Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations
Minta Thomas, Yu‐Ru Su, Elisabeth A. Rosenthal, Lori C. Sakoda, Stephanie L. Schmit, Maria Timofeeva, Zhishan Chen, Ceres Fernández–Rozadilla, Philip Law, Neil Murphy, Robert Carreras‐Torres, Virginia Díez‐Obrero, Fränzel J.B. van Duijnhoven, Shangqing Jiang, Aesun Shin, Alicja Wolk, Amanda I. Phipps, Andrea N. Burnett‐Hartman, Andrea Gsur, Andrew T. Chan, Ann G. Zauber, Anna H. Wu, Annika Lindblom, Caroline Y. Um, Catherine M. Tangen, Chris Gignoux, Christina C. Newton, Christopher A. Haiman, Conghui Qu, D. Timothy Bishop, Daniel D. Buchanan, David R. Crosslin, David V. Conti, Dong-Hyun Kim, Elizabeth R. Hauser, Emily White, Erin M. Siegel, Fredrick R. Schumacher, Gad Rennert, Graham G. Giles, Heather Hampel, Hermann Brenner, Isao Oze, Jae Hwan Oh, Jeffrey K. Lee, Jennifer L. Schneider, Jenny Chang‐Claude, Jeongseon Kim, Jeroen R. Huyghe, Jiayin Zheng, Jochen Hampe, Joel K. Greenson, John L. Hopper, Julie R. Palmer, Kala Visvanathan, Keitaro Matsuo, Koichi Matsuda, Keum Ji Jung, Li Li, Loı̈c Le Marchand, Ludmila Vodičková, Luís Bujanda, Marc J. Gunter, Marco Matejcic, Mark A. Jenkins, Martha L. Slattery, Mauro D’Amato, Meilin Wang, Michael Hoffmeister, Michael O. Woods, Michelle Kim, Mingyang Song, Motoki Iwasaki, Mulong Du, Natalia Udaltsova, Norie Sawada, Pavel Vodička, Peter T. Campbell, Polly A. Newcomb, Qiuyin Cai, Rachel Pearlman, Rish K. Pai, Robert E. Schoen, Robert S. Steinfelder, Robert W. Haile, Rosita Vandenputtelaar, Ross L. Prentice, Sébastien Küry, Sergi Castellvı́-Bel, Shoichiro Tsugane, Sonja I. Berndt, Soo Chin Lee, Stefanie Brezina, Stephanie J. Weinstein, Stephen J. Chanock, Sun Ha Jee, Sun‐Seog Kweon, Susan T. Vadaparampil, Tabitha A. Harrison, Taiki Yamaji
Abstract
Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values < 0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice.