CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation. (October 2018)

• Record Type: Journal Article
• Authors: Whiffin, Nicola; Walsh, Roddy; Govind, Risha; Edwards, Matthew; Ahmad, Mian; Zhang, Xiaolei; Tayal, Upasana; Buchan, Rachel; Midwinter, William; Wilk, Alicja; Najgebauer, Hanna; Francis, Catherine; Wilkinson, Sam; Monk, Thomas; Brett, Laura; O'Regan, Declan; Prasad, Sanjay; Morris-Rosendahl, Deborah; Barton, Paul; Edwards, Elizabeth; Ware, James; Cook, Stuart
• Is Part Of: Genetics in medicine. Volume 20:Number 10(2018)
• Page Start: 1246
• Page End: 1254
• Publication Date: 2018-10
• Subjects: bioinformatics -- clinical genomics -- inherited cardiac conditions -- next-generation sequencing -- variant interpretation; Medical genetics -- Periodicals; Genetic disorders -- Periodicals; 616.04205
• DOI: 10.1038/gim.2017.258
• Languages: English
• ISSNs: 1098-3600

Abstract

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 (http://www.cardioclassifier.org ), a semiautomated 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 variant 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'sP = 1.1 × 10−18 ), with important false positives, illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data. Conclusion CardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible, and interactive variant pathogenicity reports, according to best practice guidelines.