Expression-Based Empirical Candidate Genes Influencing Body Mass Index

基于表达的影响体重指数的经验候选基因

• 批准号: 7737468
• 负责人: JOANNE E. CURRAN
• 金额: $ 85.35万
• 依托单位: TEXAS BIOMEDICAL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7737468
• 关键词: Arthritis; Body mass index; Candidate Disease Gene; Chronic Disease; Cis-Acting Sequence; Coronary heart disease; DNA Resequencing; Data; Data Set; Deposition; Development; Diabetes Mellitus; Disease; Disease susceptibility; Economic Burden; Energy Intake; Enrollment; Environmental Risk Factor; Epidemic; Epidemiologic Studies; European; Exhibits; Family; Gallbladder; Gene Expression; Genes; Genetic; Genetic Transcription; Genotype; Habits; Health; Heart; Human; Hypertension; Individual; Knowledge; Life Style; Longevity; Lymphocyte; Malignant Neoplasms; Measures; Metabolic; Mexican Americans; Molecular; Morbidity - disease rate; Nucleotides; Obesity; Overweight; Participant; Pathogenesis; Patients; Phenotype; Population; Predisposition; Probability; Promoter Regions; Public Health; Regulation; Resources; Risk; Risk Factors; Sampling; Sampling Studies; Stroke; Testing; Transcript; United States; Variant; Wisconsin; abdominal fat; base; cohort; evidence base; gene discovery; genetic linkage analysis; genetic variant; genome-wide; novel; novel strategies; novel therapeutic intervention; obesity risk; pandemic disease; promoter; public health relevance; sedentary; trait; waist circumference

DESCRIPTION (provided by applicant): The escalating obesity epidemic is now one of the most significant threats to human health in the 21st century. In this project, we will employ a novel approach to the empirical identification of novel candidate genes influencing body mass index (BMI), in a large family based study. Using genome-wide transcriptional profiles obtained from lymphocyte samples from 1,240 San Antonio Family Heart Study participants, we identified over 1,400 transcripts that exhibit highly significant evidence for cis-regulation as inferred from quantitative linkage analysis. Each of these cis-regulated transcripts has been examined for association with BMI, with 247 transcripts showing evidence for significant correlations of quantitative gene expression levels and BMI, a major risk factor for obesity. This project will exploit this genome-wide expression-based information to rapidly identify regulatory sequence variants that influence transcriptional levels of these novel candidate genes and to assess their influence on BMI and waist circumference, as obesity risk predictors. Using this unique family-based resource of quantitative genome-wide transcriptional profiles, we will empirically nominate and examine 100 novel candidate genes that exhibit both strong evidence for cis-regulation of expression levels and significant correlations between expression levels and BMI. Our prior linkage-based evidence for cis-acting sequence variation can be exploited as a probabilistic causal anchor that should maximize our chance for finding functional variation within proximal promoters. For each of these objectively chosen genes, we will: 1) resequence approximately two kilobases of putative promoter, upstream of transcription start, in 182 founder individuals to identify promoter variants; 2) genotype all detected promoter variation in each of the 100 candidate genes in the 1,240 SAFHS samples for whom we have transcriptional profiles; 3) test whether promoter sequence variants are associated with gene expression levels of the appropriate candidate gene; 4) test for associations between promoter sequence variants, BMI and waist circumference; 5) confirm observed associations with BMI and/or waist circumference in two independent sample populations and 6) perform preliminary functional analyses of promoter variants influencing BMI and/or waist circumference. Obesity has reached pandemic proportions in the United States and throughout the world, with an estimated economic burden of approximately $93 billion per year in the United States alone. The results of this project should increase the pace of discovery of novel genes underlying human variation in BMI; a task that has been somewhat slow and unsuccessful to date. By focusing on genes whose transcripts show evidence for both cis- regulatory variation and a strong relationship with BMI, we should maximize our probability for finding causal genetic variants influencing obesity risk. PUBLIC HEALTH RELEVANCE: The estimated economic burden of overweight and obesity in the United States alone is approximately $93 billion per year, making this disease one of major public health importance. In this project, we will employ a novel strategy that should increase the pace of discovery of genes that influence body mass index and waist circumference, major indicators of obesity. The knowledge gained will help contribute to the understanding of the genetics of obesity through the identification of novel and potentially functional candidate genes, assisting in the development of new preventative measures and/or therapies.

描述(申请人提供)：不断升级的肥胖流行病现在是21世纪对人类健康的最大威胁之一。在这个项目中，我们将使用一种新的方法来对影响体重指数(BMI)的新候选基因进行经验性识别，这是一项基于大型家庭的研究。使用从1,240名圣安东尼奥家庭心脏研究参与者的淋巴细胞样本中获得的全基因组转录图谱，我们识别了超过1,400个转录本，这些转录本从定量连锁分析中推断出具有高度显著的顺式调控证据。这些顺式调控的转录本中的每一个都被检测出与BMI有关，247个转录本显示出定量基因表达水平与BMI(肥胖的一个主要风险因素)显著相关的证据。该项目将利用这种全基因组表达的信息来快速识别影响这些新候选基因转录水平的调控序列变体，并评估它们对体重指数和腰围的影响，作为肥胖风险预测因素。利用这一独特的基于家族的定量全基因组转录图谱资源，我们将经验性地提名和检验100个新的候选基因，这些基因既显示出表达水平的顺式调控的强有力证据，也显示出表达水平与BMI之间的显著相关性。我们先前基于连锁的顺式作用序列变异的证据可以被用作概率因果锚，这应该会最大限度地增加我们在近端启动子中发现功能变异的机会。对于这些客观选择的基因中的每一个，我们将：1)对182名创始人中大约2个碱基的假定启动子进行重测序，以确定启动子变体；2)在我们拥有转录谱的1,240个SAFHS样本中，对所有检测到的候选基因的启动子变异进行分型；3)测试启动子序列变异是否与适当候选基因的基因表达水平相关；4)测试启动子序列变体与BMI和/或腰围之间的关联；5)确认在两个独立样本群体中观察到的与BMI和/或腰围的关联；6)对影响体重指数和/或腰围的启动子变体进行初步的功能分析。肥胖症在美国和全世界已经达到了流行病的程度，仅在美国，估计每年的经济负担就约为930亿美元。这个项目的结果应该会加快发现人类BMI变异背后的新基因的速度；这项任务到目前为止一直有些缓慢和不成功。通过关注那些转录结果显示顺式调节变异和与BMI密切相关的基因，我们应该最大限度地发现影响肥胖风险的因果遗传变量。 与公共卫生相关：仅在美国，超重和肥胖造成的经济负担估计就约为每年930亿美元，使这种疾病成为主要的公共卫生问题之一。在这个项目中，我们将采用一种新的策略，加快发现影响体重指数和腰围的基因的速度，这些基因是肥胖的主要指标。所获得的知识将有助于通过识别新的和潜在的功能候选基因来帮助理解肥胖的遗传学，帮助开发新的预防措施和/或治疗方法。