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

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http://hdl.handle.net/10138/167568

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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

Titel: metaCCA : summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis
Författare: Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J.; Soininen, Pasi; Lehtimaki, Terho; Raitakari, Olli T.; Jarvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti
Medarbetare: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Clinicum
University of Helsinki, Institute for Molecular Medicine Finland
Datum: 2016-07-01
Språk: eng
Sidantal: 9
Tillhör serie: Bioinformatics
ISSN: 1367-4803
Permanenta länken (URI): http://hdl.handle.net/10138/167568
Abstrakt: 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.
Subject: CARDIOVASCULAR RISK
RARE VARIANTS
TRAITS
3111 Biomedicine
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