RT Journal Article
SR Electronic
T1 metaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 022665
DO 10.1101/022665
A1 Anna Cichonska
A1 Juho Rousu
A1 Pekka Marttinen
A1 Antti J Kangas
A1 Pasi Soininen
A1 Terho Lehtimäki
A1 Olli T Raitakari
A1 Marjo-Riitta Järvelin
A1 Veikko Salomaa
A1 Mika Ala-Korpela
A1 Samuli Ripatti
A1 Matti Pirinen
YR 2015
UL http://biorxiv.org/content/early/2015/07/16/022665.abstract
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.