapitulates the Bayesian formulation of the ACMG/AMP guidelines. The strengths of this system are its simplicity and that the connection between point values and odds of pathogenicity allows empirical calibration of the strength of evidence for individual data types. Weaknesses include that a narrow range of prior probabilities is locked in and that the Bayesian nature of the system is inapparent. We conclude that a points‐based system has the practical attribute of user‐friendliness and can be useful so long as the underlying Bayesian principles are acknowledged. Abstract : Building from our Bayesian formulation of the American College of Medical Genetics and Genomics/Association for Medical Pathology (ACMG/AMP) sequence variant classification guidelines, we have now derived a point system for variant classification. Two key features are (1) the points are proportional to Log(odds), and (2) the classification thresholds are derived from the probabilistic thresholds of the parent ACMG/AMP guidelines. We conclude that a points‐based system has the practical attribute of user‐friendliness and can be useful so long as the underlying Bayesian principles are acknowledged.