Genetic Architecture of Adiposity in Multiple Large Cohorts

多个大群体中肥胖的遗传结构

• 批准号: 8774098
• 负责人: Thomas John Baranski
• 金额: $ 73.67万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-10 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8774098
• 关键词: Accounting; African American; Age; Aging; Architecture; Behavior Therapy; Behavioral; Bioinformatics; Biological; Biological Models; Biology; Body mass index; Cohort Studies; Collaborations; Data; Databases; Development; Diabetes Mellitus; Diet; Drosophila genus; Drosophila melanogaster; Dyslipidemias; Elements; Epidemiology; Ethnic Origin; European; Evaluation; Family Study; Fatty Liver; Fatty acid glycerol esters; Functional RNA; Funding; Gender; Gene Expression; Gene Targeting; Genes; Genetic; Genomics; Haplotypes; Heart; Heart Diseases; Human; Human Gene Mapping; Hypertension; Intervention; Investigation; Knowledge; Lead; Link; Liver diseases; Maps; Measures; Metabolic; Metabolic Diseases; Modeling; Morbid Obesity; Morbidity - disease rate; Obesity; Orthologous Gene; Parents; Participant; Pathway interactions; Pharmacologic Substance; Phenotype; Physical activity; Population; Predisposing Factor; Prevalence; Quantitative Trait Loci; Regulator Genes; Research; Research Personnel; Resources; Role; Sample Size; Sampling; Sex Functioning; Smoking; Specificity; Staging; System; Testing; Tissues; Variant; Waist-Hip Ratio; Wit; Work; abdominal fat; base; cohort; exome; exome sequencing; fly; follow-up; genetic analysis; genetic epidemiology; genetic linkage analysis; genome sequencing; genome wide association study; innovation; insight; knock-down; novel; operation; population based; public health relevance; rare variant; screening; sex; stem; tool; trait

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.

描述（由申请人提供）：肥胖症的患病率持续上升沿着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干预的重要目标，无论是行为还是药物。在我们项目的过去4年中，在识别3个主要种族中与肥胖、局部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;识别与体育活动、吸烟、性别和年龄的相互作用;识别多效性基因座解释与代谢特征相关的结构;以及重要途径、组织特异性和预测的细胞/有机体功能的生物信息学鉴定。在这次更新申请中，我们建议继续扩大我们对肥胖性状遗传基础的理解，特别是体重指数（BMI），集中性肥胖的衡量标准（针对BMI调整的腰臀比（WHRaBMI）和CT通过关注通过全外显子组和全基因组测序测量的罕见变异来评估腹部脂肪体积，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，以及使用公共知识数据库的途径，并利用我们与ENCODE研究人员的合作。最后，我们建议在肥胖和饮食诱导的糖尿病的果蝇模型中对我们在人类中的发现进行功能映射和评估。我们将询问GWAS鉴定的基因组区域，通过评估这些区域中的功能元件（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。这种功能定位将确定影响肥胖性状的相关基因，为人类序列资源的研究提供基因靶点。我们的业务基础是在CHARGE财团内，该财团拥有出色的资源和调查人员，并与其他财团，特别是GIANT建立了合作关系。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/靶点。