Predictive prioritization of enhancers associated with pancreatic disease risk

作者：Li Wang, Songjoon Baek, Gauri Prasad, John Wildenthal, Konnie Guo, David Sturgill, Thucnhi Truongvo, Erin Char, Gianluca Pegoraro, Katherine McKinnon, Jun Zhong, Demetrius Albanes, Gabriella Andreotti, Alan A. Arslan, Laura E. Beane Freeman, Sonja I. Berndt, Julie E. Buring, Daniele Campa, Federico Canzian, Stephen J. Chanock, Yu Chen, Sandra M. Colorado‐Yohar, A. Heather Eliassen, J. Michael Gaziano, Graham G. Giles, Phyllis J. Goodman, Christopher A. Haiman, Mattias Johansson, Verena Katzke, Charles Kooperberg, Peter Kraft, Manolis Kogevinas, I‐Min Lee, Loı̈c Le Marchand, Núria Malats, Satu Männistö, Marjorie L. McCullough, Roger Milne, Steven C. Moore, Lorelei A. Mucci, Salvatore Panico, Alpa V Patel, Ulrike Peters, Miquel Porta, Francisco X. Real, Howard D. Sesso, Xiao-Ou Shu, Meir J. Stampfer, Geoffrey S. Tobias, Kala Visvanathan, Elisabete Weiderpass, Nicolas Wentzensen, Emily White, Chen Yuan, Wei Zheng, Jean Wactawski‐Wende, Rachael Z. Stolzenberg‐Solomon, Brian M. Wolpin, Laufey T. Ámundadóttir, Samuel O. Antwi, Paige M. Bracci, Steven Gallinger, Michael Goggins, Md. Imtaiyaz Hassan, Elizabeth A. Holly, Rayjean J. Hung, Donghui Li, Núria Malats, Rachel Ε. Neale, Kari G. Rabe, Harvey A. Risch, Herbert Yu, Alison P. Klein, Jason W. Hoskins, Laufey T. Ámundadóttir, H. Efsun Arda · 发表于：Cell Genomics · 年份：2025 · DOI：10.1016/j.xgen.2025.101040 · 被引用次数：1 · 研究领域：Pancreatic function and diabetes、Genomics and Chromatin Dynamics、Gene expression and cancer classification

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.