Jul 16, 2015

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

BioRxiv : the Preprint Server for Biology
Anna CichonskaMatti Pirinen


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 ...Continue Reading

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Mentioned in this Paper

Genome-Wide Association Study
Lipid Droplet
Meta-Analysis (Publications)
Meta Analysis (Statistical Procedure)
Magnetic Resonance Imaging
High Throughput Screening
Genetic Association Studies
Health Care Program
Statistical Technique

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