Integrative Approaches to Identifying Function and Clinical Significance of Adiposity Susceptibility Genes

摘要 2019年，约有1亿美国人肥胖，这加剧了与肥胖相关的发病率、死亡率和健康水平的上升 护理费用，主要来自心脏代谢性疾病(CMD)。大规模的基因研究奠定了基础 对于疾病发病机制的许多下游研究以及将这些信息转化为 公共卫生应用。在过去的十年中，全基因组关联研究(GWAS)在很大程度上 提高了我们对肥胖相关特征的遗传结构的理解。这些研究的潜力 为了阐明肥胖的生物学或病理生理学基础，研究结果怎么夸大都不为过。 其代价高昂的病态。尽管通用变体上的GWA在识别&gt；1000信号方面取得了很大进展 对于肥胖相关的特征，这些研究本身就是有限的，没有进一步转化为更具可操作性的研究 调查结果。在这项提议中，我们将缩小关联信号，并绘制出因果基因和潜在的路径 通过应用创新方法集成多个组学(基因组学， 表观基因组学、转录组学和代谢组学)。此外，我们还将探讨其临床相关性。 与电子健康记录(EHR)相关的大型生物库中的肥胖易感性变异、基因和途径 验证预期的表型关联并揭示新的表型关联。最后，我们将进行 生理相关细胞中关键变异和基因的体外功能研究揭示可能的调控 变异和对代谢物的影响的机制以及对肥胖至关重要的潜在机制 发病机制。因此，在这项提案中，我们利用了心脏和衰老研究队列中的合作 基因组流行病学(CHARE)，精准医学转组学(TOPMed)计划，基因组 测序项目(GSP)和盖辛格MyCODE社区卫生倡议的EHR数据库 研究(MyCode)以缩小Gwas信号的基因范围，执行临床特征，以及 进行体外功能研究，以确定分子基础和生物学特性 肥胖风险基因座的机制。我们的方法将在很大程度上将领域从标签变体和位置中转移出来 到因果变异、基因和机制。我们预计，这项工作将产生根本性和重要的 深入了解肥胖的根本原因，并最终为预防和治疗肥胖指明方向 治疗。

SUMMARY In 2019, ~100 million Americans were obese, fueling increases in obesity-related morbidity, mortality, and health care costs, largely from cardiometabolic diseases (CMD). Large scale genetic studies have laid the foundation for many downstream investigations into the pathogenesis of disease and the translation of this information into public health applications. Over the last decade genome-wide association studies (GWAS) have substantially improved our understanding of the genetic architecture of obesity related traits. The potential of these study findings cannot be overstated for elucidating the biological or pathophysiological underpinnings of obesity and its costly morbidities. Although GWAS on common variants have made strides in identifying > 1,000 signals for obesity related traits, these studies are inherently limited without further translation into more actionable findings. In this proposal, we will narrow association signals and map causal genes and pathways underlying known obesity risk loci by applying innovative methods to integrate multiple OMICs (genOMICs, epigenOMICs, transcriptOMICs and metabolOMICs). Additionally, we will explore the clinical relevance of obesity susceptibility variants, genes, and pathways in a large BioBank linked to electronic health records (EHR) to validate expected phenotypic associations and reveal novel phenotypic associations. Finally, we will conduct in vitro functional studies of key variants and genes in physiologically relevant cells to reveal putative regulatory mechanisms of variants and effects on metabolites and thus the underlying mechanisms critical to obesity pathogenesis. Thus, in this proposal we leverage collaborations in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), TransOMICs for Precision Medicine (TOPMed) Program, the Genome Sequencing Project (GSP), and the EHR database from the Geisinger MyCODE Community Health Initiative study (MyCode) to narrow in on genes underlying GWAS signals, perform clinical characterization, and conduct in vitro functional studies to characterize the molecular underpinnings and biological mechanisms of obesity-risk loci. Our approach will substantially move the field away from tag variants and loci to causal variants, genes, and mechanisms. We anticipate that this work will generate fundamental and important insights into the underlying etiology of obesity and ultimately point the way forward towards prevention and treatment.