调整主要成分可能会在全基因组关联研究中引发碰撞器偏差
Kelsey E Grinde1, Brian L Browning2, Alexander P Reiner3,4
1Department of Mathematics, Statistics, and Computer Science, Macalester College, Saint Paul, Minnesota, United States of America.
PLoS genetics
|December 16, 2024
概括
主要成分分析 (PCA) 可以在混合种群的全基因组关联研究 (GWAS) 中引入偏差. 仔细的数据预处理和诊断至关重要,以避免虚假的关联和偏见的结果.
科学领域:
- 遗传学 遗传学 是一个
- 人口遗传学 人口遗传学
- 统计基因组学 统计基因组学
背景情况:
- 主要成分分析 (PCA) 是一种常见的方法,用于计算全基因组关联研究 (GWAS) 中的人口结构.
- 确定主要组件 (PC) 的最佳数量,并确保它们不会捕捉诸如链接不平衡 (LD) 等混因素,是相当大的挑战.
- 建议进行前处理步骤,如LD修剪或排除高LD区域,但并非普遍应用,对混合种群的影响尚不清楚.
研究的目的:
- 调查PCA预处理策略的影响和包括在GWAS结果中的PC数量在非洲裔美国混杂人口中.
- 在混合样本中识别PCA产生的潜在偏差和虚假关联.
- 为各种人群在GWAS中提供强有力的PCA实施建议.
主要方法:
- 来自妇女健康倡议和两个Trans-Omics精准医学研究的非洲裔美国人样本的分析.
- 在PCA之前评估不同的预处理技术,包括LD修剪和排除高LD区域.
- 评估PC和全基因组祖先与本地基因组特征之间的相关性.
- 检查将各种PC纳入GWAS模型的下游后果,重点关注影响大小估计和虚假关联.
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的变异相关性模式与欧洲人口不同,导致偏差的效果大小估计和膨胀的假阳性率 (碰撞偏差).
- 排除高LD区域并没有解决这些问题;LD修剪更有效,但最佳值因数据集而异.
结论:
- 混合种群中的PCA存在独特的挑战,标准预处理方法无法完全解决这些挑战.
- 后来的PC可以捕获本地基因组特征,导致GWAS中的碰撞器偏差和虚假关联.
- 仔细的预处理和诊断检查是必不可少的,以防止将混PC纳入混合祖先的GWAS模型.
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