metaCCA : summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

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dc.contributor University of Helsinki, Institute for Molecular Medicine Finland en
dc.contributor University of Helsinki, Helsinki Institute for Information Technology en
dc.contributor University of Helsinki, Helsinki Institute for Information Technology en
dc.contributor University of Helsinki, Clinicum en
dc.contributor University of Helsinki, Institute for Molecular Medicine Finland en
dc.contributor.author Cichonska, Anna
dc.contributor.author Rousu, Juho
dc.contributor.author Marttinen, Pekka
dc.contributor.author Kangas, Antti J.
dc.contributor.author Soininen, Pasi
dc.contributor.author Lehtimaki, Terho
dc.contributor.author Raitakari, Olli T.
dc.contributor.author Jarvelin, Marjo-Riitta
dc.contributor.author Salomaa, Veikko
dc.contributor.author Ala-Korpela, Mika
dc.contributor.author Ripatti, Samuli
dc.contributor.author Pirinen, Matti
dc.date.accessioned 2016-10-07T11:18:01Z
dc.date.available 2016-10-07T11:18:01Z
dc.date.issued 2016-07-01
dc.identifier.citation Cichonska , A , Rousu , J , Marttinen , P , Kangas , A J , Soininen , P , Lehtimaki , T , Raitakari , O T , Jarvelin , M-R , Salomaa , V , Ala-Korpela , M , Ripatti , S & Pirinen , M 2016 , ' metaCCA : summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis ' , Bioinformatics , vol. 32 , no. 13 , pp. 1981-1989 . https://doi.org/10.1093/bioinformatics/btw052 en
dc.identifier.issn 1367-4803
dc.identifier.other PURE: 67013655
dc.identifier.other PURE UUID: 87e268ca-c1e8-472e-8107-f217e16251ce
dc.identifier.other WOS: 000379761500009
dc.identifier.other Scopus: 85007276054
dc.identifier.other ORCID: /0000-0002-1664-1350/work/29075050
dc.identifier.other ORCID: /0000-0003-1072-8858/work/29953687
dc.identifier.uri http://hdl.handle.net/10138/167568
dc.description.abstract Motivation: 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. en
dc.format.extent 9
dc.language.iso eng
dc.relation.ispartof Bioinformatics
dc.rights en
dc.subject CARDIOVASCULAR RISK en
dc.subject RARE VARIANTS en
dc.subject TRAITS en
dc.subject 3111 Biomedicine en
dc.title metaCCA : summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis en
dc.type Article
dc.description.version Peer reviewed
dc.identifier.doi https://doi.org/10.1093/bioinformatics/btw052
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/publishedVersion
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