Beyond Diagnosis: Computational Frontiers in Type 2 Diabetes Research

Type 2 diabetes account for 90-95% of all diabetes cases [1]. Qatar faces an increase in prevalence from 17.8% to 29.5% by the year 2050 [2]. The aim of this research will be to get an in-depth analysis about the genes related to Type 2 Diabetes and how the potential SNPs affect the structure of the proteins that eventually lead to the Diabetes phenotype. We started by analyzing the disease through GWAS catalog and identified all SNPs associated with Type 2 Diabetes. Missense SNPs were further shortlisted from the list of all SNPs and mapped to their respective proteins. Domain architecture of deleterious proteins were determined using Alpha Fold. After an in-silico analysis of potential SNPs related to Type 2 diabetes, we have identified 4 different polymorphisms that could be potential drivers to the observed phenotype. The proteins identified are: CEP120, FAM63A, and PAM. This research will allow us to investigate how the missense mutations in certain Type 2 Diabetes-related genes can lead to the disease and help us find therapeutic targets for its prevention.