Methods for multiple outcome meta-analysis of gene-expression data

Abstract

Meta-analysis is a valuable tool for the synthesis of evidence across a wide range study types including high-throughput experiments such as genome-wide association studies (GWAS) and gene expression studies. There are situations though, in which we have multiple outcomes or multiple treatments, in which the multivariate meta-analysis framework which performs a joint modeling of the different quantities of interest may offer important advantages, such as increasing statistical power and allowing performing global tests. In this work we adapted the multivariate meta-analysis method and applied it in gene expression data. With this method we can test for pleiotropic effects, that is, for genes that influence both outcomes or discover genes that have a change in expression not detectable in the univariate method. We tested this method on data regarding inflammatory bowel disease (IBD), with its two main forms, Crohn’s disease (CD) and Ulcerative colitis (UC), sharing many clinical manifestations, but differing in the location and extent of inflammation and in complications. The Stata code is given in the Appendix and it is available at: www.compgen.org/tools/multivariate-microarrays. • Multivariate meta-analysis method for gene expression data. • Discover genes with pleiotropic effects. • Differentially Expressed Genes (DEGs) identification in complex traits.

Keywords

• Multiple outcome
• Pleiotropic effects
• Microarrays
• Meta-analysis