Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix

Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate independent ‘super traits’ from the original multivariate phenotypic traits for the univariate analysis. However, parameter estimates in this framework may not be the same as those from the joint analysis of all traits, leading to spurious linkage results. In this paper, we propose to perform the PCA for residual covariance matrix instead of the phenotypical covariance matrix, based on which multiple traits are transformed to a group of pseudo principal components. The PCA for residual covariance matrix allows analyzing each pseudo principal component separately. In addition, all parameter estimates are equivalent to those obtained from the joint multivariate analysis under a linear transformation. However, a fast least absolute shrinkage and selection operator (LASSO) for estimating the sparse ...针对全基因组关联研究（Genome-Wide Association Study, GWAS）中应用多变量分析定位多性状时存在的缺陷，主成分分析（Principal Component Analysis, PCA）已被广泛用于从原始多变量表型性状中生成独立的“超级性状”，以供单变量分析使用。然而该框架下的参数估计结果与所有性状联合分析得到的参数估计并不一致，会导致虚假连锁结果。本文提出对残差协方差矩阵而非表型协方差矩阵进行主成分分析，据此将多性状转换为一组伪主成分。基于残差协方差矩阵的主成分分析允许对每个伪主成分分别开展分析，且所有参数估计结果在线性变换下与联合多变量分析得到的结果完全等价。然而，目前尚缺乏用于估计稀疏……的快速最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）。