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CardioClassifier: demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation

By Nicola Whiffin, Roddy Walsh, Risha Govind, Matthew Edwards, Mian Ahmad, Xiaolei Zhang, Upasana Tayal, Rachel J Buchan, William Midwinter, Alicja E Wilk, Hanna Najgebauer, Catherine Francis, Sam Wilkinson, Thomas Monk, Laura Brett, Declan P O'Regan, Sanjay K. Prasad, Deborah J Morris-Rosendahl, Paul J Barton, Elizabeth Edwards, James S Ware, Stuart Alexander Cook

Posted 23 Aug 2017
bioRxiv DOI: 10.1101/180109 (published DOI: 10.1038/gim.2017.258)

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 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, Fishers P=1.1x10-18), 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.

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