Genomic selection based on the single-step genomic best linear unbiased prediction (ssGBLUP) approach is becoming an important tool in forest tree breeding. The quality of the variance components and the predictive ability of the estimated breeding values (GEBV) depends on how well marker-based genomic relationships describe the actual genetic relationships at unobserved causal loci. We investigated the performance of GEBV obtained when fitting models with genomic covariance matrices based on two identity-by-descent (IBD) and two identity-by-state (IBS) relationship measures. Multiple-trait multiple-site ssGBLUP models were fitted to diameter and stem straightness in five open-pollinated progeny trials of Eucalyptus dunnii, genotyped using the EUChip60K. We also fitted the conventional ABLUP model with a pedigree-based covariance matrix. Estimated relationships from the IBD estimators displayed consistently lower standard deviations than those from the IBS approaches. Although ssGBLUP based in IBS estimators resulted in higher trait-site heritabilities, the gain in accuracy of the relationships using IBD estimators has resulted in higher predictive ability and lower bias of GEBV, especially for low-heritability trait-site. ssGBLUP based on IBS and IBD approaches performed considerably better than the traditional ABLUP. In summary, our results advocate the use of the ssGBLUP approach jointly with the IBD relationship matrix in open-pollinated forest tree evaluation.
Environmental heterogeneity and/or genetic and environmental competition were quantified on two growth traits, diameter at breast height and total height, and wood density in a progeny trial of Corymbia citriodora subsp. variegata. Three single-trait mixed models with random spatial and/or competition effects were compared to a standard analysis by analyzing fit, dispersion parameters, accuracy of breeding values, genetic gains, and ranking of trees. In addition, a multiple-trait spatial-competition model was fitted to estimate correlations among direct and indirect additive genetic effects, and to explore relations between traits. Single-trait analyses with spatial and/or competition effects outperformed the standard model. However, the performance of these models depended on the sensitivity of each trait to detect each effect. Direct–indirect genetic correlations from the multiple-trait spatial-competition model showed inverse and strong relations among growth traits and wood density, suggesting that growth traits can be affected by competition and environmental heterogeneity, but also wood density might be influenced by these effects. The approach proposed was useful to improve the genetic analysis of the species as well as to gain an understanding of the genetic relations between traits under the influence of environmental heterogeneity and competition.
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