# Predicting 3D physical gene-enhancer interactions through integration of GTEx and 4DN data

> **NIH NIH R03** · UNIVERSITY OF ILLINOIS AT CHICAGO · 2023 · $298,222

## Abstract

Program Director/Principal Investigator (Liang, Jie):
PROJECT SUMMARY/ABSTRACT
We will develop computational tools that facilitate investigation of the fundamental relationship
between gene expression and genome topology. Specifically, we will develop machine learning tools
that can link enhancer and its targeted gene at genome wide scale. The ability of establishing
relationship between enhancers and their target genes is critically important, as it will aid in our
understanding of gene regulation and in establishing the relationship between noncoding risk variants
from GWAS studies to potential causal genes. Our approach will be based on 3D polymer models of
chromatin interactions derived from Hi-C data in the common fund 4D Nucleome (4DN) database,
and will integrate data from the common fund supported Genotype-Tissue Expression (GTEx)
databaseas, as well as data from ENCODE database. We will 1) construct a database of trusted high-
quality database of candidate enhancer-gene target pairs. We will then 2) use this database to train a
machine learning predictor that can predict enhancer-gene target pairs at genome wide scale. For 1),
we will develop a pipeline to identify a small set of critical specific chromatin 3D interactions through
simulation of large scale folding of 3D chromatin ensembles. The small set of specific interactions will
be tested for sufficiency of chromatin folding. We will then identify computationally enhancers based
on epigenetic histone modifications and chromatin accessibility data from ENCODE as well as the
Roadmap Epigenomics Project. We will then select enhancers containing eQTLs from the GTEx
databases, which are known to affect the expression of the target gene. The end result will be a high-
quality and trustworthy database of enhance-gene pairs, which will be provided by the predicted
critical specific 3D physical chromatin interactions connecting the eQTL-containing enhancer and the
target gene. For 2), we will develop a machine-learning predictor that predicts enhancer-gene
interactions from genomic, epigenomic, and Hi-C data at genome-wide scale. We will combine
epigenetic data with genomic information (such as sequence motifs of TFs) as features. We will then
train a machine learning predictor through hold-outs and cross-validations of the constructed
database of enhancer-target gene pairs from 1). The efficacy of the predictor will then be assessed
with the gold-standard of the CRISPRi-FlowFISH data. We will then carry out large scale
computational and will construct databases of predicted enhancer-gene relationship for selected cell
types. Overall, we will demonstrate significant added-power of integrating two important Common
Fund data resources and will provide tools to facilitate understanding the relationship between
genome topology and gene expression. Our computational tools will lead to new insight into the
relationship of genome structure and genome function important for improving human health.
0...

## Key facts

- **NIH application ID:** 10776871
- **Project number:** 1R03OD036492-01
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT CHICAGO
- **Principal Investigator:** Jie Liang
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $298,222
- **Award type:** 1
- **Project period:** 2023-09-20 → 2024-09-19

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10776871

## Citation

> US National Institutes of Health, RePORTER application 10776871, Predicting 3D physical gene-enhancer interactions through integration of GTEx and 4DN data (1R03OD036492-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10776871. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*