联合PRS:一个数据适应框架用于多种群遗传风险预测,包括遗传相关性
Leqi Xu1, Geyu Zhou1, Wei Jiang1,2,3
1Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Nature communications
|April 23, 2025
概括
联合PRS通过在多个群体中使用遗传相关性来增强对不同人群的遗传风险预测. 这种数据适应性框架提高了准确性,特别是在代表性不足的人群中,而不需要大型个人级数据集.
科学领域:
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
- 人口健康 人口健康
背景情况:
- 在非欧洲人群中,遗传风险预测的准确性是有限的,因为基因组广泛关联研究 (GWAS) 的样本大小较小.
- 现有的方法在有限的调整数据集中扎,阻碍了对不同祖先的有效多基因风险评分 (PRS) 开发.
研究的目的:
- 引入JointPRS,这是一个新的数据适应框架,旨在改善跨多种人群的遗传风险预测.
- 为了实现准确的PRS开发,而不需要个人级调数据,即使是小调集.
主要方法:
- 联合PRS利用使用GWAS总结统计数据来利用人口之间的遗传相关性.
- 该框架采用数据适应性方法来优化预测.
- 通过使用英国生物银行 (UKBB) 和我们所有人 (AoU) 数据进行广泛的模拟和现实世界的应用来评估性能.
主要成果:
- 联合PRS在各种数据场景 (无调,同一队列,交叉队列) 中始终优于六种最先进的方法.
- 在混合美国人群中观察到显著的改善,在AoU队列中,脂质特征预测的准确性增加了6.46%-172.00%.
- 该方法在22个定量和4个二进制特征中表现出有效性.
结论:
- 联合PRS提供了一个强大的解决方案,用于增强不同人群中的遗传风险预测,解决GWAS当前的局限性和调整数据可用性.
- 该框架的数据适应性和利用跨种群遗传相关性的能力提供了实质性的改进,特别是对于代表性不足的群体.
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