Cross-Population Analysis for Functional Characterization of Type II Diabetes Variants

Abstract Objective. As Genome-Wide Association Studies (GWAS) have been increasingly used with data from various populations, it has been observed that data from different populations reveal different sets of Single Nucleotide Polymorphisms (SNPs) that are associated with the same disease. Using Type II Diabetes (T2D) as a test case, we develop measures and methods to characterize the functional overlap of SNPs associated with the same disease across populations. Materials and methods. We introduce the notion of an Overlap Matrix as a general means of characterizing the functional overlap between different SNP sets at different genomic and functional granularities. Using SNP-to-gene mapping, functional annotation databases, and interaction networks, we assess the degree of functional overlap in T2D-associated loci identified across nine populations from Asian and European ethnic origins. Results. Our results show that more overlap is captured as more functional data is incorporated as we go through the pipeline, starting from SNPs and ending at network overlap analyses. We hypothesize that these observed differences in the functional mechanisms of T2D across populations can also explain the popularity of different prescription drugs in different populations. We show that this hypothesis is concordant with the literature on the functional mechanisms of prescription drugs. Discussion. Our results show that although there exist distinct T2D processes that are affected in different populations, network-based annotations can capture more functional overlap across populations, and that the functional similarity is more substantial between populations with similar ethnic origins. Conclusion. These results support the notion that ethnicity needs to be taken into account in making personalized treatment decisions for complex diseases.