
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

10.1101/2024.09.07.611794
preprint
1
Article
Predictive Prioritization of Enhancers Associated with Pancreas Disease Risk
Wang Li
Baek Songjoon
Prasad Gauri
Wildenthal John
Guo Konnie
Sturgill David
Truongvo Thucnhi
Char Erin
Pegoraro Gianluca http://orcid.org/0000-0003-2843-9464

McKinnon Katherine
The Pancreatic Cancer Cohort Consortium, The Pancreatic Cancer Case-Control Consortium
Hoskins Jason W.
Amundadottir Laufey T.
Arda H. Efsun http://orcid.org/0000-0002-5294-2521

13 9 2024
2024.09.07.611794https://creativecommons.org/publicdomain/zero/1.0/ To the extent possible under law, the person who associated CC0 with this work has waived all copyright and related or neighboring rights to this work.
http://biorxiv.org/lookup/doi/10.1101/2024.09.07.611794
nihpp-2024.09.07.611794.pdf
SUMMARY

Genetic and epigenetic variations in regulatory enhancer elements increase susceptibility to a range of pathologies. Despite recent advances, linking enhancer elements to target genes and predicting transcriptional outcomes of enhancer dysfunction remain significant challenges. Using 3D chromatin conformation assays, we generated an extensive enhancer interaction dataset for the human pancreas, encompassing more than 20 donors and five major cell types, including both exocrine and endocrine compartments. We employed a network approach to parse chromatin interactions into enhancer-promoter tree models, facilitating a quantitative, genome-wide analysis of enhancer connectivity. With these tree models, we developed a machine learning algorithm to estimate the impact of enhancer perturbations on cell type-specific gene expression in the human pancreas. Orthogonal to our computational approach, we perturbed enhancer function in primary human pancreas cells using CRISPR interference and quantified the effects at the single-cell level through RNA FISH coupled with high-throughput imaging. Our enhancer tree models enabled the annotation of common germline risk variants associated with pancreas diseases, linking them to putative target genes in specific cell types. For pancreatic ductal adenocarcinoma, we found a stronger enrichment of disease susceptibility variants within acinar cell regulatory elements, despite ductal cells historically being assumed as the primary cell-of-origin. Our integrative approach—combining cell type-specific enhancer-promoter interaction mapping, computational models, and single-cell enhancer perturbation assays—produced a robust resource for studying the genetic basis of pancreas disorders.
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pmc
