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dc.contributor.authorMarttinen, Pekkaen_US
dc.contributor.authorPirinen, Mattien_US
dc.contributor.authorSarin, Antti-Pekkaen_US
dc.contributor.authorGillberg, Jussien_US
dc.contributor.authorKettunen, Johannesen_US
dc.contributor.authorSurakka, Idaen_US
dc.contributor.authorKangas, Antti J.en_US
dc.contributor.authorSoininen, Pasien_US
dc.contributor.authorO’Reilly, Paulen_US
dc.contributor.authorKaakinen, Marikaen_US
dc.contributor.authorKähönen, Mikaen_US
dc.contributor.authorLehtimäki, Terhoen_US
dc.contributor.authorAla-Korpela, Mikaen_US
dc.contributor.authorRaitakari, Olli T.en_US
dc.contributor.authorSalomaa, Veikkoen_US
dc.contributor.authorJärvelin, Marjo-Riittaen_US
dc.contributor.authorRipatti, Samulien_US
dc.contributor.authorKaski, Samuelen_US
dc.date.accessioned2014-08-13T13:58:34Z
dc.date.issued2014en_US
dc.identifier.citationMarttinen, P., M. Pirinen, A. Sarin, J. Gillberg, J. Kettunen, I. Surakka, A. J. Kangas, et al. 2014. “Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression.” Bioinformatics 30 (14): 2026-2034. doi:10.1093/bioinformatics/btu140. http://dx.doi.org/10.1093/bioinformatics/btu140.en
dc.identifier.issn1367-4803en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12717411
dc.description.abstractMotivation: A typical genome-wide association study searches for associations between single nucleotide polymorphisms (SNPs) and a univariate phenotype. However, there is a growing interest to investigate associations between genomics data and multivariate phenotypes, for example, in gene expression or metabolomics studies. A common approach is to perform a univariate test between each genotype–phenotype pair, and then to apply a stringent significance cutoff to account for the large number of tests performed. However, this approach has limited ability to uncover dependencies involving multiple variables. Another trend in the current genetics is the investigation of the impact of rare variants on the phenotype, where the standard methods often fail owing to lack of power when the minor allele is present in only a limited number of individuals. Results: We propose a new statistical approach based on Bayesian reduced rank regression to assess the impact of multiple SNPs on a high-dimensional phenotype. Because of the method’s ability to combine information over multiple SNPs and phenotypes, it is particularly suitable for detecting associations involving rare variants. We demonstrate the potential of our method and compare it with alternatives using the Northern Finland Birth Cohort with 4702 individuals, for whom genome-wide SNP data along with lipoprotein profiles comprising 74 traits are available. We discovered two genes (XRCC4 and MTHFD2L) without previously reported associations, which replicated in a combined analysis of two additional cohorts: 2390 individuals from the Cardiovascular Risk in Young Finns study and 3659 individuals from the FINRISK study. Availability and implementation: R-code freely available for download at http://users.ics.aalto.fi/pemartti/gene_metabolome/. Contact: samuli.ripatti@helsinki.fi; samuel.kaski@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online.en
dc.language.isoen_USen
dc.publisherOxford University Pressen
dc.relation.isversionofdoi:10.1093/bioinformatics/btu140en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4080737/pdf/en
dash.licenseLAAen_US
dc.titleAssessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regressionen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalBioinformaticsen
dc.date.available2014-08-13T13:58:34Z
dc.identifier.doi10.1093/bioinformatics/btu140*
dash.authorsorderedfalse


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