# Diabetes risk variants affecting transcription factor-regulated cellular networks

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2022 · $387,500

## Abstract

Type 2 diabetes is a complex disease that affects 1 in 10 Americans and is influenced by many
common genetic risk factors. Genetic association studies have identified over 100 loci that
influence T2D risk, although how these loci mechanistically contribute to diabetes pathogenesis
is largely unknown. The majority of these risk loci map to non-coding sequence, and likely alter
gene regulatory processes in specific cell-types. Translating this breadth of diabetes regulatory
variation into their molecular mechanisms can thus profoundly inform on diabetes
pathophysiology, although remains challenging. In this study we propose a novel approach to
identify T2D-relevant transcription factors and gene networks regulated by these factors by
combining statistical human genetics, epigenomics, high-throughput assay and quantitative trait
locus (QTL) mapping. In this approach we identify T2D risk variants that affect the cell-type
expression of a transcription factor gene, characterize the genomic binding sites and target
gene network regulated by these transcription factors, and broadly determine the effects of
variants disrupting transcription factor-regulated networks on diabetes risk. In preliminary
findings we have identified several diabetes risk variants that affect the cell-type expression
level of a transcription factor gene, almost none of which have known involvement in diabetes-
relevant pathways. In Aim 1 we will combine genetic fine-mapping with epigenomic annotation
and eQTL data from diabetes-relevant cells to identify diabetes risk variants that affect the cell-
type expression of a transcription factor. In Aim 2 we will perform ChIP-seq assays of five
transcription factors in pancreatic islet samples combined with eQTL data to map the trans
network of target genes affected by transcription factor regulatory variants. In Aim 3 we will
combine allelic imbalance mapping and in silico motif prediction of islet ChIP-seq data to
quantify the genome-wide effects of variants disrupting transcription factor-regulated networks
on diabetes risk. The results of these studies will reveal specific transcription factors that are
regulated by diabetes risk variants, and the gene networks regulated by these factors that in
turn impact diabetes pathophysiology. Together these studies will provide insight into critical
transcription factors and gene networks involved in diabetes pathogenesis.

## Key facts

- **NIH application ID:** 10395986
- **Project number:** 5R01DK114650-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Kyle Jeffrie Gaulton
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $387,500
- **Award type:** 5
- **Project period:** 2018-06-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10395986

## Citation

> US National Institutes of Health, RePORTER application 10395986, Diabetes risk variants affecting transcription factor-regulated cellular networks (5R01DK114650-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10395986. Licensed CC0.

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