Classify and identify the risky loci of type 2 diabetes with computational method

Abstract

Genome-wide association studies (GWAS) of T2D have discovered a number of loci that contribute to susceptibility to the disease. In this paper, we classified and identified the suspected risky Loci of T2D with computational method based on the known T2D GWAS-associated SNPs. The framework includes two parts: we first classified the SNPs based on their features of position and function through a simplified classification decision tree which was constructed by C4.5 decision tree algorithm; we then identified whether the genes associated with the suspected risky SNPs are associated with T2D by using random walk algorithm with Restart Model on the PPI network of T2D GWAS-associated genes among proteins and interactors. Based on the classification of SNP associated with T2D, we analyzed molecular pathogenesis of T2D. We verified the accuracy and reliability of the classification and identification framework with the data set of GWAS-associated SNPs. The result shows that this method is reliable. It provides a significant way to identify and classify the suspected risky Loci associated with T2D and further insights into the molecular pathogenesis of T2D.