  Meta-analysis of multiple genome-wide association studies (GWAS) is effective
for detecting single or multi marker associations with complex traits. We
develop a flexible procedure ("STAMP") based on mixture models to perform
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 $\leq$ 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 risk of fourteen cancers, and the associations of
expression of quantitative traits loci (eQTLs) from two genetic regions with
their cis-SNPs measured in seventeen tissue types using data from The Cancer
Genome Atlas (TCGA).
