@article{ab31ea227bea4da5857304d9636cc503,
title = "Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions",
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{\textquoteright}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.",
keywords = "Brugada syndrome, cardiomyopathy, long QT syndrome, missense variant interpretation, pathogenicity prediction",
author = "Xiaolei Zhang and Roddy Walsh and Nicola Whiffin and Rachel Buchan and William Midwinter and Alicja Wilk and Risha Govind and Nicholas Li and Mian Ahmad and Francesco Mazzarotto and Angharad Roberts and Theotokis, {Pantazis I.} and Erica Mazaika and Mona Allouba and {de Marvao}, Antonio and Pua, {Chee Jian} and Day, {Sharlene M.} and Euan Ashley and Colan, {Steven D.} and Michelle Michels and Pereira, {Alexandre C.} and Daniel Jacoby and Ho, {Carolyn Y.} and Iacopo Olivotto and Gunnarsson, {Gunnar T.} and Jefferies, {John L.} and Chris Semsarian and Jodie Ingles and O{\textquoteright}Regan, {Declan P.} and Yasmine Aguib and Yacoub, {Magdi H.} and Cook, {Stuart A.} and Barton, {Paul J. R.} and Leonardo Bottolo and Ware, {James S.}",
note = "Funding Information: We thank Hugh Watkins and Kate Thomson (University of Oxford and Oxford Medical Genetics Laboratory) for making data available and for constructive discussion, and Mark Hazebroek (Maastricht University) for helpful feedback. The research was supported by the Wellcome Trust [107469/Z/15/Z; 200990/A/16/ Z], British Heart Foundation [NH/17/1/32725; RE/18/4/34215], Medical Research Council (UK), National Institute for Health Research (NIHR) Royal Brompton Biomedical Research Unit, NIHR Imperial College Biomedical Research Centre, Science and Technology Development Fund (Egypt), Al-Alfi Foundation, Magdi Yacoub Heart Foundation, and the Alan Turing Institute under the Engineering and Physical Sciences Research Council grant [EP/ N510129/1 to L.B.]. N.W. is supported by a Rosetrees and Stoneygate Imperial College Research Fellowship. J.I. is the recipient of a National Health and Medical Research Council (Australia) Career Development Fellowship (1162929). C.S. is the recipient of a National Health and Medical Research Council (Australia) Practitioner Fellowship (1154992). Publisher Copyright: {\textcopyright} 2020, The Author(s).",
year = "2021",
month = jan,
doi = "https://doi.org/10.1038/s41436-020-00972-3",
language = "English",
volume = "23",
pages = "69--79",
journal = "Genetics in medicine",
issn = "1098-3600",
publisher = "Lippincott Williams and Wilkins",
number = "1",
}