Population stratification (PS) is a major confounder in genome-wide association studies (GWAS) and can lead to false-positive associations. To adjust for PS, principal component analysis (PCA)-based ancestry prediction has been widely used. Simple projection (SP) based on principal component loadings and the recently developed data augmentation, decomposition and Procrustes (ADP) transformation, such as LASER and TRACE, are popular methods for predicting PC scores. However, the predicted PC scores from SP can be biased toward NULL. On the other hand, ADP has a high computation cost because it requires running PCA separately for each study sample on the augmented dataset.

中文翻译：

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使用主成分分析进行快速、稳健的血统预测。


群体分层（PS）是全基因组关联研究（GWAS）中的主要混杂因素，可能导致假阳性关联。为了调整 PS，基于主成分分析 (PCA) 的祖先预测已被广泛使用。基于主成分载荷的简单投影（SP）和最近开发的数据增强、分解和Procrustes（ADP）变换，例如LASER和TRACE，是预测PC分数的流行方法。然而，SP 预测的 PC 分数可能会偏向 NULL。另一方面，ADP 的计算成本很高，因为它需要对增强数据集上的每个研究样本单独运行 PCA。