Genetic Architecture of Adiposity in Multiple Large Cohorts

描述(申请人提供)：肥胖症的患病率持续上升，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。清楚地了解肥胖症的遗传结构及其相关的代谢特征可以确定重要的干预目标，无论是行为干预还是药物干预。在我们项目的最后4年中，在以下方面取得了重大进展：识别与肥胖、区域脂肪分布和异位脂肪有关的数百个常见变异；识别与体力活动、吸烟、性别和年龄的交互作用；识别与代谢特征相关的结构的多效性基因座；以及重要途径、组织特异性和预测的细胞/器官功能的生物信息学识别。在这一更新应用中，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)、集中式肥胖测量(根据BMI调整的腰臀比)和CT评估腹部脂肪体积，方法是专注于通过全外显子组和全基因组测序测量的罕见变异，利用公开可用的知识数据库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、调节功能和通路，并利用我们与ENCODE研究人员的合作。最后，我们建议在果蝇肥胖和饮食诱导的糖尿病模型中对我们在人类中的发现进行功能图谱和评估。我们将通过评估这些区域中功能元件(基因、调控基因)的敲除和敲除对果蝇肥胖和代谢表型的影响来询问GWAS识别的基因组区域。这一功能图谱将识别影响肥胖性状的关联区基因，为人类序列资源的研究提供基因靶点。我们的运营基础是在Charge财团内部，拥有出色的资源和调查人员，并与其他财团，特别是巨人财团建立了合作。这些发现、注释和筛选功能重要性的强大方法将使我们能够扩大我们对肥胖症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/靶点。

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.