Developing a network view of type 2 diabetes risk pathways through integration of genetic, genomic and functional data

Abstract

Background Genome-wide association studies (GWAS) have identified several hundred susceptibility loci for type 2 diabetes (T2D). One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes. However, the causal variants identified by GWAS mostly fall into a non-coding sequence, complicating the task of defining the effector transcripts through which they operate. Methods Here, we describe implementation of an analytical pipeline to address this question. First, we integrate multiple sources of genetic, genomic and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals. Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner tree approach) which uses external, experimentally confirmed protein-protein interaction (PPI) data to generate high-confidence sub-networks. Third, we use GWAS data to test the T2D association enrichment of the “non-seed” proteins introduced into the network, as a measure of the overall functional connectivity of the network. Results We find (a) non-seed proteins in the T2D protein-interaction network so generated (comprising 705 nodes) are enriched for association to T2D ( p = 0.0014) but not control traits, (b) stronger T2D-enrichment for islets than other tissues when we use RNA expression data to generate tissue-specific PPI networks and (c) enhanced enrichment ( p = 3.9 × 10 − 5 ) when we combine the analysis of the islet-specific PPI network with a focus on the subset of T2D GWAS loci which act through defective insulin secretion. Conclusions These analyses reveal a pattern of non-random functional connectivity between candidate causal genes at T2D GWAS loci and highlight the products of genes including YWHAG , SMAD4 or CDK2 as potential contributors to T2D-relevant islet dysfunction. The approach we describe can be applied to other complex genetic and genomic datasets, facilitating integration of diverse data types into disease-associated networks. Electronic supplementary material The online version of this article (10.1186/s13073-019-0628-8) contains supplementary material, which is available to authorized users.

Bioinformatic Reconstruction and Analysis of Gene Networks Related to Glucose Variability in Diabetes and Its Complications

“…The reconstruction and analysis of gene networks is a widely used approach for studying the mechanisms of complex biological processes and multifactorial diseases [ 25 , 26 ]. Specifically, this approach was applied for identification of key genes and pathways involved in pathogenesis of diabetes and its complications [ 27 , 28 , 29 , 30 ]. However, to our knowledge, GV-related networks have not been reconstructed and analyzed yet.…”

CONQUER: an interactive toolbox to understand functional consequences of GWAS hits

“…With the user-friendly and interactive dashboard, we were able to pinpoint SNPs and linked them to biological pathways in specific tissues. In contrast to previous studies that have had similar approaches ( 42 , 43 ), we have developed open-source software that is available as an R-package where only the SNPs and tissues of interest have to be specified.…”

Ion Transporters, channelopathies, and glucose disorders

“…Elevated ATP/ADP ratio closes the ATP-sensitive potassium channel (K ATP ). The Kir6.2 subunits of the K ATP channels are responsible for K + ion efflux from the pancreatic beta-cell, and thus maintains a steady state membrane potential (4). The closure of the K ATP channels results in a depolarization of the pancreatic beta-cell membrane and the activation of voltage-gated calcium channels located on the beta-cell membrane (5).…”