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dc.contributor.authorZhang, Xiaolei
dc.contributor.authorWalsh, Roddy
dc.contributor.authorWhiffin, Nicola
dc.contributor.authorBuchan, Rachel
dc.contributor.authorMidwinter, William
dc.contributor.authorWilk, Alicja
dc.contributor.authorGovind, Risha
dc.contributor.authorLi, Nicholas
dc.contributor.authorAhmad, Mian
dc.contributor.authorMazzarotto, Francesco
dc.contributor.authorRoberts, Angharad
dc.contributor.authorTheotokis, Pantazis I.
dc.contributor.authorMazaika, Erica
dc.contributor.authorAllouba, Mona
dc.contributor.authorde Marvao, Antonio
dc.contributor.authorPua, Chee Jian
dc.contributor.authorDay, Sharlene M.
dc.contributor.authorAshley, Euan
dc.contributor.authorColan, Steven D.
dc.contributor.authorMichels, Michelle
dc.contributor.authorPereira, Alexandre C.
dc.contributor.authorJacoby, Daniel
dc.contributor.authorHo, Carolyn Y.
dc.contributor.authorOlivotto, Iacopo
dc.contributor.authorGunnarsson, Gunnar T.
dc.contributor.authorJefferies, John L.
dc.contributor.authorSemsarian, Chris
dc.contributor.authorIngles, Jodie
dc.contributor.authorO’Regan, Declan P.
dc.contributor.authorAguib, Yasmine
dc.contributor.authorYacoub, Magdi H.
dc.contributor.authorCook, Stuart A.
dc.contributor.authorBarton, Paul J. R.
dc.contributor.authorBottolo, Leonardo
dc.contributor.authorWare, James S.
dc.date.accessioned2021-01-16T16:06:25Z
dc.date.available2021-01-16T16:06:25Z
dc.date.issued2020-10-13
dc.date.submitted2020-06-20
dc.identifier.issn1098-3600
dc.identifier.others41436-020-00972-3
dc.identifier.other972
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/316313
dc.descriptionFunder: Science and Technology Development Fund; doi: https://doi.org/10.13039/
dc.descriptionFunder: Al-Alfi Foundation
dc.descriptionFunder: Magdi Yacoub Heart Foundation
dc.descriptionFunder: Rosetrees and Stoneygate Imperial College Research Fellowship
dc.descriptionFunder: National Health and Medical Research Council (Australia)
dc.description.abstractAbstract: 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.
dc.languageen
dc.publisherNature Publishing Group US
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectArticle
dc.subjectarticle
dc.subjectpathogenicity prediction
dc.subjectmissense variant interpretation
dc.subjectcardiomyopathy
dc.subjectlong QT syndrome
dc.subjectBrugada syndrome
dc.titleDisease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions
dc.typeArticle
dc.date.updated2021-01-16T16:06:25Z
prism.endingPage79
prism.issueIdentifier1
prism.publicationNameGenetics in Medicine
prism.startingPage69
prism.volume23
dc.identifier.doi10.17863/CAM.63422
dcterms.dateAccepted2020-09-09
rioxxterms.versionofrecord10.1038/s41436-020-00972-3
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidWare, James S. [0000-0002-6110-5880]
dc.identifier.eissn1530-0366
pubs.funder-project-idAlan Turing Institute (EP/N510129/1)


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)