基因与环境相互作用的坚固混合模型协会测试
Mengyu Zhang1, Jingxian Tang2, Michael R Brown3
1Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston.
medRxiv : the preprint server for health sciences
|November 19, 2025
概括
一个新的强大的混合模型关联测试 (RoM) 改进了对相关个体的基因环境相互作用 (GEI) 分析. 这种方法为大规模遗传研究提供了更好的错误控制和高效的计算.
科学领域:
- 遗传学 是一个遗传学.
- 生物统计学 生物统计学
- 人口遗传学 人口遗传学
背景情况:
- 线性混合模型 (LMM) 是基因与环境相互作用 (GEI) 研究的标准,考虑了人口结构和相关性.
- 用LMM进行全基因组GEI测试在计算上很苛刻,容易出现膨胀的I型错误和错误指定的环境影响.
- 现有的可靠方法往往局限于与人无关的样本,这对与人有关联的基于家庭或基于人口的研究构成了挑战.
研究的目的:
- 开发和评估一项强大的混合模型关联测试 (RoM),用于在相关样本中高效准确的全基因组GEI分析.
- 为了解决与传统基于LMM的GEI测试相关的计算强度和I型错误膨胀问题.
- 为涉及相关个人的大规模GEI研究提供可靠的方法.
主要方法:
- 建议使用Huber-White三明治估计器进行强大的混合模型关联测试 (RoM).
- 开发了RoM以进行高效的计算,实现了在受界集群大小下的样本大小的线性缩放.
- 通过模拟将RoM的性能与基于LMM的两步方法和其他方法进行比较.
主要成果:
- 模拟表明,与两步方法和替代方法相比,RoM在全基因组显著水平上提供了优越的I型错误控制.
- 当应用到现实世界的GEI分析时,RoM表现出强大的错误控制和可比的信号检测.
- 将RoM应用于大型数据集,包括弗雷明汉心脏研究,ARIC和英国生物银行,用于对人类特征和性别的GEI分析.
结论:
- RoM是一种高效,强大的方法,用于在相关样本中进行大规模基因环境相互作用分析.
- 提出的方法有效地控制了I型错误率,优于现有策略.
- 对于涉及复杂家族结构或人口分层的遗传关联研究,RoM提供了可靠的替代方案.
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