UC Riverside

Genome-wide association studies (GWAS) have been widely used to identify common type 2 diabetes (T2D) variants. However, the known variants just explain less than 20% of the overall estimated genetic contribution to T2D. Pathway-based methods have been applied into T2D GWAS datasets to investigate the biological mechanisms and reported some novel T2D risk pathways. However, few pathways were shared in these studies. Here, we performed a pathway analysis using the summary results from a large-scale meta-analysis of T2D GWAS to investigate more genetic signals in T2D. Here, we selected PLNK and VEGAS to perform the gene-based test and WebGestalt to perform the pathway-based test. We identified 8 shared KEGG pathways after correction for multiple tests in both methods. We confirm previous findings, and highlight some new T2D risk pathways. We believe that our results may be helpful to study the genetic mechanisms of T2D.