Fitting only the leading principal components allows genetic covariance matrices to be modelled parsimoniously, yielding reduced rank estimates. If principal components with non-zero variances are omitted from the model, genetic variation is moved into the covariance matrices for residuals or other random effects. The resulting bias in estimates of genetic eigen-values and -vectors is examined.