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

项目总结 心血管疾病和其他相关的心脏代谢特征，如肥胖和高血 胆固醇对全球健康造成重大负担。影响这些基因的检测 心脏代谢特征将有利于公众健康，为发育提供数据 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基因座，然而大多数潜在的 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 它们的生物学途径。表达数量性状基因座(EQTL)是变异体之间的关联 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，并能阐明基因的潜在机制 在与疾病相关的组织中，例如脂肪具有心脏代谢的特征。可以集成eQTL数据 通过共定位研究基因与心脏代谢性状的关联 分析表明，一个或多个变体与基因和 这就是特点。进一步的因果中介分析可以提供变异体可能通过基因发挥作用的证据 影响心脏代谢特性的基因表达。以前的脂肪eQTL研究力量有限 由于他们的样本大小。我的项目的目标是结合脂肪eQTL研究来识别 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将在疾病相关脂肪中涉及新的基因 途径和远端eQTL将识别新的针对基因的主要调控转录因子 与心脏新陈代谢有关。我将从脂肪组织样本中分析新的RNA测序数据 参与男性代谢综合征(METSIM)研究的个人，荟萃分析这些 利用现有研究中的eQTL获得新的eQTL数据，并进行条件分析 协同定位和调解分析。我将鉴定皮下脂肪的多基因调控因子 应用远端eQTL Meta分析心脏代谢特征相关的组织表达水平。这个 结果将检测新的候选基因并掌握调节转录因子与潜在的 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 并有助于减轻这些特征造成的当前健康负担。

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.