Journal Article

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

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

2018

Overview

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.

• Publisher: Nature Publishing Group US,Elsevier Limited
• Subject: 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