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dc.contributor.authorBurren, Oliveren
dc.contributor.authorGuo, Huien
dc.contributor.authorWallace, Chrisen
dc.date.accessioned2014-09-29T11:12:40Z
dc.date.available2014-09-29T11:12:40Z
dc.date.issued2014-08-27en
dc.identifier.citationBioinformatics (2014) DOI: 10.1093/bioinformatics/btu571en
dc.identifier.issn1367-4803
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/246057
dc.description.abstractMotivation: Genome-wide association studies (GWAS) have identified many loci implicated in disease susceptibility. Integration of GWAS summary statistics (P-values) and functional genomic datasets should help to elucidate mechanisms. Results: We extended a non-parametric SNP set enrichment method to test for enrichment of GWAS signals in functionally defined loci to a situation where only GWAS P-values are available. The approach is implemented in VSEAMS, a freely available software pipeline. We use VSEAMS to identify enrichment of type 1 diabetes (T1D) GWAS associations near genes that are targets for the transcription factors IKZF3, BATF and ESRRA. IKZF3 lies in a known T1D susceptibility region, while BATF and ESRRA overlap other immune disease susceptibility regions, validating our approach and suggesting novel avenues of research for T1D.
dc.description.sponsorshipThis work was funded by the JDRF (9-2011-253), the Wellcome Trust (091157) and the National Institute for Health Research Cambridge Biomedical Research Centre. The research leading to these results has received funding from the European Unions seventh Framework Programme (FP7/2007-2013) under grant agreement no. 241447 (NAIMIT). The Cambridge Institute for Medical Research is in receipt of a Wellcome Trust Strategic Award (100140). C.W. and H.G. are supported by the Wellcome Trust (089989). ImmunoBase.org is supported by Eli Lilly and Company.
dc.languageEnglishen
dc.language.isoenen
dc.publisherOxford Journals
dc.rightsAttribution 2.0 UK: England & Wales*
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/uk/*
dc.titleVSEAMS: A pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetesen
dc.typeArticle
dc.description.versionThis is the final published version, also available from OUP at http://bioinformatics.oxfordjournals.org/content/early/2014/09/18/bioinformatics.btu571.short?rss=1.en
prism.publicationDate2014en
prism.publicationNameBioinformaticsen
dc.rioxxterms.funderJDRF
dc.rioxxterms.funderWellcome Trust
dc.rioxxterms.funderEU
dc.rioxxterms.projectid9-2011-253
dc.rioxxterms.projectid091157
dc.rioxxterms.projectidFP7/2007-2013
dc.rioxxterms.projectid241447
dc.rioxxterms.projectid100140
rioxxterms.versionofrecord10.1093/bioinformatics/btu571en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2014-08-27en
dc.contributor.orcidBurren, Oliver [0000-0002-3388-5760]
dc.contributor.orcidWallace, Chris [0000-0001-9755-1703]
dc.identifier.eissn1367-4811
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idNational Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (U01DK062418)
pubs.funder-project-idWellcome Trust (091157/Z/10/B)
pubs.funder-project-idWellcome Trust (089989/Z/09/Z)


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Attribution 2.0 UK: England & Wales
Except where otherwise noted, this item's licence is described as Attribution 2.0 UK: England & Wales