In summary, recent studies [11,73–84] have repeatedly demonstrated that gene set analysis is a promising approach for analyzing and interpreting GWAS datasets in order to better understand the genetic architecture underlying complex diseases. In this paper, we have provided an up-to-date review of the current progress, as well as the limitations in gene set analysis methods for GWAS. The power and potential performance of these methods may be further improved by integrating additional biological and environmental information at the systems level. For example, network-based approaches that combine association signals in GWAS with local PPI information can help account for gene-gene interactions and identify genes playing central roles in protein networks by interconnecting many disease genes that are weakly associated with disease themselves [26,63–66]. Similarly, analysis that models gene pathways with environmental interactions will help investigators identify novel genes with weak marginal effects that act jointly with exposure factors [85]. As many more GWAS datasets are expected to be generated in the near future, meta-analyses, which integrate multiple independent GWAS datasets, can be included in gene set analysis methods to increase
