Mapping Adiposity QTLS in the NHLBI Family Heart Study

描述（由申请人提供）：肥胖已成为一种全国性的流行病。我们建议寻找与较高BMI相关的DNA多态性，以确定与肥胖有关的基因。该应用程序基于NHLBI家族心脏研究（FHS）对718个家庭的4，211名受试者进行的BMI基因组扫描。我们将通过在染色体13 q14（lod=3.2）上最大的连锁峰之一下的不平衡作图来扩展这项工作。我们将把工作重点放在对联系支助贡献最大的144个家庭（715人）上。我们将通过对上位效应、性别效应和可能的多效性效应进行统计建模来精确定位潜在的QTL。将通过SNP分型来评估落在连锁区域中的一系列候选基因，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域。我们将开始与三个动机良好的候选基因-HRT 2A，EDNRB和LMO 7-我们已经确定了12个其他使用生物信息学资源。在一个相关的目标中，我们还将使用病例对照样本，包括研究中BMI最高和最低的受试者，检测位于其他暗示连锁区域（1.5<1 od <2.0）的肥胖候选基因。将对两组各350例年龄为40-70岁的无关受试者进行基因分型，两组受试者平均分布在4个田间中心，男女各半。我们将检测脂联素和apoD（3q 29）、PPARgamma（3 p25）和ADRB 2（5 q31）。将使用各种统计建模技术，包括单倍型分析，分层连锁不平衡映射纳入信息块结构，和贝叶斯方法来评估所有可能的单倍型在一个区域内。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的经验-研究设计，基因分型和生物信息学，统计模型开发和分析-将导致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.