PLEIOTROPIC EFFECTS ON OBESITY AND LIPOPROTEINS

肥胖是动脉粥样硬化的重要危险因素。然而，这些疾病之间的关系的原因仍然知之甚少。来自身体成分研究的证据表明，肥胖症与几项重要的内分泌指标高度相关，包括与葡萄糖和脂蛋白代谢相关的表型。关于影响肥胖的基因及其对这些内分泌参数的多效性影响以及与动脉粥样硬化相关的危险因素，如脂蛋白表型，我们知之甚少。在这个项目中，我们将测量几种与肥胖相关的表型，包括全身脂肪(使用 生物阻抗)，以及几种脂肪细胞衍生的内分泌因子(例如，瘦素、脂联素、酰化刺激蛋白和肿瘤坏死因子α)的血清浓度。为了更好地研究可变基因表达的潜在遗传决定因素，我们还将继续测量大网膜脂肪组织中几个候选基因(瘦素、脂蛋白脂酶和葡萄糖转运蛋白4基因)以及另外三个基因(瘦素受体、脂联素和抵抗素基因)的定量mRNA水平。我们将检测并本地化 影响肥胖相关表型的数量性状基因座(QTL)及其对脂蛋白性状和基因多效性影响的检验假说？年龄互动。数量性状基因座的定位将通过使用候选基因和STR多态的基因组筛选来完成，这些基因和STR多态来自于750只非近交系的单个家系。统计关联分析将使用多点方差分量法进行，该方法有效地利用了所有可用信息，我们已将其扩展以适应 近交系的并发症。当找到一个数量性状基因座时，我们将利用多变量连锁分析来确定肥胖相关基因是否对脂蛋白表型具有多效性效应。最后，我们将尝试在有希望的QTL区域中发现位置较强的候选基因，并将使用连锁/不平衡分析和一种新的贝叶斯数量性状核苷酸分析方法来评估这些基因的多态是否可以解释观察到的连锁信号。

Obesity is an important risk factor for atherosclerosis. However, the reasons for the relationship between these disorders are still poorly understood. Evidence from body composition studies suggests that adiposity is highly correlated with several important endocrine measures including phenotypes related to glucose and lipoprotein metabolism. Little is known regarding the genes that influence adiposity and their pleiotropic effects on these endocrine parameters and on correlated risk factors for atherosclerosis such as lipoprotein phenotypes. In this Project, we will measure several adiposity-related phenotypes including total body fat (estimated using bioimpedance), and serum concentrations of several adipocyte derived endocrine factors (e.g., leptin, adiponectin, acylation stimulating protein, and TNFalpha) in pedigreed baboons. To better examine the underlying genetic determinants of variable gene expression, we will also continue to measure quantitative mRNA levels of several candidate genes (the leptin, lipoprotein lipase, and glucose transporter 4 genes) as well as three additional genes (the leptin receptor, adiponectin, and resistin genes) in biopsied omental fat tissue. We will detect and localize quantitative trait loci (QTLs) influencing adiposity-related phenotypes and test hypotheses regarding their pleiotropic effects on lipoprotein traits and genotype ? age interaction. Localization of quantitative trait loci will be accomplished via a genomic screen using candidate gene and STR polymorphisms in a single pedigree of 750 non-inbred baboons. Statistical linkage analyses will be performed using the multipoint variance component method which makes efficient use of all available information and which we have extended to accommodate the complications of inbred pedigrees. When a quantitative trait locus is found, we will utilize multivariate linkage analysis to determine if adiposity-related genes have pleiotropic effects on lipoprotein phenotypes. Finally we will attempt to identify strong positional candidate genes in the regions of promising QTLs and will use combined linkage/disequilibrium analyses and a novel Bayesian quantitative trait nucleotide analysis method to assess whether polymorphisms in these genes account for the observed linkage signal.