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

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Title: CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation
Authors: Whiffin, N
Walsh, R
Govind, R
Edwards, M
Ahmad, M
Zhang, X
Tayal, U
Buchan, R
Midwinter, W
Wilk, A
Najgebauer, H
Francis, C
Wilkinson, S
Monk, T
Brett, L
O'Regan, D
Prasad, S
Morris-Rosendahl, D
Barton, P
Edwards, E
Ware, J
Cook, S
Item Type: Journal Article
Publication Date: 2018-01-25
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 (, 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.
Issue Date: 25-Jan-2018
Date of Acceptance: 28-Nov-2017
ISSN: 1098-3600
Publisher: Nature Publishing Group
Start Page: 1246
End Page: 1254
Journal / Book Title: Genetics in Medicine
Volume: 20
Copyright Statement: © The Author(s) 2018. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit
Sponsor/Funder: British Heart Foundation
Fondation Leducq
Fondation Leducq
Wellcome Trust
Department of Health
Royal Brompton & Harefield NHS Foundation Trust
Wellcome Trust
Royal Brompton & Harefield NHS Foundation Trust
British Heart Foundation
Imperial College Healthcare NHS Trust- BRC Funding
Funder's Grant Number: SP/10/10/28431
11 CVD-01
11 CVD-01
infoed 59322
Keywords: Science & Technology
Life Sciences & Biomedicine
Genetics & Heredity
clinical genomics
inherited cardiac conditions
next-generation sequencing
variant interpretation
0604 Genetics
1103 Clinical Sciences
Genetics & Heredity
Publication Status: Published
Appears in Collections:Clinical Sciences
Imaging Sciences
National Heart and Lung Institute
Faculty of Medicine

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