Biological Insights from Genetic Investigation of ANthropometric Traits (GIANT) Across the Allelic Spectrum

跨等位基因谱的人体测量特征 (GIANT) 遗传研究的生物学见解

• 批准号: 9766263
• 负责人: JOEL N HIRSCHHORN
• 金额: $ 71.51万
• 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 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9766263
• 关键词: Adult; Alleles; Benchmarking; Biological; Biological Factors; Biological Process; Biology; Body mass index; Cardiovascular Diseases; Cessation of life; Childhood; Code; Collection; Computer software; Computing Methodologies; Data; Data Set; Developmental Process; Diabetes Mellitus; Disease; Environment; Epidemic; Epigenetic Process; Frequencies; Funding; Future; Generations; Genes; Genetic; Genetic Variation; Genetic screening method; Genetic study; Genotype; Goals; Growth; Haplotypes; Height; Heritability; Human Genetics; Individual; Infrastructure; Investigation; Knowledge; Light; Malignant Neoplasms; Measures; Medical; Meta-Analysis; Metabolic; Methods; Modeling; Obesity; Pathway interactions; Plant Roots; Polygenic Traits; Predisposition; Public Health; Randomized; Regulatory Element; Resistance; Resources; Route; Sample Size; Sampling; Signal Transduction; Site; Testing; Therapeutic Intervention; Untranslated RNA; Variant; Waist-Hip Ratio; Work; base; causal variant; design; effective therapy; exome; genetic approach; genome wide association study; genome-wide; genomic data; human disease; improved; innovation; insight; novel; novel therapeutics; obesity genetics; obesity risk; rare variant; success; trait; whole genome

For diseases without safe and long-term effective therapies, such as obesity, human genetics offers an unbiased route to biological insights that may provide valuable new therapeutic hypotheses. Genome-wide association studies (GWAS) have implicated both known and novel genes for many polygenic traits, including obesity. However, moving from genetic discovery to biological insight requires overcoming some key hurdles. Because associations from GWAS typically identify clusters of correlated noncoding variants, associated loci typically do not pinpoint either specific regulatory elements or causal genes. In addition, little is known about the function of most genes, so it is often difficult to recognize the biological implications of new discoveries. Fortunately, there is a path forward – considering associated loci in combination can reveal shared biology and causal genes not apparent from any individual association – but powerful computational methods and large numbers of associated loci are needed for this approach to work. For height, a model polygenic trait with many known loci, this approach highlights many relevant pathways and genes, both known and novel. Similar insights have only just begun to emerge when applied to measures of obesity, where there are fewer known loci and likely less well-annotated causal biology. The main goal of these genetic studies is to achieve a clearer view of underlying biology, and progress has been more dramatic for height than for obesity. As such, the current success with height shows the promise for a greatly expanded genetic discovery effort for obesity. This proposal aims to fulfill the promise of human genetics to provide critical insights into the root biological causes of obesity. It builds on the collaborative infrastructure we successfully created within the GIANT consortium and have used to discover most of the common variants known to be associated with anthropometric traits. The work will leverage newly feasible genetic approaches and unprecedented sample sizes to study anthropometric measures of obesity (a major public health problem and unmet medical need) and height (the classical model polygenic trait). To increase the number of genetic discoveries, which is vital to recognizing underlying biology, the proposal encompasses the largest collection of genotyped samples yet assembled (up to 2 million individuals from multiple ancestries), imputed to state-of-the-art reference panels. Association analysis for anthropometric traits will also be performed in large whole genome and whole exome sequence data sets (N>100,000), to discover rare variants that may have larger effects and more precisely pinpoint causal genes/regulatory elements. Computational methods that integrate genetic, expression and epigenetic data will be benchmarked on results from height, and then applied to recognize shared biology across obesity-associated loci and across the allelic spectrum, providing insights into likely causal genes and mechanisms. Finally, Mendelian randomization will be used to infer causal relationships between obesity and circulating metabolites, to define metabolic consequences of obesity as well as new therapeutic opportunities.

对于没有安全和长期有效治疗方法的疾病，如肥胖症，人类遗传学提供了一个 无偏见的路线，生物学的见解，可能提供有价值的新的治疗假设。全基因 关联研究（GWAS）已经暗示了许多多基因性状的已知和新基因，包括 肥胖然而，从基因发现到生物学洞察需要克服一些关键障碍。 因为来自GWAS的关联通常识别相关的非编码变体簇， 通常不能精确定位特定的调控元件或致病基因。此外，人们对 大多数基因的功能，所以它往往是很难认识到新发现的生物学意义。 幸运的是，有一条前进的道路-考虑相关基因座的组合可以揭示共同的生物学， 因果基因不明显从任何个人协会-但强大的计算方法和大 这种方法的工作需要许多相关的基因座。对于身高，一个模型多基因性状与许多 在已知的基因座中，这种方法突出了许多相关的途径和基因，包括已知的和新的。类似 当应用于肥胖的测量时，见解才刚刚开始出现， 基因座和可能不太好注释的因果生物学。这些基因研究的主要目标是实现更清晰的 从基础生物学的角度来看，身高的进步比肥胖更引人注目。因此， 目前在身高方面的成功显示了肥胖症基因发现工作将大大扩展的前景。 这项提案旨在履行人类遗传学的承诺，为根生物学提供关键的见解。 肥胖的原因它建立在我们在GIANT中成功创建的协作基础设施之上 协会，并已用于发现已知与 人体测量特征这项工作将利用新的可行的遗传方法和前所未有的样本 研究肥胖的人体测量（一个主要的公共卫生问题和未满足的医疗需求） 和身高（经典模型多基因性状）。增加基因发现的数量，这对 认识到潜在的生物学，该提案包括迄今为止最大的基因型样本收集 收集（来自多个祖先的多达200万人），估算到最先进的参考面板。 还将在大型全基因组和全外显子组中进行人体测量性状的关联分析 序列数据集（N> 100，000），以发现可能具有更大影响的罕见变异， 查明致病基因/调控元件。整合遗传、表达和 表观遗传数据将以身高为基准，然后应用于识别共同的生物学特征。 跨肥胖相关基因座和跨等位基因谱，提供对可能的因果基因的见解， 机制等最后，将使用孟德尔随机化来推断肥胖和肥胖之间的因果关系。 循环代谢物，以确定肥胖症的代谢后果以及新的治疗机会。