Whiffin, N;
Walsh, R;
Govind, R;
Edwards, M;
Ahmad, M;
Zhang, X;
Tayal, U;
... Cook, SA; + view all
(2018)
CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation.
Genetics in Medicine
, 20
(10)
pp. 1246-1254.
10.1038/gim.2017.258.
Preview |
Text
gim.2017.258.pdf - Published Version Download (3MB) | Preview |
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’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.
| Type: | Article |
|---|---|
| Title: | CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/gim.2017.258 |
| Publisher version: | https://doi.org/10.1038/gim.2017.258 |
| Language: | English |
| Additional information: | Copyright © the authors. 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 http://creativecommons.org/ licenses/by/4.0/. |
| Keywords: | bioinformatics; clinical genomics; inherited cardiac conditions; next-generation sequencing; variant interpretation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10065557 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
