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Genetic Variation in the Uncoupling Protein and Fatty Acid Binding Protein Gene Families: A Multi-Locus Approach to Investigating Obesity and Type 2 Diabetes

Obesity and type 2 diabetes are heterogeneous conditions caused by a combination of genetic and environmental factors. A number of candidate gene families have been identified that influence obesity- and diabetes-related traits, including the uncoupling proteins (UCPs) and fatty acids binding proteins (FABPs). The UCPs are mitochondrial transport proteins that promote proton leakage across the inner mitochondrial membrane, uncoupling oxidative phosphorylation from ATP production and releasing energy as heat. The FABPs are intracellular transporters of fatty acids that facilitate lipid metabolism and gene transcription regulation. The UCPs and FABPs influence energy metabolism, fuel substrate partitioning, glucose and lipid metabolism, and insulin action. In this study, we identified variation in UCP and FABP genes, explored the influence of that variation on phenotype through multi-locus analyses, and assayed the functional consequences of promoter variation on gene expression. Using the multi-locus analysis approach, we constructed regression models that explained a relatively large portion of the variation in phenotypes in comparison to the individual effects of single loci. Several of the models explained upwards of 10% of the variation in traits. This suggests that a multi-locus approach to studying complex disease is much more informative than considering single loci individually. In addition to the statistical analyses, functional studies were performed to assess the effects of promoter variation on gene expression. Variation in the FABP2 promoter region was associated with levels of promoter activity, suggesting a biological explanation for effects of this polymorphism on phenotype. This exploratory analysis identified a number of interesting multi-locus genetic effects on traits related to obesity and type 2 diabetes, suggesting that consideration of multiple gene effects is a more comprehensive approach to understanding complex disease.

Identiferoai:union.ndltd.org:PITT/oai:PITTETD:etd-04242002-111809
Date29 April 2002
CreatorsDamcott, Coleen Mae
ContributorsMichael Barmada, Eleanor Feingold, David Finegold, Robert Ferrell
PublisherUniversity of Pittsburgh
Source SetsUniversity of Pittsburgh
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.library.pitt.edu:80/ETD/available/etd-04242002-111809/
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