Polygenic risk scores for pan-cancer risk prediction in the Chinese population: A population-based cohort study based on the China Kadoorie Biobank.
<h4>Background</h4>Polygenic risk scores (PRSs) have been extensively developed for cancer risk prediction in European populations, but their effectiveness in the Chinese population remains uncertain.<h4>Methods and findings</h4>We constructed 80 PRSs for the 13 most common c...
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Public Library of Science (PLoS)
2025-02-01
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| Series: | PLoS Medicine |
| Online Access: | https://doi.org/10.1371/journal.pmed.1004534 |
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| author | Meng Zhu Xia Zhu Yuting Han Zhimin Ma Chen Ji Tianpei Wang Caiwang Yan Ci Song Canqing Yu Dianjianyi Sun Yue Jiang Jiaping Chen Ling Yang Yiping Chen Huaidong Du Robin Walters Iona Y Millwood Juncheng Dai Hongxia Ma Zhengdong Zhang Zhengming Chen Zhibin Hu Jun Lv Guangfu Jin Liming Li Hongbing Shen China Kadoorie Biobank Collaborative Group |
| author_facet | Meng Zhu Xia Zhu Yuting Han Zhimin Ma Chen Ji Tianpei Wang Caiwang Yan Ci Song Canqing Yu Dianjianyi Sun Yue Jiang Jiaping Chen Ling Yang Yiping Chen Huaidong Du Robin Walters Iona Y Millwood Juncheng Dai Hongxia Ma Zhengdong Zhang Zhengming Chen Zhibin Hu Jun Lv Guangfu Jin Liming Li Hongbing Shen China Kadoorie Biobank Collaborative Group |
| author_sort | Meng Zhu |
| collection | DOAJ |
| description | <h4>Background</h4>Polygenic risk scores (PRSs) have been extensively developed for cancer risk prediction in European populations, but their effectiveness in the Chinese population remains uncertain.<h4>Methods and findings</h4>We constructed 80 PRSs for the 13 most common cancers using seven schemes and evaluated these PRSs in 100,219 participants from the China Kadoorie Biobank (CKB). The optimal PRSs with the highest discriminatory ability were used to define genetic risk, and their site-specific and cross-cancer associations were assessed. We modeled 10-year absolute risk trajectories for each cancer across risk strata defined by PRSs and modifiable risk scores and quantified the explained relative risk (ERR) of PRSs with modifiable risk factors for different cancers. More than 60% (50/80) of the PRSs demonstrated significant associations with the corresponding cancer outcomes. Optimal PRSs for nine common cancers were identified, with each standard deviation increase significantly associated with corresponding cancer risk (hazard ratios (HRs) ranging from 1.20 to 1.76). Compared with participants at low genetic risk and reduced modifiable risk scores, those with high genetic risk and elevated modifiable risk scores had the highest risk of incident cancer, with HRs ranging from 1.97 (95% confidence interval (CI): 1.11-3.48 for cervical cancer, P = 0.020) to 8.26 (95% CI: 1.92-35.46 for prostate cancer, P = 0.005). We observed nine significant cross-cancer associations for PRSs and found the integration of PRSs significantly increased the prediction accuracy for most cancers. The PRSs contributed 2.6%-20.3%, while modifiable risk factors explained 2.3%-16.7% of the ERR in the Chinese population.<h4>Conclusions</h4>The integration of existing evidence has facilitated the development of PRSs associated with nine common cancer risks in the Chinese population, potentially improving clinical risk assessment. |
| format | Article |
| id | doaj-art-9a748508e9df4403beb4e6bbeda10bd7 |
| institution | Kabale University |
| issn | 1549-1277 1549-1676 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Medicine |
