? ? The dramatic rise in obesity and diabetes has fueled interest in understanding the influence of environmental factors on triglyceride (TG) concentrations. For example, the influence of a high fat meal on plasma TGs is dramatic and variable. Similarly, while drugs such as fenofibrate are, on average, efficacious for treatment of elevated TGs, therapeutic response varies considerably among individuals. Genetic variation is reported to play a role in both of these variable responses, although the specific genetic basis is not well understood. The goal of the proposed research is to identify genetic loci that determine gene-environment interactions that predict triglyceride response to two interventions, one to lower TGs (fenofibrate) and one to raise TGs (intake of a high-fat meal to induce post-prandial lipemia (PPL). In the current phase of this project, The Genetics of Lipid-lowering and Diet Network (GOLDN) study recruited and examined family members from pedigrees in two ethnically-homogenous field centers in Minnesota and Utah. Building on the work done to date, we will conduct the proposed study within a well characterized trial cohort. Specifically, we will: (1) Conduct genome-wide linkage analysis to identify QTLs for the response of TGs and TG-related phenotypes to fenofibrate, a fat meal, and a fat meal in the context of fenofibrate treatment. A large phenotypic profile was collected on all subjects, including NMR measures of particle size, RBC fatty acids, insulin, glucose, adiponectin, and inflammatory markers (CRP, TNFa, MCP1, IL2SR, IL6). Linkage analysis for the intervention phenotypes will be implemented using a mixed model approach implemented in a variance components model. (2) Characterize 2 linkage regions using gene-based dense SNP mapping and linkage disequilibrium methods. We will use linear mixed models with structured association tests to control for population stratification and FDR to correct for multiple testing. We will identify functionally relevant positional candidate genes within linkage peaks, and we will select the most relevant candidate genes for genotyping. (3) Conduct epidemiological and candidate gene association analyses to exploit the rich phenotypic resources of the GOLDN study. We will extend our statistical methods in combinatorial partitioning and neural networks to identify combinations of genes and/or environments that predict variation in response to fat intake and fenofibrate. (4) Replicate genetic associations for post-prandial lipemia measures identified in the HAPI-HEART Study, and serve as a genotyping lab resource to replicate findings from other PROGENI networks. Future studies will extend these discoveries by examination of the functional relevance of SNPs that are associated with TG-lowering effects of fenofibrate or TG-raising effects of a high-fat meal. Genotypic characterization of individuals who respond favorably to fenofibrate may enable targeted interventions to reduce TGs and identify the most effective treatments in clinical practice. ? ? ?
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