Identification of the potential type 2 diabetes susceptibility genetic elements in South Asian populations

Currently, type 2 diabetes (T2D) is a significant health risk to humanity. Regardless of many comprehensive genetic studies, the potential role of many candidate genes involved in the pathogenesis of T2D remains unclear. Identification of disease-associated genes and their variants through conventional experimental techniques is an expensive and time-consuming job. In this data-driven study, we tried to predict novel T2D related genetic signatures in South Asian (SAS) populations which may make these population susceptible to the T2D. Gene expression and SNPs association data related to T2D was retrieved from the GWAS catalogue and GEO database for the predictions. After cleaning and fetching the most relevant genes related to diabetes from GWAS and GEO data, functional annotation of the selected genes was performed using DAVID, resulting in the core gene list. SAS-specific genes from the core gene list were selected based on minor allele frequency (MAF < 0.05). The list was further shortlisted for already reported genes in SAS populations. Finally, we were able to identify a total of seven unreported candidate genes and their associated gene variants. We presume our computational gene collection data using various bioinformatics tools would not only contribute towards a knowledge-base but also throw in ideas for the development of genotyping arrays for better-targeted therapeutics and management of the T2D.