Predictive prioritization of enhancers associated with pancreatic disease risk

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.

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Publication Details

Journal
Cell Genomics
Published
2025-10-17
DOI
https://doi.org/10.1016/j.xgen.2025.101040
Primary Topic
Pancreatic function and diabetes
Type
article
Field-Weighted Citation Impact
0.00
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article

Predictive prioritization of enhancers associated with pancreatic disease risk

Alpa V Patel, Phyllis J. Goodman, Geoffrey S. Tobias, Emily White et al.
Cell Genomics
Pancreatic function and diabetes
article

Predictive prioritization of enhancers associated with pancreatic disease risk

Alpa V Patel, Phyllis J. Goodman, Geoffrey S. Tobias, Emily White, Federico Canzian, Harvey A. Risch, Howard D. Sesso, Marjorie L. McCullough, Jean Wactawski‐Wende, Samuel O. Antwi, Salvatore Panico, Graham G. Giles, Manolis Kogevinas, Alan A. Arslan, Alison Patricia Klein, Mattias Johansson, Núria Malats, I‐Min Lee, Gianluca Pegoraro, Satu Männistö, Laufey T. Ámundadóttir, Steven Gallinger, Chen Yuan, Songjoon Baek, Konnie Guo, Brian M. Wolpin, Lorelei Ann Mucci, Roger Milne, Loı̈c Le Marchand, Paige M. Bracci, Rayjean J. Hung, Gauri Prasad, Francisco X. Real, Miquel Porta, Sonja I. Berndt, Daniele Campa, Verena Katzke, Herbert Yu, Rachael Z. Stolzenberg‐Solomon, Rachel Ε. Neale, Kari G. Rabe, Sandra M. Colorado‐Yohar, J. Michael Gaziano, Elisabete Weiderpass, H. Efsun Arda, Jason W. Hoskins, Charles Kooperberg, Katherine McKinnon, Laura E. Beane Freeman, Gabriella Andreotti, Annette Peters, Kala Visvanathan, Michael Goggins, Julie E. Buring, Steven C. Moore, Erin Char, Donghui Li, Yu Chen, Md. Imtaiyaz Hassan, Peter Kraft, A. Heather Eliassen, David Sturgill, John Wildenthal, Thucnhi Truongvo, Meir J. Stampfer, Wei Zheng, Stephen J. Chanock, Demetrius Albanes, Nicolas Wentzensen, Elizabeth A. Holly, Xiao-Ou Shu, Christopher A. Haiman, Li Wang
article en

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.

Cell GenomicsVol. 6(1)
National Institutes of Health (US), National Cancer Institute (US), Center for Cancer Research (US)
Openalex Percentile: Top 54%
Pancreatic function and diabetes
DOIOpen Access PDFOpenAlex
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