多基因风险得分和特征之间的关联分析:实用指南和教程,带有精神分裂症说明数据集
Itziar Irigoien1, Patricia Mas-Bermejo2,3, Sergi Papiol4,5,6
1Department of Computation and Artificial Intelligence, Euskal Herriko Unibertsitatea (UPV/EHU), Donostia, Spain.
Frontiers in psychiatry
|September 8, 2025
概括
本指南简化了多基因风险评分 (PRS) 关联研究,提供了统计学上正确的方法和现实世界的例子. 它涉及复杂的建模和验证,用于准确的PRS特征分析.
科学领域:
- 遗传学 遗传学 是一个
- 统计遗传学 统计遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 多基因风险评分 (PRS) 的计算通常是详细的,但关联分析方法需要进一步澄清.
- 对于PRS关联研究的标准回归模型可能会忽略关键的相互作用和验证条件.
- 自动化分析可能导致PRS计算中的重要信息丢失.
研究的目的:
- 为统计学上正确的多基因风险评分 (PRS) 和感兴趣的特征之间的关联研究提供全面的指南.
- 解决PRS关联分析的复杂性,包括相互作用和验证.
- 通过填补当前方法解释中的空白来促进准确的PRS分析.
主要方法:
- 详细解释PRS关联的回归建模,包括共变量调整 (例如主要组件).
- 对现实世界的数据集进行说明性分析,以证明其实际应用.
- 包括四个"工作示例"与R代码用于各种研究场景.
主要成果:
- 通过适当的建模,展示如何在PRS关联研究中避免信息丢失.
- 关于解释回归模型输出的指导,包括差异解释和PRS术语意义.
- 为各种PRS关联分析情况提供可重复的R代码.
结论:
- 本指南提高了多基因风险评分关联研究的准确性和完整性.
- 研究人员可以通过遵循所介绍的方法来实现统计学上合理的结果.
- 提供的示例和代码有助于先进的PRS分析技术的实际应用.
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