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Title: Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions
Authors: Zhang, Xiaolei
Walsh, Roddy
Whiffin, Nicola
Buchan, Rachel
Midwinter, William
Wilk, Alicja
Govind, Risha
Li, Nicholas
Ahmad, Mian
Mazzarotto, Francesco
Roberts, Angharad
Theotokis, Pantazis I.
Mazaika, Erica
Allouba, Mona
de Marvao, Antonio
Pua, Chee Jian
Day, Sharlene M.
Ashley, Euan
Colan, Steven D.
Michels, Michelle
Pereira, Alexandre C.
Jacoby, Daniel
Ho, Carolyn Y.
Olivotto, Iacopo
Gunnarsson, Gunnar T.
Jefferies, John L.
Semsarian, Chris
Ingles, Jodie
O’Regan, D.P.
Aguib, Yasmine
Yacoub, Magdi H.
Cook, Stuart A. 
Barton, Paul J. R.
Bottolo, Leonardo
Ware, James S.
Keywords: Brugada syndrome
long QT syndrome
missense variant interpretation
pathogenicity prediction
Issue Date: 13-Oct-2020
Publisher: Springer Nature
Citation: Zhang, Xiaolei, Walsh, Roddy, Whiffin, Nicola, Buchan, Rachel, Midwinter, William, Wilk, Alicja, Govind, Risha, Li, Nicholas, Ahmad, Mian, Mazzarotto, Francesco, Roberts, Angharad, Theotokis, Pantazis I., Mazaika, Erica, Allouba, Mona, de Marvao, Antonio, Pua, Chee Jian, Day, Sharlene M., Ashley, Euan, Colan, Steven D., Michels, Michelle, Pereira, Alexandre C., Jacoby, Daniel, Ho, Carolyn Y., Olivotto, Iacopo, Gunnarsson, Gunnar T., Jefferies, John L., Semsarian, Chris, Ingles, Jodie, O’Regan, D.P., Aguib, Yasmine, Yacoub, Magdi H., Cook, Stuart A., Barton, Paul J. R., Bottolo, Leonardo, Ware, James S. (2020-10-13). Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions. Genetics in Medicine 23 (1) : 69-79. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
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 (, highlighting broad opportunities for improved pathogenicity prediction through disease specificity. © 2020, The Author(s).
Source Title: Genetics in Medicine
ISSN: 1098-3600
DOI: 10.1038/s41436-020-00972-3
Rights: Attribution 4.0 International
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