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

基本信息

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
项目类别：
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

项目摘要

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.

概括 2019年，约有1亿美国人肥胖，加剧了与肥胖相关的发病率，死亡率和健康的增加护理费用主要来自心脏代谢疾病（CMD）。大规模的遗传研究已经奠定了基础对于许多对疾病发病机理的下游调查以及将此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）大大具有提高了我们对肥胖相关特征的遗传结构的理解。这些研究的潜力发现结果不能被夸大，以阐明肥胖症的生物学或病理生理基础它的昂贵病情。尽管公共变体上的GWA在识别> 1,000个信号方面取得了长足的进步对于与肥胖相关的特征，这些研究本质上受到限制，而没有进一步转化为更可行的发现。在此提案中，我们将缩小关联信号和绘制因果基因和途径的缩小已知的肥胖风险基因座通过应用创新方法来整合多种OMIC（基因组学，表观基因组学，转录组学和代谢组学）。此外，我们将探讨与电子健康记录相关的大生物库中的肥胖易感性变异，基因和途径（EHR）验证预期的表型关联并揭示新的表型关联。最后，我们将进行在生理相关细胞中关键变异和基因的体外功能研究，以揭示推定的调节变体的机制和对代谢产物的影响，因此对肥胖至关重要的基本机制发病。因此，在此提案中，我们利用在同伙中进行心脏和衰老研究的合作基因组流行病学（电荷），精密医学的跨性跨科学（Topmed）程序，基因组测序项目（GSP）和Geisinger MyCode社区健康计划的EHR数据库研究（Mimode）以缩小GWAS信号的基因，进行临床表征，并进行体外功能研究以表征分子基础和生物学肥胖风险基因座的机制。我们的方法将实质上将田野远离标签变体和基因座因果变异，基因和机制。我们预计这项工作将产生基本和重要洞悉肥胖的潜在病因，并最终指向预防和治疗。