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

识别肥胖易感基因的功能和临床意义的综合方法

• 批准号: 10267228
• 负责人: Anne Justice
• 金额: $ 68.4万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-22 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10267228
• 关键词: Adipose tissue; Affect; Aging; American; Biological; Blood; Body mass index; Brain; CRISPR/Cas technology; Candidate Disease Gene; Cardiometabolic Disease; Cells; Clinical; Collaborations; Collection; Community Health; Critical Pathways; DNA Sequence Alteration; Data; Databases; Disease; Electronic Health Record; Etiology; Foundations; Frequencies; Gene Expression; Genes; Genetic; Genetic Predisposition to Disease; Genetic Variation; Genetic study; Genomics; Health; Health Care Costs; Heart; In Vitro; Individual; Investigation; Knowledge; Link; Liver; Maps; Measures; Medical; Meta-Analysis; Methods; Molecular; Morbidity - disease rate; Obesity; Obesity associated disease; Outcome; Participant; Pathogenesis; Pathway interactions; Penetrance; Phenotype; Physiological; Precision therapeutics; Prevention; Procedures; Process; Public Health Applications Research; Regulation; Regulatory Element; Reporting; Research; Role; Scanning; Signal Transduction; Susceptibility Gene; Tissues; Transcript; Translating; Translations; Untranslated RNA; Variant; Waist-Hip Ratio; Work; base; biobank; causal variant; clinically relevant; clinically significant; cohort; cost; disorder risk; efficacy evaluation; epigenomics; exome sequencing; follow-up; genetic architecture; genome editing; genome sequencing; genome wide association study; genome-wide; genomic epidemiology; improved; individualized prevention; innovation; insight; metabolomics; methylomics; mortality; multiple omics; novel; obesity risk; phenome; polygenic risk score; precision medicine; programs; rare variant; risk variant; trait; transcriptomics; waist circumference; whole genome

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.

总结 在2019年，约有1亿美国人肥胖，助长了肥胖相关的发病率，死亡率和健康的增加 护理费用，主要来自心脏代谢疾病（CMD）。大规模的遗传学研究为 对于许多下游研究疾病的发病机制和翻译这些信息， 公共卫生应用。在过去的十年里，全基因组关联研究（GWAS）已经取得了实质性进展 提高了我们对肥胖相关性状的遗传结构的理解。这些研究的潜力 对于阐明肥胖的生物学或病理生理学基础， 其昂贵的发病率。尽管GWAS在识别> 1,000个信号方面取得了长足的进步， 对于肥胖相关的特征，这些研究本身就有局限性，没有进一步转化为更可操作的研究。 调查结果。在这个建议中，我们将缩小关联信号，并绘制因果基因和潜在的途径， 通过应用创新方法整合多种OMIC（基因OMIC， 表观基因OMICs、转录OMICs和代谢OMICs）。此外，我们还将探讨 与电子健康记录（EHR）相关的大型生物银行中的肥胖易感性变体，基因和途径 以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 对生理相关细胞中的关键变体和基因进行体外功能研究，以揭示推定的调控基因。 变异体的机制和对代谢物的影响，从而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（CHARGE），精准医学的transOMIC（TOPMed）计划，基因组 测序项目（GSP），以及Geisinger MyCODE社区健康倡议的EHR数据库 研究（MyCode）以缩小GWAS信号的潜在基因，进行临床表征， 进行体外功能研究，以表征分子基础和生物学特性 肥胖风险基因座的机制。我们的方法将大大地把这个领域从标签变异和基因座转移开 到致病变异基因和机制我们预计，这项工作将产生基本的和重要的 深入了解肥胖的根本病因，并最终指出预防和治疗的方向， 治疗