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<p>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.</p>

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Polygenic risk score for type 2 diabetes shows context-dependent effects across populations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月1日

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  • Boya Guo,
  • Yanwei Cai,
  • Daeeun Kim,
  • Roelof A. J. Smit,
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  • Kruthika R. Iyer,
  • Austin T. Hilliard,
  • Jeffrey Haessler,
  • Ran Tao,
  • K. Alaine Broadaway,
  • Yujie Wang,
  • Nikita Pozdeyev,
  • Frederik F. Stæger,
  • Chaojie Yang,
  • Brett Vanderwerff,
  • Amit D. Patki,
  • Lauren Stalbow,
  • Meng Lin,
  • Nicholas Rafaels,
  • Jonathan Shortt,
  • Laura Wiley,
  • Maggie Stanislawski,
  • Jack Pattee,
  • Lea Davis,
  • Peter S. Straub,
  • Megan M. Shuey,
  • Nancy J. Cox,
  • Nanette R. Lee,
  • Marit E. Jørgensen,
  • Peter Bjerregaard,
  • Christina 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,
  • Charles B. Easton,
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  • JoAnn E. Manson,
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  • Michael Boehnke,
  • Stephen S. Rich,
  • Ani Manichaikul,
  • Jerome I. Rotter,
  • Noha A. Yousri,
  • Ryan M. Irvin,
  • Heather D. Anderson,
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  • Christopher H. Arehart,
  • Ian M. Brooks,
  • Tonya M. Brunetti,
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  • Emily B. Todd,
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  • Amy Kilbourne,
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  • Deepak Voora,
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摘要

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.