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dc.contributor.authorHutchinson, Anna
dc.contributor.authorReales, Guillermo
dc.contributor.authorWillis, Thomas
dc.contributor.authorWallace, Chris
dc.date.accessioned2021-11-01T19:27:18Z
dc.date.available2021-11-01T19:27:18Z
dc.date.issued2021-10-20
dc.date.submitted2021-07-01
dc.identifier.issn1553-7390
dc.identifier.otherpgenetics-d-21-00900
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330146
dc.descriptionFunder: GlaxoSmithKline; funder-id: http://dx.doi.org/10.13039/100004330
dc.description.abstractGenome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions (“Flexible cFDR”). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource.
dc.languageen
dc.publisherPublic Library of Science
dc.subjectResearch Article
dc.subjectBiology and life sciences
dc.subjectMedicine and health sciences
dc.subjectResearch and analysis methods
dc.subjectPhysical sciences
dc.subjectComputer and information sciences
dc.subjectEngineering and technology
dc.titleLeveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR
dc.typeArticle
dc.date.updated2021-11-01T19:27:17Z
prism.issueIdentifier10
prism.publicationNamePLOS Genetics
prism.volume17
dc.identifier.doi10.17863/CAM.77589
dcterms.dateAccepted2021-09-30
rioxxterms.versionofrecord10.1371/journal.pgen.1009853
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
datacite.contributor.supervisoreditor: Yang, Jingjing
dc.contributor.orcidHutchinson, Anna [0000-0002-9224-4410]
dc.contributor.orcidReales, Guillermo [0000-0001-9993-3916]
dc.contributor.orcidWillis, Thomas [0000-0001-6138-3496]
dc.contributor.orcidWallace, Chris [0000-0001-9755-1703]
dc.identifier.eissn1553-7404
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/R511870/1)
pubs.funder-project-idWellcome Trust (WT107881)
pubs.funder-project-idMedical Research Council (MC UU 00002/4)
pubs.funder-project-idNIHR Cambridge Biomedical Research Centre (BRC-1215-20014)


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