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dc.contributor.authorGe, Tian
dc.contributor.authorChen, Chia-Yen
dc.contributor.authorSabuncu, Mert R.
dc.contributor.authorNeale, Benjamin
dc.contributor.authorSmoller, Jordan
dc.date.accessioned2017-06-20T13:53:35Z
dc.date.available2017-06-20T13:53:35Z
dc.date.issued2017-04
dc.date.submitted2016-11
dc.identifier.issn1553-7404
dc.identifier.issn1553-7390
dc.identifier.urihttp://hdl.handle.net/1721.1/110042
dc.description.abstractHeritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient heritability estimation method that can handle large sample sizes, and report the SNP heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting heritability.en_US
dc.description.sponsorshipUnited States. National Institutes of Health (S10RR023043)en_US
dc.description.sponsorshipUnited States. National Institutes of Health (S10RR023401)en_US
dc.description.sponsorshipUnited States. National Institutes of Health (R01NS083534)en_US
dc.description.sponsorshipUnited States. National Institutes of Health (R01NS070963)en_US
dc.description.sponsorshipUnited States. National Institutes of Health (1K25EB013649-01)en_US
dc.description.sponsorshipUnited States. National Institutes of Health (K24MH094614)en_US
dc.language.isoen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pgen.1006711en_US
dc.rightsCreative Commons Attribution 4.0 International Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourcePLoSen_US
dc.titlePhenome-wide heritability analysis of the UK Biobanken_US
dc.typeArticleen_US
dc.identifier.citationGe, Tian; Chen, Chia-Yen; Neale, Benjamin M.; Sabuncu, Mert R. and Smoller, Jordan W. “Phenome-Wide Heritability Analysis of the UK Biobank.” Edited by Benjamin W. Domingue. PLOS Genetics 13, no. 4 (April 2017): e1006711 © 2017 Ge et alen_US
dc.contributor.departmentBroad Institute of MIT and Harvarden_US
dc.contributor.mitauthorNeale, Benjamin
dc.contributor.mitauthorSmoller, Jordan
dc.relation.journalPLoS Geneticsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsGe, Tian; Chen, Chia-Yen; Neale, Benjamin M.; Sabuncu, Mert R.; Smoller, Jordan W.en_US
dspace.embargo.termsNen_US
mit.licensePUBLISHER_CCen_US


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