Dataset Information

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

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.

SUBMITTER: Whiffin N

PROVIDER: S-EPMC6558251 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Whiffin Nicola N Walsh Roddy R Govind Risha R Edwards Matthew M Ahmad Mian M Zhang Xiaolei X Tayal Upasana U Buchan Rachel R Midwinter William W Wilk Alicja E AE Najgebauer Hanna H Francis Catherine C Wilkinson Sam S Monk Thomas T Brett Laura L O'Regan Declan P DP Prasad Sanjay K SK Morris-Rosendahl Deborah J DJ Barton Paul J R PJR Edwards Elizabeth E Ware James S JS Cook Stuart A SA

Genetics in medicine : official journal of the American College of Medical Genetics 20180125 10

<h4>Purpose</h4>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).<h4>Methods</h4>CardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, ...[more]

PMID: 29369293

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Dataset Information

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

Purpose

Methods

Results

Conclusion

Publications

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

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