CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation

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 i...

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Published in:	Genetics in medicine Vol. 20; no. 10; pp. 1246 - 1254
Main Authors:	Whiffin, Nicola, Walsh, Roddy, Govind, Risha, Edwards, Matthew, Ahmad, Mian, Zhang, Xiaolei, Tayal, Upasana, Buchan, Rachel, Midwinter, William, Wilk, Alicja E, Najgebauer, Hanna, Francis, Catherine, Wilkinson, Sam, Monk, Thomas, Brett, Laura, O’Regan, Declan P, Prasad, Sanjay K, Morris-Rosendahl, Deborah J, Barton, Paul J R, Edwards, Elizabeth, Ware, James S, Cook, Stuart A
Format:	Journal Article
Language:	English
Published:	New York Nature Publishing Group US 01-10-2018 Elsevier Limited
Subjects:	Biomedical and Life Sciences Biomedicine Cardiomyopathy Cardiovascular Abnormalities - diagnosis Cardiovascular Abnormalities - genetics Cardiovascular Abnormalities - pathology Computational Biology Decision Support Techniques Genetic Testing Genome, Human - genetics Genomics High-Throughput Nucleotide Sequencing Human Genetics Humans Laboratory Medicine Mutation Software clinical genomics next-generation sequencing bioinformatics inherited cardiac conditions variant interpretation
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Summary:	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’s P = 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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors jointly supervised this work
ISSN:	1098-3600 1530-0366
DOI:	10.1038/gim.2017.258