调整主要成分可以在混合种群的全基因组关联研究中诱导虚假关联
Kelsey E Grinde1, Brian L Browning2, Alexander P Reiner3,4
1Department of Mathematics, Statistics, and Computer Science, Macalester College, Saint Paul, Minnesota, 55105, USA.
bioRxiv : the preprint server for biology
|April 15, 2024
概括
对于全基因组关联研究 (GWAS) 的主要成分分析 (PCA) 在混合种群中可能存在问题. 后来的主要组件 (PCs) 可能会捕获本地基因组特征,从而导致偏见的结果和虚假的关联.
科学领域:
- 遗传学 是一个遗传学.
- 人口遗传学 人口遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 主要成分分析 (PCA) 是一种用于控制全基因组关联研究 (GWAS) 人口结构的常用方法.
- 确定主要组件 (PC) 的最佳数量,并防止它们捕获诸如链接不平衡 (LD) 等非人口文物,是关键的挑战.
- 建议进行预处理步骤,例如LD修剪或排除高LD区域,但不普遍应用,对混合种群的影响尚不清楚.
研究的目的:
- 调查前处理和PC数量对GWAS在非洲裔美国混杂人口中的影响.
- 与欧洲人口相比,确定PC如何在混合样本中捕获基因组特征.
- 了解将有问题的PC纳入GWAS模型的下游后果.
主要方法:
- 来自妇女健康倡议SNP健康协会资源,杰克逊心脏研究和慢性阻塞性肺病遗传流行病学研究的非洲裔美国人样本的分析.
- 检查PC与全基因组祖先和局部基因组特征之间的相关性.
- 评估不同预处理策略 (LD修剪,排除高LD区域) 对PCA结果的影响.
主要成果:
- 第一个PC与全基因组祖先有很强的相关性,而随后的PC在所有三个混合样本中都捕获了局部基因组特征.
- 与PC的变体相关性模式与欧洲人群不同,导致偏差的效果大小估计和虚假的关联 (碰撞偏差).
- 排除高LD区域并没有解决问题;LD修剪更有效,但最佳值有所不同.
结论:
- 混合种群中的PCA存在独特的挑战,后来的PC可以捕获本地基因组特征,而不仅仅是人口结构.
- 将这些PC纳入GWAS模型可以引入偏见和虚假关联.
- 仔细的预处理和诊断检查对于确保GWAS结果在混合种群中的有效性至关重要.
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