多基因预测在特征和种群中的差异性表现取决于基因型发现方法
bioRxiv : the preprint server for biology
|April 1, 2025
概括
整基因组测序 (WGS) 通常会提高多基因分数 (PGS) 预测准确度,而不是基因型阵列,特别是复杂的特征. 然而,这种优势因特征,种群和用于计算多基因风险得分的方法而异.
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.
- 计算生物学 计算生物学
背景情况:
- 多基因分数 (PGS) 汇总遗传变异以估计对复杂特征的倾向.
- 基因型阵列和全基因组测序 (WGS) 是PGS计算中发现变异的主要方法.
- 将数组衍生和WGS衍生PGS进行比较对于优化预测准确性和适用性至关重要.
研究的目的:
- 为了比较从基因类型化数组获得的PGS的预测性能,人口可移植性和计算效率与WGS.
- 评估不同PGS计算方法 (聚合和值与PRS-CS) 对基于数组和WGS的得分的影响.
- 在"我们所有人"研究计划中评估这些跨不同特征和祖先的比较.
主要方法:
- 计算了10个特征的PGS,使用来自"我们所有人"队列的数组和WGS数据.
- 经过训练的PGS使用来自泛英国生物银行的多祖先元分析.
- 在PGS计算中应用了Clumping和Thresholding (C+T) 和LD-informed PRS-CS方法.
- 在欧洲,非洲裔美国人和混合美国人的祖先群体中评估了表现.
主要成果:
- 基于WGS的PGS在使用C+T方法的高度多基因特征上优于基于数组的PGS.
- 一般来说,PRS-CS方法显示了比C+T更好的预测性能,WGS在大多数非癌症特征方面表现优于数组.
- 对于稀疏的特征,观察到精度差异,可能是由于WGS聚类变异体中的等位基因频率较低.
结论:
- 一般来说,WGS为PGS提供了优越的预测能力,特别是在使用像PRS-CS这样的方法时.
- 与数组相比,WGS的好处取决于上下文,受特定特征,人口祖先和所选择的PGS方法论的影响.
- 研究结果突出了在选择基因型发现方法的复杂性,用于多基因预测在不同的人口.
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