| spelling | doaj-art-9a748508e9df4403beb4e6bbeda10bd72025-08-20T03:47:27ZengPublic Library of Science (PLoS)PLoS Medicine1549-12771549-16762025-02-01222e100453410.1371/journal.pmed.1004534Polygenic risk scores for pan-cancer risk prediction in the Chinese population: A population-based cohort study based on the China Kadoorie Biobank.Meng ZhuXia ZhuYuting HanZhimin MaChen JiTianpei WangCaiwang YanCi SongCanqing YuDianjianyi SunYue JiangJiaping ChenLing YangYiping ChenHuaidong DuRobin WaltersIona Y MillwoodJuncheng DaiHongxia MaZhengdong ZhangZhengming ChenZhibin HuJun LvGuangfu JinLiming LiHongbing ShenChina Kadoorie Biobank Collaborative Group<h4>Background</h4>Polygenic risk scores (PRSs) have been extensively developed for cancer risk prediction in European populations, but their effectiveness in the Chinese population remains uncertain.<h4>Methods and findings</h4>We constructed 80 PRSs for the 13 most common cancers using seven schemes and evaluated these PRSs in 100,219 participants from the China Kadoorie Biobank (CKB). The optimal PRSs with the highest discriminatory ability were used to define genetic risk, and their site-specific and cross-cancer associations were assessed. We modeled 10-year absolute risk trajectories for each cancer across risk strata defined by PRSs and modifiable risk scores and quantified the explained relative risk (ERR) of PRSs with modifiable risk factors for different cancers. More than 60% (50/80) of the PRSs demonstrated significant associations with the corresponding cancer outcomes. Optimal PRSs for nine common cancers were identified, with each standard deviation increase significantly associated with corresponding cancer risk (hazard ratios (HRs) ranging from 1.20 to 1.76). Compared with participants at low genetic risk and reduced modifiable risk scores, those with high genetic risk and elevated modifiable risk scores had the highest risk of incident cancer, with HRs ranging from 1.97 (95% confidence interval (CI): 1.11-3.48 for cervical cancer, P = 0.020) to 8.26 (95% CI: 1.92-35.46 for prostate cancer, P = 0.005). We observed nine significant cross-cancer associations for PRSs and found the integration of PRSs significantly increased the prediction accuracy for most cancers. The PRSs contributed 2.6%-20.3%, while modifiable risk factors explained 2.3%-16.7% of the ERR in the Chinese population.<h4>Conclusions</h4>The integration of existing evidence has facilitated the development of PRSs associated with nine common cancer risks in the Chinese population, potentially improving clinical risk assessment.https://doi.org/10.1371/journal.pmed.1004534 |
| spellingShingle | Meng Zhu Xia Zhu Yuting Han Zhimin Ma Chen Ji Tianpei Wang Caiwang Yan Ci Song Canqing Yu Dianjianyi Sun Yue Jiang Jiaping Chen Ling Yang Yiping Chen Huaidong Du Robin Walters Iona Y Millwood Juncheng Dai Hongxia Ma Zhengdong Zhang Zhengming Chen Zhibin Hu Jun Lv Guangfu Jin Liming Li Hongbing Shen China Kadoorie Biobank Collaborative Group Polygenic risk scores for pan-cancer risk prediction in the Chinese population: A population-based cohort study based on the China Kadoorie Biobank. PLoS Medicine |
| title | Polygenic risk scores for pan-cancer risk prediction in the Chinese population: A population-based cohort study based on the China Kadoorie Biobank. |
| title_full | Polygenic risk scores for pan-cancer risk prediction in the Chinese population: A population-based cohort study based on the China Kadoorie Biobank. |
| title_fullStr | Polygenic risk scores for pan-cancer risk prediction in the Chinese population: A population-based cohort study based on the China Kadoorie Biobank. |
| title_full_unstemmed | Polygenic risk scores for pan-cancer risk prediction in the Chinese population: A population-based cohort study based on the China Kadoorie Biobank. |
| title_short | Polygenic risk scores for pan-cancer risk prediction in the Chinese population: A population-based cohort study based on the China Kadoorie Biobank. |
| title_sort | polygenic risk scores for pan cancer risk prediction in the chinese population a population based cohort study based on the china kadoorie biobank |
| url | https://doi.org/10.1371/journal.pmed.1004534 |
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