To our best knowledge, the conditional FDR approach is the first approach that statistically addresses the issue of pleiotropy between two GWAS. and GSEA is presently the most popular approach to evaluating the enrichment of gene sets. In fact, GPA provides a unified framework for systematically integrating both sources of information, pleiotropy and annotation. Rigorous statistical inference of pleiotropic effects and annotation enrichment has been established in this framework. As demonstrated in our extensive simulation study, GPA has better performance of identifying disease-associated markers than the conditional FDR approach, and it shows greater power of evaluating annotation enrichment than GSEA as well. With real data analysis, we have demonstrated how to use GPA to incorporate pleiotropy information and multiple annotation data for prioritizing GWAS results.
