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Meta-analysis of multiple genome-wide association studies (GWAS) is effectivefor detecting single or multi marker associations with complex traits. Wedevelop a flexible procedure ("STAMP") based on mixture models to performregion based meta-analysis of different phenotypes using data from differentGWAS and identify subsets of associated phenotypes. Our model framework helpsdistinguish true associations from between-study heterogeneity. As a measure ofassociation we compute for each phenotype the posterior probability that thegenetic region under investigation is truly associated. Extensive simulationsshow that STAMP is more powerful than standard approaches for meta analyseswhen the proportion of truly associated outcomes is $\leq$ 50\%. For othersettings, the power of STAMP is similar to that of existing methods. Weillustrate our method on two examples, the association of a region onchromosome 9p21 with risk of fourteen cancers, and the associations ofexpression of quantitative traits loci (eQTLs) from two genetic regions withtheir cis-SNPs measured in seventeen tissue types using data from The CancerGenome Atlas (TCGA).

Subset Testing and Analysis of Multiple Phenotypes (STAMP)