Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions
Creators: name: Zhang, Xiaolei
name: Walsh, Roddy
name: Whiffin, Nicola
name: Buchan, Rachel
name: Midwinter, William
name: Wilk, Alicja
name: Govind, Risha
name: Li, Nicholas
name: Ahmad, Mian
name: Mazzarotto, Francesco
name: Roberts, Angharad
name: Theotokis, Pantazis I.
name: Mazaika, Erica
name: Allouba, Mona
name: De Marvao, Antonio
name: Pua, Chee Jian
name: Day, Sharlene M.
name: Ashley, Euan
name: Colan, Steven D.
name: Michels, Michelle
name: Pereira, Alexandre C.
name: Jacoby, Daniel
name: Ho, Carolyn Y.
name: Olivotto, Iacopo
name: Gunnarsson, Gunnar T.
name: Jefferies, John L.
name: Semsarian, Chris
name: Ingles, Jodie
name: O’Regan, Declan P.
name: Aguib, Yasmine
name: Yacoub, Magdi H.
name: Cook, Stuart A.
name: Barton, Paul J. R.
name: Bottolo, Leonardo
name: Ware, James S.
Publisher: name: Apollo - University of Cambridge Repository
Year: 2020
Subjects: Article
article
pathogenicity prediction
missense variant interpretation
cardiomyopathy
long QT syndrome
Brugada syndrome

description: Funder: Science and Technology Development Fund; doi: https://doi.org/10.13039/
description: Funder: Al-Alfi Foundation
description: Funder: Magdi Yacoub Heart Foundation
description: Funder: Rosetrees and Stoneygate Imperial College Research Fellowship
description: Funder: National Health and Medical Research Council (Australia)
description: 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 &gt;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.