Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices.
Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices.
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DOI：
10.1186/1297-9686-37-1-1
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发表时间：
2005-01
期刊：
影响因子：
--
通讯作者：
Kirkpatrick M
中科院分区：
文献类型：
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作者：
Meyer K;Kirkpatrick M
主成分分析是一种广泛使用的降维技术，尽管通常是在表型水平上。结果表明，我们可以通过对通常的线性混合模型进行简单的重新参数化来直接估计遗传主成分。这适用于任何符合多个相关遗传效应的分析，无论是对单个性状的影响还是对模拟轨迹的随机回归系数集的影响。根据遗传相关性的大小，主成分的子集通常足以捕捉大部分遗传变异。遗传协方差矩阵的相应估计更简约，具有降阶和平滑，对于k效应和m个主成分，建模离散结构所需的参数数量从k(k+1)/2减少到m(2k-m+1)/2。描述了通过使用似然导数的受限最大似然来估计遗传协方差矩阵的这些参数、最大特征值和相关特征向量。结果表明，降阶估计可以大大降低多变量分析的计算量。给出了用超声活体扫描技术对肉牛8个性状进行分析的应用实例。
Principal component analysis is a widely used 'dimension reduction' technique, albeit generally at a phenotypic level. It is shown that we can estimate genetic principal components directly through a simple reparameterisation of the usual linear, mixed model. This is applicable to any analysis fitting multiple, correlated genetic effects, whether effects for individual traits or sets of random regression coefficients to model trajectories. Depending on the magnitude of genetic correlation, a subset of the principal component generally suffices to capture the bulk of genetic variation. Corresponding estimates of genetic covariance matrices are more parsimonious, have reduced rank and are smoothed, with the number of parameters required to model the dispersion structure reduced from k(k + 1)/2 to m(2k - m + 1)/2 for k effects and m principal components. Estimation of these parameters, the largest eigenvalues and pertaining eigenvectors of the genetic covariance matrix, via restricted maximum likelihood using derivatives of the likelihood, is described. It is shown that reduced rank estimation can reduce computational requirements of multivariate analyses substantially. An application to the analysis of eight traits recorded via live ultrasound scanning of beef cattle is given.