Across-site heterogeneity of genetic and environmental variances in the genetic evaluation of
Eucalyptus globulus trials for height growth
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Costa e Silva, J and Dutkowski, GW and Borralho, NMG, Across-site heterogeneity of genetic and environmental variances in the genetic evaluation of
Eucalyptus globulus trials for height growth, Annals of Forest Science, 62, (2) pp. 183-191. ISSN 1286-4560 (2005) [Refereed Article]
Height data from six 3-year-old Eucalyptus globulus trials with cloned progenies were jointly analysed with a heterogeneous variances model. Significant heterogeneity between trial sites was detected for additive genetic and environmental variances, corresponding to coefficients of variation of 41% and 26%, respectively. Two additive genetic and four environmental variances were significantly different from common estimates across all trials. Significant heterogeneity was also detected for heritability estimates, which ranged from 13.5% to 40.3%. Genetic evaluations of parents and clones within full-sib families were obtained from the heterogeneous variances model, and from a simpler model assuming variance homogeneity across trial sites and using either unadjusted data or data pre-adjusted by scale transformations. Changes in predictions of breeding values, top ranking genotypes and selection responses were examined to assess the impact of ignoring heterogeneous variances on the genetic evaluation. Clones were more sensitive than parents to the assumption of homogeneous variances in the evaluation model. Nevertheless, ignoring variance heterogeneity decreased the response to clonal selection by only 2% relatively to the evaluation based on the heterogeneous variances model. Pre-adjusting the data to constant phenotypic or environmental variances reduced the variance heterogeneity. The latter scale transformation was somewhat more effective in increasing fairness of selection, and resulted in close to optimal ranking and selection response. On the basis of the results of this study, Best Linear Unbiased Prediction was fairly robust to erroneously assuming homogeneous variances in a genetic evaluation model. © INRA, EDP Sciences, 2005.
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