The Taiwan Precision Medicine Initiative: A Cohort for Large-Scale Studies
Journal
bioRxiv
Date Issued
2024-10-17
Author(s)
Yang, Hsin-Chou
Kwok, Pui-Yan
Li, Ling-Hui
Liu, Yi-Min
Jong, Yuh-Jyh
Lee, Kang-Yun
Wang, Da-Wei
Tsai, Ming-Fang
Yang, Jenn-Hwai
Chen, Chien-Hsiun
Yeh, Erh-Chan
Wei, Chun-yu
Fann, Cathy S.-J.
Huang, Yen-Tsung
Chen, Chia-Wei
Lee, Yi-Ju
Chu, Shih-Kai
Ho, Chih-hsing
Yang, Cheng-Shin
Lee, Yungling Leo
Chen, Hung-Hsin
Hou, Ming-Chih
Chiou, Jeng-Fong
Yang, Shun-Fa
Wang, Chih-Hung
Huang, Chih-Yang
Chiu, Kuan-Ming
Chen, Ming
Lee, Sing-Lian
Chiang, Fu-Tien
Chen, Shiou-Sheng
Yao, Wei-Jen
Chien, Chih-Cheng
Lin, Shih-Yao
Chang, Fu-Pang
Ho, Hsiang-Ling
Yeh, Yi-Chen
Tseng, Wei-Cheng
Lin, Ming-Hwai
Chang, Hsiao-Ting
Tseng, Ling-Ming
Liang, Wen-Yih
Chen, Paul Chih-Hsueh
Hang, Jen-Fan
Lin, Shih-Chieh
Chan, Yu-Jiun
Kuo, Ying-Ju
Wang, Lei-Chi
Pan, Chin-Chen
Hsieh, Yu-Cheng
Chen, Yi-Ming
Hsiao, Tzu-Hung
Lin, Ching-Heng
Chen, Yen-Ju
Chen, I-Chieh
Mao, Chien-Lin
Chang, Shu-Jung
Chang, Yen-Lin
Liao, Yi-Ju
Lai, Chih-Hung
Lee, Wei-Ju
Tung, Hsin
Yen, Ting-Ting
Yen, Hsin-Chien
Shih, Chun-Ming
Chou, Teh-Ying
Liou, Tsan-Hon
Chiang, Chen-Yuan
Cherng, Yih-Giun
Chen, Chih-Hwa
Chiu, Chao-Hua
Tseng, Sung-Hui
Lin, Emily Pei-Ying
Chen, Ying-Ju
Chuang, Hui-Ping
Chen, Tsai-Chuan
Huang, Wei-Ting
Sin, Joey
Liu, I-Ling
Chao, Kuo-Kuang
Chen, Yi-Chen
Wu, Yu-Min
Yu, Pin-Pin
Chang, Lung-Pao
Yen, Kuei-Yao
Chang, Li-Ching
Sheen, Yi-Jing
Chen, Yuan-Tsong
Kan, Kamhon
Tsai, Hsiang-Lin
Wang, Yao-Kuang
Hou, Ming-Feng
Yang, Yuan-Han
Kuo, Chao-Hung
Wu, Wen-Jeng
Huang, Jee-Fu
Chong, Inn-Wen
Tsai, Jong-Rung
Lin, Cheng-Yu
Yu, Ming-Chin
Lee, Tsong-Hai
Ou, Yu-Che
Chen, Pin-Yuan
Hu, Tsung-Hui
Shyu, Yu-Chiau
Cheng, Chih-Kuang
Tsai, Meng-Han
Fang, Yu-Jen
Hsieh, Song-Chou
Chen, Chieh-Chang
Chen, Chien-Hung
Li, Ko-Jen
Chu, Shi-Jye
Wu, Chen-Chi
Liu, Feng-Cheng
Lin, Chin-Hsien
Yang, Fu-Ch
Chen, Chun-Yen
Chang, Hsin-An
Chen, Wei-liang
Yang, Sung-Sen
Sung, Yueh-feng
Wang, Tso-Fu
Lin, Shinn-Zong
Wu, Yen-Wen
Wu, Chien-Sheng
Jiang, Ju-Ying
Ma, Gwo-Chin
Chang, Ting-Yu
Hwang, Juey-Jen
Kao, Kuo-Jang
Hung, Chen-Fang
Chiu, Ting-Fang
Chen, Po-Yueh
Tsui, Kochung
Pang, See-Tong
Wu, Ming-Shiang
Chen, Shih-Ann
Chen, Wei-Ming
Chen, Chun-houh
Sheu, Wayne Huey-Herng
Wu, Jer-Yuarn
Abstract
The Taiwan Precision Medicine Initiative (TPMI), a project initiated by the Academia Sinica in collaboration with 16 major medical centers around Taiwan, has recruited 565,390 participants who consented to provide DNA samples for genetic profiling and grant access to their electronic medical records (EMR) for studies to develop precision medicine. Access to the EMR is both retrospective and prospective, allowing researchers to conduct prospective studies over time. Genetic profiling is done with population-optimized SNP arrays for the Han Chinese populations that enable genetic analyses such as genome-wide association, phenome-wide association, and polygenic risk score studies to evaluate common disease risk and pharmacogenetic response. Furthermore, the TPMI participants agree to be contacted for future research opportunities related to their genetic risks and receive personalized genetic risk profiles with health management recommendations. TPMI has established the TPMI Data Access Platform (TDAP), a central database and analysis platform that both safeguards the security of the data and facilitates academic research. The TPMI is the largest non-European cohort that merges genetic profiles with EMR in the world. With a cohort that can be followed over time, it can be utilized to validate genetic risk prediction models, conduct clinical trials to show the efficacy of risk-based health management, and optimize health policies based on genetic risks. In this report, we describe the TPMI study design, the population and genetic characteristics of the TPMI cohort, and the power it provides to conduct crucial studies in developing precision medicine on a population and personal level. As Han Chinese represent almost 20% of the world’s population, the results of TPMI studies will benefit >1.4 billion people around the world and serve as a model for developing population-based precision medicine. The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Publisher
Cold Spring Harbor Laboratory
Type
other