Whiffin, N;
Walsh, R;
Govind, R;
Edwards, M;
Ahmad, M;
Zhang, X;
Tayal, U;
... Cook, SA; + view all
(2017)
CardioClassifier – demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation.
BioRxiv: Cold Spring Harbor, NY, USA.
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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 (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 varian 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=1.1x10-18), 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.
| Type: | Working / discussion paper |
|---|---|
| Title: | CardioClassifier – demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1101/180109 |
| Publisher version: | https://doi.org/10.1101/180109 |
| Language: | English |
| Additional information: | The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| 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/10065760 |
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