One of the first was a weighted False Discovery Rate (FDR) approach104, which uses external information to prioritize some SNPs or regions while maintaining a fixed overall FDR. Bayesian versions of the FDR have also been described105,106, as well as the use of Bayes factors107 and empirical Bayes shrinkage108. Both GSEA and hierarchical modeling approaches are also amenable to incorporating external knowledge. Several authors109-111 have described applications of the hierarchical Bayes modeling approach to GWA data using prior covariates extracted from genomic or pathway ontologies. While these have focused on main effects, the methods are also applicable to GEWIS11, the limiting factor presently being the lack of suitable ontologies for interaction effects. Meanwhile, a growing literature is discussing various ways of using GSEA or other methods of integrating pathway knowledge into GWA analyses9,62-64,112-116. Few studies have explicitly included G×E interactions in formal pathway-based analyses of GWA data117. A promising approach entails incorporating metabolomics, as in the first GWA of a large panel of metabolite phenotypes118, which found associations of 4 genes with metabolite concentration ratios for enzymatic activities that matched the pathways in which these enzymes act.
