| Title: | Population-specific polygenic risk scores for people of Han Chinese ancestry |
| Journal: | Nature |
| Published: | 15 Oct 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41094136/ |
| DOI: | https://doi.org/10.1038/s41586-025-09350-y |
| URL: | https://www.nature.com/articles/s41586-025-09350-y.pdf |
Publication 19170
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Abstract
Predicting complex disease risks on the basis of individual genomic profiles is an advancing field in human genetics1,2. However, most genetic studies have focused on populations of European ancestry, creating a global imbalance in precision medicine and underscoring the need for genomic research in non-European groups3,4. The Taiwan Precision Medicine Initiative recruited more than half a million Taiwanese residents, providing a large dataset of genetic profiles and electronic medical record data for people with Han Chinese ancestry. Using extensive phenotypic data, we conducted comprehensive genomic analyses across the medical phenome with individuals genetically similar to Han Chinese reference populations. These analyses identified population-specific genetic risk variants and new findings for various complex traits. We developed polygenic risk scores, demonstrating strong predictive performance for conditions such as cardiometabolic diseases, autoimmune disorders, cancers and infectious diseases. We observed consistent findings in an independent dataset, Taiwan Biobank, and among people of East Asian ancestry in the UK Biobank and the All of Us Project. The identified genetic risks accounted for up to 10.3% of the overall health variation in the Taiwan Precision Medicine Initiative cohort. Our approach of characterizing the phenome-wide genomic landscape, developing population-specific risk-prediction models, assessing their performance and identifying the genetic effect on health serves as a model for similar studies in other diverse study populations.</p>
129 Authors
- Hung-Hsin Chen
- Chien-Hsiun Chen
- Ming-Chih Hou
- Yun-Ching Fu
- Ling-Hui Li
- Che-Yu Chou
- Erh-Chan Yeh
- Ming-Fang Tsai
- Chun-houh Chen
- Hsin-Chou Yang
- Yen-Tsung Huang
- Yi-Min Liu
- Chun-yu Wei
- Jen-Ping Su
- Wan-Jia Lin
- Elin H. F. Wang
- Chi-Lu Chiang
- Jeng-Kai Jiang
- I-Hui Lee
- Kung-Hao Liang
- Wei-Sheng Chen
- Hung-Cheng Tsai
- Shih-Yao Lin
- Fu-Pang Chang
- Hsiang-Ling Ho
- Yi-Chen Yeh
- Wei-Cheng Tseng
- Ming-Hwai Lin
- Hsiao-Ting Chang
- Ling-Ming Tseng
- Wen-Yih Liang
- Paul Chih-Hsueh Chen
- Yu-Cheng Hsieh
- Yi-Ming Chen
- Tzu-Hung Hsiao
- Ching-Heng Lin
- Yen-Ju Chen
- I-Chieh Chen
- Chien-Lin Mao
- Shu-Jung Chang
- Yen-Lin Chang
- Yi-Ju Liao
- Chih-Hung Lai
- Wei-Ju Lee
- Hsin Tung
- Ting-Ting Yen
- Hsin-Chien Yen
- Ming-Yao Chen
- Ying-Chin Lin
- Yung-Ta Kao
- Bi-Zhen Kao
- Jing-Er Lee
- Chi-Li Chung
- Ju-Chi Liu
- Paul Chan
- Chang-Hsien Lin
- Chia-Hsin Chen
- I-Chen Wu
- Lung-Chang Lin
- Jiunn-Wei Wang
- Shen-liang Shih
- Sun-Wung Hsieh
- Chih-Hsing Hung
- Wei-Ming Li
- Chih-Jen Yang
- Cheng-Shin Yang
- Ru-Hui Weng
- Yu-Chi Chen
- Chun-Ping Chang
- Tai-Hsun Wu
- Yu-Chang Lin
- Yi-Jing Sheen
- Shi-Heng Wang
- Sye-Pu Chen
- Timothy Raben
- Erik Widen
- Stephen Hsu
- Feng-Jen Hsieh
- Dong-Ru Ho
- Yu-Huei Huang
- Chung-Han Yang
- Yu-Shu Huang
- Yen-Fu Chen
- Hsien-Ming Wu
- Ping-Han Tsai
- Kuan-Gen Huang
- Chih-Yen Chien
- Yi-Lwun Ho
- Ming-Shiang Wu
- Jia-Horng Kao
- Yen-Bin Liu
- Jyh-Ming Jimmy Juang
- Mao-Hsin Lin
- Yen-Hung Lin
- Ji-Yuh Lee
- Hsueh-Ju Lu
- Chieh-Hua Lu
- An-Chieh Feng
- Jhih-Syuan Liu
- Chien-Ping Chiang
- Nain-Feng Chu
- Jung-Chun Lin
- Yi-Wei Yeh
- En Meng
- Chih-Yang Huang
- Chi-Cheng Li
- Tso-Fu Wang
- Kuei-Ying Su
- Jia-Kang Wang
- Mei-Hsiu Chen
- Hua-Fen Chen
- Gwo-Chin Ma
- Ting-Yu Chang
- Fu-Tien Chiang
- Hsing-Jung Chang
- Kuo-Jang Kao
- Chen-Fang Hung
- Ching-Yao Tsai
- Po-Yueh Chen
- Kochung Tsui
- Yuan-Tsong Chen
- Pui-Yan Kwok
- Wayne Huey-Herng Sheu
- Shun-Fa Yang
- Jyh-Ming Liou
- Jaw-Yuan Wang
- Jeng-Fong Chiou
- Jer-Yuarn Wu
- Cathy S. J. Fann
1 Application
| Application ID | Title |
|---|---|
| 15326 | Compressed Sensing and high-dimensional statistical methods in complex trait genomics |
Enabling scientific discoveries that improve human health