# Integrative analysis of bulk and single-cell RNA-seq data for cardiometabolic disease

> **NIH NIH R21** · UNIVERSITY OF PENNSYLVANIA · 2022 · $121,875

## Abstract

PROJECT SUMMARY
Cardiometabolic diseases (CMD), including obesity, type 2 diabetes, heart attack, stroke, and atherosclerosis,
are caused by the effects and complex interplay of genetic and lifestyle factors. In the past decade, there has
been a dramatic increase of CMD, which represents important causes of morbidity and mortality worldwide.
Therefore, there is a great interest to understand the etiology and pathophysiology of CMD. Recent genome-
wide association studies (GWAS) have provided increased insight into the genetic basis for CMD and related
traits. Although GWAS have identified strong and highly replicated association of genetic loci for CMD and their
related traits, GWAS findings can only suggest locations of associated variants and not directly link any one
gene within a region to disease. Since most GWAS-identified single nucleotide polymorphisms (SNPs) are
located in non-coding regions of the genome, their influence on disease is likely to be on modulating RNA
expression by acting as expression quantitative trait loci (eQTL). Excess adipose tissue, especially in central
abdominal depots, is associated with increased risk of CMD. Subcutaneous adipose tissue stores additional
lipids and acts as a buffering system for lipid energy balance, thus providing a protective role for CMD.
Previous eQTL studies in subcutaneous adipose tissue have implicated genes involved in obesity and
metabolic traits. However, the role of candidate genes identified in GWAS is still not yet clear because
published eQTL studies were based on bulk tissue gene expression in adipose. Adipose tissue is a loose
connective tissue that is composed mostly of adipocytes. In addition to adipocytes, adipose tissue also
contains adipocyte progenitor cells, endothelial cells, fibroblasts, and various immune cells such as
macrophages. The resulting heterogeneity between samples can confound the analysis of bulk tissue data. To
overcome these limitations, we propose to perform integrative secondary data analysis of publically available
bulk RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) data from human adipose. We will test
the hypothesis that measurable molecular deficits that include cell types and gene expression occur in adipose
for CMD. We will further integrate with publically available GWAS data on CMD to advance post-GWAS
interpretation of CMD genetic results. By detailed characterization of cell-type composition and cell-type-
specific gene expression changes in human adipose, our results will elucidate the functional roles of GWAS
findings that are still poorly understood and can power precision therapeutic targeting of CMD.

## Key facts

- **NIH application ID:** 10448317
- **Project number:** 5R21HL156234-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Mingyao Li
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $121,875
- **Award type:** 5
- **Project period:** 2021-07-15 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10448317

## Citation

> US National Institutes of Health, RePORTER application 10448317, Integrative analysis of bulk and single-cell RNA-seq data for cardiometabolic disease (5R21HL156234-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10448317. Licensed CC0.

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