Genetics in medicine open · 2026
Population-specific optimization of polygenic risk scores for breast cancer in East Asians.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Liu Shu-Hsuan, Wang Yu-Chun, Shieh Yun-Jer, Tan Hai-Feng, Kao Kuo-Jang, Hung Chih-Chiang, Hou Ming-Feng, Huang Chi-Cheng, Lin Ching-Hung, Feng An-Chieh, Chiou Jeng-Fong, Chen Chien-Hsiun, Li Ling-Hui, Taipei Medical University Hospital Consortium, Hsiao Tzu-Hung, Shao Yu-Hsuan Joni
Аннотация
PURPOSE: Polygenic risk scores (PRS) have shown promise in predicting disease susceptibility, but their accuracy varies across populations due to the limited representation of non-European ancestries in genomic studies. While European-derived PRS demonstrate some transferability to Asian populations, optimizing these scores for specific populations remains crucial for improving risk prediction.
METHODS: Using data from the Taiwan Precision Medicine Initiative (n = 46,422) and an external validation cohort (n = 891), we developed an approach to optimize European-derived PRS for breast cancer prediction in East Asians. Our method involved 2 key steps: (1) replacing European PRS variants with Asian-specific single nucleotide polymorphisms within the same linkage disequilibrium blocks (r 2 > 0.8) and (2) incorporating additional population-specific variants. We validated this approach using both breast and prostate cancer data sets.
RESULTS: The optimized PRS (PRSEUR_replace+TPMI) showed improved predictive performance compared with the original European PRS (PRSEUR) in our main cohort, with an increased area under the curve from 0.608 to 0.617 and odds ratios per SD from 1.47 (95% CI 1.36-1.60) to 1.51 (95% CI 1.39-1.63). Individuals in the top 5% of PRSEUR_replace+TPMI demonstrated a 2.09-fold increased risk of developing breast cancer compared with those in the 40th to 60th percentile. External validation confirmed enhanced performance, and similar improvements were observed when this method was applied to prostate cancer prediction.
CONCLUSION: Our study presents a practical approach for optimizing European-derived PRS in non-European populations, which is particularly beneficial for populations with limited genetic data. As genetic data collection expands in these populations, this approach may help reduce health care disparities in the implementation of precision medicine.
METHODS: Using data from the Taiwan Precision Medicine Initiative (n = 46,422) and an external validation cohort (n = 891), we developed an approach to optimize European-derived PRS for breast cancer prediction in East Asians. Our method involved 2 key steps: (1) replacing European PRS variants with Asian-specific single nucleotide polymorphisms within the same linkage disequilibrium blocks (r 2 > 0.8) and (2) incorporating additional population-specific variants. We validated this approach using both breast and prostate cancer data sets.
RESULTS: The optimized PRS (PRSEUR_replace+TPMI) showed improved predictive performance compared with the original European PRS (PRSEUR) in our main cohort, with an increased area under the curve from 0.608 to 0.617 and odds ratios per SD from 1.47 (95% CI 1.36-1.60) to 1.51 (95% CI 1.39-1.63). Individuals in the top 5% of PRSEUR_replace+TPMI demonstrated a 2.09-fold increased risk of developing breast cancer compared with those in the 40th to 60th percentile. External validation confirmed enhanced performance, and similar improvements were observed when this method was applied to prostate cancer prediction.
CONCLUSION: Our study presents a practical approach for optimizing European-derived PRS in non-European populations, which is particularly beneficial for populations with limited genetic data. As genetic data collection expands in these populations, this approach may help reduce health care disparities in the implementation of precision medicine.