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dc.contributor.authorCichonska Anna
dc.contributor.authorRousu Juho
dc.contributor.authorMarttinen Pekka
dc.contributor.authorKangas Antti J
dc.contributor.authorSoininen Pasi
dc.contributor.authorLehtimäki Terho
dc.contributor.authorRaitakari Olli T
dc.contributor.authorJärvelin Marjo-Riitta
dc.contributor.authorSalomaa Veikko
dc.contributor.authorAla-Korpela Mika
dc.contributor.authorRipatti Samuli
dc.contributor.authorPirinen Matti
dc.date.accessioned2017-02-14T13:43:14Z
dc.date.available2017-02-14T13:43:14Z
dc.date.issued2016
dc.identifier10.1093/bioinformatics/btw052fi_FI
dc.identifier.issn1367-4803
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/274
dc.descriptionArticle
dc.description.abstractMotivation A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. Results We introduce metaCCA , a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA , using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies.fi_FI
dc.language.isoENGfi_FI
dc.publisherOxford University Press (OUP)fi_FI
dc.relation.ispartofseriesBioinformatics;
dc.relation.urihttps://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btw052fi_FI
dc.rights© Authorsfi_FI
dc.titlemetaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysisfi_FI
dc.typehttp://purl.org/eprint/type/JournalArticle
dc.rights.licenseCC BY http://creativecommons.org/licenses/by/4.0/
dc.description.versionpublisher's pdffi_FI
dc.contributor.departmentSchool of Pharmacy, Activities
uef.solecris.id40173910
eprint.statushttp://purl.org/eprint/status/PeerReviewedfi_FI
dc.type.publicationinfo:eu-repo/semantics/article
dc.rights.accessrightsopenAccessfi_FI
uef.citationinfo.issue32(13)
uef.citationinfo.pages1981-1989
dc.relation.doi10.1093/bioinformatics/btw052
dc.description.reviewstatushttp://purl.org/eprint/status/PeerReviewed
dc.format.pagerange1981-1989
dc.relation.issn1367-4803
dc.relation.issue32(13)


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