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

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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.