对各种人群构建多基因分数的方法进行比较
Sophia Gunn1, Xin Wang2, Daniel C Posner3
1Biostatistics, Boston University School of Public Health, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA.
HGG advances
|September 26, 2024
概括
开发多基因分数 (PGSs) 对于不同种群来说至关重要. 结合全基因组关联研究 (GWAS) 数据的多祖先方法与单祖先方法相比,在代表性不足的群体中显著提高了PGS性能.
科学领域:
- 遗传学 遗传学 是一个
- 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:
- 多基因分数 (PGS) 使用全基因组关联研究 (GWAS) 数据估计遗传疾病风险.
- 由于祖先偏见,目前的PGS在非欧洲人群中表现不佳.
- 在未被充分研究的人群中,急需改进PGS.
研究的目的:
- 为了比较构建PGS的多祖先和单祖先方法.
- 通过使用大型队列数据,在不同人群中评估PGS表现.
- 确定最佳的方法,以提高PGS在代表性不足的群体的实用性.
主要方法:
- 来自百万退伍军人计划 (MVP) 和我们所有人 (AoU) 队伍的数据.
- 使用贝叶斯方法为五个连续和五个二进制特征构建了PGS.
- 比较多祖先和单祖先GWAS数据,包括MVP META和特定人口的GWAS结果.
- 在三个AoU群体 (非洲人,混合美国人,欧洲人) 中使用基于关联的测试评估PGS性能.
主要成果:
- 多祖先的PGS方法表现优于单祖先的方法.
- 使用结合的GWAS数据构建的PGS在不同人群中表现出卓越的表现.
- 具体来说,使用PRS-CSx开发的多祖先分数在评估的AoU群体中表现最好.
结论:
- 结合来自多个祖先的GWAS数据,可以提高PGS的性能.
- 多祖先的PGS在不同的人口中比单祖先的PGS更有效.
- PRS-CSx为构建高性能多祖先多基因分数提供了一个有前途的方法.
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