College of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
The Department of Obstetrics and Gynaecology, Heilongjiang Provincial Forestry General Hospital, Harbin, Heilongjiang, China.
Sci Rep. 2017 Oct 2;7(1):12546. doi: 10.1038/s41598-017-12873-8.
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.
全基因组关联研究（GWAS）已被广泛用于鉴定常见的 2 型糖尿病（T2D）变异。然而，已知的变异仅能解释 T2D 总体遗传贡献的不到 20%。基于途径的方法已被应用于 T2D GWAS 数据集，以研究生物学机制，并报告了一些新的 T2D 风险途径。然而，这些研究中很少有途径是共同的。在这里，我们使用 T2D GWAS 大规模荟萃分析的汇总结果进行了途径分析，以研究 T2D 中的更多遗传信号。在这里，我们选择了 PLNK 和 VEGAS 进行基于基因的测试，以及 WebGestalt 进行基于途径的测试。我们在两种方法中均对多次测试进行了校正后，确定了 8 个共享的 KEGG 途径。我们证实了之前的发现，并强调了一些新的 T2D 风险途径。我们相信我们的结果可能有助于研究 T2D 的遗传机制。
