Multi-Environment Trial Analysis For 'Pinus Radiata'
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.
Industry wide genetic analysis of tree breeding data using TREEPLAN®
TREEPLAN is an advanced analytical tool providing accurate and precise predictions of genetic values to operational tree breeders. This project aimed to exploit TREEPLAN's analytical power for the benefit of all sections of the forest growing industry; in particular, the deployment sector and programs not traditionally associated with the Southern Tree Breeding Association. The outputs include: the routine inclusion of reproductive and fitness traits into the assessment framework; better prediction of stand performance through competition models; the undertaking of pilot studies using data from large industry programs; and the better modelling of the genetic structures of hybrid populations.
Managing the rate of increase in average co-ancestry in a rolling front tree breeding strategy
In breeding forest trees, as for livestock, the goal is to capture as much genetic gain as possible for the breeding objective, while limiting longand short-term inbreeding. The Southern Tree Breeding Association STBA) is responsible for breeding Australia's two main commercial forest tree species and has adopted algorithms and methods commonly used in animal breeding to achieve this balance. Discrete generation breeding is the norm for most tree breeding programmes. However, the STBA uses an overlapping generation strategy, with a new stream of breeding initiated each year. A feature of the species bred by the STBA ('Pinus radiata' and 'Eucalyptus globulus') is the long interval (up to 7 years) between when an individual is mated and when its progeny is first assessed in field trials and performance data included in the national performance database. Mate selection methods must therefore recognize the large pool of unmeasured progeny generated over recent years of crossing. In addition, the substantial delay between when an individual is selected in a field trial and when it is clonally copied into a mating facility (breeding arboretum) means that selection and mating must occur as a two-step process. In this article, we describe modifications to preselection and mate selection algorithms that allow unmeasured progeny (juveniles) to be recognized. We also demonstrate that the addition of hypothetical new progeny to the juvenile pool is important for computing the increase in average co-ancestry in the population. Methods outlined in this article may have relevance to animal breeding programmes where between mating and progeny measurement, new rounds of mating are initiated.
Simulation of hybrid forest tree breeding strategies
Computer simulation is the only realistic method of evaluating alternative methods of breeding hybrid forest trees. Empirical tests would be very long term and expensive. This paper describes the development of a simulation program, called XSIM, which generates two different but closely related outcrossing tree species. The genetic correlation between performance in each parental species and performance in the resulting hybrid can be set, in addition to the amounts and types of variances in each parental species. The breeding strategies available for testing include conventional reciprocal recurrent selection, reciprocal recurrent selection with forward selection, recurrent selection within each pure species, and the creation of a synthetic species. XSIM allows the strategies to be compared using the same base populations, equivalent selection intensities, and comparable mating patterns. Innovative best linear unbiased prediction procedures allow all ancestral and current progeny generation data, from both parental species and the hybrid, to be analysed together. The theoretical basis for the simulation is given, and genetic and statistical models are described. In summary, XSIM allows rigorous comparisons of the strategies in terms of genetic gain per time and provides useful insight into hybrid forest tree breeding.
Simulation of the comparative gains from four different hybrid tree breeding strategies
There is increasing interest in the deployment of interspecific hybrids in forest tree planting. The associated breeding programs are usually an adaptation of the reciprocal recurrent selection (RRS) strategy outlined by Comstock et al. (R. Comstock, H. Robonson, and P. Harvey. 1949. Agron. J. 41: 360-367) or use recurrent selection for general combining ability (GCA) in the pure species. This study uses a computer simulation tool known as XSIM, which has been described in a previous paper, to investigate the efficiency of four hybrid strategies. In addition to conventional RRS, we considered RRS with forward selection (RRS-SF), a strategy that approximately halves the generation interval needed for RRS, because hybrid and pure species progeny are bred simultaneously. Forward and backward selections are also made simultaneously and not in successive generations as is the case for RRS. An innovative best linear unbiased prediction analysis makes this possible. The development of a synthetic species (SYN) and pure species selection (PSS) were other strategies tested. The strategies were tested across a wide range of genetic structures. Genetic structures were defined as particular combinations of the correlation between pure species and hybrid performance for each species and the proportion of the genetic variance that is additive, dominance, and epistatic for each species. The results of the simulation have shown that the SYN strategy is the most cost effective across a wider range of genetic structures. This is especially so for those structures where there is less dominance variance and the pure-hybrid correlations in both species are greater than zero. Where the SYN strategy is not cost effective, the RRS-SF strategy is then the best option.
Application Of GGE Biplot Analysis To Evaluate Genotype (G), Environment (E), And G×E Interaction On 'Pinus Radiata': A Case Study
Genetics, genetics x environment (GGE) biplot analysis is an effective method, based on principal component analysis, to fully explore multi-environment trial data. It allows visual examination of the relationships among the test environments, genotypes, and the genotype x environment (GxE) interactions. Data from multi-environment trials of 'P. radiata' D. Don containing 165 to 216 families in five environments were used to demonstrate the results and application of GGE biplot analysis. There were non-overlapping clusters of two and three sites, which indicated two distinct environments. The best family for both of the distinct environments was also identified. Genetic correlations among sites ranged from 0.98 to -0.50, indicating that there were large GxE interactions among the test environments.