Multivariate estimation fitting a common structure to estimates of genetic and environmental covariance matrices is examined in a simple simulation study. It is shown that such parsimonious estimation can considerably reduce sampling variation. However, if the assumption of similarity in structure does not hold at least approximately, bias in estimates of the genetic covariance matrix can be substantial. For small samples and more than a few traits, structured estimation is likely to reduce mean square error even if bias is quite large. Hence such models should be used cautiously
In plant breeding, one of the main purpose of multi-environment trial (MET) is to assess the intensi...
Covariance functions have been proposed to predict breeding values and genetic (co)variances as a fu...
The coefficient of variation (CV) measures variability relative to the mean, and can be useful when ...
Fitting only the leading principal components allows genetic covariance matrices to be modelled par-...
The additive genetic variance-covariance matrix (G) summarizes the multivariate genetic relationship...
Genetic models partitioning additive and non-additive genetic effects for populations tested in repl...
Abstract Background Analysis of data on genotypes with different expression in different environment...
Genetic models partitioning additive and non-additive genetic effects for populations tested in repl...
Covariance functions have been proposed to predict breeding values and genetic (co)variances as a f...
Genetic correlations between traits determine the multivariate response to selection in the short te...
Observed differences in phenotypic means between groups such as parents and their offspring or male ...
Because of its importance in directing evolutionary trajectories there has been considerable interes...
The additive genetic variance–covariance matrix (G) summarizes the multivariate genetic relationship...
Overt computational constraints in the formation of mixed models for the analy-sis of large extended...
For many multivariate twin models, the numerical Type I error rates are lower than theoretically exp...
In plant breeding, one of the main purpose of multi-environment trial (MET) is to assess the intensi...
Covariance functions have been proposed to predict breeding values and genetic (co)variances as a fu...
The coefficient of variation (CV) measures variability relative to the mean, and can be useful when ...
Fitting only the leading principal components allows genetic covariance matrices to be modelled par-...
The additive genetic variance-covariance matrix (G) summarizes the multivariate genetic relationship...
Genetic models partitioning additive and non-additive genetic effects for populations tested in repl...
Abstract Background Analysis of data on genotypes with different expression in different environment...
Genetic models partitioning additive and non-additive genetic effects for populations tested in repl...
Covariance functions have been proposed to predict breeding values and genetic (co)variances as a f...
Genetic correlations between traits determine the multivariate response to selection in the short te...
Observed differences in phenotypic means between groups such as parents and their offspring or male ...
Because of its importance in directing evolutionary trajectories there has been considerable interes...
The additive genetic variance–covariance matrix (G) summarizes the multivariate genetic relationship...
Overt computational constraints in the formation of mixed models for the analy-sis of large extended...
For many multivariate twin models, the numerical Type I error rates are lower than theoretically exp...
In plant breeding, one of the main purpose of multi-environment trial (MET) is to assess the intensi...
Covariance functions have been proposed to predict breeding values and genetic (co)variances as a fu...
The coefficient of variation (CV) measures variability relative to the mean, and can be useful when ...