Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions
Zhang, Xiaolei Walsh, Roddy Whiffin, Nicola Buchan, Rachel Midwinter, William Wilk, Alicja Govind, Risha Li, Nicholas Ahmad, Mian Mazzarotto, Francesco
Zhang, Xiaolei
Walsh, Roddy
Whiffin, Nicola
Buchan, Rachel
Midwinter, William
Wilk, Alicja
Govind, Risha
Li, Nicholas
Ahmad, Mian
Mazzarotto, Francesco
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Alternative Title
Abstract
Purpose: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. Methods: We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost’s ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. Results: CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4–24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11–29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. Conclusions: A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions (https://www.cardiodb.org/cardioboost/), highlighting broad opportunities for improved pathogenicity prediction through disease specificity. © 2020, The Author(s).
Keywords
Brugada syndrome, cardiomyopathy, long QT syndrome, missense variant interpretation, pathogenicity prediction
Source Title
Genetics in Medicine
Publisher
Springer Nature
Series/Report No.
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Rights
Attribution 4.0 International
Date
2020-10-13
DOI
10.1038/s41436-020-00972-3
Type
Article