Principals about principal components in statistical genetics
Principals about principal components in statistical genetics
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DOI：
10.1093/bib/bby081
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发表时间：
2019-11-01
影响因子：
9.5
通讯作者：
Van Steen, Kristel
中科院分区：
文献类型：
--
作者：
Abegaz, Fentaw;Chaichoompu, Kridsadakorn;Van Steen, Kristel
主成分 (PC) 广泛用于统计学，指的是从初始变量池中导出的相对少量的不相关变量，同时尽可能多地解释总方差。同样在统计遗传学中，主成分分析（PCA）是一种流行的技术。为了获得最佳结果，与其他方法相比，需要全面了解 PCA 的不同实施方式及其对研究结果的影响。在这篇综述中，我们重点关注 PC 在祖先预测、全基因组关联研究、罕见变异分析、插补策略、荟萃分析和上位检测中的可能性、局限性和作用。我们还描述了经典 PCA 的几种变体，这些变体在统计遗传学应用中值得更多关注。
Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.