Genetic and Environmental Determinants of Triglycerides

描述（由申请人提供）： 肥胖症和糖尿病的急剧上升激发了人们对了解环境因素对甘油三酯（TG）浓度影响的兴趣。例如，高脂肪膳食对血浆TG的影响是显著的和可变的。类似地，虽然药物如非诺贝特平均而言对于治疗升高的TG是有效的，但个体之间的治疗反应差异很大。据报道，遗传变异在这两种可变反应中发挥作用，尽管具体的遗传基础还不清楚。这项研究的目的是确定决定基因-环境相互作用的遗传位点，这些基因-环境相互作用预测甘油三酯对两种干预措施的反应，一种是降低TG（非诺贝特），另一种是提高TG（摄入高脂肪膳食以诱导餐后脂血症（PPL））。在该项目的当前阶段，降脂和饮食网络遗传学（GOLDN）研究招募并检查了来自明尼苏达州和犹他州两个种族同质野外中心的家系成员。在迄今为止所做工作的基础上，我们将在一个特征良好的试验队列中进行拟议研究。具体而言，我们将：（1）进行全基因组连锁分析，以确定TG和TG相关表型对非诺贝特、脂肪餐和非诺贝特治疗背景下脂肪餐反应的QTL。收集所有受试者的大量表型特征，包括粒度、RBC脂肪酸、胰岛素、葡萄糖、脂联素和炎症标志物（CRP、TNF α、MCP 1、IL 2SR、IL 6）的NMR测量。干预表型的连锁分析将使用方差分量模型中实施的混合模型方法实施。(2)使用基于基因的密集SNP作图和连锁不平衡方法表征2个连锁区域。我们将使用线性混合模型和结构化关联检验来控制人群分层，并使用FDR来校正多重检验。我们将确定功能相关的连锁峰内的位置候选基因，我们将选择最相关的候选基因进行基因分型。(3)进行流行病学和候选基因关联分析，以利用GOLDN研究的丰富表型资源。我们将扩展我们的组合分区和神经网络的统计方法，以确定基因和/或环境的组合，预测响应脂肪摄入量和非诺贝特的变化。(4)复制HAPI-HEART研究中确定的餐后脂血测量的遗传关联，并作为基因分型实验室资源复制其他PROGENI网络的发现。未来的研究将通过检查与非诺贝特的TG降低作用或高脂餐的TG升高作用相关的SNP的功能相关性来扩展这些发现。对非诺贝特有良好反应的个体的基因型特征可能使有针对性的干预措施能够降低TG并确定临床实践中最有效的治疗方法。

DESCRIPTION (provided by applicant): 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.