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

对于没有安全和长期有效治疗的疾病，如肥胖，人类遗传学提供了一种 不偏不倚地获得可能提供有价值的新治疗假说的生物学见解。全基因组 关联研究已经发现了许多多基因性状的已知和新基因，包括 肥胖。然而，从基因发现转向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异体的簇，相关的基因座 通常既不确定特定的调节元件，也不确定因果基因。此外，人们对此知之甚少。 大多数基因的功能，因此往往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和 因果基因从任何个体的关联中都不明显--但强大的计算方法和大量的 这种方法需要多个相关的基因座才能奏效。对于身高来说，这是一个多基因的典型性状 已知的位点，这种方法突出了许多相关的途径和基因，既已知的，也是新的。类似 当应用于肥胖症的测量时，洞察力才刚刚开始浮现，在这方面已知的情况较少。 基因座和可能没有那么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 从潜在的生物学角度来看，身高方面的进展比肥胖方面的进展更显著。因此， 目前在身高方面的成功表明，大大扩展肥胖的基因发现工作是有希望的。 这项提议旨在实现人类遗传学的承诺，为根部生物学提供批判性的见解 肥胖的原因。它建立在我们在巨人内部成功创建的协作基础设施之上 并用来发现大多数已知与 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 研究人体测量肥胖的尺寸(一个主要的公共卫生问题和未得到满足的医疗需求) 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基础，该提案涵盖了迄今为止最大的基因分型样本收集 组装(多达200万个来自多个祖先的个体)，归因于最先进的参考小组。 人体测量性状的关联分析也将在大的全基因组和整个外显子组中进行 测序数据集(N&>；100,000)，以发现可能具有更大影响和更精确的稀有变体 精确定位因果基因/调控元素。集成了遗传、表达和 表观遗传学数据将以身高的结果为基准，然后应用于识别共享的生物学 跨肥胖相关基因座和跨等位基因谱，提供对可能的原因基因和 机械装置。最后，孟德尔随机化将被用于推断肥胖和肥胖之间的因果关系。 循环代谢物，以确定肥胖的代谢后果以及新的治疗机会。

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.