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

> **NIH NIH F31** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2021 · $37,550

## Abstract

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.

## Key facts

- **NIH application ID:** 10156641
- **Project number:** 1F31HL154730-01A1
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Sarah Zweifel
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $37,550
- **Award type:** 1
- **Project period:** 2021-02-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10156641

## Citation

> US National Institutes of Health, RePORTER application 10156641, Analyzing gene expression in adipose tissue to identify candidate genes at cardiometabolic trait GWAS loci (1F31HL154730-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10156641. Licensed CC0.

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