# Predictive prioritization of enhancers associated with pancreatic disease risk

## TL;DR

This study maps enhancer-promoter interactions in human pancreatic cells to identify enhancers linked to disease risk and gene expression.

## Contribution

A novel network-based approach using 3D chromatin data and machine learning to prioritize enhancers critical for pancreatic gene regulation and disease risk.

## Key findings

- 3D enhancer-promoter interactions were mapped across five pancreatic cell types using chromatin assays.
- Tree models identified enhancers with the strongest influence on cell-type-specific gene expression.
- Enhancers prioritized by the algorithm were enriched for germline variants associated with pancreatic diseases.

## Abstract

Genetic and epigenetic variation in enhancers is associated with disease susceptibility; however, linking enhancers to target genes and predicting enhancer dysfunction remain challenging. We mapped enhancer-promoter interactions in human pancreas using 3D chromatin assays across 28 donors and five cell types. Using a network approach, we parsed these interactions into enhancer-promoter tree models, enabling quantitative, genome-wide analysis of enhancer connectivity. A machine learning algorithm built on these trees estimated enhancer contributions to cell-type-specific gene expression. To test predictions, we perturbed enhancers in primary human pancreas cells with CRISPR interference and quantified effects at single-cell resolution using RNA fluorescence in situ hybridization (FISH) and high-throughput imaging. Tree models also annotated germline risk variants linked to pancreatic disorders, connecting them to candidate target genes. For pancreatic ductal adenocarcinoma risk, acinar regulatory elements showed greater variant enrichment, challenging the ductal cell-of-origin view. Together, these datasets and models provide a resource for studying pancreatic disease genetics.

•3D enhancer-promoter contacts in five primary human pancreatic cell types•Graph “tree” models reveal enhancer connectivity controlling cell identity•Algorithm based on tree models identifies enhancers most critical for gene expression•Predicted key enhancers are enriched for germline pancreatic disease risk variants

3D enhancer-promoter contacts in five primary human pancreatic cell types

Graph “tree” models reveal enhancer connectivity controlling cell identity

Algorithm based on tree models identifies enhancers most critical for gene expression

Predicted key enhancers are enriched for germline pancreatic disease risk variants

Identifying key enhancers underlying disease susceptibility is a significant challenge. By mapping chromatin contacts between enhancers and promoters across five pancreatic cell types, Wang et al. built graph “tree” models capturing multi-enhancer control of genes. A ranking algorithm identified enhancers with the strongest influence on cell-type-specific expression, and perturbations in primary cells supported the predictions. The enhancers prioritized solely by this framework were enriched for risk variants, including those for diabetes and pancreatic cancer. The resulting maps and models provide a resource for variant-to-function studies in the human pancreas.