# Bridging the gap between type 2 diabetes GWAS and therapeutic targets

> **NIH NIH UM1** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2021 · $1,948,933

## Abstract

Type 2 diabetes (T2D) is a heterogeneous disorder characterized by resistance of hepatic, skeletal muscle and
adipose tissues to insulin and a relative deficiency of insulin secretion by pancreatic β cells. T2D has a substantial
genetic component, and over the past decade human genetic studies have identified over 400 association
signals across diverse populations. However, in most cases the specific variants and genes responsible for these
association signals are not known. T2D signals include loci for which functions of the protein products encoded
by nearby genes are poorly characterized, the closest known gene is distant, or more than one gene appears to
be a plausible biological candidate. Identifying the causal variants, the regulatory gene networks affected by the
change in DNA sequence, and the mechanisms by which such variation leads to disease are critical steps toward
understanding the genetic architecture of T2D, validating potential drug targets, and developing novel therapeutic
strategies. Here, we propose large-scale multi-disciplinary functional genomics projects in islet, liver, adipose
and muscle cells to determine the contributions and mechanisms underlying T2D risk-associated variants and
their downstream effector transcripts. Throughout the project, we leverage our prior and ongoing generation of
genomic data sets and genome-wide and targeted screens for function of variants and genes. To complement
these efforts, we will first collect genome-wide array and sequencing-based association study results, identify
conditionally distinct association signals and construct credible sets of variants. We propose to link variants to
effector transcripts through analyses of genome-wide transcriptomic and epigenomic data, perturbation assays
that alter thousands of variant-containing regulatory elements and effector transcripts, perturbations of tens of
specific variants, and integrative computational analyses. Next, we propose systematic evaluation of hundreds
of potential effector transcripts through use of genome-wide and targeted screens of insulin secretion, lipid
accumulation, mitochondrial function, glucose uptake, and differentiation state, with assay selection depending
on cell type. Based on these results, we propose focused studies on tens to hundreds of potential effector
transcripts to evaluate electrophysiology, gluconeogenesis, lipid metabolism and signaling pathways, and we
propose thorough investigation the context-specific mechanism of action of individual genes. Finally, we propose
to analyze, integrate, and visualize all data by placing effector transcripts into cell-type and environmental
context-specific networks, selecting network nodes as candidate biomarkers and modulation points for drugs,
and building a framework to understand the tissue-specific contribution of variants and transcripts to individual
disease heterogeneity. Successful completion of these aims will translate T2D association signals into biological...

## Key facts

- **NIH application ID:** 10242210
- **Project number:** 5UM1DK126185-02
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** JOSE CARLOS FLOREZ
- **Activity code:** UM1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,948,933
- **Award type:** 5
- **Project period:** 2020-08-20 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10242210

## Citation

> US National Institutes of Health, RePORTER application 10242210, Bridging the gap between type 2 diabetes GWAS and therapeutic targets (5UM1DK126185-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10242210. Licensed CC0.

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