超越预测R2:量子回归和非等价性测试揭示了特征和多基因分数的复杂关系
Joel Mefford1, Molly Smullen2, Felix Zhang3
1Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA.
American journal of human genetics
|June 6, 2025
概括
多基因分数 (PGSs) 预测特征,但在不同个体的准确性上有所不同. 这项研究揭示了PGS预测能力在表型范围之间存在差异,强调了在应用中需要谨慎.
科学领域:
- 遗传学 是一个遗传学.
- 生物统计学 生物统计学
- 人口健康 人口健康
背景情况:
- 多基因分数 (PGS) 对于预测疾病风险和特征至关重要.
- 由于环境和个人因素,PGS预测准确度在人群中可能会有所不同.
- 识别PGS异质性对于准确的临床和研究应用至关重要.
研究的目的:
- 开发一种用于选特征-PGS对进行预测值异质性的方法.
- 评估PGS预测准确度在表型分布中如何变化.
- 识别需要仔细考虑PGS应用的特征.
主要方法:
- 使用英国生物库数据进行分析.
- 采用定量回归线性模型来估计定量特异效应大小.
- 进行模拟以探索基因与环境相互作用作为异质性来源.
主要成果:
- 对于分析的25个连续性特征中的大多数,PGS预测准确性在表型量级上有显著的变化.
- 只有三种特征在多量体中没有PGS效应大小的实质变化.
- 模拟证实基因与环境之间的相互作用可以导致这种观察到的异质性.
结论:
- 一个群体内的所有个体的PGS表现并不均.
- 开发的方法可以标记需要谨慎解释PGS结果的特征.
- 根据相关变量进行分层可能是必要的,当PGS性能在人口群体之间有显著差异时.
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