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：
10474778
负责人：
Anne Justice
金额：
$ 10.83万
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/10474778
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 1 亿美国人肥胖，导致肥胖相关发病率、死亡率和健康状况增加护理费用主要来自心脏代谢疾病（CMD）。大规模遗传学研究奠定了基础用于对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）已经取得了显着的成果提高了我们对肥胖相关性状遗传结构的理解。这些研究的潜力对于阐明肥胖和肥胖的生物学或病理生理学基础而言，这一发现无论如何强调都不为过。其昂贵的发病率。尽管常见变体的 GWAS 在识别超过 1,000 个信号方面取得了长足进步对于肥胖相关特征，这些研究本质上是有限的，没有进一步转化为更可行的发现。在本提案中，我们将缩小关联信号范围并绘制因果基因和潜在途径通过应用创新方法整合多个 OMIC（genOMIC、表观基因组、转录组和代谢组）。此外，我们将探讨以下临床相关性：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、基因和通路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异和基因进行体外功能研究，以揭示假定的调控变异的机制和对代谢物的影响，以及对肥胖至关重要的潜在机制发病。因此，在本提案中，我们利用心脏和衰老研究队列中的合作基因组流行病学 (CHARGE)、TransOMICs 精准医学 (TOPMed) 计划、基因组测序项目 (GSP) 以及 Geisinger MyCODE 社区健康计划的 EHR 数据库研究 (MyCode)，以缩小 GWAS 信号背后的基因范围，进行临床表征，以及进行体外功能研究，以表征分子基础和生物学肥胖风险位点的机制。我们的方法将大大推动该领域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根本性和重要的成果深入了解肥胖的根本病因，并最终指出预防和治疗肥胖的前进方向治疗。