Abstract
Meta-analysis of multiple genome-wide association studies (GWAS) is effective for detecting single- or multimarker associations with complex traits. We develop a flexible procedure (subset testing and analysis of multiple phenotypes [STAMP]) based on mixture models to perform a region-based meta-analysis of different phenotypes using data from different GWAS and identify subsets of associated phenotypes. Our model framework helps distinguish true associations from between-study heterogeneity. As a measure of association, we compute for each phenotype the posterior probability that the genetic region under investigation is truly associated. Extensive simulations show that STAMP is more powerful than standard approaches for meta-analyses when the proportion of truly associated outcomes is between 25% and 50%. For other settings, the power of STAMP is similar to that of existing methods. We illustrate our method on two examples, the association of a region on chromosome 9p21 with the risk of 14 cancers, and the associations of expression of quantitative trait loci from two genetic regions with their cis-single-nucleotide polymorphisms measured in 17 tissue types using data from The Cancer Genome Atlas.