PT - JOURNAL ARTICLE AU - Anna Cichonska AU - Juho Rousu AU - Pekka Marttinen AU - Antti J Kangas AU - Pasi Soininen AU - Terho Lehtimäki AU - Olli T Raitakari AU - Marjo-Riitta Järvelin AU - Veikko Salomaa AU - Mika Ala-Korpela AU - Samuli Ripatti AU - Matti Pirinen TI - metaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis AID - 10.1101/022665 DP - 2015 Jan 01 TA - bioRxiv PG - 022665 4099 - http://biorxiv.org/content/early/2015/07/16/022665.short 4100 - http://biorxiv.org/content/early/2015/07/16/022665.full AB - A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analysing 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.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.Code is available at https://github.com/aalto-ics-kepaco.