Multi-environment trial analysis for Pinus radiata. Ding, M., Tier, B., Dutkowski, G., Wu, H., Powell, M., & McRae, T. New Zealand Journal of Forestry Science, 38:143–159, January, 2008. abstract bibtex A stem-diameter data set of five combined trials of Pinus radiata D. Don was used to identify and determine the nature of genetics by environment (GxE) interaction. The restricted maximum likelihood approach was applied to handle the main issues of the multi-environment trial analysis: (1) Testing sources of heterogeneity of variance and lack of between-sites genetic correlation; (2) Modelling the heterogeneity of error variance among trials and micro-environmental variation within each trial; and (3) Selecting the best model for prediction of breeding values. Model comparison was based on the criterion of log-likelihood. The significance of variance components was tested by the likelihood ratio test which showed that all sources of GxE interactions were highly significant, indicating that GxE interactions occurred in these five trials due to both the heterogeneity of variances and the lack of correlation. Estimates of Type B genetic correlations were increased slightly by correcting for the heterogeneity of variances. The full model, which accommodated heterogeneity of error variances between trials, spatial variation within trials, and fitting a separate GxE interaction variance for each trial, was superior to other models for this multi-environment trial.
@article{ding_multi-environment_2008,
title = {Multi-environment trial analysis for {Pinus} radiata},
volume = {38},
abstract = {A stem-diameter data set of five combined trials of Pinus radiata D. Don was used to identify and determine the nature of genetics by environment (GxE) interaction. The restricted maximum likelihood approach was applied to handle the main issues of the multi-environment trial analysis: (1) Testing sources of heterogeneity of variance and lack of between-sites genetic correlation; (2) Modelling the heterogeneity of error variance among trials and micro-environmental variation within each trial; and (3) Selecting the best model for prediction of breeding values. Model comparison was based on the criterion of log-likelihood. The significance of variance components was tested by the likelihood ratio test which showed that all sources of GxE interactions were highly significant, indicating that GxE interactions occurred in these five trials due to both the heterogeneity of variances and the lack of correlation. Estimates of Type B genetic correlations were increased slightly by correcting for the heterogeneity of variances. The full model, which accommodated heterogeneity of error variances between trials, spatial variation within trials, and fitting a separate GxE interaction variance for each trial, was superior to other models for this multi-environment trial.},
journal = {New Zealand Journal of Forestry Science},
author = {Ding, M. and Tier, Bruce and Dutkowski, G.W. and Wu, Harry and Powell, Michael and McRae, T.A.},
month = jan,
year = {2008},
keywords = {⛔ No DOI found},
pages = {143--159},
}
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