CardioClassifier – demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation - Whiffin,N; Walsh,R; Govind,R; Edwards,M; Ahmad,M; Zhang,X; Tayal,U; Buchan,R; Midwinter,W; Wilk,AE; Najgebauer,H; Francis,C; et al. (2017)
ABSTRACT Purpose Internationally-adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier ( www.cardioclassifier.org ), a semi-automated decision-support tool for inherited cardiac conditions (ICCs). Methods CardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support varian interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules. Results We benchmarked CardioClassifier on 57 expertly-curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically-actionable variants (64/219 vs 156/219, Fisher’s P =1.1x10-18), with important false positives; illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putati