Polygenic scores for risk of pancreatic ductal adenocarcinoma: evaluation of novel and published models
Samuel O Antwi, Brandon J Coombes, Erin E Carlson, Nicholas B Larson, Kari G Rabe, Hunter J Atkinson, Shounak Majumder, William R Bamlet, Daniel J Schaid, Jun Zhong, David McKean, Alan A Arslan, Laura E Beane Freeman, Paige M Bracci, Federico Canzian, Th�r�se Truong, Josh Atkins, Mengmeng Du, Steven Gallinger, Phyllis J Goodman, Verena Katzke, Daniele Campa, Charles Kooperberg, Loic Le Marchand, Rachel E Neale, Alpa V Patel, Jean Wactawski-Wende, Sandra Perdomo, Xiao-Ou Shu, Kala Visvanathan, Stephen K Van Den Eeden, Emily White, Wei Zheng, Demetrius Albanes, Gabriella Andreotti, Paul Brennan, Stephen J Chanock, Yu Chen, Burcu Darst, Pietro Ferrari, Edward L Giovannucci, Michael Goggins, Christopher Haiman, Manal Hassan, Rayjean J Hung, Miranda R Jones, Peter Kraft, N�ria Malats, Steven C Moore, Kimmie Ng, Ulrike Peters, Miquel Porta, Nathaniel Rothman, Maria-Jos� S�nchez, Howard D Sesso, Debra T Silverman, Melissa C Southey, Roger L Milne, Caroline Y Um, Herbert Yu, Chen Yuan; MAYO-RGC Project Generation; Regeneron Genetics Center; Harvey A Risch, Brian M Wolpin, Rachael Z Stolzenberg-Solomon, Alison P Klein, Laufey T Amundadottir, Ann L Oberg
NPJ Precis Oncol. 2026 May 27. doi: 10.1038/s41698-026-01479-x. Online ahead of print.
Abstract
Polygenic risk scores (PRSs) may enhance risk stratification for pancreatic ductal adenocarcinoma (PDAC), but existing models vary widely in design, predictive performance, and cross-ancestry transferability. We developed genome-wide PRSs using Bayesian methods (LDpred2 and PRS-CS) and p value thresholding (PRSice-2) and systematically evaluated these alongside 13 published PRSs to identify models with robust predictive performance across ancestries. Using GWAS summary statistics from 7531 cases and 10,631 controls, we derived the PRSs and tested associations in an independent sample of 4508 PDAC cases and 46,189 controls, with adjustment for well-established PDAC risk factors. Among all models, the genome-wide LDpred2-based PRS showed the strongest association with PDAC (OR = 1.57 per standard deviation increase; 95% CI: 1.51-1.62) and significantly improved discrimination beyond established risk factors alone (AUC = 0.74-0.76; p < 0.0001). Importantly, the genome-wide LDpred2 PRS demonstrated consistent associations across African, Admixed American, and European ancestry groups, whereas the best-performing published PRS was associated with PDAC risk only in individuals of European ancestry. These findings support genome-wide PRSs as a promising framework for multi-ancestry risk stratification for PDAC and to inform targeted early detection strategies.