Estimates of genetic principal components (PCs) have generally been obtained by first estimating a full rank covariance matrix and then determining its eigendecomposition. A better alternative is to estimate PCs directly, disregarding any PCs which do not explain a significant amount of variation. It is shown that this is readily implemented within the usual linear mixed model framework, requiring, in essence, only a simple reparameterisation, and that standard estimation techniques are applicable. Considering the important PCs only can yield highly parsimonious models, gives estimates of covariance matrices of reduced rank, and can reduce sampling variances. Moreover, computational requirements can be reduced dramatically, making routine higher-dimensional multivariate analyses of large data sets more feasible. PC analysis is illustrated considering a set of 14 carcass traits recorded on beef cattle.
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume , , 25.03, 2006
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