联合PRS:多种人群遗传风险预测的数据适应框架,包括遗传相关性
Leqi Xu1, Geyu Zhou1, Wei Jiang1
1Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
bioRxiv : the preprint server for biology
|November 14, 2023
概括
联合PRS通过同时建模多个祖先来提高非欧洲人群的遗传预测准确度. 这种新的框架提高了多基因风险评分 (PRS) 的性能,即使调整数据有限.
科学领域:
- 人口遗传学 人口遗传学
- 统计基因组学 统计基因组学
- 生物信息学是一种生物信息学.
背景情况:
- 全基因组关联研究 (GWAS) 在非欧洲人群中表现出有限的遗传预测准确性,原因是样本规模较小.
- 预测模型的参数调整对于小数据集来说具有挑战性,特别是对于非欧洲祖先.
研究的目的:
- 引入JointPRS,一个新的,数据适应性框架,用于使用GWAS总结统计数据同时进行多种群的遗传预测.
- 通过利用遗传关联结构来提高代表性不足的人群中的遗传预测准确性.
主要方法:
- 开发了JointPRS,一个使用GWAS总结统计数据同时模拟多个种群的框架.
- 将遗传相关性结构纳入预测模型.
- 使用了数据适应方法,以在有限的调整数据下实现强大的性能.
主要成果:
- 与六种最先进的方法相比,JointPRS在各种数据场景 (无调,同队列调/测试,不同队列调/测试) 中表现优越.
- 该框架在非欧洲人群 (东亚人,非洲人,南亚人,混合美国人) 的大多数特征上取得了高准确性.
- 评估包括使用英国生物银行和我们所有人的数据的22个定量和4个二进制特征.
结论:
- 联合PRS提供了一种强大且计算效率高的解决方案,用于改善多种人群中的遗传预测.
- 该框架有效地解决了有限的样本大小和对非欧洲祖先的调整数据所带来的挑战.
- 联合PRS保持了模型的简单性,同时提高了跨多个大陆人口的预测准确性.
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