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https://doi.org/10.1038/gim.2017.258
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.E Najgebauer, H Francis, C Wilkinson, S Monk, T Brett, L O’Regan, D.P Prasad, S.K Morris-Rosendahl, D.J Barton, P.J.R Edwards, E Ware, J.S Cook, S.A |
Keywords: | Article automation CardioClassifier cardiomyopathy clinical assessment tool clinical decision support system controlled study genetic disorder genetic screening genetic variation heart disease human molecular genetics pathogenicity practice guideline reproducibility biology cardiovascular malformation decision support system genetics genomics high throughput sequencing human genome mutation pathology software Cardiovascular Abnormalities Computational Biology Decision Support Techniques Genetic Testing Genome, Human Genomics High-Throughput Nucleotide Sequencing Humans Mutation Software |
Issue Date: | 2018 | Publisher: | Nature Publishing Group | Citation: | Whiffin, N, Walsh, R, Govind, R, Edwards, M, Ahmad, M, Zhang, X, Tayal, U, Buchan, R, Midwinter, W, Wilk, A.E, Najgebauer, H, Francis, C, Wilkinson, S, Monk, T, Brett, L, O’Regan, D.P, Prasad, S.K, Morris-Rosendahl, D.J, Barton, P.J.R, Edwards, E, Ware, J.S, Cook, S.A (2018). CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation. Genetics in Medicine 20 (10) : 1246-1254. ScholarBank@NUS Repository. https://doi.org/10.1038/gim.2017.258 | Rights: | Attribution 4.0 International | 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. © 2018, American College of Medical Genetics and Genomics. | Source Title: | Genetics in Medicine | URI: | https://scholarbank.nus.edu.sg/handle/10635/179019 | ISSN: | 10983600 | DOI: | 10.1038/gim.2017.258 | Rights: | Attribution 4.0 International |
Appears in Collections: | Elements Staff Publications |
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