Genetic Architecture of Adiposity in Multiple Large Cohorts

描述（由申请人提供）：肥胖的患病率持续上升，伴随其代谢后果，包括糖尿病、血脂异常、高血压、脂肪肝、心脏病和许多其他疾病。清楚地了解肥胖的遗传结构及其相关的代谢特征可以确定干预的重要目标，无论是行为还是药物。在过去的4年里，我们的项目在确定与肥胖、区域脂肪分布和异位脂肪相关的数百种常见变异方面取得了重大进展。确定与身体活动、吸烟、性别和年龄的相互作用；鉴定与代谢性状相关结构的多效位点；以及重要途径、组织特异性和预测细胞/有机体功能的生物信息学鉴定。在这个更新的申请中，我们建议继续扩大我们对肥胖特征的遗传基础的理解，特别是身体质量指数（BMI），集中肥胖的测量（腰臀比调整BMI (WHRaBMI)）和CT评估腹部脂肪体积，重点关注全外显子组和全基因组测序测量的罕见变异，对我们的发现进行详细的生物信息学注释，包括预测的功能意义，调节功能，使用公开可用的知识数据库和途径，并利用我们与ENCODE研究者的合作。最后，我们建议在肥胖和饮食诱导糖尿病的果蝇模型中对我们在人类中的发现进行功能定位和评估。我们将通过评估这些区域的功能元件（基因、调控位点）的敲除和敲除对果蝇肥胖和代谢表型的影响，来询问gwas鉴定的基因组区域。这种功能定位将识别影响肥胖特征的相关区域的基因，为人类序列资源的研究提供基因靶点。我们的运作基础是在CHARGE财团内，拥有其优秀的资源和研究人员，以及我们与其他财团的合作，特别是GIANT。这些强大的发现、注释和筛选功能意义的方法将使我们能够扩展我们对肥胖遗传结构的知识和理解，并有可能确定适合药物或行为干预的途径/目标。

DESCRIPTION (provided by applicant): The prevalence of obesity continues to rise, along with its metabolic consequences including diabetes, dyslipidemia, hypertension, fatty liver disease, heart disease, and a host of other morbidities. A clear understanding of the genetic architecture of adiposity and its correlated metabolic traits can identify important targets for intervention, either behavioral or pharmaceutical. Significant progress was achieved in the last 4 years of our project in identifying hundreds of common variants associated with adiposity, regional fat distribution, and ectopic fat across 3 major ethnicities; identifying interactions wit physical activity, smoking, gender, and age; identifying pleiotropic loci accounting for the correlated architecture with metabolic traits; and bioinformatic identification of important pathways, tissue specificities, and predicted cellular / organismal functions. In this renewal application, we propose to continue to expand our understanding of the genetic underpinnings of adiposity traits, specifically, body mass index (BMI), measures of centralized obesity (waist- to-hip ratio adjusted for BMI (WHRaBMI) and CT assessed abdominal fat volumes by focusing on rare variation measured by whole exome and whole genome sequencing, carrying out detailed bioinformatic annotation of our findings including predicted functional significance, regulatory function, and pathways using publicly available knowledge databases, and leveraging our collaboration with ENCODE investigators. Finally, we propose to carry out functional mapping and evaluation of our discoveries in humans in a Drosophila model of adiposity and diet-induced diabetes. We will interrogate GWAS-identified genomic regions, by assessing the effect of knock-downs and knock-outs of functional elements (genes, regulatory loci) in those regions on Drosophila adiposity and metabolic phenotypes. This functional mapping will identify genes in the regions of association that influence adiposity traits, providing gene targets for investigation in the human sequence resource. Our basis of operation is within the CHARGE consortium with its outstanding resources and investigators, and with our established collaboration with other consortia, in particular, GIANT. These powerful approaches for discovery, annotation, and screening for functional significance will allow us to expand our knowledge and understanding of the genetic architecture of obesity with the potential to identify pathways / targets amenable to pharmaceutical or behavioral intervention.