Polygenic risk score for type 2 diabetes shows context-dependent effects across populations
Boya Guo, Yanwei Cai, Daeeun Kim, Roelof A. J. Smit, Zhe Wang, Kruthika Iyer, Austin T. Hilliard, Jeffrey Haessler, Ran Tao, K. Alaine Broadaway, Yujie Wang, Nikita Pozdeyev, Frederik Filip Stæger, Chaojie Yang, Brett Vanderwerff, Amit D. Patki, Lauren Stalbow, Meng Lin, Nicholas Rafaels, Jonathan Shortt, Laura K. Wiley, Maggie A. Stanislawski, Jack Pattee, Lea K. Davis, Péter Straub, Megan M. Shuey, Nancy J. Cox, Nanette R. Lee, Marit E. Jørgensen, Peter Bjerregaard, Christina Viskum Lytken Larsen, Torben Hansen, Ida Moltke, James B. Meigs, Daniel O. Stram, Xianyong Yin, Xiang Zhou, Kyong‐Mi Chang, Shoa L. Clarke, Rodrigo Guarischi‐Sousa, Joanna Lankester, Philip S. Tsao, Steven Buyske, Mariaelisa Graff, Laura M. Raffield, Quan Sun, Lynne R. Wilkens, Christopher S. Carlson, C. Easton, Simin Liu, JoAnn E. Manson, Loı̈c Le Marchand, Christopher A. Haiman, Karen L. Mohlke, Penny Gordon‐Larsen, Anders Albrechtsen, Michael Boehnke, Stephen S. Rich, Ani Manichaikul, Jerome I. Rotter, Noha A. Yousri, Ryan Irvin, Heather Anderson, Christina L. Aquilante, Kelsey L. Arbogast, Christopher H. Arehart, Ian M. Brooks, Tonya M. Brunetti, Judith Brutus-Lestin, Elizabeth A. Burke, Emily M. Casteel, Joanne B. Cole, Curtis R. Coughlin, Kristy Crooks, Jacob E. Crawford, Erin Culver, Michelle N. Edelmann, Matthew J. Fisher, Alan W. Franklin, Teresa C. Frye, George Hunter, Christopher R. Gignoux, Elizabeth K. Gilliland, Casey S. Greene, Brooke Hawkes, Emily C Hearst, Audrey E. Hendricks, Randi K. Johnson, Colleen G. Julian, Dave Kao, Iain R. Konigsberg, Lisa Ku, Elizabeth Kudron, Ronalda J De Lacy, E.M. Lange, Yee Ming Lee, Joe A. Lesny, Jan T. Lowery, Luciana B. Vargas, Betzaida L. Maldonado
Abstract
Polygenic risk scores hold prognostic value for identifying individuals at higher risk of type 2 diabetes. However, further characterization is needed to understand the generalizability of type 2 diabetes polygenic risk scores in diverse populations across various contexts. We systematically characterize a multi-ancestry type 2 diabetes polygenic risk score among 244,637 cases and 637,891 controls across diverse populations from the Population Architecture Genomics and Epidemiology Study and 13 additional biobanks and cohorts. Polygenic risk score performance is context dependent, with better performance in those who are younger, male, without hypertension, and not obese or overweight. Additionally, the polygenic risk score is associated with various diabetes-related cardiometabolic traits and type 2 diabetes complications, suggesting its utility for stratifying risk of complications and identifying shared genetic architecture between type 2 diabetes and other diseases. These findings highlight the need to account for context when evaluating polygenic risk score as a tool for type 2 diabetes risk prognostication and the potentially generalizable associations of type 2 diabetes polygenic risk score with diabetes-related traits, despite differential performance in type 2 diabetes prediction across diverse populations. Our study provides a comprehensive resource to characterize a type 2 diabetes polygenic risk score.