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

项目摘要 心血管疾病和其他相关的心脏代谢特征，如肥胖和高血压 胆固醇对全球健康造成重大负担。检测影响这些的基因 心脏代谢特征将通过提供数据用于开发 心脏代谢疾病的治疗。全基因组关联研究（GWAS）发现， 与这些心脏代谢特征相关的数百个遗传位点，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， 需要与心脏代谢性状相关的组织来阐明潜在的潜在基因， 他们的生物学途径。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的各种基因量的表达水平。eQTL可以是 与基因表达的局部或远端相关，可以阐明基因表达的潜在机制。 在疾病相关的组织中，例如脂肪中的心脏代谢特征。eQTL数据可以整合 GWAS信号通过共定位研究与心脏代谢性状的遗传关联 分析，表明一个或多个变体与基因的表达和 特质进一步的因果中介分析可以提供证据表明，变异可能通过基因作用， 表达来影响心脏代谢特征。先前的脂肪eQTL研究效力有限 因为他们的样本量。我的项目的目标是联合收割机的脂肪eQTL研究，以确定 候选基因和与心脏代谢性状相关的生物学途径。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， 而远端eQTL将识别新的主调节转录因子， 与心脏代谢特征有关我会分析从脂肪组织样本中提取的新的RNA测序数据 参与男性代谢综合征（METSIM）研究的个体，对这些进行荟萃分析。 新的eQTL数据与现有研究的eQTL，并进行条件分析，GWAS 共定位和中介分析。我会找出皮下脂肪的多基因调节因子 使用远端eQTL荟萃分析与心脏代谢性状相关的组织表达水平。的 结果将检测新的候选基因和主调节转录因子， 与心脏代谢特征相关的生物学途径，这将为未来的相关治疗提供信息。 疾病，并帮助减少这些特征目前的健康负担。

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.