of genes. One potentially promising strategy is to take advantage of the information from gene expression studies. Veyrieras et al. [15] estimated that the majority of genetic variants influencing gene expression are located within 20kb of the genes. Recently, to identify T2D associated pathways, Zhong et al. [16] assessed the impact of the SNPs on gene expressions in liver and adipose tissues and summarized each gene by the SNP significantly associated with the gene’s transcript abundance. For general reference, Gamazon et al. [17] developed the SCAN database, which provides information on mapping genetic variants associated with gene expression based on the samples in the HapMap project [18,19]. More comprehensive databases will be developed in the future, for example, those for expression quantitative trait loci (eQTL, regions of the genome that impact gene expression) measured in disease relevant tissues. Thus, we expect that utilizing the information from gene expression studies will improve the power of the gene set analysis approach for GWAS.
