弥合多样性差距:分析和研究设计考虑,以提高跨祖先遗传预测的准确性
Ozvan Bocher1, Arthur Gilly1, Young-Chan Park1
1ITG, Helmholtz Zentrum München, Munich, Germany.
HGG advances
|July 14, 2023
概括
精准医学需要精确的基因风险预测跨祖先. 像跨祖先元回归 (TAMR) 这样的新方法可以改善多基因分数 (PGS) 的多样性,通过考虑到遗传异质性来优于传统方法.
科学领域:
- 遗传学 是一个遗传学.
- 生物医学信息学 生物医学信息学
- 人口遗传学 人口遗传学
背景情况:
- 全基因组关联研究 (GWASs) 主要使用欧洲祖先样本,导致多基因分数 (PGSs) 与其他祖先的可转移性差.
- 遗传影响在不同种群中可能是异质的,这限制了标准跨祖先元分析 (FETA,RETA) 和祖先特定 (AS) 评分的准确性.
研究的目的:
- 评估不同多基因评分 (PGS) 方法的预测性能,包括固定效应 (FETA) 和随机效应 (RETA) 跨祖先元分析,祖先特异性 (AS) 评分和跨祖先元回归 (TAMR).
- 在各种遗传架构和祖先配置下评估这些方法,重点关注它们在不同人群的精准医学中的实用性.
主要方法:
- 在不同的基因架构和祖先组成下模拟人类复杂的特征.
- 比较FETA,RETA,AS和TAMR多基因分数 (PGS) 的预测性能.
- 在模拟的非欧洲种群中,每个PGS方法解释的量化表型差异.
主要成果:
- 跨祖先遗传异质性对FETA和RETA PGS的表现产生了重大影响.
- 在代表不足的目标人群中,AS PGS业绩下降.
- 在不同场景中,TAMR PGS表现出强大的预测性能,解释了非欧洲人群中甘油三水平和2型糖尿病的表型变异比AS和FETA方法更多.
结论:
- 跨祖先元回归 (TAMR) 提供了一种有前途的方法来构建祖先意识的多基因分数 (PGS),以减轻当前GWAS祖先组成的偏差.
- 在GWAS中增加非欧洲人的比例对于实现与欧洲祖先研究相比的预测准确性至关重要.
- 超回归方法对于弥补GWAS中的人口偏差至关重要,提高了所有人群的精准医学中遗传风险预测的准确性.
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