Genome-wide association studies (GWAs) for type 2 diabetes (T2D) have been successful in identifying many loci with robust association signals. Nevertheless, there is a clear need for post-GWAs strategies to understand mechanism of action and clinical relevance of these variants. The association of several comorbidities with T2D suggests a common etiology for these phenotypes and complicates the management of the disease. In this study, we focused on the genetics underlying these relationships, using systems genomics to identify genetic variation associated with T2D and 12 other traits. GWAs studies summary statistics for pairwise comparisons were obtained for glycemic traits, obesity, coronary artery disease, and lipids from large consortia GWAs meta-analyses. We used a network medicine approach to leverage experimental information about the identified genes and variants with cross traits effects for biological function interpretation. We identified a set of 38 genetic variants with cross traits effects that point to a main network of genes that should be relevant for T2D and its comorbidities. We prioritized the T2D associated genes based on the number of traits they showed association with and the experimental evidence showing their relation to the disease etiology. In this study, we demonstrated how systems genomics and network medicine approaches can shed light into GWAs discoveries, translating findings into a more therapeutically relevant context.
Keywords: Network medicine; Post-GWA study strategy; Systems genomics; Type 2 diabetes genes.
Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.