Use of Analytic Factor Structure to Increase Heritability of Clonal Progeny Tests of Pinus taeda L.

Abstract

Advanced variance-covariance structures are commonly used in genetic evaluation of crops to account for micro-site variability and achieve higher accuracy of predictions to increase selection efficiency. Various genetic variance-covariance structures were explored to predict best linear unbiased genetic merits of 453 loblolly pine (Pinus taeda L.) cloned progeny tested at 16 different locations in the southern U.S. Statistical models were compared using model fit statistics, variance components and genetic parameters. Among the models explored, spatial autoregressive error correlation with independent residual term for the R side with a factor analytic structure for the G side of the mixed model was superior. The model produced one of the smallest fit statistics (LogL equal to -2694), a small error variance (12.72), and the highest broad-sense heritability (0.45), compared with the default homogeneous error and genetic variance-covariance structure (statistical significance at P < 0.05). We concluded that the combination of specific structure for error and genetic design was effective to remove spatial-related variance, and to increase the accuracy of predictions of clonal genetic values, which could be used as analytical tool for increasing the selection efficiencies in forest genetic trials.

Genotype-by-environment interactions and the dynamic relationship between tree vitality and height in northern Pinus sylvestris

“…In crop and tree breeding, multi-environmental trials (MET) are established to evaluate the degree and pattern of genotype-by-environment interactions (G × E interactions, or G × E), as well as to test the robustness in performance of genotypes in different environments. In this context, type B genetic correlations (Burdon 1977), i.e., correlations between the same trait in different environments, can reveal rank changes among genotypes across different environments and are therefore widely used to evaluate the degree of G × E Kelly et al 2007;Li et al 2017;Smith et al 2015;Ukrainetz et al 2018;Zapata-Valenzuela 2012).…”

Factor analytic and reduced animal models for the investigation of additive genotype-by-environment interaction in outcrossing plant species with application to a Pinus radiata breeding programme

“…However, FA models for the genotype within family effects revealed substantial G × e, with the estimated genetic correlations ranging from −0.53 to 0.99. Zapata-Valenzuela (2012) used an FA1 model for the variance structure of the additive genetic by environment effects in the analysis of a MeT dataset on loblolly pines with 16 environments and 463 clones. They investigated two other variance models, namely the compound symmetric and heterogenous variance common correlation model.…”