Mapping Adiposity QTLS in the NHLBI Family Heart Study

描述(申请人提供)：肥胖已成为全国性的流行病。我们建议寻找与较高BMI相关的DNA多态性，以确定与肥胖相关的基因。这一应用建立在NHLBI家庭心脏研究(FHS)对718个家庭的4211名受试者进行的BMI基因组扫描的基础上。我们将通过在染色体13q14(LOD=3.2)上最大的连锁峰之一下进行不平衡作图来扩展这项工作。集中力量对接支持力度最大的144个家庭(715名个人)。我们将通过对上位性效应、性别效应和可能的多效性效应进行统计建模来精炼潜在QTL的位置。一系列落在连锁区域的候选基因将通过SNP分型进行评估，如果没有发现关联，则将不平衡作图技术应用于连锁峰下的匿名区。我们将从三个动机良好的候选基因开始-HRT2A、EDNRB和LMO7-我们已经利用生物信息学资源确定了另外12个基因。在一个相关的目标中，我们还将使用病例对照样本，测试位于暗示连锁的其他区域(1.5；10d；2.0)的肥胖候选基因，该样本由研究中体重指数最高和最低的受试者组成。两组各350名无关受试者，平均分布在4个领域中心和2个性别，年龄在40-70岁之间，将进行基因分型。我们将检测脂联素和APOD(3q29)、PPARGamma(3p25)和ADRB2(5q31)。将使用各种统计建模技术，包括单倍型分析、结合区块结构信息的层级连锁不平衡作图，以及评估区域内所有可能的单倍型的贝叶斯方法。我们相信，这里提出的最先进的方法，以及研究团队在所有相关领域--研究设计、基因分型和生物信息学，以及统计模型开发和分析--的经验，将导致对影响人类肥胖的基因和特定变异的新发现。

DESCRIPTION (provided by applicant): Obesity has become a national epidemic. We propose to seek DNA polymorphisms associated with higher BMI, to identify genes involved in obesity. This application builds upon a genome scan for BMI performed in the NHLBI Family Heart Study (FHS) on 4,211 subjects in 718 families. We will extend this work by disequilibrium mapping under one of the largest linkage peaks on chromosome 13q 14 (lod=3.2). We will focus our efforts on 144 families (715 individuals) with the highest contribution to the support for the linkage. We will refine the location of the underlying QTL by statistically modeling epistatic effects, sex effects, and possible pleiotropic effects. A series of candidate genes falling in the linkage region will be evaluated by SNP-typing, and if no associations are found, disequilibrium mapping techniques will be applied to the anonymous region under the linkage peak. We will begin with three well-motivated candidate genes - HRT2A, EDNRB, and LMO7 - and we have identified 12 others using bioinformatics resources. In a related aim, we will also test obesity candidate genes located in other regions of suggestive linkage (1.5<1od<2.0), using a case-control sample, comprised of subjects with the highest and lowest BMIs in the study. Two groups of 350 unrelated subjects each, equally distributed over the 4 field centers and 2 sexes, aged 40-70 years, will be genotyped. We will test adiponectin and apoD (3q29), PPARgamma (3p25), and ADRB2 (5q31). A variety of statistical modeling techniques will be used including haplotype analysis, hierarchical linkage disequilibrium mapping incorporating information on block structure, and a Bayesian approach to evaluating all possible haplotypes within a region. We believe that the state-of-the-art approaches proposed here, as well as the experience of the investigative team in all relevant realms - study design, genotyping and bioinformatics, and statistical model development and analysis - will lead to new discoveries of the genes and specific variants influencing human obesity.