Analyzing gene expression in adipose tissue to identify candidate genes at cardiometabolic trait GWAS loci

分析脂肪组织中的基因表达以确定心脏代谢特征 GWAS 位点的候选基因

• 批准号: 10156641
• 负责人: Sarah Brotman
• 金额: $ 3.76万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10156641
• 关键词: Adipose tissue; Affect; Biological; Candidate Disease Gene; Cardiometabolic Disease; Cardiovascular Diseases; Cause of Death; Cessation of life; Cholesterol; Clinical; Collaborations; Complex; Data; Data Set; Detection; Disease; Distal; Ensure; Exons; Future; Gene Expression; Gene Expression Profiling; Genes; Genotype; Goals; Health; Heart Diseases; Human Genome; Individual; Lead; Link; Measures; Mediation; Meta-Analysis; Metabolic syndrome; Obesity; Participant; Pathway interactions; Phenotype; Process; Public Health; Quality Control; Quantitative Trait Loci; RNA analysis; Resources; Risk Factors; Sample Size; Sampling; Signal Transduction; Testing; Tissue Sample; Tissues; United States; Variant; Woman; base; cardiometabolism; cell type; disorder risk; genetic association; genetic information; genetic variant; genome wide association study; genomic locus; global health; hypercholesterolemia; insulin sensitivity; men; subcutaneous; trait; transcription factor; transcriptome sequencing; treatment strategy

PROJECT SUMMARY Cardiovascular disease and other related cardiometabolic traits such as obesity and high blood cholesterol pose significant global health burdens. The detection of genes influencing these cardiometabolic traits would be beneficial to public health by providing data to use towards developing treatments of cardiometabolic diseases. Genome-wide association studies (GWAS) have discovered hundreds of genetic loci associated with these cardiometabolic traits, however most of the underlying genes remain unknown. Genetic variants that influence changes in the expression level of genes in tissues related to cardiometabolic traits are needed to elucidate the potential underlying genes and their biological pathways. Expression quantitative trait loci (eQTL) are associations between variants and the expression level of various gene quantities including genes and exons. eQTLs can be associated with gene expression locally or distally and can illuminate potential mechanisms of genes in a disease-relevant tissue, such as adipose for cardiometabolic traits. eQTL data can be integrated with GWAS signals to investigate genetic associations with cardiometabolic traits through colocalization analysis, suggesting that one or more variants are correlated with both the expression of a gene and the trait. Further causal mediation analysis can provide evidence that variants may act through gene expression to influence the cardiometabolic trait. Previous adipose eQTL studies have limited power due to their sample sizes. The goal of my project is to combine adipose eQTL studies to identify candidate genes and biological pathways that are associated with cardiometabolic traits. I hypothesize that local eQTLs colocalized with GWAS signals will implicate new genes in disease-relevant adipose pathways and that distal eQTLs will identify new master regulator transcription factors that target genes involved in cardiometabolic traits. I will analyze new RNA-sequencing data from adipose tissue samples of individuals participating in the METabolic Syndrome In Men (METSIM) study, meta-analyze these new eQTL data with eQTL from existing studies, and perform conditional analysis, GWAS colocalization, and mediation analysis. I will identify multi-gene regulators of subcutaneous adipose tissue expression levels associated with cardiometabolic traits using a distal eQTL meta-analysis. The results will detect new candidate genes and master regulator transcription factors with underlying biological pathways related to cardiometabolic traits, which will inform future treatments of related diseases and help decrease the current health burden of those traits.

项目概要 心血管疾病和其他相关的心脏代谢特征，例如肥胖和高血压 胆固醇造成严重的全球健康负担。检测影响这些的基因 心脏代谢特征将通过提供用于发展的数据来有益于公共健康 心脏代谢疾病的治疗。全基因组关联研究（GWAS）发现 数百个与这些心脏代谢特征相关的遗传位点，但是大多数潜在的 基因仍然未知。影响基因表达水平变化的遗传变异 需要与心脏代谢特征相关的组织来阐明潜在的潜在基因和 他们的生物学途径。表达数量性状位点 (eQTL) 是变体之间的关联 以及包括基因和外显子在内的各种基因量的表达水平。 eQTL 可以是 与局部或远端的基因表达相关，可以阐明基因的潜在机制 与疾病相关的组织，例如心脏代谢特征的脂肪。 eQTL数据可以整合 利用 GWAS 信号通过共定位研究与心脏代谢特征的遗传关联 分析，表明一个或多个变异与基因的表达和 特质。进一步的因果中介分析可以提供证据表明变异可能通过基因起作用 表达影响心脏代谢特征。之前的脂肪 eQTL 研究效力有限 由于它们的样本量。我的项目的目标是结合脂肪 eQTL 研究来确定 与心脏代谢特征相关的候选基因和生物途径。我假设 与 GWAS 信号共定位的局部 eQTL 将暗示疾病相关脂肪中的新基因 途径，并且远端 eQTL 将识别针对基因的新主调控转录因子 参与心脏代谢特征。我将分析来自脂肪组织样本的新 RNA 测序数据 参与男性代谢综合症 (METSIM) 研究的个体，对这些进行荟萃分析 使用现有研究中的 eQTL 获得新的 eQTL 数据，并进行条件分析、GWAS 共定位和中介分析。我将确定皮下脂肪的多基因调节因子 使用远端 eQTL 荟萃分析确定与心脏代谢特征相关的组织表达水平。这 结果将检测新的候选基因并掌握具有潜在作用的调节转录因子 与心脏代谢特征相关的生物途径，这将为相关的未来治疗提供信息 疾病并有助于减轻这些特征当前的健康负担。