Dorothy Hodgkin Lecture 20141 Understanding genes identified by genome‐wide association studies for Type 2 diabetes. Issue 12 (December 2014)

• Record Type: Journal Article
• Authors: Rutter, G. A.
• Is Part Of: Diabetic medicine. Volume 31:Issue 12(2014:Dec.)
• Page Start: 1480
• Page End: 1487
• Publication Date: 2014-12
• Subjects: Diabetes -- Periodicals; 616.462
• DOI: 10.1111/dme.12579
• Languages: English
• ISSNs: 0742-3071

Abstract

<abstract abstract-type="main" id="dme12579-abs-0001"> <title>Abstract</title> <p>Whilst the heritable nature of Type 2 diabetes has been recognized for many years, only in the past two decades have linkage analyses in families and genome‐wide association studies in large populations begun to reveal the genetic landscape of the disease in detail. Whilst the former have provided a powerful means of identifying the genes responsible for monogenic forms of the disease, the latter highlight relatively large genomic regions. These often harbour multiple genes, whose relative contribution to exaggerated disease risk is uncertain. In the present study, the approaches that have been used to dissect the role of just a few (<italic>TCF7L2, SLC30A8, ADCY5, MTNR1B and CDKAL1</italic>) of the ~ 500 genes identified at dozens of implicated loci are described. These are usually selected based on the strength of their effect on disease risk, and predictions as to their likely biological role. Direct determination of the effects of identified polymorphisms on gene expression in disease‐relevant tissues, notably the pancreatic islet, are then performed to identify genes whose expression is affected by a particular polymorphism. Subsequent functional analyses then involve perturbing gene expression <italic>in vitro</italic> in β‐cell lines or isolated islets and <italic>in vivo</italic> in animal models. Although the majority of polymorphisms affect insulin production rather than action, and mainly affect the β cell, effects via other tissues may also contribute, requiring careful consideration in the design and interpretation of experiments in model systems. These considerations illustrate the scale of the task needed to exploit genome‐wide association study data for the development of new therapeutic strategies.</p> </abstract>