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Current Pharmaceutical Design

Editor-in-Chief

ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

Obesity and Diabetes Gene Discovery Approaches

Author(s): K. Walder, D. Segal, J. Jowett, J. Blangero and G. R. Collier

Volume 9, Issue 17, 2003

Page: [1357 - 1372] Pages: 16

DOI: 10.2174/1381612033454739

Price: $65

Abstract

New treatments are currently required for the common metabolic diseases obesity and type 2 diabetes. The identification of physiological and biochemical factors that underlie the metabolic disturbances observed in obesity and type 2 diabetes is a key step in developing better therapeutic outcomes. The discovery of new genes and pathways involved in the pathogenesis of these diseases is critical to this process, however identification of genes that contribute to the risk of developing these diseases represents a significant challenge as obesity and type 2 diabetes are complex diseases with many genetic and environmental causes. A number of diverse approaches have been used to discover and validate potential new targets for obesity and diabetes. To date, DNA-based approaches using candidate gene and genome-wide linkage analysis have had limited success in identifying genomic regions or genes involved in the development of these diseases. Recent advances in the ability to evaluate linkage analysis data from large family pedigrees using variance components based linkage analysis show great promise in robustly identifying genomic regions associated with the development of obesity and diabetes. RNA-based technologies such as cDNA microarrays have identified many genes differentially expressed in tissues of healthy and diseased subjects. Using a combined approach, we are endeavouring to focus attention on differentially expressed genes located in chromosomal regions previously linked with obesity and / or diabetes. Using this strategy, we have identified Beacon as a potential new target for obesity and diabetes.

Keywords: obesity, diabetes, genetics, linkage, quantitative trait loci, gene expression, microarrays

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