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World J Diabetes. Apr 15, 2014; 5(2): 97-114
Published online Apr 15, 2014. doi: 10.4239/wjd.v5.i2.97
Expression quantitative trait analyses to identify causal genetic variants for type 2 diabetes susceptibility
Swapan Kumar Das, Neeraj Kumar Sharma
Swapan Kumar Das, Neeraj Kumar Sharma, Section on Endocrinology and Metabolism, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, United States
Author contributions: Das SK performed the literature review, integrated key scientific concepts, and wrote the paper; Sharma NK performed the literature and data mining and reviewed the manuscript.
Supported by National Institutes of Health (NIH/NIDDK), Nos. R01 DK090111 and DK039311
Correspondence to: Swapan K Das, PhD, Section on Endocrinology and Metabolism, Department of Internal Medicine, Wake Forest School of Medicine, Medical Center Boulevard, NRC Building # E159, Winston-Salem, NC 27157, United States. sdas@wakehealth.edu
Telephone: +1-336-7136057 Fax: +1-336-7137200
Received: November 27, 2013
Revised: February 21, 2014
Accepted: March 13, 2014
Published online: April 15, 2014
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.

Keywords: Type 2 diabetes, Single nucleotide polymorphisms, Expression quantitative trait locus, Expression regulatory SNPs, Gene-environment interaction, Genome-wide association study

Core tip: Identification of genetic variants that modulate the susceptibility to disease and elucidating their function at the molecular level is a major focus of type 2 diabetes (T2D) research. This article highlights the utility of expression quantitative trait analysis in discovering regulatory variants that increase susceptibility to T2D by modulating the expression of transcripts in tissues important for glucose homeostasis.