Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12716
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dc.contributor.authorLewin, A-
dc.contributor.authorSaadi, H-
dc.contributor.authorPeters, JE-
dc.contributor.authorMoreno-Moral, A-
dc.contributor.authorLee, JC-
dc.contributor.authorSmith, KGC-
dc.contributor.authorPetretto, E-
dc.contributor.authorBottolo, L-
dc.contributor.authorRichardson, S-
dc.date.accessioned2016-06-03T14:09:04Z-
dc.date.available2016-02-15-
dc.date.available2016-06-03T14:09:04Z-
dc.date.issued2016-
dc.identifier.citationBioinformatics, 32(4): pp. 523 - 532, (2016)en_US
dc.identifier.issn1367-4803-
dc.identifier.urihttp://bioinformatics.oxfordjournals.org/content/32/4/523-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/12716-
dc.description.abstractAnalysing the joint association between a large set of responses and predictors is a fundamental statistical task in integrative genomics, exemplified by numerous expression Quantitative Trait Loci (eQTL) studies. Of particular interest are the so-called ‘hotspots’, important genetic variants that regulate the expression of many genes. Recently, attention has focussed on whether eQTLs are common to several tissues, cell-types or, more generally, conditions or whether they are specific to a particular condition. Results: We have implemented MT-HESS, a Bayesian hierarchical model that analyses the association between a large set of predictors, e.g. SNPs, and many responses, e.g. gene expression, in multiple tissues, cells or conditions. Our Bayesian sparse regression algorithm goes beyond ‘one-at-a-time’ association tests between SNPs and responses and uses a fully multivariate model search across all linear combinations of SNPs, coupled with a model of the correlation between condition/tissue-specific responses. In addition, we use a hierarchical structure to leverage shared information across different genes, thus improving the detection of hotspots. We show the increase of power resulting from our new approach in an extensive simulation study. Our analysis of two case studies highlights new hotspots that would remain undetected by standard approaches and shows how greater prediction power can be achieved when several tissues are jointly considered.en_US
dc.description.sponsorshipThis research was funded by Medical Research Council Grant G1002319 (A.L., H.S., L.B., S.R.), a Wellcome Trust Clinical PhD Programme Fellowship (J.E.P.), Wellcome Trust Grant 083650/Z/07/Z and Medical Research Council Grant MR/L19027/1 (K.G.C.S.), British Heart Foundation PhD Studentship Grant FS/11/25/28740 (A.M., E.P.) and Engineering and Physical Sciences Research Council Grant EP/K030760/1 (L.B.).en_US
dc.format.extent523 - 532 (10)-
dc.languageEnglish-
dc.language.isoenen_US
dc.publisherOxford University Pressen_US
dc.subjectMT-HESSen_US
dc.subjectBayesian hierarchical modelen_US
dc.subjectPredictorsen_US
dc.subjectGene expressionen_US
dc.subjectRegression algorithmen_US
dc.titleMT-HESS: An efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissuesen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1093/bioinformatics/btv568-
dc.relation.isPartOfBIOINFORMATICS-
pubs.issue4-
pubs.publication-statusPublished-
pubs.volume32-
Appears in Collections:Dept of Mathematics Research Papers

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