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Expression quantitative trait analyses to identify causal genetic variants for type 2 diabetes susceptibility.

ABSTRACT: Type 2 diabetes (T2D) is a common metabolic disorder which is caused by multiple genetic perturbations affecting different biological pathways. Identifying genetic factors modulating the susceptibility of this complex heterogeneous metabolic phenotype in different ethnic and racial groups remains challenging. Despite recent success, the functional role of the T2D susceptibility variants implicated by genome-wide association studies (GWAS) remains largely unknown. Genetic dissection of transcript abundance or expression quantitative trait (eQTL) analysis unravels the genomic architecture of regulatory variants. Availability of eQTL information from tissues relevant for glucose homeostasis in humans opens a new avenue to prioritize GWAS-implicated variants that may be involved in triggering a causal chain of events leading to T2D. In this article, we review the progress made in the field of eQTL research and knowledge gained from those studies in understanding transcription regulatory mechanisms in human subjects. We highlight several novel approaches that can integrate eQTL analysis with multiple layers of biological information to identify ethnic-specific causal variants and gene-environment interactions relevant to T2D pathogenesis. Finally, we discuss how the eQTL analysis mediated search for "missing heritability" may lead us to novel biological and molecular mechanisms involved in susceptibility to T2D.

SUBMITTER: Das SK

PROVIDER: S-EPMC3990322 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Expression quantitative trait analyses to identify causal genetic variants for type 2 diabetes susceptibility.

Das Swapan Kumar SK Sharma Neeraj Kumar NK

World journal of diabetes 20140401 2

Type 2 diabetes (T2D) is a common metabolic disorder which is caused by multiple genetic perturbations affecting different biological pathways. Identifying genetic factors modulating the susceptibility of this complex heterogeneous metabolic phenotype in different ethnic and racial groups remains challenging. Despite recent success, the functional role of the T2D susceptibility variants implicated by genome-wide association studies (GWAS) remains largely unknown. Genetic dissection of transcript ...[more]

PMID: 24748924

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Project description:BackgroundType 2 diabetes (T2D) is a prevalent chronic disease associated with several comorbidities.ObjectivesThis study investigated whether the risk of T2D varied with genetically predicted insulin (INS), insulin receptor (INS-R), or insulin-like growth factor 1 receptor (IGF-1R) using genetic variants in a Mendelian randomization (MR) study.MethodsA 2-sample MR study was conducted using summary statistics from 2 genome-wide association studies (GWASs). Genetic predictors of the exposures (INS, INS-R, and IGF-1R) were obtained from a publicly available proteomics GWAS of the INTERVAL randomized controlled trial of blood donation in the United Kingdom. For T2D, the study leveraged the DIAbetes Meta-ANalysis of Trans-Ethnic association studies (DIAMANTE) consortium. The estimated associations of INS, INS-R, and IGF-1R proteins with T2D were based on independent single nucleotide polymorphisms (SNPs) strongly (P < 5 × 10-6) predicting each exposure. These SNPs were applied to publicly available genetic associations with T2D from the DIAMANTE case (n = 74,124) and control (n = 824,006) study of people of European descent. SNP-specific Wald estimates were meta-analyzed using inverse variance weighting with multiplicative random effects. Sensitivity analysis was conducted using the weighted median (WM) and MR-Egger.ResultsINS-R (based on 13 SNPs) was associated with a lower risk of T2D (OR: 0.95 per effect size; 95% CI: 0.92, 0.98; P = 0.001), with similar estimates from the WM and MR-Egger. Insulin (8 SNPs) and IGF-1R (10 SNPs) were not associated with T2D. However, 1 of the SNPs for INS-R was from the ABO blood group gene.ConclusionsThis study is consistent with a causally protective association of the INS-R with T2D. INS-R in RBCs regulates glycolysis and thus may affect their functionality and integrity. However, a pleiotropic effect via the blood group ABO gene cannot be excluded. The INS-R may be a target for intervention by repurposing existing therapeutics or otherwise to reduce the risk of T2D.

Dataset Information

Expression quantitative trait analyses to identify causal genetic variants for type 2 diabetes susceptibility.

Publications

Expression quantitative trait analyses to identify causal genetic variants for type 2 diabetes susceptibility.

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