Paper of the week:
“metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis”, Cichonska et al. ,2016. Bioinformatics, Volume 32, Issue 13, Pages 1981–1989
Presented by: Dr Schneider-Luftman
GWAS (Genome-wide association study) is a method widely used nowadays for the analysis of association between the genome and any phenotypic trait. In its classical form, it works as a “giant” univariate regression of 1 outcome against the allelic variation of all the SNPs (Single-nucleotide polymorphism) in the genome *. (*= except rare SNPs, chrm. X and Y, and other conditions….). However, there is increased interest in analysis multiple related traits together. It only makes sense to analyse them jointly, in order to increase statistical power.
This paper introduce a framework to perform multi-outcome meta-analysis of GWA data, from a single of several studies. The underlying method relies on CCA (canonical correlation analysis) + shrinkage. The paper also discusses an application to several Finnish population cohorts – notably the Northern Finland Birth Cohort, very widely used at EBS – across 81 lipid NMR (nuclear magnetic resonance) measures.