Candidate Gene Studies of Obesity Guided by Whole Genome Association Data

全基因组关联数据指导的肥胖候选基因研究

• 批准号: 7628614
• 负责人: JOEL N HIRSCHHORN
• 金额: $ 61.12万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-08 至 2012-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7628614
• 关键词: Adult; Affect; African American; Alleles; Behavior; Body mass index; Candidate Disease Gene; Cardiovascular Diseases; Cessation of life; DNA Resequencing; Data; Diabetes Mellitus; Disease; Energy Intake; Epidemic; Ethnic group; Family; Follow-Up Studies; Future; Genes; Genetic; Genetic Variation; Genome; Genomics; Genotype; Human; Individual; Knowledge; Maps; Measures; Methods; Obesity; Obesity associated disease; Outcome; Pathogenesis; Pathway interactions; Phenotype; Population; Prevention therapy; Research Personnel; Risk; SNP genotyping; Sampling; Scanning; Screening procedure; Signal Transduction; Solutions; Staging; Sterols; Stratification; Surveys; Susceptibility Gene; Testing; Transcription Coactivator; Validation; Variant; base; cohort; follow-up; gene environment interaction; genetic epidemiology; genetic variant; genome wide association study; genome-wide; meetings; obesity risk; sedentary; tool; trend

DESCRIPTION (provided by applicant): The obesity epidemic in the U.S. continues unabated, and is accompanied by substantial complications, including diabetes, cardiovascular disease, and death. Obesity is strongly influenced by sequence variation in as yet unknown susceptibility genes. Whole genome association scans offer a potentially powerful method for identifying common variants with modest effects on obesity, but the deluge of data from these studies will require careful analysis and rigorous follow-up. The first whole genome association studies are now being performed in large samples in which body mass index has been measured, and we will have access to whole genome association data from 3,000 individuals with measures of obesity (body mass index, BMI). We hypothesize that these studies will identify many potential associations between genetic variants and obesity, representing a mix of largely false positive results interspersed with a smaller number of true associations. It will be critical to perform appropriate and rigorous follow-up to distinguish the true causal variants from the false leads. The combination of multiple large well-characterized cohorts with measures of obesity, high throughput genotyping and robust analytic methods will permit us to rapidly follow up the preliminary results from the whole genome association scans. Specifically, we will be able to extract from the mass of data those genetic variants that are truly associated with obesity, even if the effects are modest. This will lay the groundwork for future studies of the phenotypic consequences of these variants on other obesity-related phenotypes and the risks of complications such as diabetes and cardiovascular disease, and for studies of gene-gene and gene-environment interactions. Successful identification of genes that are convincingly associated with obesity would highlight key pathways that influence obesity in humans, guiding efforts at therapy and prevention.

描述(申请人提供)：肥胖症在美国的流行有增无减，并伴随着严重的并发症，包括糖尿病、心血管疾病和死亡。肥胖很大程度上受到未知易感基因序列变异的影响。全基因组关联扫描为识别对肥胖影响不大的常见变异提供了一种潜在的强大方法，但来自这些研究的大量数据将需要仔细分析和严格的后续行动。第一个全基因组关联研究现在正在大样本中进行，其中已经测量了体重指数，我们将获得3000个测量肥胖(身体质量指数，BMI)的个体的全基因组关联数据。我们假设，这些研究将确定基因变异和肥胖之间的许多潜在联系，表现为主要是假阳性结果的混合，夹杂着少量的真实联系。关键是要进行适当和严格的跟踪，以区分真正的因果变异和错误的线索。将多个大型特征良好的队列与肥胖测量、高通量基因分型和稳健的分析方法相结合，将使我们能够快速跟进全基因组关联扫描的初步结果。具体地说，我们将能够从海量数据中提取出真正与肥胖相关的基因变异，即使影响不大。这将为未来研究这些变异对其他肥胖相关表型的表型影响以及糖尿病和心血管疾病等并发症的风险，以及基因-基因和基因-环境相互作用的研究奠定基础。成功识别出令人信服的与肥胖相关的基因将突出影响人类肥胖的关键途径，指导治疗和预防工作。